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InstaDeepAI/multi_species_genomes
|
InstaDeepAI
| 2023-11-01T14:07:25Z | 37 | 7 |
[
"DNA",
"Genomics",
"Nucleotide",
"region:us"
] | null | 2023-04-06T19:05:46Z |
---
tags:
- DNA
- Genomics
- Nucleotide
pretty_name: Human Reference Genome
---
# 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}
}
```
|
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] |
arbml/tashkeela
|
arbml
| 2023-04-06T19:09:05Z | 37 | 2 |
[
"region:us"
] | null | 2023-04-06T19:07:05Z |
---
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
---
# Dataset Card for "tashkeela"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
mstz/shuttle
|
mstz
| 2023-04-16T17:58:41Z | 37 | 0 |
[
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"shuttle",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-14T12:03:39Z |
---
language:
- en
tags:
- shuttle
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: Shuttle
size_categories:
- 10K<n<100K
task_categories:
- tabular-classification
configs:
- shuttle
- shuttle_binary
license: cc
---
# Shuttle
The [Shuttle dataset](https://archive-beta.ics.uci.edu/dataset/146/statlog+shuttle+satellite) from the [UCI repository](https://archive-beta.ics.uci.edu/).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-----------------------|---------------------------|-------------------------|
| shuttle | Multiclass classification.| |
| shuttle_0 | Binary classification. | Is the image of class 0? |
| shuttle_1 | Binary classification. | Is the image of class 1? |
| shuttle_2 | Binary classification. | Is the image of class 2? |
| shuttle_3 | Binary classification. | Is the image of class 3? |
| shuttle_4 | Binary classification. | Is the image of class 4? |
| shuttle_5 | Binary classification. | Is the image of class 5? |
| shuttle_6 | Binary classification. | Is the image of class 6? |
|
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shareAI/ShareGPT-Chinese-English-90k
|
shareAI
| 2023-11-03T09:27:34Z | 37 | 126 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-04-15T16:23:35Z |
---
license: apache-2.0
configs:
- config_name: default
data_files: sharegpt_jsonl/*.jsonl
---
# ShareGPT-Chinese-English-90k 中英文双语人机问答数据集
中英文平行双语优质人机问答数据集，覆盖真实复杂场景下的用户提问。用于训练高质量的对话模型 （比那些通过反复调用api接口生成机器模拟问答的数据在指令分布上更鲁棒）
特点：
- 1.同时提供意义表达完全相同的中英文平行对照语料，可进行双语对话模型训练。
- 2.所有问题均非人为臆想加上api轮询拟造的假数据（如Moss)，更加符合真实用户场景的指令分布和提问表达。
- 3.sharegpt数据集是由网友自发分享而收集到的，相当于有一层非常天然的过滤（通过人类感觉），筛除了大部分体验不好的对话。
补充：该数据收集于chatGPT还未表现出明显智力退化的时间点。（猜测一方面可能是官方为了减小开支把150B的gpt3.5替换成10b左右的蒸馏版本了，另一方面可能是由于引入了更多的拒绝答复导致模型连接知识逻辑的程度退化）
优秀对话llm的训练离不开高质量的多轮对话数据集，如果你也想成为志愿者
欢迎加入数据集QQ群：130920969，共同进行优质数据集的交流、收集和建设工作
|
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] |
taka-yayoi/databricks-dolly-15k-ja
|
taka-yayoi
| 2023-04-17T09:18:13Z | 37 | 2 |
[
"license:cc-by-sa-3.0",
"region:us"
] | null | 2023-04-17T08:58:32Z |
---
license: cc-by-sa-3.0
---
こちらのデータセットを活用させていただき、Dollyのトレーニングスクリプトで使えるように列名の変更とJSONLへの変換を行っています。
https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja
Dolly
https://github.com/databrickslabs/dolly
|
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Blgn94/mongolian-ner
|
Blgn94
| 2023-05-01T08:29:43Z | 37 | 0 |
[
"region:us"
] | null | 2023-05-01T08:29:42Z |
---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence: int64
splits:
- name: train
num_bytes: 4006355
num_examples: 10162
download_size: 1026335
dataset_size: 4006355
---
# Dataset Card for "mongolian-ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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a6kme/minds14-mirror
|
a6kme
