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333e848980523b939cd84d7a9d68a6e4ba224772
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# Dataset Card for the Multi-species genome
## Dataset Description
- **Repository:** [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer)
- **Paper:** [The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics](https://www.biorxiv.org/content/10.1101/2023.01.11.523679v1)
### Dataset Summary
The Multi-species dataset was constructed by parsing the genomes available on [NCBI](https://www.ncbi.nlm.nih.gov/), before arbitrarily selecting only one species from each genus. Plant and virus genomes were not taken into account, as their regulatory elements differ from those of interest in the paper's tasks. The resulting collection of genomes was downsampled to a total of 850 species, in which several genomes that are heavily studied in the literature have been incorporated. The collection represents 174B nucleotides, resulting in roughly 29B tokens. The distribution of each genomics class in the dataset is displayed below:
```
| Class | Number of species | Number of nucleotides (B) |
| ---------------------| -------------------| --------------------------|
| Bacteria | 667 | 17.1 |
| Fungi | 46 | 2.3 |
| Invertebrate | 39 | 20.8 |
| Protozoa | 10 | 0.5 |
| Mammalian Vertebrate | 31 | 69.8 |
| Other Vertebrate | 57 | 63.4 |
```
### Supported Tasks and Leaderboards
This dataset has been used as a pre-training corpus for the Nucleotide Transformers models. Depending on the configuration used, each sequence is 6,200 or 12,200 base pase pairs long. If the dataset is iterated without being shuffled, the first 100 nucleotides of a sequence are the same as the last 100 base pairs of the previous sequence, and the last 100 nucleotides are the same as the first 100 base pairs of the next sequence. During training, this allows for randomly selecting a nucleotide between the first 200 nucleotides of the sequence and start the tokenization from this nucleotide. That way, all the chromosome is covered and the model sees different tokens for a given sequence at each epoch.
### Languages
DNA
## Dataset Structure
[N/A]
### Data Instances
For each instance, there is a string representing the sequence, a string indicating the description of the sequence, two integers representing the index of the first and last nucleotide respectively and the link to the genome's fasta URL. An instance is shown below:
```python
{'sequence': 'AAACTACCACTGGCTAAATTTCGACCATCTGGGCTAATAGCAACTGACCGCACCCAATATTTATGTCCTTTAAGTGTGCGAATTAGCTTTCCTGTGCCTAAATTCCAAACTTTGAGAGTGTTGTCATCGCTACCACTCACCAAAATTTTCCCATTAGGACTAATTGTTAATGCTTGAATGGAGTCAGTATGTCCTGTTAATGTGTAGACTATTTTACCTGTTGCCAAATTCCAGGCTTTAATAGTTTGATCATCACTCCCGCTAACCAAAGTTTTGCCATTGGGACTGATAGCCACAGCATTAACTTTTTGCGAATGTCCACTCAGGGTTAGTATTTCTTTTCCTGTGGTCAGATTCCACATTTTAATTATGCGTTCCCCTTCGCCACTACTAGCAATTGTCTGCCCATCGGGACTAATGGCGACAGAGACAACAGATTTTGCCCCACCTTTGAGGGTGTTAGCTAAGGAAATATTTTTAACTGGAACATTGGGTGACTGACCAAAAACAACTTCACCCTGAGTAGGACTGTAATTTCCTGGCTTTAGTCTCGATAACAAACTGGTTTGAATTTGGTGATATTTTTGATACCAAGTATCACTAAAACCAAATAACAAAATGAAAGCAGCGCCTAAAACTAAACTTTTGACAAAAGCATATTTAAAGGAGAACTTTGCACTCGGTTGAGTTACGGTGAATTTTCCTGATGATTGTCCGGCGGCTGGTAAGGCGCGTGGGAGTGATGGAATCAAATCTTTAATCACTTCATCGGCTGACTGGTAGCGTTGACTTAAGTCTTTTTGCAACAGCTTCGTCATCACCCCTTCCAATTCTGGCGACAAAGGACTACGCAAATATTCCCGCCAACTGTTCGCCCAGCCATAGCCATGTTCCATCCACAATTGAAAAGGGGATGTTCCTGTTAAGAGATGAAAACAGGTAGCCCCCAAACTGAACAAATCACTAGCTGGGTAAGCTTTACCGTCTCTGATTTGTTCCAGTGGAGAATAACCATGCGAACCAATGGATGTACCATTTTTATTCTTGACTTTTTCGGTTAATTGCTTAGAAGAACCAAAATCAATCAAGCTAAGTCGCCCATCATAACGACAGCGAATTAAATTTTCTGGTTTAATGTCTCGGTGAATCACACCGCGATCGTGAATGAATTTGAGTACAGGCAGTAAATCAAGTAAAATTGCTTGAATTTCATTCGCTTTATAGACTTTGCGCTGTTGTAATTCTTTTAACAAGTTCTGCCCATTAATAAACTGTTGTACCAAATAAAGGCAGTTATCTTGTTCAAAGTAAGCAATCAGTGTAGGAATTTGCGGATGTTCGCCGAGTTCTTGCAGTCGCTTGGCTTCTTCTGCAAATAACTCCATTGCTTTTTTCTGCGACCAAGTTCCTTGAAATTTCGGTGCTAATTGCTTAATTACACACAGTTCATTGAGTTTATCGGTATCTTCAGATAAATAAGTTCTGCCAAATCCCCCCTCATCGGAAAGCACCCGAATCACTCGAAAGCGATTTCTTAATAGTGGCACCAAGGGGGTGCTACAAGTTTGGCATGACTGCTTTCCTTTGGGATTTAGGGGATTTGGACAATCGGGATTTAAGCAGCAGATCATTATCTGACAGGCGCAACTGCATAAAAATTTTTACTAAATTAACCCCGATATTTCCCTAGATGATGATTGACTCTCACGTATTGATGGTAGATCCCGCTGGTAGTGGGGAGTGGGGAATCAATTATATAGTCAATTTTGGTAAATGCTCATAAGTTTTCTTCAATGCAGGAAAACTACGAGAGTCATCAGCTGAATTTTATCGATTATAGCAGCAGGCAAAAGTAGCAGACAGGTTAAGAGTGTCATTAGTCAAGACAAATGACTCATGACTAATGACTCATGACTAATAACTAAGGCTTTTGGGTGGCGATCGCTAATTTTGCCCCCTGGACTTGTCTGACTTGATCCATCACTGCCACTACTTTACCGTGGGTGACTGTTGCATCAGCATTCACAATTACTAATGCTTCTTGGTTATCGCCTACCAAGGTACGCAATTGTCCGGCTAAACCGTCAACAGTGCTTGGTTGACGGTTAACACTTACTATTCCATCTTTATCTACTGTGACGGTAATTTTGGCTGGAACTTGCTGCTGTTTGGCTGTCGCCGCTTTGGGTAAGTTGACGGGTAAACCTTCTGAGCGAGTTAAAAATAACGTTGACATGATAAAAAATGTCAAAATCGCAAATATCACATCAATCATTGGCACGATGTTGATTTGCGGTGGTAAATCTGGCTCATCTTGTAGACGCATAGGTTCTGTCTCCTCGTTCAAAGCGGCGGCGATAGAGCAGTTCTAATTGTCCACCATATTCTTGTATTGCGGCAATCTGTCGTTGATATAACCCTCGAAAGGTATTAGCAAATAAAAGTATAAAAATAGCCACAATTAAACCTGAAGCTGTAGATACCAGCGCTTCACTAATACCTGCGGTAACTCCTGCGGTTTTTGTCCCGCCTACATCACCCAAGTTTAATGATGCAAAAGAAGCAATCAAACCTAATACAGTACCCAGTAGACCTAAAAGTGGTGCAAGACCAATAATTGTGTCAAACATATTTTGAAAACGTTTGAGAACTGGGATTTCGGCTTGCGCTTCACTTTCTAGTGCAAGCCGAAATTCTTCTGGGGTTGGTTCTTCTAATTGCAACGCCGCTAAAAAAATCCGTGTCATGGGCAAATCTGCATTCTTTTGCAATTTATCCAACGCGCCAACAACATTATCAAGGCGGTAAAGATTCAACACTTCTCTGACTATGCGGTTTTGCCGAGTATTGATGCGATACCAAAAGCGGACTCGCTCGATAATTAAAGCAATTCCCACCACACTAAACGCCAGCAGGGGCCACATGACTACGCCACCTGCTACAAACAACTCATACATGGGCAATATCTCTAGGAACTAAATGGACAACGTTACAGTTAGACTAGCAGTTTACGGTACTAAATGATATATCTTATCAATAAGGAGTAGACAAAATAAAAAGCTATGTCAAATTCGGTTGAGTTTTGATGACATAATTATTCATTCTTGTTCAAGGCTTGATTCGCTACAATCCTGATGATGAAAGTATTTGTGTAAGTATACAGTTGATGAAAGCTAACTCAGGAATTTTTTTCTTTATTGCTTGACTTTTGCGAGAGATGGTTTTGAACAGAGTAATTACTAATAAGAACTTGCAATAAATTTAAACAGAACAGTAGTTTGTAGCTTTGCTTGAGAAGCGATCGCCCGACGTTGAGAGTTAAAGTATATTTTGCGTACTAACTTACCCAACGCCCAAAAAATTACATCATTTGAATATCGTCAATTTGTACTCTTAATCATCTATGGCTAAACTATTTGACTCAATCACAGAAGAACTGCAAGAGTTTATTGCAGCCCAAAACCTTTTCTTTGTAGGAACCGCGCCTCTGAGTGCTACAGGTCACGTTAATTTATCTCCCAAAGGTCTCGATTGCTTGCGGATTTTATCACCCCACAAAGTCGCCTATCTCGATCTCACAGGTAGCGGTAACGAAACTTCAGCCCATCTGCAAGAAAATGGTCGCATTACCTTCATGTTTTGCGCCTTCACTGAACCAGCGCGCATCTTGCGACTTTACGGTCAAGGACACGTAATTTTACCTAGCTATCCTGATTGGGATTCTGTATATTCAGTGTTTCCGCCGCTACCAGGAACTCGTCAAATTATCGTAGCTGATATTGAGATTGTGCAAAGTTCCTGTGGTTTCGGCGTTCCTCTTTACGAATACCAAGGTCAACGCCAAACACTAGTAAATTGGGCTGCTAAAAAAGGCGAACAGGGAGTCCGAGAATATCAACAACAAAAAAACAGCATCAGCATTGATGGTTTACCGACACCATTAGGCCAATTATCTGACGGTTAAAGCGGCGTTTCATATATTTTTAGTTAATCTGAACCAAAAAATCTCAAATTTTTTGTCAATAGTCTCTAGTCCAAAGAAGCTTGATTTTTGACCATAGATTGTAGGCTTTTGACAAAAATAACCTTTATAGAGAAAATTTATCCTTGCTGACACTCTATAACTAAGTTTATAAAACATAGCGTCAAAAATCGATACATATCAGTTCTATTTTCTGCCTCTATTCCTAATTAAATTTGGTGTAAAGGAACTATTATGCGGTTTCCGTGTCTTGACGTAATGATTTGCAACGAATTATGATTCGAGTTTAGTCCGGATCAACCGAGACATCCTCGAAAATTGGTGCAAGTAAATTCAACTTTCGCTCTACATAATCACACGCATGAGATTACGCTTATTTCTGTTTAGCGTTGTCAGTATTGTCCTGCTTTCTTCTCCAGTAAGAGCATCTCGCTTAGAATCTTGGAGCTTTGACACCGCACAAAATCAACTGAATATTACTACTGTATCTGGTGTTAAACCAAGAGCATTTTTAATTCAAAATCCCACGCGGTTAGTTATCGATCTTCCTGGTACACAACTGAACACAAATACAGTTCGGAAAAACTTTGGTTCCACAGTACGTGAAATCCGTGTTGGTAAGGTTGACGATAACACAACAAGATTAGTAGTTGAATTAGCACCTGGATACACTGTAGACCCTAACAAGTTACTGCTGCAAGGTGATTCTTCCACTCATTGGATAGTGAAATTTCCATCGGTAGAACGGGTTCAAAATCCTGTTGATAATAATTTTTCTTTATCTAGTGAAGAGCAAATTCCGGTTTCTGTGAGTGATGTTTCTTTGTTTGCGGGAGTTGTACCGTTAGGTAAGGAAATACCACAATTGCGATCGCAGGTACAAGCCTTAGCTGCTCGTTATCGTTCCCTGGATGCAGGAATGTTCTTTTTAGATTTAGATACTGGTAACTATCTAGATTTAAATGGTGAGAAAGTCTTTCCTGCTGCTAGTACAATAAAGTTTCCCATTTTAGTAGCGTTATTTCAAGAAGTAGATGCAGGTAGAGTCAAACTGAATGAAACCTTAGTTATGCGGCGCGACTTAATAACTGGAGGTTCTGGAGAATTTCAATACAAGCGTGCAGGAAGTCGTTTTAGTCTGATAGAAACCGTGACTAAGATGATTACCATCAGCGACAACACAGCTACCAATATGGTAATTGACCGATTAGGTGGTAAAGCTAAGTTAAATCAGCGTTTTCGTGGTTGGGGTCTGCAAAACACCGTTGTGCGGAATTTACTCGGCGACTTTAAGGGAACGAATACAACTAGCGCCAAAGATTTAGTCAGGCTGTCTGCGTTGGTTGCAAAAAATCAATTATTGACTGATTCCAGCCGTAGCAAAGTTTTGGATATTATGCAGCGTGTTCACAACACCAAGTTATTACCTGCTGGTTTGGGTAAAGGTGCGGTAATTGCTCACAAAACCGGAACTCTAGGCATTGTACTAGGTGATGCCGGGATTATTCAAATGCCATCTGGTAAGCGCTACTTAGCCGGAATTTTTGTCAGAAGACCTTTTAATGATTTAAAAGCGCGAGATTTTATCAATCAAGTTTCTCGAATTGTTTACGGCTATTTAGACCAACCAAGAGTCGCCAGCAAGCCTTAATACTCCTGATGTAAAAAAGAAAAATTTTAATTGACGTAAGCCCCTGATATTCATTAATATCTAGGGGTTTTTGCATATCTATTTATAGCAGTGCTTAACGCACCCTATCTCTCAGTGCGTTACGGCTAATCCTTATTCTCTTAAACTAACAAATTCTTGCATAGCCGTAACACATTCTAATTCATATTGGCTTTGAAGGATATTGACTGTATTCCTGCCAAGTTGGCTACATATACCTAAGCCGCACTGCTAAATTATGAATGGGAAATAACTTGCGGGCTTGATAAACCAACTTTTACTACACTAAACATGCTAAAGCATTAACAACGGACGGATTTAGGTTAGTTGCTTATTTTGCTCACTCTTGTGAGAGATTGCTGCTGTTTTTATTGTAGCGATCGACATCAAACTTCTTTATCTCTAAAAGGACAAATATAACAGGAAGTCCTCATTGATTACTCCTATCCTCACCTCGTTCATCGCAAAATGTACGAGGGCTTTTTTTATTTGGCAGAATTTACCCCTATTACGCCAATGATAATTAAAGCTATCGAGAAAAGTTTGGTAAGAGACATTGATTCACGAAACCAAATTACCCCAATAGTAGCGATTACAGTTGTGCCTAAACCTGACCAAACAGCATACGCAATGCTGACTTCAATTTTTTTAAGAGCTAAAGTTAAAAAACTAAAACAAATTCCATAACAGATAAAAATTAAAACCGAGGGAATAGTTCTTGTAAACCCCTCAGACAATTTCATGGAAGTTGTACCAGCGACTTCAAATAAGATTGCTGCAATGAGATAAAGCCAACTATTTACCATGTTTATTGATTGATTATAAGGTGATGATGGGAATATGATTTTTCGACAAGCATAATGAGTCAAAATTCTATATTTAATCTATTAACTAATTCTGCTATTTTGACAACATTTATAGTTAGCTGATGAGATAGGCAAAAATCAAAATATTCATATTTCCGAATTAGTAAAGAAGTTGGTAATCTCTAAAGTTCAGTTTACCACACCAATATTATGGGGGTTTACCGTACTAATACTAAGGTTCGGAAATCATGATGTAATTGGTGATAAAAACCGAATTTACACTGTACTGGATTGTGAATACTATAAAAACAACGCAAATGATTTAAACCTAAATCAACTACACAAAATTAGAAATTAAACGAGGTGGAGACATGACATTAGTGCGTTGGAATCCTTGGCAAGAAATGAACACTCTCCAAAGACAAATCAACAATTTATTTGCAGACGAAATGCTCCCATCTACTTTACTTGAAAGAAGCCTTACAAAAGTTCCGGCGGCTGAATTACACGAATCTGAAGAAGCTATTCATCTCAAGCTAGAATTACCAGGAATTGAAGCCAAAGACCTAGATGTGCAAGTTACAGAAAAAGCTGTGTATATCAGCGGTGAACGGAAATCTGAAACTAAAACAGAAGGGAAAGGTGTAACCAAGAGTGAATTTCATTATGGGAAATTCCAACGTTTGATTCCTTTACCAACTCGCATTCAAAATACCAATGTTACTGCTGATTATAAAGATGGTATTTTGACTCTGACTTTGCCTAAAGCCGAAGAAGAAAAGAAAAAGGTTGTCAAGCTGAATCTTGAATCTATTGGCTAATATCAATTTTGGATTAGCGCTAAAATACCCGACTTCTTTAAGAAGTCGGGTATTTTGTTGTTCACTAATGATTTAAAATTGCTATAAGCTGCGATTTCTGCCTGTTGATTGTTGTCTGTCTACGGGAAAAACGTCAAAATCGAAAGTTGCAATTAGACGCTCATCAACGTATACCTGTATTTTATGCTTACCAGGAGGATCACCTGCGGCGATCGTCCAATAGTTTTCAATTACACCATCATTAGCTATAGTTTTGCGCCTCATTACCGACTCTGTACCGTCAGCGGAGACTGTGAAGTTTTCACCATCATCTGTAGCCCAAGTTTCTGGGGGTTTTGGTAAGCGTAGGACTTCTCGCCATGTAACTTCGCCTTGGTAGTCTTTGAGTTGAATTCGCCACCCATATTTACTACCTTCTTGTAGTGGGACTCTGAATGTGGGGATGAAGTTAACTTTACCTCTAGCATCGACTCTCGCTATGCCAAACTCAGCTTTGTCGATCGCTACCGACTTTTTAGTATTGTTTGCTTGAGAAATTGACCCTGATGATGCTATTTTTTCGTCGGAGATCGCTACTGTAGCATTGATTGGCTGAGACGCTACCAACCCGGAAACTAGCCAAGAAGAAGTTAGTACAACTATTGCAGTCCAAATTCTCATCAGCAAAATTTTTGGTCATTTACTAGTACTTATTCCCGCCTTCCCATTGGCTTCCGGGTACAGTCCCGATAAATAGCCAAGTTGGCAGAATAAAAGTTGCAGAATTAATAGTCAGTTTATAGTTAAATCGGCAACACCAGATCAAGCCACTCAAACTACTTTACTCTCGGGCCAGTTGCCAGAACTGCGAAAACTATCATCGCAGGTTTTCGGTGTAGGTGCTAAATATGCGTTTATTCTTAACTATTTTGTGTTCAATACGGAATTTTTAATATGTAAGCAATTGCTGACAGTCGGCTATTTGATCAATTGTCATTTCCTAGAGTTTCATCCCCTTGAGGGGAAGGAGTTTGGGAAATGTCAAAAACTGTCAAATGCTTAATGCAAAGATTAACAGTTGTGCCTAAGTGCGATCGCACTTAGGCATGACAAAGCATCAAAAATTAGCATTGGAGAACCGATATTTTCCTATTACCTGACTGCTATATATTGATAGTGAGGCGTTTTTGAGCAGCAAACAGCATGGCAGATATTCCAAATTCCATCGCATCATACCGTGCCTTAGCACTGCAAGTTACCTGTCATGCTGTGAATCAAGCGAGCGATCGCCACGCTGTCCAAGAAATCATTCATCATACTATCAACCGCCTGGCGCAACAAATCGCCGCCAGTATTGCTTTTATTGGTTTTGACTGTCGTTTAATTGTTTTACCAGAATATTTTCTGACAGGTTTCCCGATGGGTGAACCTTTGGCTGTTTGGGGAGAAAAGGCTTGTATAGAAATGCACGGTGCCGAGTATGAAGCCCTCAGTAAAATTGCTCAAAAACATCAGATATTTTTAGCTGGTAACGCCTACGAACTCGACCCCAATTTTCCTGGCTTATACTTTCAAACTTGCTTTGTGATTGACCCGGCTGGTGCTATTGTCTTGCGGTATCGGCGGCTAAATTCGTTATTTGCACCCACACCTCATGATGTTTGGGATAAATATCTTGATTGTTACGGCCTAGAAGGGGTGTTTCCTGTAGCGAAAACTGCAATTGGCAATTTAGCCGCTTTAGCTTCCGAAGAAATTTTGTATCCAGAAGTAGCGCGGTGTTTAGCAATGCGTGGTGCAGAAATTTTTCTGCATTCCACTTCTGAAATTTATAGCAAAAACCTCACACCTAAAGATGCGGCGAAAATTTCTCGCGCTGTGGAAAATATGGCTTACGTTGTGTCTGCGAATACCGCAGGTCTAGCTAATAGTTCTATACCCAGCGCTTCTGTTGATGGTGGCTCAAAAATAGTTGACTATCGCGGTATCGTATTAGCAGAAACAGGTGCAGGCGAAAGTATGGCAGCTTTTGCAGAGATAGATTTAACTGCTTTAAGACGCGATCGCCGTCGTCCAGGGTTAAATAATTTACTGTCTCGCCAGCGATTTGAACTCTACGCCCAAAGCTACAGCCAGTCACAATTTTATCCAGCAAACACTATGCTAAATCAAGAATGCGATCGCCAACACTTCATCCAAACACAGCAACAAACCATAGAACGTCTATCTCAGTTAGGAGTGATTTAAAAGTCTAAAGTCTGAAATTAGATTCTTTTGACCATTGACTATTGACAAATGACAAATGACAAAACCAATCGAAGTCCGTAACCCGCGAACGGGAAAATATGATTATGTAATTATCCCACCGCCGCCGAAACTGCTGGCGCAGCAATGTAACCGAGCGCGAAGGGCGCAAGTGCGTTGGCAAAAACTGGGCGTAGAAGGGAGAGTTGCAGCTTTAAAAGAATGGAAGCAAGCAGTTTTGGCTGGACGCGAAAAGCTCACAGATGCTTTGGTCAATGATACGGGTAGATTATCTATATCAGTGATGGAAATCGACTCATTCCTTTCTAGCATCGATCGCTGGTGTGGATTAGCGCCAGATTTATTACAAGATTCGGCCAAAAATACATCAATTCCGTTCATCGCCTTACAACAAACATCAACGCCTTACCCTGTAGTTGGGGTAATTAGTCCTTGGAATTTCCCTCTGTTGCTGTCTACGATAGATACCATTCCCGCACTGTTGGCGGGTTGTGCTGTAGTTGTCAAACCCAGTGAAATTGCACCGCGTTTCATCGCCCCACTGATAGCTGCAATTAATCAAGTACCCGCCTTGCGCGATGTTTTCAGTTTTGTGGAAGGTGCGGGAGAAACTGGCGCGGCTTTGATGGAGAATGTAGATTTAGTTTGTTTTACCGGTAGTGTCGCTACTGGACGCAAAGTTGCAGAAGTCGCCGCACAAAGATTTATCCCCGCTTTTTTGGAATTGGGCGGGAAAGATCCGGCGATCGTGTTGGAATCTGCCGATTTAGAATTAGCCACATCAGCGATTTTATGGGGTTCCGTCGTTAACACCGGACAGTCTTGTTTATCAATTGAGCGTATTTACGTTGCCGAATCTATCTTTGAAAAGTTTTATCATCAGTTAGTAGCCAAAGCACATCGCCTACAACTAGCCCATCCCACCATTGAAAGTGGCGAAATCGGCCCCATTATTGCTGAAAGACAAGCTGGCATAATTAACGAGCATATCTCCGATGCAGTGCAAAAAGGTGCAGTAATTCATTGTGGCGGTAAAGTTGAAGAGTTAGGCGGTGGTTGGTGGTGTCATCCCACAGTGCTGACTCATGTTAACCATACAATGAAAGTCATGACCGAAGAGACTTTTGGCCCGATCATGCCAATCATGCCTTTTGCCACAGTAGAGGAAGCTGTTAACTTAGCCAACGATTCAATTTATGGACTGAGTGCGGCGGTGTTTGCGGAAACCGAAACTGAAGCGTTAACAGTTGCCCAGCAAATAGATGCAGGTGCTATCAGTATTAATGATGCCGCCCTCACCGCCATTATGCACGAAGGTGAAAAAAACGCTTTCAAATTATCCGGTTTAGGCGGTTCACGTATGGGTGCAGCCGCCATCAAACGATTTTTGCGGAAAAAAGCGTTTTTGATTAAAACCAACTCAAATCAAGACCCTTGGTGGTTTGAGCCTAAAGTGTAGTGCAATCTTCTCTCAGCGACCTCTGCGTCTCTGTAGTTCGTTAAAAACCGTATTAGATTCTGTTTGTTGGGTTTCGCTGTCGCTTCACCCAACCTACTTTCCTTAAACCCCTACTACAGATTCATTCACAGTTTCACTAGCCGCAACACCATTAGTCAAAATCGCTTGCCGAGTTTTCAGGTTAAATTTATAACCATGTGGCAAAATATGCAGCTTCGCACCACAAATTGCCAAAGGTTCATCCCGGAGAATTGTATCTGCGTTGTTATATGTAGATTCAGACTCATCCACAATGGTGACTGAACCTTCACCAATAATTTCGATTTGGTCATCAGTCACGGCGATCGCTGTATTCTCATCAATCCCAAATCCTAACACCGCAGGTTCATGAATTAAAGCTGTAATTAAACGCCCTAAGCGTCCCCGTTGTAAGAAATGTTGGTCAATCACCACCCCTGGGAGAAAACCCATACCAGGCCCCATTTCCACAATTTCCATCCGTGGTGTACTTTGAGAATCACCCTCAACAATCATTTTATCGGGCATCACAGCCGCACCCGCACTAGTACCTGCAATTACTGCACCTTCAGCATAGCGTTGGTGAATAGCCGCATCGATTTCGGTATCCTTGAGGATACTAGTAATTCGCGCTTGGTCTCCTCCAGTAAAAAATATCCCAGTCGCCTTAGCAATAGCTTCTAAAGCCGTAGAAGACCTAGCATCTTCACGAGTTTCTGTATCAATAATGCGAACGTGTTCTGCACCTAGCCGTTCAAAAACTCTAATATAATTTTCCCCCACTTCTCTAGGCAGTTCTGTGGCGGCCGTCATAATTACAATATTGGCTTTTGTACCCCCAGCCCGACGGACAAATTCTCGCAGAATCACACAATCTCCTTCTTTATCTTCTGCGCCACCAATAATTACCAACTGGCGTTTATGTGCAGTTTCTGTCATAATGCCCCCCGGATAACCGGATTAGAATTTAATTTAGATTAATTTCAATAAAACATGACAATTATCACAATCAAATCATCCATTTGATAGATTAATTTTTAATGGCAAAAGTTAAATTATATATAACTTTATGTATATATAAACTCTTGCCAAATTTAGCATTTTTAATAATTGGTAATTCATTTAGCAGAATTACCAATTACTTATACAGTAATAATTTATGTATAACTCTTCTCAAGTAATAGCACTAAAATCTCATAGT',