| 2023-05-13T11:42:15Z | 37 | 0 |
[
"task_categories:automatic-speech-recognition",
"task_ids:keyword-spotting",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"language:en",
"language:fr",
"language:it",
"language:es",
"language:pt",
"language:de",
"language:nl",
"language:ru",
"language:pl",
"language:cs",
"language:ko",
"language:zh",
"license:cc-by-4.0",
"arxiv:2104.08524",
"region:us"
] |
[
"automatic-speech-recognition",
"speech-processing"
] | 2023-05-13T07:56:01Z |
---
annotations_creators:
- expert-generated
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
- fr
- it
- es
- pt
- de
- nl
- ru
- pl
- cs
- ko
- zh
language_bcp47:
- en
- en-GB
- en-US
- en-AU
- fr
- it
- es
- pt
- de
- nl
- ru
- pl
- cs
- ko
- zh
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: 'MInDS-14'
size_categories:
- 10K<n<100K
task_categories:
- automatic-speech-recognition
- speech-processing
task_ids:
- speech-recognition
- keyword-spotting
---
# MInDS-14
## Dataset Description
- **Fine-Tuning script:** [pytorch/audio-classification](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification)
- **Paper:** [Multilingual and Cross-Lingual Intent Detection from Spoken Data](https://arxiv.org/abs/2104.08524)
- **Total amount of disk used:** ca. 500 MB
MINDS-14 is training and evaluation resource for intent detection task with spoken data. It covers 14
intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties.
## Example
MInDS-14 can be downloaded and used as follows:
```py
from datasets import load_dataset
minds_14 = load_dataset("PolyAI/minds14", "fr-FR") # for French
# to download all data for multi-lingual fine-tuning uncomment following line
# minds_14 = load_dataset("PolyAI/all", "all")
# see structure
print(minds_14)
# load audio sample on the fly
audio_input = minds_14["train"][0]["audio"] # first decoded audio sample
intent_class = minds_14["train"][0]["intent_class"] # first transcription
intent = minds_14["train"].features["intent_class"].names[intent_class]
# use audio_input and language_class to fine-tune your model for audio classification
```
## Dataset Structure
We show detailed information the example configurations `fr-FR` of the dataset.
All other configurations have the same structure.
### Data Instances
**fr-FR**
- Size of downloaded dataset files: 471 MB
- Size of the generated dataset: 300 KB
- Total amount of disk used: 471 MB
An example of a datainstance of the config `fr-FR` looks as follows:
```
{
"path": "/home/patrick/.cache/huggingface/datasets/downloads/extracted/3ebe2265b2f102203be5e64fa8e533e0c6742e72268772c8ac1834c5a1a921e3/fr-FR~ADDRESS/response_4.wav",
"audio": {
"path": "/home/patrick/.cache/huggingface/datasets/downloads/extracted/3ebe2265b2f102203be5e64fa8e533e0c6742e72268772c8ac1834c5a1a921e3/fr-FR~ADDRESS/response_4.wav",
"array": array(
[0.0, 0.0, 0.0, ..., 0.0, 0.00048828, -0.00024414], dtype=float32
),
"sampling_rate": 8000,
},
"transcription": "je souhaite changer mon adresse",
"english_transcription": "I want to change my address",
"intent_class": 1,
"lang_id": 6,
}
```
### Data Fields
The data fields are the same among all splits.
- **path** (str): Path to the audio file
- **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio
- **transcription** (str): Transcription of the audio file
- **english_transcription** (str): English transcription of the audio file
- **intent_class** (int): Class id of intent
- **lang_id** (int): Id of language
### Data Splits
Every config only has the `"train"` split containing of *ca.* 600 examples.
## Dataset Creation
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/).
### Citation Information
```
@article{DBLP:journals/corr/abs-2104-08524,
author = {Daniela Gerz and
Pei{-}Hao Su and
Razvan Kusztos and
Avishek Mondal and
Michal Lis and
Eshan Singhal and
Nikola Mrksic and
Tsung{-}Hsien Wen and
Ivan Vulic},
title = {Multilingual and Cross-Lingual Intent Detection from Spoken Data},
journal = {CoRR},
volume = {abs/2104.08524},
year = {2021},
url = {https://arxiv.org/abs/2104.08524},
eprinttype = {arXiv},
eprint = {2104.08524},
timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2104-08524.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset
|
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] |
clarin-knext/msmarco-pl-qrels
|
clarin-knext
| 2023-06-07T08:21:32Z | 37 | 0 |
[
"language:pl",
"arxiv:2305.19840",
"region:us"
] | null | 2023-06-06T22:03:21Z |
---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: [email protected]
|
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] |
FredZhang7/toxi-text-3M
|
FredZhang7
| 2023-07-20T21:33:29Z | 37 | 8 |
[
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:zero-shot-classification",
"size_categories:1M<n<10M",
"language:ar",
"language:es",
"language:pa",
"language:th",
"language:et",
"language:fr",
"language:fi",
"language:hu",
"language:lt",
"language:ur",
"language:so",
"language:pl",
"language:el",
"language:mr",
"language:sk",
"language:gu",
"language:he",
"language:af",
"language:te",
"language:ro",
"language:lv",
"language:sv",
"language:ne",
"language:kn",
"language:it",
"language:mk",
"language:cs",
"language:en",
"language:de",
"language:da",
"language:ta",
"language:bn",
"language:pt",
"language:sq",
"language:tl",
"language:uk",
"language:bg",
"language:ca",
"language:sw",
"language:hi",
"language:zh",
"language:ja",
"language:hr",
"language:ru",
"language:vi",
"language:id",
"language:sl",
"language:cy",
"language:ko",
"language:nl",
"language:ml",
"language:tr",
"language:fa",
"language:no",
"language:multilingual",
"license:apache-2.0",
"nlp",
"moderation",
"region:us"
] |
[
"text-classification",
"token-classification",
"zero-shot-classification"
] | 2023-06-28T23:28:34Z |
---
license: apache-2.0
task_categories:
- text-classification
- token-classification
- zero-shot-classification
size_categories:
- 1M<n<10M
language:
- ar
- es
- pa
- th
- et
- fr
- fi
- hu
- lt
- ur
- so
- pl
- el
- mr
- sk
- gu
- he
- af
- te
- ro
- lv
- sv
- ne
- kn
- it
- mk
- cs
- en
- de
- da
- ta
- bn
- pt
- sq
- tl
- uk
- bg
- ca
- sw
- hi
- zh
- ja
- hr
- ru
- vi
- id
- sl
- cy
- ko
- nl
- ml
- tr
- fa
- 'no'
- multilingual
tags:
- nlp
- moderation
---
[A demo for a model finetuned on this and other datasets](https://huggingface.co/spaces/aivance/one-for-all-toxicity-v3)
This is a large multilingual toxicity dataset with 3M rows of text data from 55 natural languages, all of which are written/sent by humans, not machine translation models.
The preprocessed training data alone consists of 2,880,667 rows of comments, tweets, and messages. Among these rows, 416,529 are classified as toxic, while the remaining 2,463,773 are considered neutral. Below is a table to illustrate the data composition:
| | Toxic | Neutral | Total |
|-------|----------|----------|----------|
| [multilingual-train-deduplicated.csv](./train/multilingual-train-deduplicated.csv) | 416,529 | 2,464,138 | 2,880,667 |
| [mulilingual-validation(new).csv](./validation/mulilingual-validation(new).csv) | 10,613 | 19,028 | 29,641 |
| [multilingual-test.csv](./test/multilingual-test.csv) | 14,410 | 49,402 | 63,812 |
Each CSV file has three columns: `text`, `is_toxic`, and `lang`.
Supported types of toxicity:
- Identity Hate/Homophobia
- Misogyny
- Violent Extremism
- Hate Speech
- Offensive Insults
- Sexting
- Obscene
- Threats
- Harassment
- Racism
- Trolling
- Doxing
- Others
Supported languages:
- Afrikaans
- Albanian
- Arabic
- Bengali
- Bulgarian
- Catalan
- Chinese (Simplified)
- Chinese (Traditional)
- Croatian
- Czech
- Danish
- Dutch
- English
- Estonian
- Finnish
- French
- German
- Greek
- Gujarati
- Hebrew
- Hindi
- Hungarian
- Indonesian
- Italian
- Japanese
- Kannada
- Korean
- Latvian
- Lithuanian
- Macedonian
- Malayalam
- Marathi
- Nepali
- Norwegian
- Persian
- Polish
- Portuguese
- Punjabi
- Romanian
- Russian
- Slovak
- Slovenian
- Somali
- Spanish
- Swahili
- Swedish
- Tagalog
- Tamil
- Telugu
- Thai
- Turkish
- Ukrainian
- Urdu
- Vietnamese
- Welsh
<br>
### Original Source?
Around 11 months ago, I downloaded and preprocessed 2.7M rows of text data, but completely forgot the original source of these datasets...