'description': 'NZ_AP018174.1 Anabaenopsis circularis NIES-21 DNA, nearly complete genome',
'start_pos': 1824000,
'end_pos': 1836200,
'fasta_url': 'https://ftp.ncbi.nlm.nih.gov/genomes/refseq/bacteria/Anabaenopsis_circularis/latest_assembly_versions/GCF_002367975.1_ASM236797v1/GCF_002367975.1_ASM236797v1_genomic.fna.gz'}
```
### Data Fields
- `sequence`: a string containing a DNA sequence from the human reference genome
- `desciption`: a string indicating the Species of the sequence as well as the NCBI id.
- `start_pos`: an integer indicating the index of the sequence's first nucleotide
- `end_pos`: an integer indicating the index of the sequence's last nucleotide
- `fasta_url`: a string indicating the URL used to download the fasta from which the sequence was taken.
### Data Splits
The Multi-species dataset has 3 splits: train, validation, and test. |
## Dataset Creation
[N/A]
### Curation Rationale
[N/A]
### Source Data
#### Initial Data Collection and Normalization
The data consists of sequences cut from the the whole genome sequences of the 850 species sampled that can be found in the `urls.csv` file of this dataset's repository.
#### Who are the source language producers?
[N/A]
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
[N/A]
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
[N/A]
### Licensing Information
[N/A]
### Citation Information
```bibtex
@article{dalla2023nucleotide,
title={The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics},
author={Dalla-Torre, Hugo and Gonzalez, Liam and Mendoza Revilla, Javier and Lopez Carranza, Nicolas and Henryk Grywaczewski, Adam and Oteri, Francesco and Dallago, Christian and Trop, Evan and Sirelkhatim, Hassan and Richard, Guillaume and others},
journal={bioRxiv},
pages={2023--01},
year={2023},
publisher={Cold Spring Harbor Laboratory}
}
```
|
InstaDeepAI/multi_species_genomes
|
[
"DNA",
"Genomics",
"Nucleotide",
"region:us"
] |
2023-04-06T18:05:46+00:00
|
{"pretty_name": "Human Reference Genome", "tags": ["DNA", "Genomics", "Nucleotide"]}
|
2023-11-01T14:07:25+00:00
|
96cc2daa40aa7357476ebcaf62392913d75e5a21
|
# Dataset Card for "tashkeela"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
arbml/tashkeela
|
[
"region:us"
] |
2023-04-06T18:07:05+00:00
|
{"dataset_info": {"features": [{"name": "diacratized", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1419585102, "num_examples": 979982}, {"name": "test", "num_bytes": 78869542, "num_examples": 54444}, {"name": "dev", "num_bytes": 78863352, "num_examples": 54443}], "download_size": 747280703, "dataset_size": 1577317996}}
|
2023-04-06T18:09:05+00:00
|
f2be934bc7a4c788c37e0fd2a54c27b2e9f65156
|
# Musk
The [Musk dataset](https://archive.ics.uci.edu/ml/datasets/Musk) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Census dataset including personal characteristic of a person, and their income threshold.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|------------------------|
| musk | Binary classification | Is the molecule a musk?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/musk", "musk")["train"]
```
|
mstz/musk
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"musk",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] |
2023-04-06T18:08:11+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Musk", "tags": ["musk", "tabular_classification", "binary_classification", "multiclass_classification", "UCI"], "configs": ["musk"]}
|
2023-04-16T16:34:46+00:00
|
a1d78cd8a4001b4e40f36ee4a608defdd3b41701
|
# Dataset Card for "MULTI_VALUE_rte_yall"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_rte_yall
|
[
"region:us"
] |
2023-04-06T18:14:01+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 22293, "num_examples": 42}, {"name": "train", "num_bytes": 17700, "num_examples": 34}], "download_size": 36392, "dataset_size": 39993}}
|
2023-04-06T18:14:04+00:00
|
04e4f951a987a668cd90e43c5db1bcbf74b986c2
|
# Dataset Card for "acgn_face_control_60k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
huanngzh/anime_face_control_60k
|
[
"region:us"
] |
2023-04-06T18:14:05+00:00
|
{"dataset_info": {"features": [{"name": "item_id", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "landmarks", "sequence": {"sequence": "float64"}}, {"name": "source", "dtype": "image"}, {"name": "target", "dtype": "image"}, {"name": "visual", "dtype": "image"}, {"name": "origin_path", "dtype": "string"}, {"name": "source_path", "dtype": "string"}, {"name": "target_path", "dtype": "string"}, {"name": "visual_path", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5359477272.0, "num_examples": 60000}], "download_size": 0, "dataset_size": 5359477272.0}}
|
2023-04-07T01:20:48+00:00
|
f36bc85e6b03861dd512b65ef939308f37d00bf8
|
# Dataset Card for "MULTI_VALUE_rte_comparative_than"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_rte_comparative_than
|
[
"region:us"
] |
2023-04-06T18:14:06+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 8322, "num_examples": 16}, {"name": "train", "num_bytes": 9148, "num_examples": 22}], "download_size": 24114, "dataset_size": 17470}}
|
2023-04-06T18:14:08+00:00
|
126c48cb518e3da915bdc526b9467351a3e910fe
|
# Dataset Card for "MULTI_VALUE_rte_present_modals"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_rte_present_modals
|
[
"region:us"
] |
2023-04-06T18:14:06+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 120227, "num_examples": 257}, {"name": "train", "num_bytes": 113082, "num_examples": 237}], "download_size": 161901, "dataset_size": 233309}}
|
2023-04-06T18:14:09+00:00
|
f235818670f95b89859ed34be10c3d7a80a1c1f7
|
# Dataset Card for "MULTI_VALUE_rte_medial_object_perfect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_rte_medial_object_perfect
|
[
"region:us"
] |
2023-04-06T18:14:10+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 130021, "num_examples": 302}, {"name": "train", "num_bytes": 117795, "num_examples": 243}], "download_size": 169255, "dataset_size": 247816}}
|
2023-04-06T18:14:13+00:00
|
bf4e2c0855525fb9d038e9cb2595e857bfac76d2
|
# Dataset Card for "MULTI_VALUE_rte_drop_aux_be_gonna"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_rte_drop_aux_be_gonna
|
[
"region:us"
] |
2023-04-06T18:14:20+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 4587, "num_examples": 8}, {"name": "train", "num_bytes": 4110, "num_examples": 6}], "download_size": 18134, "dataset_size": 8697}}
|
2023-04-06T18:14:23+00:00
|
34888135b588dac7b08375218a8b8bfa5a2784c9
|
# Dataset Card for "MULTI_VALUE_rte_plural_interrogative"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_rte_plural_interrogative
|
[
"region:us"
] |
2023-04-06T18:14:21+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 38643, "num_examples": 71}, {"name": "train", "num_bytes": 31784, "num_examples": 66}], "download_size": 57752, "dataset_size": 70427}}
|
2023-04-06T18:14:24+00:00
|
8a1f7b1b9148ae12cf17efea62f0ef28278a2156
|
# Dataset Card for "MULTI_VALUE_rte_fronting_pobj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_rte_fronting_pobj
|
[
"region:us"
] |
2023-04-06T18:14:26+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 650133, "num_examples": 2022}, {"name": "train", "num_bytes": 570848, "num_examples": 1692}], "download_size": 789565, "dataset_size": 1220981}}
|
2023-04-06T18:14:29+00:00