All I remember is that I downloaded datasets from everywhere I could: HuggingFace, research papers, GitHub, Kaggle, SurgeAI, and Google search. I even fetched 20K+ tweets using the Twitter API.
Recently, I came across 6 datasets, so I remembered to credit them below.
Known datasets:
- tomekkorbak/pile-toxicity-balanced2 (HuggingFace)
- datasets/thai_toxicity_tweet (HuggingFace)
- datasets/ethos (HuggingFace)
- inspection-ai/japanese-toxic-dataset (GitHub)
- mathigatti/sexting-dataset (GitHub)
- omar-sharif03/BAD-Bangla-Aggressive-Text-Dataset (GitHub)
I manually collected and wrote 100 rows of data.
<br>
### Limitations
Limitations include:
- All labels were rounded to the nearest integer. If a text was classified as 46%-54% toxic, the text itself might not be noticeably toxic or neutral.
- There were disagreements among moderators on some labels, due to ambiguity and lack of context.
- When there're only URL(s), emojis, or anything that's unrecognizable as natural language in the "text" column, the corresponding "lang" is "unknown".
Have fun modelling!
|
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] |
EgilKarlsen/BGL
|
EgilKarlsen
| 2023-07-21T01:13:21Z | 37 | 0 |
[
"region:us"
] | null | 2023-07-21T01:12:56Z |
---
dataset_info:
features:
- name: log
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 830216355
num_examples: 4753370
- name: test
num_bytes: 237311703
num_examples: 1358106
- name: validation
num_bytes: 118629381
num_examples: 679054
download_size: 476009078
dataset_size: 1186157439
---
# Dataset Card for "BGL"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
PL-MTEB/cbd
|
PL-MTEB
| 2023-08-11T12:22:44Z | 37 | 0 |
[
"license:bsd-3-clause",
"region:us"
] | null | 2023-08-11T12:22:32Z |
---
license: bsd-3-clause
---
|
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PL-MTEB/polemo2_in
|
PL-MTEB
| 2023-08-11T12:40:43Z | 37 | 0 |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-08-11T12:40:29Z |
---
license: cc-by-nc-sa-4.0
---
|
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] |
duxprajapati/symptom-disease-dataset
|
duxprajapati
| 2023-08-22T12:39:19Z | 37 | 0 |
[
"task_categories:text-classification",
"language:en",
"region:us"
] |
[
"text-classification"
] | 2023-08-22T12:38:28Z |
---
task_categories:
- text-classification
language:
- en
---
|
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] |
ContextualAI/tiny-wiki100-chunks
|
ContextualAI
| 2023-09-22T17:47:30Z | 37 | 0 |
[
"region:us"
] | null | 2023-09-22T17:47:26Z |
---
dataset_info:
features:
- name: doc_id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 63619
num_examples: 100
download_size: 43300
dataset_size: 63619
---
# Dataset Card for "tiny-wiki100-chunks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
tanvirsrbd1/dataset1_two_app_annotated1
|
tanvirsrbd1
| 2023-10-01T05:32:23Z | 37 | 0 |
[
"region:us"
] | null | 2023-10-01T05:32:15Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: xml
dtype: string
- name: html
dtype: string
- name: response
dtype: string
- name: annotated
dtype: string
splits:
- name: train
num_bytes: 1894327.1470588236
num_examples: 66
download_size: 265891
dataset_size: 1894327.1470588236
---
# Dataset Card for "dataset1_two_app_annotated1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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approximatelabs/tablib-v1-sample
|
approximatelabs
| 2023-10-13T22:34:05Z | 37 | 8 |
[
"size_categories:1M<n<10M",
"license:other",
"arxiv:2310.07875",
"region:us"
] | null | 2023-10-04T16:55:20Z |
---
license: other
pretty_name: TabLib
size_categories:
- 1M<n<10M
extra_gated_prompt: >-
Access to this dataset is automatically granted once this form is completed.