|
2c6c0ce74fe60d29b6de30d680df56f0c38a4fa1
|
# Dataset Card for "MULTI_VALUE_rte_object_pronoun_drop"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_rte_object_pronoun_drop
|
[
"region:us"
] |
2023-04-06T18:14:29+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 99406, "num_examples": 193}, {"name": "train", "num_bytes": 92593, "num_examples": 182}], "download_size": 139152, "dataset_size": 191999}}
|
2023-04-06T18:14:33+00:00
|
963901591644e2d7a9dd938ea7830157397a1527
|
# Dataset Card for "MULTI_VALUE_rte_regularized_past_tense"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_rte_regularized_past_tense
|
[
"region:us"
] |
2023-04-06T18:14:40+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 334657, "num_examples": 815}, {"name": "train", "num_bytes": 278765, "num_examples": 659}], "download_size": 401674, "dataset_size": 613422}}
|
2023-04-06T18:14:46+00:00
|
9538d49f3b6239f0a3e9868d99e22501cee6635f
|
# Dataset Card for "MULTI_VALUE_rte_it_is_referential"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_rte_it_is_referential
|
[
"region:us"
] |
2023-04-06T18:14:43+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 10806, "num_examples": 22}, {"name": "train", "num_bytes": 5089, "num_examples": 12}], "download_size": 22066, "dataset_size": 15895}}
|
2023-04-06T18:14:49+00:00
|
cd9759f46ba4b72f057f46a748d0d1dc41f8cd53
|
# Dataset Card for "MULTI_VALUE_rte_volition_changes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_rte_volition_changes
|
[
"region:us"
] |
2023-04-06T18:14:58+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 7326, "num_examples": 14}, {"name": "train", "num_bytes": 6754, "num_examples": 15}], "download_size": 17966, "dataset_size": 14080}}
|
2023-04-06T18:15:05+00:00
|
189821714a9d1b4b6d30dbee11fdebc7e84934ff
|
# Musk
The [Musk dataset](https://archive.ics.uci.edu/ml/datasets/Musk) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Census dataset including personal characteristic of a person, and their income threshold.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|------------------------|
| musk | Binary classification | Is the molecule a musk?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/muskV2")["train"]
```
|
mstz/muskV2
|
[
"task_categories:tabular-classification",
"size_categories:100<n<1K",
"language:en",
"musk",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"region:us"
] |
2023-04-06T18:25:22+00:00
|
{"language": ["en"], "size_categories": ["100<n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Musk", "tags": ["musk", "tabular_classification", "binary_classification", "multiclass_classification"], "configs": ["musk"]}
|
2023-04-07T13:32:09+00:00
|
1f9966fe1b508b882d2df2e784e8b13dc81552ad
|
# Dataset Card for "BroadleafCommerce_broadleaf_3_0_10_GA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nguyenminh871/BroadleafCommerce_broadleaf_3_0_10_GA
|
[
"region:us"
] |
2023-04-06T18:28:41+00:00
|
{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "func", "dtype": "string"}, {"name": "target", "dtype": "bool"}, {"name": "project", "dtype": "string"}], "splits": [{"name": "BroadleafCommerce_broadleaf_3_0_10_GA", "num_bytes": 6257292, "num_examples": 2094}], "download_size": 1631435, "dataset_size": 6257292}}
|
2023-04-06T18:28:54+00:00
|
47cafbb0dd81f834cd177f9f067fe2f3fd0ddabb
|
# Dataset Card for "hazelcast_3_3_EA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nguyenminh871/hazelcast_3_3_EA
|
[
"region:us"
] |
2023-04-06T18:29:18+00:00
|
{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "func", "dtype": "string"}, {"name": "target", "dtype": "bool"}, {"name": "project", "dtype": "string"}], "splits": [{"name": "hazelcast_3_3_EA", "num_bytes": 8353741, "num_examples": 3765}], "download_size": 1934896, "dataset_size": 8353741}}
|
2023-04-06T18:29:31+00:00
|
f6ce577082493a927a672292dc8a2ae7cdfba7f9
|
# Dataset Card for "orientdb_1_6_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nguyenminh871/orientdb_1_6_2
|
[
"region:us"
] |
2023-04-06T18:29:44+00:00
|
{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "func", "dtype": "string"}, {"name": "target", "dtype": "bool"}, {"name": "project", "dtype": "string"}], "splits": [{"name": "orientdb_1_6_2", "num_bytes": 8348430, "num_examples": 2098}], "download_size": 2141816, "dataset_size": 8348430}}
|
2023-04-06T18:30:01+00:00
|
e9e7d7b9a5b048c19eb80a97b61de388fe3a9f34
|
mskov/misophoniaSounds
|
[
"task_categories:audio-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
2023-04-06T18:30:09+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["audio-classification"]}
|
2023-04-06T21:12:22+00:00
|
|
577588a3a5e7eff4d100e35e59262d24256d5d3d
|
# Dataset Card for "processed_gpt_dataset_big"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sanagnos/processed_gpt_dataset_big
|
[
"region:us"
] |
2023-04-06T18:39:47+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": 23584245444.0, "num_examples": 3831099}], "download_size": 6899066299, "dataset_size": 23584245444.0}}
|
2023-04-06T19:05:27+00:00
|
7eecddd2a1341a49c8f30687bdcfd546f043cadb
|
# Dataset Card for CoCoCON
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
CocoCON is a challenging dataset for evaluating cross-task consistency in vision-and-language models. We use contrast sets created by modifying COCO test instances for multiple tasks in small but semantically meaningful ways to change the gold label, and outline metrics for measuring if a model is consistent by ranking the original and perturbed instances across tasks. We find that state-of-the-art systems suffer from a surprisingly high degree of inconsistent behavior across tasks, especially for more heterogeneous tasks.
- **Homepage:**
https://adymaharana.github.io/cococon/
- **Repository:**
https://github.com/adymaharana/cococon
- **Paper:**
https://arxiv.org/abs/2303.16133
- **Point of Contact:**
[email protected];
### Languages
English.
## Dataset Structure
Each sample in this dataset corresponds to a COCO image, a set of ground truth annotations for the image captioning, visual question-answering (VQA), and localization (optional) tasks, and their respective contrast sets.
### Data Fields
caption (string): ground truth caption.
query (string): VQA question.
answer (string): ground truth VQA answer.
question_id (int64): unordered unique identifier for sample.
image_id (int64): COCO image id.
detection (string): (optional) localization query.
boxes (list): (optional) list of ground truth bounding boxes for the localization query.
contrast_sets: Each sample in "contrast_sets" is a set of perturbed annotations corresponding to the ground truth annotations. Perturbed annotations are prefixed with "mutex_".
file_name (string): COCO filename for the image.
coco_url (string): url for downloading the image from the COCO server.
flickr_url (string): url for downloading the image from Flickr.
height (int64): height of image.
width (int64): width of image.
id (int64): ordered unique identifier for sample.
### Data Splits
The CocoCON benchmark is an evaluation-only dataset. The data accessible through this link should be considered as the test split.
## Dataset Creation
The CoCoCON dataset is created by a combination of machine + expert annotators who perturbed ground truth COCO annotations to create contrast sets.
## Considerations for Using the Data
### Licensing Information
CC-By 4.0
### Citation Information
@article{maharana2023cococon,
author = {Maharana, Adyasha and Kamath, Amita and Clark, Christopher and Bansal, Mohit and Kembhavi, Aniruddha},
title = {Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models.},
journal = {arxiv},
year = {2023},
}
|
adymaharana/cococon
|
[
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"consistency",
"visual-reasoning",
"arxiv:2303.16133",
"region:us"
] |
2023-04-06T18:41:52+00:00
|
{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "task_ids": [], "pretty_name": "CoCoCON", "tags": ["consistency", "visual-reasoning"]}
|
2023-04-10T15:25:13+00:00
|
d11db01bffb24ddab9d02595892d435f9b37f454
|
Amirkid/jokes
|
[
"license:creativeml-openrail-m",
"region:us"
] |
2023-04-06T18:45:00+00:00
|
{"license": "creativeml-openrail-m", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 131818111, "num_examples": 578634}], "download_size": 86215403, "dataset_size": 131818111}}
|
2023-04-06T18:45:55+00:00
|
|
04ce8aa84f9fd6ef27441f0accd7e69be2f6bca0
|
Amirkid/reddit
|
[
"license:creativeml-openrail-m",
"region:us"
] |
2023-04-06T18:58:22+00:00
|
{"license": "creativeml-openrail-m", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 136321153, "num_examples": 574159}], "download_size": 89389496, "dataset_size": 136321153}}
|
2023-04-06T18:59:10+00:00
|
|
0e79d81e9819d2b75bad3faa8e3bb251c97248cc
|
rosaaldama/books
|
[
"license:openrail",
"region:us"
] |
2023-04-06T19:35:51+00:00
|
{"license": "openrail"}
|
2023-04-06T19:35:51+00:00
|
|
6fd469cff5b9711b4ddc566067ba9186008c08ae
|
# Dataset Card for "chunk_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_2
|
[
"region:us"
] |
2023-04-06T20:04:34+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 17130352896.0, "num_examples": 178352}], "download_size": 15246910271, "dataset_size": 17130352896.0}}
|
2023-04-06T20:19:08+00:00
|
1606b44f73c3db2714b2a76e364da67230e85d69
|
# Dataset Card for "chunk_6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_6
|
[
"region:us"
] |
2023-04-06T20:06:03+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 21413517408.75, "num_examples": 222946}], "download_size": 18385746022, "dataset_size": 21413517408.75}}
|
2023-04-06T20:17:36+00:00
|
a49364b2c9165c0cf903004660de75c964b6c02e
|
# Dataset Card for "chunk_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_4
|
[
"region:us"
] |
2023-04-06T20:08:14+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 17303239296.0, "num_examples": 180152}], "download_size": 15078034325, "dataset_size": 17303239296.0}}
|
2023-04-06T20:19:41+00:00
|
cee0c0536b1ee783d18c91f00a74ae42254b86c2
|
# Dataset Card for "chunk_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_0
|
[
"region:us"
] |
2023-04-06T20:08:53+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 21981833424.125, "num_examples": 228863}], "download_size": 18831760350, "dataset_size": 21981833424.125}}
|
2023-04-06T20:46:09+00:00
|
912bac6badb30c1f08803c5e0a72c96a2f83d296
|
# Dataset Card for "chunk_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_1
|
[
"region:us"
] |
2023-04-06T20:09:26+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 17666588880.125, "num_examples": 183935}], "download_size": 15417818418, "dataset_size": 17666588880.125}}
|
2023-04-06T20:26:57+00:00
|
3deb3f9ac1410bd1e6abe5f8f3e54d30c2da32e2
|
# AutoTrain Dataset for project: chewan
## Dataset Description
This dataset has been automatically processed by AutoTrain for project chewan.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "One of the other reviewers has mentioned that after watching just 1 Oz episode you'll be hooked. They are right, as this is exactly what happened with me.<br /><br />The first thing that struck me about Oz was its brutality and unflinching scenes of violence, which set in right from the word GO. Trust me, this is not a show for the faint hearted or timid. This show pulls no punches with regards to drugs, sex or violence. Its is hardcore, in the classic use of the word.<br /><br />It is called OZ as that is the nickname given to the Oswald Maximum Security State Penitentary. It focuses mainly on Emerald City, an experimental section of the prison where all the cells have glass fronts and face inwards, so privacy is not high on the agenda. Em City is home to many..Aryans, Muslims, gangstas, Latinos, Christians, Italians, Irish and more....so scuffles, death stares, dodgy dealings and shady agreements are never far away.<br /><br />I would say the main appeal of the show is due to the fact that it goes where other shows wouldn't dare. Forget pretty pictures painted for mainstream audiences, forget charm, forget romance...OZ doesn't mess around. The first episode I ever saw struck me as so nasty it was surreal, I couldn't say I was ready for it, but as I watched more, I developed a taste for Oz, and got accustomed to the high levels of graphic violence. Not just violence, but injustice (crooked guards who'll be sold out for a nickel, inmates who'll kill on order and get away with it, well mannered, middle class inmates being turned into prison bitches due to their lack of street skills or prison experience) Watching Oz, you may become comfortable with what is uncomfortable viewing....thats if you can get in touch with your darker side.",
"target": 1
},
{
"text": "A wonderful little production. <br /><br />The filming technique is very unassuming- very old-time-BBC fashion and gives a comforting, and sometimes discomforting, sense of realism to the entire piece. <br /><br />The actors are extremely well chosen- Michael Sheen not only \"has got all the polari\" but he has all the voices down pat too! You can truly see the seamless editing guided by the references to Williams' diary entries, not only is it well worth the watching but it is a terrificly written and performed piece. A masterful production about one of the great master's of comedy and his life. <br /><br />The realism really comes home with the little things: the fantasy of the guard which, rather than use the traditional 'dream' techniques remains solid then disappears. It plays on our knowledge and our senses, particularly with the scenes concerning Orton and Halliwell and the sets (particularly of their flat with Halliwell's murals decorating every surface) are terribly well done.",
"target": 1
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['negative', 'positive'], 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 | 1471 |
| valid | 602 |
|
Chewan/autotrain-data-chewan
|
[
"task_categories:text-classification",
"language:en",
"region:us"
] |
2023-04-06T20:10:47+00:00
|
{"language": ["en"], "task_categories": ["text-classification"]}
|
2023-04-07T08:52:53+00:00
|
0825adc61264482470aac02edb3010855aedd213
|
# Dataset Card for "chunk_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_3
|
[
"region:us"
] |
2023-04-06T20:11:55+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 18734066352.875, "num_examples": 195049}], "download_size": 15931724724, "dataset_size": 18734066352.875}}
|
2023-04-06T20:28:45+00:00
|
d3e6f988de7cd440693f443a56a4e953012e8590
|
# Dataset Card for "chunk_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_5
|
[
"region:us"
] |
2023-04-06T20:15:08+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 18436029408.75, "num_examples": 191946}], "download_size": 15886893185, "dataset_size": 18436029408.75}}
|
2023-04-06T20:30:55+00:00
|
6ec69e50d0c9caca14b22499193d4074db257029
|
# Dataset Card for "somos-alpaca-es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Sebastian77/somos-alpaca-es
|
[
"region:us"
] |
2023-04-06T20:20:51+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "tr-flag-2-input", "dtype": "bool"}, {"name": "tr-flag-3-output", "dtype": "bool"}]}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 703591, "num_examples": 37}], "download_size": 0, "dataset_size": 703591}}
|
2023-04-15T12:35:27+00:00
|
29371fc4e510f1447275011aca65bc7b53915a73
|
# Dataset Card for "chunk_9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_9
|
[
"region:us"
] |
2023-04-06T20:26:16+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 20343446640.375, "num_examples": 211805}], "download_size": 18016130134, "dataset_size": 20343446640.375}}
|
2023-04-06T20:44:10+00:00
|
8d4f8c9ea291c85f92a525ac2f61b1cbd3b9d394
|
## 🐥 🇧🇷 Canarim Instruct Dataset
<p align="center">
<img width="250" alt="Camarim Logo" src="https://raw.githubusercontent.com/DominguesM/Canarim-Instruct-PTBR/main/assets/canarim.png">
</p>
<p align="center">
<a href="https://github.com/DominguesM/Canarim-Instruct-PTBR">[🐱 Github]</a>
</p>
<hr>
## What's Canarim?
Canarim is a dataset with over 300,000 instructions in Portuguese, ranging from simple instructions like "Descreva os efeitos do aquecimento global" to more complex instructions like "Nesta tarefa, você precisa ser capaz de resumir uma determinada lista de pontos-chave" where additional context is provided.
## Why it's called Canarim?
"Canarim" is spoken in some regions of Brazil (mainly by grandparents), and it could be translated as "canarinho," which means "little canary" in English. "Canarim" (is pronounced: kɑnɑrɪm) or canary is a bird very present in Brazilian daily life, living for up to 30 years. Every Brazilian at some point in their life has come across this bird, which is why I chose this name for my project.
## Source Data
This dataset was created through translation and adaptation from the following sources:
* [**dominguesm/alpaca-data-pt-br**](https://huggingface.co/datasets/dominguesm/alpaca-data-pt-br) (*51759 rows*)
* [**cahya/instructions-pt**](https://huggingface.co/datasets/cahya/instructions-pt) (*57692 rows*)
* [**HuggingFaceH4/self_instruct**](https://huggingface.co/datasets/HuggingFaceH4/self_instruct) (*74350 rows*)
* [**HuggingFaceH4/helpful_instructions (self_instruct)**](https://huggingface.co/datasets/HuggingFaceH4/helpful_instructions/viewer/self_instruct/train) (*82612 rows*)
* [**HuggingFaceH4/helpful_instructions (super_natural_instructions)**](https://huggingface.co/datasets/HuggingFaceH4/helpful_instructions/viewer/super_natural_instructions/train) (*50000 rows*)
Totaling 316k lines in the training split and 1519 lines in the test split, all lines must contain an "instruction" and an "output", and may or may not also contain a "context".
There may be translation errors in the data.
## Prompt
Below is the prompt template I am using with this dataset:
```python
def generate_prompt(instruction, input=None):
if input:
return f"""Abaixo está uma instrução que descreve uma tarefa, emparelhada com uma entrada que fornece mais contexto. Escreva uma resposta que conclua adequadamente a solicitação.
### Instruções:
{instruction}
### Entrada:
{input}
### Resposta:"""
else:
return f"""Abaixo está uma instrução que descreve uma tarefa. Escreva uma resposta que conclua adequadamente a solicitação.
### Instruções:
{instruction}
### Resposta:"""
```
## Examples of prompts
```json
{
"instruction": "Sugira uma estratégia de marketing eficaz para um programa de TV.",
"input": "",
"output": "Uma estratégia de marketing eficaz para um programa de TV é criar um trailer de teaser a ser lançado antes da estréia do programa na televisão e outras plataformas digitais. O trailer de teaser deve ser curto, dinâmico e atraente, a fim de desencadear a curiosidade e o interesse nos espectadores. Ele também deve conter uma linha de tags convincente ou "gancho" que resume a premissa do programa. Além disso, criar uma hashtag associada ao programa de conteúdo da TV e pedir aos espectadores que se juntem à conversa nas mídias sociais."
}
```
```json
{
"instruction": "Compare os dois assuntos a seguir",
"input": "Inteligência Artificial e Machine Learning",
"output": "Inteligência Artificial (IA) e Machine Learning (ML) são campos da ciência da computação que envolvem o desenvolvimento de máquinas inteligentes que podem aprender com dados e executar tarefas de forma autônoma. A IA se concentra no comportamento semelhante ao humano e envolve o desenvolvimento de algoritmos e estruturas de dados que permitem que as máquinas aprendam através do reconhecimento de padrões e do raciocínio lógico. A ML, por outro lado, é um subconjunto de IA que se concentra em ensinar computadores a otimizar tarefas com um certo grau de precisão."
}
```
## License Information
### Overview
The Canarim-Instruct dataset is now licensed under CC-BY-NC (Creative Commons Attribution-NonCommercial). This change is a result of my commitment to ethical data usage and legal compliance, particularly in the realm of derived data and AI-generated content.
### Why CC-BY-NC?
My decision to adopt the CC-BY-NC license comes from a detailed assessment of the origins and intended use of the Canarim-Instruct dataset. Portions of our dataset derive from or are influenced by models from OpenAI (e.g., Self-instruct, Alpaca). In light of this, adherence to specific guidelines on the usage of such data is essential.
The policy of OpenAI limits the use of its model generations for training other models, especially in commercial scenarios. To align with these guidelines and ensure the responsible use of AI-generated data, the CC-BY-NC license was selected as the most appropriate.
### What Does This Mean for Users?
- **Remixing and Adaptation**: Users are free to remix, adapt, and build upon the Canarim-Instruct dataset non-commercially.
- **Credit**: Proper attribution must be given to me as the creator of the dataset, with a link to the license and an indication of any changes made.
- **Non-Commercial Use**: The dataset is not to be used for commercial purposes under this license.
I believe that the CC-BY-NC license strikes a balance between open accessibility and the legal and ethical considerations surrounding AI-generated data. My aim is to create an environment where the community can utilize this valuable resource for research and development while respecting the boundaries set by the origins of the data and relevant policies.