Note that this access request is for the TabLib sample, not [the full TabLib dataset](https://huggingface.co/datasets/approximatelabs/tablib-v1-full).
extra_gated_fields:
I agree to abide by the license requirements of the data contained in TabLib: checkbox
---
[](https://discord.gg/kW9nBQErGe)
<img src="https://approximatelabs.com/tablib.png" width="800" />
# TabLib Sample
**NOTE**: This is a 0.1% sample of [the full TabLib dataset](https://huggingface.co/datasets/approximatelabs/tablib-v1-full).
TabLib is a minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl.
This includes 867B tokens of "context metadata": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc.
For more information, read the [paper](https://arxiv.org/abs/2310.07875) & [announcement blog](https://approximatelabs.com/blog/tablib).
# Dataset Details
## Sources
* **GitHub**: nearly all public GitHub repositories
* **Common Crawl**: the `CC-MAIN-2023-23` crawl
## Reading Tables
Tables are stored as serialized Arrow bytes in the `arrow_bytes` column. To read these, you will need to deserialize the bytes:
```python
import datasets
import pyarrow as pa
# load a single file of the dataset
ds = datasets.load_dataset(
'approximatelabs/tablib-v1-sample',
token='...',
)
df = ds['train'].to_pandas()
tables = [pa.RecordBatchStreamReader(b).read_all() for b in df['arrow_bytes']]
```
## Licensing
This dataset is intended for research use only.
For specific licensing information, refer to the license of the specific datum being used.
# Contact
If you have any questions, comments, or concerns about licensing, pii, etc. please contact using [this form](https://forms.gle/C74VTWP7L78QDVR67).
# Approximate Labs
TabLib is a project from Approximate Labs. Find us on [Twitter](https://twitter.com/approximatelabs), [Github](https://github.com/approximatelabs), [Linkedin](https://www.linkedin.com/company/approximate-labs), and [Discord](https://discord.gg/kW9nBQErGe).
# Citations
If you use TabLib for any of your research, please cite the TabLib paper:
```
@misc{eggert2023tablib,
title={TabLib: A Dataset of 627M Tables with Context},
author={Gus Eggert and Kevin Huo and Mike Biven and Justin Waugh},
year={2023},
eprint={2310.07875},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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] |
OpenPipe/hacker-news
|
OpenPipe
| 2023-11-02T13:41:53Z | 37 | 4 |
[
"region:us"
] | null | 2023-10-13T19:44:25Z |
---
dataset_info:
features:
- name: id
dtype: int64
- name: type
dtype: string
- name: by
dtype: string
- name: time
dtype: timestamp[us]
- name: title
dtype: string
- name: text
dtype: string
- name: url
dtype: string
- name: score
dtype: float64
- name: parent
dtype: float64
- name: top_level_parent
dtype: int64
- name: descendants
dtype: float64
- name: kids
sequence: int64
- name: deleted
dtype: bool
- name: dead
dtype: bool
splits:
- name: train
num_bytes: 16886975696
num_examples: 38109500
download_size: 9948795138
dataset_size: 16886975696
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Hacker News posts and comments
This is a dataset of all HN posts and comments, current as of November 1, 2023.
|
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] |
kardosdrur/opensubtitles-no-da
|
kardosdrur
| 2023-10-26T07:09:53Z | 37 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-10-25T10:46:28Z |
---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: link_id
dtype: string
- name: da
dtype: string
- name: 'no'
dtype: string
- name: overlap
dtype: float64
splits:
- name: train
num_bytes: 270499727.08648384
num_examples: 1772983
- name: test
num_bytes: 67624969.91351616
num_examples: 443246
download_size: 201396375
dataset_size: 338124697.0
---
# OpenSubtitles Danish-Norwegian
Aligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Norwegian.
The source code for producing the dataset is included in the repository.