## Citation
If you want to cite **Canarim Instruct PTBR dataset**, you could use this:
```
@misc {maicon_domingues_2023,
author = { {Maicon Domingues} },
title = { Canarim-Instruct-PTBR-Dataset (Revision c2de751) },
year = 2023,
url = { https://huggingface.co/datasets/dominguesm/Canarim-Instruct-PTBR-Dataset },
doi = { 10.57967/hf/0983 },
publisher = { Hugging Face }
}
```
|
dominguesm/Canarim-Instruct-PTBR-Dataset
|
[
"language:pt",
"license:cc-by-nc-4.0",
"doi:10.57967/hf/0983",
"region:us"
] |
2023-04-06T20:36:49+00:00
|
{"language": "pt", "license": "cc-by-nc-4.0", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 113100060, "num_examples": 316413}, {"name": "test", "num_bytes": 687328, "num_examples": 1519}], "download_size": 63510092, "dataset_size": 113787388}}
|
2023-11-17T09:03:46+00:00
|
7efb770cfb96a3a0094fff73e113791d21f545df
|
# Ozone
The [Ozone dataset](https://archive.ics.uci.edu/ml/datasets/Ozone) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-------------------------|
| 8hr | Binary classification | Is there an ozone layer?|
| 1hr | Binary classification | Is there an ozone layer?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/ozone", "8hr")["train"]
```
|
mstz/ozone
|
[
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"ozone",
"tabular_classification",
"binary_classification",
"region:us"
] |
2023-04-06T20:44:22+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["1K<n<10K"], "task_categories": ["tabular-classification"], "pretty_name": "Ozone", "tags": ["ozone", "tabular_classification", "binary_classification"], "configs": ["8hr", "1hr"]}
|
2023-04-16T16:57:24+00:00
|
cab38dadfa71d230be0022ec0b0478b19357239d
|
# Dataset Card for 'ML Articles Subset of Scientific Papers' Dataset
## Dataset Summary
The dataset consists of 32,621 instances from the 'Scientific papers' dataset, a selection of scientific papers and summaries from ArXiv repository. This subset focuses on articles that are semantically, vocabulary-wise, structurally, and meaningfully closest to articles describing machine learning. This subset was created using sentence embeddings and K-means clustering.
## Supported Tasks and Leaderboards
The dataset supports tasks related to text summarization. Particularly, the dataset was created for fine-tuning transformer models for summarization. There are no established leaderboards at this moment.
## Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
An instance in the dataset includes a scientific paper and its summary, both in English.
### Data Fields
article: The full text of the scientific paper.\
abstract: The summary of the paper.
### Data Splits
The dataset is split into:\
-training subset: 30280 articles\
-validation subset: 1196 articles\
-test subset: 1145 articles
## Dataset Creation
### Methods
The subset was created using sentence embeddings from a transformer model, SciBERT. The embeddings were clustered into 6 clusters using the K-means clustering algorithm. The cluster closest to articles strongly related to the machine learning area by cosine similarity was chosen to form this dataset.
### Source Data
The dataset is a subset of the 'Scientific papers' dataset, which includes scientific papers from the ArXiv repository.
### Social Impact
This dataset could help improve the quality of summarization models for machine learning research articles, which in turn can make such content more accessible.
### Discussion of Biases
As the dataset focuses on machine learning articles, it may not be representative of scientific papers in general or other specific domains.
### Other Known Limitations
As the dataset has been selected based on a specific methodology, it may not include all machine learning articles or may inadvertently include non-machine learning articles.
### Dataset Curators
The subset was created as part of a project aimed to build an effective summarization model for Machine Learning articles.
|
bakhitovd/ML_arxiv
|
[
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:en",
"license:cc0-1.0",
"region:us"
] |
2023-04-06T20:46:29+00:00
|
{"language": ["en"], "license": "cc0-1.0", "size_categories": ["10K<n<100K"], "task_categories": ["summarization"], "pretty_name": "ML Articles Subset of Scientific Papers"}
|
2023-05-19T20:47:33+00:00
|
de788dd6f99d68c826032e10de5d2155f98e5926
|
# Dataset Card for "chunk_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_8
|
[
"region:us"
] |
2023-04-06T20:47:34+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 21109813632.0, "num_examples": 219784}], "download_size": 18582152239, "dataset_size": 21109813632.0}}
|
2023-04-06T21:16:26+00:00
|
7d63b500b9ba89b4feeb4dadb5e270c0d1bc4560
|
# Dataset Card for "somos-clean-alpaca-es-validations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lopezjm96/somos-clean-alpaca-es-validations
|
[
"region:us"
] |
2023-04-06T21:07:52+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "tr-flag-1-instruction", "dtype": "bool"}, {"name": "tr-flag-2-input", "dtype": "bool"}, {"name": "tr-flag-3-output", "dtype": "bool"}]}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 339970, "num_examples": 18}], "download_size": 0, "dataset_size": 339970}}
|
2023-04-28T19:33:55+00:00
|
ba207c6aebb02294a71e92953b17f989b49e7f55
|
# pima
The [pima dataset](https://archive.ics.uci.edu/ml/datasets/Ozone) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Predict diabetes of a patient.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-------------------------|
| pima | Binary classification | Does the patient have diabetes?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/pima")["train"]
```
|
mstz/pima
|
[
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"pima",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
2023-04-06T21:15:13+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["1K<n<10K"], "task_categories": ["tabular-classification"], "pretty_name": "Ozone", "tags": ["pima", "tabular_classification", "binary_classification", "UCI"], "configs": ["pima"]}
|
2023-04-16T16:57:48+00:00
|
5e12e8c676a1d6751786fceb38969e24a666c82e
|
# Dataset Card for "hy_eanc_2023"
5M tokens
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
armvectores/hy_eanc_2023
|
[
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:hy",
"region:us"
] |
2023-04-06T21:31:25+00:00
|
{"language": ["hy"], "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "\u0531\u0562\u0578\u057e\u0575\u0561\u0576 \u053d\u0561\u0579\u0561\u057f\u0578\u0582\u0580\u055d \u00a0\u00a0\u0531\u057c\u0561\u057b\u056b\u0576 \u057d\u0565\u0580\u0568", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 61486730, "num_examples": 384406}], "download_size": 28553551, "dataset_size": 61486730}}
|
2023-04-06T22:02:52+00:00
|
8595fbb31322034a80260ff5f4ae555ea7decd73
|
# Dataset Card for "biored_tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
J4YL19/biored_tokenized
|
[
"region:us"
] |
2023-04-06T21:33:48+00:00
|
{"dataset_info": {"features": [{"name": "pmid", "dtype": "string"}, {"name": "passage", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 2259680, "num_examples": 387}, {"name": "val", "num_bytes": 604670, "num_examples": 98}, {"name": "test", "num_bytes": 576610, "num_examples": 97}], "download_size": 1083246, "dataset_size": 3440960}}
|
2023-04-06T21:33:57+00:00
|
6ccd5e14133fd3e30e307785999665273045a035
|
# Planning
The [Planning dataset](https://archive.ics.uci.edu/ml/datasets/Planning) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|------------------------------------|
| planning | Binary classification | Is the patient in a planning state?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/planning")["train"]
```
|
mstz/planning
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"planning",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
2023-04-06T21:38:04+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Planning", "tags": ["planning", "tabular_classification", "binary_classification", "UCI"], "configs": ["planning"]}
|
2023-04-16T16:57:54+00:00
|
bee24fc4cd2c726ed46c5fc2ef4812a4b051c716
|
# Dataset Card for "chunk_7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_7
|
[
"region:us"
] |
2023-04-06T21:42:32+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 21486802032.375, "num_examples": 223709}], "download_size": 18826565722, "dataset_size": 21486802032.375}}
|
2023-04-06T22:51:12+00:00
|
b053e3f8eec43ff8ebb63fa5f0a21af906c03329
|
Synthetic Dataset for Ad Generation using AI
Data generated using the following steps:
* Prompt GPT-3.5-turbo to create a list of 10 home products and their descriptions.
* Form into desired format `{"product" : "", "description" : ""}
* Then prompt GPT to create ads for each of the items.
Note: This data was not cleaned or verified manually.
|
almontalvao/home_products_ads
|
[
"region:us"
] |
2023-04-06T21:50:05+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4987, "num_examples": 10}], "download_size": 8185, "dataset_size": 4987}}
|
2023-04-07T03:06:39+00:00
|
321ac8a62e3663894b5fa5f8c5c8fafe1bc4b3ae
|
# Dataset Card for "generadai-sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
generativeaidemo/generadai-sample
|
[
"region:us"
] |
2023-04-06T22:26:56+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "price", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1905, "num_examples": 5}], "download_size": 5923, "dataset_size": 1905}}
|
2023-04-06T22:26:59+00:00
|
efc7e4dcf24b1f5dd4f48a719a5fcfdac16f37d7
|
EhabEM/faena
|
[
"region:us"
] |
2023-04-06T23:15:07+00:00
|
{}
|
2023-04-07T01:05:00+00:00
|
|
e8b3f935478af744bd9f53dfcc11549aa4db93d6
|
K-University/KU_Dataset
|
[
"license:apache-2.0",
"region:us"
] |
2023-04-06T23:32:30+00:00
|
{"license": "apache-2.0"}
|
2023-04-06T23:32:30+00:00
|
|
aa8e621359a9b1909f1b34b06406ae88e0dcf945
|
# Dataset Card for "sample-sklearn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
DavidMOBrien/sample-sklearn
|
[
"region:us"
] |
2023-04-06T23:33:48+00:00
|
{"dataset_info": {"features": [{"name": "before", "dtype": "string"}, {"name": "after", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5538359.416666667, "num_examples": 998}, {"name": "test", "num_bytes": 693682.2916666666, "num_examples": 125}, {"name": "valid", "num_bytes": 693682.2916666666, "num_examples": 125}], "download_size": 2782945, "dataset_size": 6925724.000000001}}
|
2023-04-06T23:33:55+00:00
|
709f675b64d0050224aca7bbb130b092c7fa198e
|
# V2
Images from [SketchyCOCO](https://mikexuq.github.io/test_building_pages/dataset.html).
Includes more variety: bicycle, car, motorcycle, airplane, traffic light, fire hydrant, cat, dog, horse, sheep, cow, elephant, zebra, and giraffe.
|
GreeneryScenery/SheepsNetV2
|
[
"art",
"SketchyCOCO",
"region:us"
] |
2023-04-06T23:48:16+00:00
|
{"pretty_name": "SketchyCOCO Objects", "tags": ["art", "SketchyCOCO"]}
|
2023-04-07T00:51:13+00:00
|
759dfeb40e4a134db0aea60efa30d40ce1b80174
|
<div align="center">
<img src="https://huggingface.co/datasets/MashiroSA/sovits-emu-dataset/resolve/main/favicon.png" height="200" alt="emu">
<h1>MashiroSA/sovits-emu-dataset</h1>
<b>A voice dataset collected from Project Sekai charactor Emu Otori</b>
</div>
## Introduction
- Size: **1398**, all WAV format.
- This is a voice dataset for project `so-vits-svc 4.0`.
- We didn't make any modifications, just extracted the voice from the game.
- All voice copyrights belong to SEGA and EmuOtori voice actors themselves, this data set is for research use only.
## License
This Project use `CC-BY-NC 4.0` as its license EXCEPT voice owner including **SEGA** and **Hina Kino** while they have right to decide their own assets.
```
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
```
## Personal Talk
I do really love the charactor of ```鳳えむ```, she gives me a lot of fun in my boring life. So I fetched the voice data from web and made a collection, tried to use so-vits-svc project to let her sing different song, or just give me a blessing.
I believe that AI can become a reliable tool for the future development of human beings, and even provide comfort in our most painful times.
I am very grateful for the game developed by SEGA and the voice actor of Otori Emu.
Personally, I hope this repo can be kept, because it is restricted by strict non-profit requirements, and it can help the development of the sovits community very well.
However, I am not very clear whether SEGA's DMCA rules have restrictions on these voices.
If you think this repo infringes your digital copyright, please write to `[email protected]`.
Although this is regrettable, we will take down the voice dataset repo and we sincerely apologize to you.
## Access Requests
All users who need to use the dataset must be given an email address to prevent abuse of the dataset.