The dataset was created to aid training sentence transformers in the Danish Foundation Models project.
|
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dim/SlimOrcaRU
|
dim
| 2023-11-07T23:49:06Z | 37 | 0 |
[
"region:us"
] | null | 2023-11-07T23:48:48Z |
---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: value_ru
dtype: string
- name: weight
dtype: float64
- name: key
dtype: int64
splits:
- name: train
num_bytes: 183635644
num_examples: 47536
download_size: 83293621
dataset_size: 183635644
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "SlimOrcaRU"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Supersaiyan1729/instruct_dataset_mcq
|
Supersaiyan1729
| 2023-11-14T21:23:22Z | 37 | 0 |
[
"region:us"
] | null | 2023-11-14T21:23:09Z |
---
dataset_info:
features:
- name: input_prompt
dtype: string
- name: input_output_prompt
dtype: string
- name: dataset
dtype: string
splits:
- name: train
num_bytes: 69681099
num_examples: 48302
download_size: 29758222
dataset_size: 69681099
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "instruct_dataset_mcq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
atmallen/qm_bob_hard_4_grader_first_1.0e
|
atmallen
| 2023-11-16T18:27:44Z | 37 | 0 |
[
"region:us"
] | null | 2023-11-16T03:20:17Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 3455633.0
num_examples: 37091
- name: validation
num_bytes: 369717.0
num_examples: 3969
- name: test
num_bytes: 365744.0
num_examples: 3926
download_size: 1060982
dataset_size: 4191094.0
---
# Dataset Card for "qm_bob_hard_4_grader_first_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Definite/my_precious
|
Definite
| 2023-11-20T10:52:05Z | 37 | 0 |
[
"region:us"
] | null | 2023-11-16T15:31:15Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': jms
'1': shin
'2': winner
'3': biblei
splits:
- name: train
num_bytes: 2691826.25
num_examples: 5000
- name: eval
num_bytes: 1615095.75
num_examples: 3000
- name: test
num_bytes: 1115019
num_examples: 2000
download_size: 3023909
dataset_size: 5421941.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
- split: test
path: data/test-*
---
|
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DBQ/Gucci.Product.prices.United.States
|
DBQ
| 2023-11-18T22:15:36Z | 37 | 0 |
[
"region:us"
] | null | 2023-11-18T22:15:31Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 2746363
num_examples: 5764
download_size: 807752
dataset_size: 2746363
---
# Gucci web scraped data
## About the website
The **luxury fashion industry** in the **United States** is a rapidly evolving market, with a significant presence of globally renowned brands such as **Gucci**. In the recent years, the industry has channelled its efforts towards online platforms, leading to an increase in **ecommerce** activity. This transition has played a crucial role in boosting the accessibility and convenience of purchasing high-end fashion products. Of particular interest is the **Ecommerce product-list page (PLP) data on Gucci** in the United States. This dataset provides invaluable insights into online fashion consumer behavior, purchase patterns, and product preference trends.
## Link to **dataset**
[United States - Gucci - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Gucci%20Product-prices%20United%20States/r/rec9JLoIyDa81dIck)
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] |
danielz01/fMoW
|
danielz01
| 2023-11-20T10:36:50Z | 37 | 0 |
[
"region:us"
] | null | 2023-11-20T08:29:33Z |
---
configs:
- config_name: WILDS
data_files:
- split: test
path: WILDS/test-*
- split: id_test
path: WILDS/id_test-*
- split: val
path: WILDS/val-*
- split: id_val
path: WILDS/id_val-*
- split: train
path: WILDS/train-*
dataset_info:
config_name: WILDS
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': airport
'1': airport_hangar
'2': airport_terminal
'3': amusement_park
'4': aquaculture
'5': archaeological_site
'6': barn
'7': border_checkpoint
'8': burial_site
'9': car_dealership
'10': construction_site
'11': crop_field
'12': dam
'13': debris_or_rubble
'14': educational_institution
'15': electric_substation
'16': factory_or_powerplant
'17': fire_station
'18': flooded_road
'19': fountain
'20': gas_station
'21': golf_course
'22': ground_transportation_station