## PR
If you have updated data, welcome to raise a Pull Request, we will merge after review.
|
chitsanfei/pjsk-emu-dataset
|
[
"size_categories:1K<n<10K",
"language:ja",
"license:cc-by-nc-4.0",
"music",
"region:us"
] |
2023-04-06T23:48:45+00:00
|
{"language": ["ja"], "license": "cc-by-nc-4.0", "size_categories": ["1K<n<10K"], "tags": ["music"]}
|
2023-04-12T10:29:32+00:00
|
57dbb37b57d78770cb94161bcb14b65e4ab2df44
|
# Dataset Card for "enwiki20230101-bysize-minilml6v2-avgembeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lsb/enwiki20230101-bysize-minilml6v2-avgembeddings
|
[
"region:us"
] |
2023-04-06T23:58:48+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "textgzlen", "dtype": "int64"}, {"name": "avg_embed", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 31169038847, "num_examples": 6593739}], "download_size": 26546783217, "dataset_size": 31169038847}}
|
2023-04-07T04:53:44+00:00
|
740390dde1af3bb24171afa19e3940392ec55e76
|
# Dataset Card for "mct_1_7b1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nguyenminh871/mct_1_7b1
|
[
"region:us"
] |
2023-04-07T01:57:02+00:00
|
{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "func", "dtype": "string"}, {"name": "target", "dtype": "bool"}, {"name": "project", "dtype": "string"}], "splits": [{"name": "mct_1_7b1", "num_bytes": 5421424, "num_examples": 2050}], "download_size": 1457592, "dataset_size": 5421424}}
|
2023-04-07T01:57:14+00:00
|
598a1926f404e74a71a2a7eb38c2ad876021a7a3
|
# Dataset Card for "titan_0_5_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nguyenminh871/titan_0_5_1
|
[
"region:us"
] |
2023-04-07T02:13:54+00:00
|
{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "func", "dtype": "string"}, {"name": "target", "dtype": "bool"}, {"name": "project", "dtype": "string"}], "splits": [{"name": "titan_0_5_1", "num_bytes": 4760562, "num_examples": 1770}], "download_size": 1279691, "dataset_size": 4760562}}
|
2023-04-07T02:14:06+00:00
|
306d12fb6ebb911c4d3f2adfe6a71d64742cdde8
|
# Dataset Card for "ceylon_ide_eclipse_1_1_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nguyenminh871/ceylon_ide_eclipse_1_1_0
|
[
"region:us"
] |
2023-04-07T02:16:10+00:00
|
{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "func", "dtype": "string"}, {"name": "target", "dtype": "bool"}, {"name": "project", "dtype": "string"}], "splits": [{"name": "ceylon_ide_eclipse_1_1_0", "num_bytes": 5826189, "num_examples": 1651}], "download_size": 1473383, "dataset_size": 5826189}}
|
2023-04-07T02:16:23+00:00
|
9dcd00e452025e0f67fd2a8a2ba7742ce0c49823
|
# Dataset Card for "Gameplay Captions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
asgaardlab/GameplayCaptions
|
[
"task_categories:image-to-text",
"task_categories:text-to-image",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"Gameplay",
"region:us"
] |
2023-04-07T03:01:01+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["image-to-text", "text-to-image"], "pretty_name": "Gameplay Captions", "dataset_info": {"features": [{"name": "img_id", "dtype": "string"}, {"name": "game", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "blip2-opt-6.7b_captions.csv", "dtype": "string"}, {"name": "coca_captions.csv", "dtype": "string"}, {"name": "git-large-coco_captions.csv", "dtype": "string"}, {"name": "git-large-r-textcaps_captions.csv", "dtype": "string"}, {"name": "vit-gpt2_captions.csv", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 69110393094.684, "num_examples": 75979}], "download_size": 66660916127, "dataset_size": 69110393094.684}, "tags": ["Gameplay"]}
|
2023-04-07T13:38:12+00:00
|
0a4c74976b4f3bd98da24f4a53c90030eb8460ca
|
# Dataset Card for "tamil_sentences_master"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AnanthZeke/tamil_sentences_master_raw
|
[
"region:us"
] |
2023-04-07T03:38:38+00:00
|
{"dataset_info": {"features": [{"name": "sent_token", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20175735014, "num_examples": 64948854}], "download_size": 6917303724, "dataset_size": 20175735014}}
|
2023-04-07T04:25:02+00:00
|
5f162a1d64d536d78462b410b65835226e63e044
|
# Dataset Card for "common_voice_10_1_th_clean_split_0_fix_spacial_char"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
DylanonWic/common_voice_10_1_th_clean_split_0
|
[
"region:us"
] |
2023-04-07T04:24:31+00:00
|
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "labels", "sequence": "int64"}, {"name": "input_values", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 12101560609, "num_examples": 50670}], "download_size": 11891879164, "dataset_size": 12101560609}}
|
2023-04-07T04:34:12+00:00
|
bec7cde26706b91b627d865db318f19617115501
|
shihabsarar29/grapeLeafDisease
|
[
"license:mit",
"region:us"
] |
2023-04-07T04:43:32+00:00
|
{"license": "mit"}
|
2023-04-07T05:08:28+00:00
|
|
cf4cdd1017deb3ebe05cf30afc3b5d80a95c7453
|
# Dataset Card for "common_voice_10_1_th_clean_split_1_fix_spacial_char"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
DylanonWic/common_voice_10_1_th_clean_split_1
|
[
"region:us"
] |
2023-04-07T05:22:36+00:00
|
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "labels", "sequence": "int64"}, {"name": "input_values", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 12036309641, "num_examples": 50300}], "download_size": 11830638341, "dataset_size": 12036309641}}
|
2023-04-07T05:32:31+00:00
|
eb8e187092979aaa37cccdb50cd8eb94e07e027b
|
LangChainDatasets/openapi-chain-klarna-products-get
|
[
"license:mit",
"region:us"
] |
2023-04-07T05:32:04+00:00
|
{"license": "mit"}
|
2023-04-07T05:44:47+00:00
|
|
2715cba83cc9a69f76757a078985cb2af8f6ca83
|
# Dataset Card for "common_voice_10_1_th_clean_split_2_fix_spacial_char"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
DylanonWic/common_voice_10_1_th_clean_split_2
|
[
"region:us"
] |
2023-04-07T05:51:03+00:00
|
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "labels", "sequence": "int64"}, {"name": "input_values", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 12088465913, "num_examples": 50594}], "download_size": 11885573096, "dataset_size": 12088465913}}
|
2023-04-07T06:02:23+00:00
|
900f1a6c3995fafdca0eefe2b43c0d31793ce1e1
|
# Dataset Card for "chunk_17"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_17
|
[
"region:us"
] |
2023-04-07T06:15:06+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 26588775744.5, "num_examples": 276828}], "download_size": 23794603806, "dataset_size": 26588775744.5}}
|
2023-04-07T06:29:56+00:00
|
652058cd171c313e55d19ae8dd8b7eebbd5d69ae
|
# Dataset Card for "chunk_19"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_19
|
[
"region:us"
] |
2023-04-07T06:15:28+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 27056049264.375, "num_examples": 281693}], "download_size": 24232487237, "dataset_size": 27056049264.375}}
|
2023-04-07T06:30:12+00:00
|
589983b44e2889e355de59beb6cf8ce9f625dffd
|
# Dataset Card for "common_voice_10_1_th_clean_split_3_augment_fix_spacial_char"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
DylanonWic/common_voice_10_1_th_clean_split_3_augment
|
[
"region:us"
] |
2023-04-07T06:17:03+00:00
|
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "labels", "sequence": "int64"}, {"name": "input_values", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 12080449267, "num_examples": 50530}], "download_size": 12068906392, "dataset_size": 12080449267}}
|
2023-04-07T06:39:56+00:00
|
e2167c0997734e5394e369b3034bfcb0cc884b48
|
# Dataset Card for "chunk_15"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_15
|
[
"region:us"
] |
2023-04-07T06:17:51+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 19173101760.5, "num_examples": 199620}], "download_size": 17499579865, "dataset_size": 19173101760.5}}
|
2023-04-07T06:31:12+00:00
|
4ae2634ba94e07584434b39f1625c82d54978d5f
|
# Dataset Card for "chunk_14"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_14
|
[
"region:us"
] |
2023-04-07T06:26:45+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 20150294112.75, "num_examples": 209794}], "download_size": 17971519323, "dataset_size": 20150294112.75}}
|
2023-04-07T06:43:43+00:00
|
dcda986001d74b2712d6d39e8827794fdeefe8ec
|
# Dataset Card for "chunk_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_10
|
[
"region:us"
] |
2023-04-07T06:27:17+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 19423883088.125, "num_examples": 202231}], "download_size": 16921334480, "dataset_size": 19423883088.125}}
|
2023-04-07T06:43:25+00:00
|
12de2e36eeffbef27893ac74970f518648cd9cc1
|
# Dataset Card for "chunk_13"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_13
|
[
"region:us"
] |
2023-04-07T06:37:21+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 22396952880.875, "num_examples": 233185}], "download_size": 19489840324, "dataset_size": 22396952880.875}}
|
2023-04-07T07:06:07+00:00
|
645cdef739323eb5fc72a286028b3185c62a27bf
|
# Spambase
The [Spambase dataset](https://archive.ics.uci.edu/ml/datasets/Spambase) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Is the given mail spam?
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|------------------|
| spambase | Binary classification | Is the mail spam?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/spambase")["train"]
```
|
mstz/spambase
|
[
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"spambase",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
2023-04-07T06:37:26+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["1K<n<10K"], "task_categories": ["tabular-classification"], "pretty_name": "Spambase", "tags": ["spambase", "tabular_classification", "binary_classification", "UCI"], "configs": ["spambase"]}
|
2023-04-16T17:02:22+00:00
|
518a873a8dcd3d92cbc731e5bacfd9968b6b8c09
|
# Dataset Card for "chunk_18"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_18
|
[
"region:us"
] |
2023-04-07T06:44:21+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 27172651536.625, "num_examples": 282907}], "download_size": 24172241448, "dataset_size": 27172651536.625}}
|
2023-04-07T07:07:11+00:00
|
f27dc8dbd82f708e314503c36f187855d17916d8
|
# Dataset Card for "chunk_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_16
|
[
"region:us"
] |
2023-04-07T06:57:57+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 23311906128.125, "num_examples": 242711}], "download_size": 20728341325, "dataset_size": 23311906128.125}}
|
2023-04-07T07:31:45+00:00
|
d0817ae1a63dfbd358d9c56805fbf4a0d021e94b
|
# Dataset Card for "chunk_21"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_21
|
[
"region:us"
] |
2023-04-07T06:58:13+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 25162655040.5, "num_examples": 261980}], "download_size": 22793446466, "dataset_size": 25162655040.5}}
|
2023-04-07T07:14:32+00:00
|
125c2853fcce0ba3ad01e2f8cf0d07ecfb6bde68
|
# Ozone
The [Ozone dataset](https://archive.ics.uci.edu/ml/datasets/Ozone) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-------------------------|
| spect | Binary classification | Is there an ozone layer?|
| spectf | Binary classification | Is there an ozone layer?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/spect", "spect")["train"]
```
|
mstz/spect
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"spect",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
2023-04-07T07:05:44+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Ozone", "tags": ["spect", "tabular_classification", "binary_classification", "UCI"], "configs": ["spect", "spectf"]}
|
2023-04-16T17:02:28+00:00
|
3c4e6fe47e618be52428ef21544643fc578c75c8
|
# Australian Credit
The [Australian Credit](https://archive-beta.ics.uci.edu/dataset/143/statlog+australian+credit+approval) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Classification of loan approval.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-------------------------|
| australian_credit | Binary classification | Is the loan granted? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/australian_credit")["train"]
```
# Features
Target feature changes according to the selected configuration and is always in last position in the dataset.
|
mstz/australian_credit
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"australian_credit",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
2023-04-07T07:20:44+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Australian Credit", "tags": ["australian_credit", "tabular_classification", "binary_classification", "UCI"], "configs": ["australian_credit"]}
|
2023-04-15T10:11:01+00:00
|
98f67938932d49d84941227747f3442c75cb3628
|
# Dataset Card for "chunk_20"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_20
|
[
"region:us"
] |
2023-04-07T07:22:44+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 26697406032.125, "num_examples": 277959}], "download_size": 23859952631, "dataset_size": 26697406032.125}}
|
2023-04-07T07:57:56+00:00
|
20df9ba7caff461e2f31fd578d58bf8d6e9f67b3
|
# Dataset Card for "chunk_25"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_25
|
[
"region:us"
] |
2023-04-07T07:25:57+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 15838891488.75, "num_examples": 164906}], "download_size": 14036326858, "dataset_size": 15838891488.75}}
|
2023-04-07T07:34:33+00:00
|
9037cc67de04e363ba4e188828a584a16b578adb
|
# Dataset Card for "chunk_24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_24
|
[
"region:us"
] |
2023-04-07T07:31:41+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 16896860208.875, "num_examples": 175921}], "download_size": 15392804768, "dataset_size": 16896860208.875}}
|
2023-04-07T07:45:54+00:00
|
d47d4aa2e7ba85a625a4655e78c614b166cb7813
|
# TicTacToe
The [TicTacToe dataset](https://archive-beta.ics.uci.edu/dataset/101/tic+tac+toe+endgame) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-------------------------|
| tic_tac_toe | Binary classification | Does the X player win? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/tic_tac_toe")["train"]
```
|
mstz/tic_tac_toe
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"TicTacToe",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
2023-04-07T07:42:16+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "TicTacToe", "tags": ["TicTacToe", "tabular_classification", "binary_classification", "UCI"], "configs": ["tic_tac_toe"]}
|
2023-04-16T17:03:22+00:00
|
43e2616b8fb57fbcbb01b667dd4ad98ae9361ce4
|
# Dataset Card for "chunk_22"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_22
|
[
"region:us"
] |
2023-04-07T07:43:41+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 20243076480.0, "num_examples": 210760}], "download_size": 17915722749, "dataset_size": 20243076480.0}}
|
2023-04-07T08:11:42+00:00
|
086468002b6a8f1a2e607cc929259afc0c4f60f0
|
# Dataset Card for "chunk_26"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_26
|
[
"region:us"
] |
2023-04-07T07:46:10+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 16222987440.875, "num_examples": 168905}], "download_size": 14476054976, "dataset_size": 16222987440.875}}
|
2023-04-07T07:59:28+00:00
|
7f178efe21de8ea95dbeb8edc3a0f1f9d8d4ba74
|
# Dataset Card for "common_voice_10_1_th_clean_split_3_fix_spacial_char"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
DylanonWic/common_voice_10_1_th_clean_split_3
|
[
"region:us"
] |
2023-04-07T07:54:29+00:00
|
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "labels", "sequence": "int64"}, {"name": "input_values", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 12097784428, "num_examples": 50545}], "download_size": 11895239991, "dataset_size": 12097784428}}
|
2023-04-07T08:16:23+00:00
|
0a97245c8687e3396ec3963435b9d6104c966a9f
|
# Dataset Card for "chunk_27"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_27
|
[
"region:us"
] |
2023-04-07T07:58:18+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 16806959280.875, "num_examples": 174985}], "download_size": 15014847500, "dataset_size": 16806959280.875}}
|
2023-04-07T08:13:10+00:00
|
c635332543e161f56b25e92519e8433f792747b0
|
BoolQ questions with semantic alteration and human verifications
```bib
@article{khashabi2020naturalperturbations,
title={Natural Perturbation for Robust Question Answering},
author={D. Khashabi and T. Khot and A. Sabhwaral},
journal={arXiv preprint},
year={2020}
}
```
|
metaeval/boolq-natural-perturbations
|
[
"task_categories:text-classification",
"language:en",
"region:us"
] |
2023-04-07T08:05:20+00:00
|
{"language": ["en"], "task_categories": ["text-classification"]}
|
2023-04-09T13:14:18+00:00
|
20577e51cf09cd6c4ba1ee5b7296bfe1b3964fd0
|
# Dataset Card for "chunk_12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_12
|
[
"region:us"
] |
2023-04-07T08:06:56+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 18789293952.0, "num_examples": 195624}], "download_size": 16796755807, "dataset_size": 18789293952.0}}
|
2023-04-07T09:08:41+00:00
|
23666484391c73aee77c4ce7cd4256573d468a96
|
language:
- "List of ISO 639-1 code for your language"
- lang1
- lang2
pretty_name: "Pretty Name of the Dataset"
tags:
- tag1
- tag2
license: "any valid license identifier"
task_categories:
- task1
- task2
|
gogogogo-1/gushen-test
|
[
"language:ch",
"license:bigscience-openrail-m",
"region:us"
] |
2023-04-07T08:07:50+00:00
|
{"language": ["ch"], "license": "bigscience-openrail-m"}
|
2023-05-30T00:35:13+00:00
|
4cee00e6022028f7fb3a903ffdeac426d3c221dd
|
# Generalization of Counterfactually-Augmented NLI Data
```bib
@inproceedings{huang2020cnligeneralization,
title={Counterfactually-Augmented {SNLI} Training Data Does Not Yield Better Generalization Than Unaugmented Data},
author={William Huang and Haokun Liu and Samuel R. Bowman},
booktitle = {Proceedings of the 2020 EMNLP Workshop on Insights from Negative Results in NLP},
year={2020},
publisher = {The Association for Computational Linguistics}
}
```
|
metaeval/cnli
|
[
"language:en",
"region:us"
] |
2023-04-07T08:08:36+00:00
|
{"language": ["en"]}
|
2023-04-07T08:09:37+00:00
|
a5860a741720711f0f208c73dd50e185bf0e8126
|
HuggingFaceGECLM/data_feedback
|
[
"license:openrail",
"region:us"
] |
2023-04-07T08:11:11+00:00
|
{"license": "openrail"}
|
2023-04-13T01:46:25+00:00
|
|
8d2e7341082841e69c0d18a43267d8fc2406d2bc
|
# Titanic
The [Titanic dataset](https://www.kaggle.com/datasets/vinicius150987/titanic3) from [Kaggle](https://www.kaggle.com/).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|----------------------------|
| survival | Binary classification | Has the passanger survived?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/titanic")["train"]
```
|
mstz/titanic
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"titanic",
"tabular_classification",
"binary_classification",
"region:us"
] |
2023-04-07T08:15:56+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Titanic", "tags": ["titanic", "tabular_classification", "binary_classification"], "configs": ["survival"]}
|
2023-04-09T22:30:09+00:00
|
d1c0a240e56cce273fd1a1dfe056e468f5ea829e
|
# Dataset Card for "hotpot_sp_label"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/hotpot_sp_label
|
[
"region:us"
] |
2023-04-07T08:16:47+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "dtype": "int64"}, {"name": "token_type_ids", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 212593284, "num_examples": 526221}, {"name": "validation", "num_bytes": 23527344, "num_examples": 58236}, {"name": "test", "num_bytes": 19387152, "num_examples": 47988}], "download_size": 52422990, "dataset_size": 255507780}}
|
2023-04-07T08:16:59+00:00
|
7eb80ce44f2f99e81c47944b4fee5aa9728623b3
|
# Dataset Card for "chunk_23"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_23
|
[
"region:us"
] |
2023-04-07T08:21:59+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 18791887248.625, "num_examples": 195651}], "download_size": 16817820198, "dataset_size": 18791887248.625}}
|
2023-04-07T08:34:22+00:00
|
fbe6e7e4fdf11edb6fbb55c0468bfae5152b2a9d
|
# Dataset Card for "chunk_28"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_28
|
[
"region:us"
] |
2023-04-07T08:23:16+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 16231343616.0, "num_examples": 168992}], "download_size": 14338516609, "dataset_size": 16231343616.0}}
|
2023-04-07T08:38:32+00:00
|
a3f5df2b17f17ac39287a85e68262726c5a43756
|
# Dataset Card for "chunk_29"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_29
|
[
"region:us"
] |
2023-04-07T08:33:38+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 16335459648.5, "num_examples": 170076}], "download_size": 14446652583, "dataset_size": 16335459648.5}}
|
2023-04-07T08:57:04+00:00
|
2178d8d46812e58bc8ecf7fd60bf13856ed952e6
|
# Dataset Card for "chunk_32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_32
|
[
"region:us"
] |
2023-04-07T08:34:42+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 13993521264.375, "num_examples": 145693}], "download_size": 12371757603, "dataset_size": 13993521264.375}}
|
2023-04-07T08:43:47+00:00
|
a37af3d082dded5a9b41c459ff5170635ddcf463
|
# Dataset Card for "chunk_30"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_30
|
[
"region:us"
] |
2023-04-07T08:34:48+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 14932294416.625, "num_examples": 155467}], "download_size": 13264513005, "dataset_size": 14932294416.625}}
|
2023-04-07T08:46:58+00:00
|
72aabeb699f9d071c1e48d0ac109515763068bce
|
# Dataset Card for "chunk_31"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_31
|
[
"region:us"
] |
2023-04-07T08:52:54+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 15164634528.25, "num_examples": 157886}], "download_size": 13646133839, "dataset_size": 15164634528.25}}
|
2023-04-07T09:06:50+00:00
|
7f33bee2340d7e5562d34e44338e35946c3b3b12
|
# TwoNorm
The [TwoNorm dataset](https://www.openml.org/search?type=data&status=active&id=1507) from the [OpenML repository](https://www.openml.org/).
# Configurations and tasks
| **Configuration** | **Task** |
|-------------------|---------------------------|
| twonorm | Binary classification |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/twonorm")["train"]
```
|
mstz/twonorm
|
[
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"twonorm",
"tabular_classification",
"binary_classification",
"region:us"
] |
2023-04-07T09:01:07+00:00
|
{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["tabular-classification"], "pretty_name": "Two Norm", "tags": ["twonorm", "tabular_classification", "binary_classification"], "configs": ["8hr", "1hr"]}
|
2023-04-07T13:58:58+00:00
|
912b56c900f0457e6d4147c81b12b68a3acc9123
|
# Dataset Card for "chunk_11"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_11
|
[
"region:us"
] |
2023-04-07T09:02:44+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 18932501520.625, "num_examples": 197115}], "download_size": 16779364500, "dataset_size": 18932501520.625}}
|
2023-04-07T09:35:50+00:00
|
612a75a578ced6acaf582a533310065dc248c71a
|
# Dataset Card for "chunk_38"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
one-sec-cv12/chunk_38
|
[
"region:us"
] |
2023-04-07T09:15:24+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 25316716032.0, "num_examples": 263584}], "download_size": 21757615757, "dataset_size": 25316716032.0}}
|
2023-04-07T09:28:33+00:00
|
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