'23': helipad
'24': hospital
'25': impoverished_settlement
'26': interchange
'27': lake_or_pond
'28': lighthouse
'29': military_facility
'30': multi-unit_residential
'31': nuclear_powerplant
'32': office_building
'33': oil_or_gas_facility
'34': park
'35': parking_lot_or_garage
'36': place_of_worship
'37': police_station
'38': port
'39': prison
'40': race_track
'41': railway_bridge
'42': recreational_facility
'43': road_bridge
'44': runway
'45': shipyard
'46': shopping_mall
'47': single-unit_residential
'48': smokestack
'49': solar_farm
'50': space_facility
'51': stadium
'52': storage_tank
'53': surface_mine
'54': swimming_pool
'55': toll_booth
'56': tower
'57': tunnel_opening
'58': waste_disposal
'59': water_treatment_facility
'60': wind_farm
'61': zoo
- name: domain_labels
sequence: int64
- name: domain_labels_readable
struct:
- name: from_source_domain
dtype: bool
- name: region
dtype: string
- name: y
dtype: 'null'
- name: year
dtype: int64
- name: split
dtype: string
- name: img_filename
dtype: string
- name: img_path
dtype: string
- name: spatial_reference
dtype: string
- name: epsg
dtype: int64
- name: category
dtype: string
- name: visible
dtype: bool
- name: img_width
dtype: int64
- name: img_height
dtype: int64
- name: country_code
dtype: string
- name: cloud_cover
dtype: int64
- name: timestamp
dtype: string
- name: lat
dtype: float64
- name: lon
dtype: float64
- name: region
dtype: int64
- name: y
dtype: int64
- name: year
dtype: float64
splits:
- name: test
num_bytes: 2283079843.392
num_examples: 22108
- name: id_test
num_bytes: 1168174637.125
num_examples: 11327
- name: val
num_bytes: 2052331276.625
num_examples: 19915
- name: id_val
num_bytes: 1191085782.625
num_examples: 11483
- name: train
num_bytes: 7946709118.125
num_examples: 76863
download_size: 14612709837
dataset_size: 14641380657.892
---
# Dataset Card for "fMoW"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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pszemraj/midjourney-messages-cleaned
|
pszemraj
| 2023-11-22T08:46:09Z | 37 | 1 |
[
"task_categories:text-generation",
"source_datasets:vivym/midjourney-messages",
"language:en",
"license:apache-2.0",
"midjourney",
"region:us"
] |
[
"text-generation"
] | 2023-11-21T16:46:17Z |
---
language:
- en
license: apache-2.0
source_datasets: vivym/midjourney-messages
task_categories:
- text-generation
dataset_info:
- config_name: deduped
features:
- name: id
dtype: string
- name: channel_id
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2538669745.0
num_examples: 14828769
download_size: 1585207687
dataset_size: 2538669745.0
- config_name: default
features:
- name: id
dtype: string
- name: channel_id
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 3575844717.3610477
num_examples: 19716685
download_size: 1514418407
dataset_size: 3575844717.3610477
configs:
- config_name: deduped
data_files:
- split: train
path: deduped/train-*
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- midjourney
---
# midjourney-messages-cleaned
This is [vivym/midjourney-messages](https://huggingface.co/datasets/vivym/midjourney-messages) but with the following cleaning steps:
- remove most columns (keep `id` columns for reference vs. original)
- Apply `clean-text` to all rows (_keep casing_)
- rename `content` to `text` (ffs)
- remove intermediate ID/tag (???) in angle brackets at the end, remove double asterisks `**`
- remove exact duplicate rows
## dataset structure
overall:
```python
DatasetDict({
train: Dataset({
features: ['id', 'channel_id', 'text'],
num_rows: 19738964
})
})
```
A single example looks like this:
```python
random.choice(dataset['train'])
{'id': '1108635049391308879',
'channel_id': '1008571088919343124',
'text': 'Warhammer 40k Chaos Space Marine with pink Armor and a guitar'}
```
## details
585M GPT-4 tiktoken tokens.
```
token_count
count 1.971668e+07
mean 2.971651e+01
std 3.875208e+01
min 1.000000e+00
25% 1.000000e+01
50% 1.900000e+01
75% 3.400000e+01
max 2.077000e+03
```
|
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] |
sankettgorey/one_layout
|
sankettgorey
| 2023-11-23T10:07:01Z | 37 | 0 |
[
"region:us"
] | null | 2023-11-23T10:06:56Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 19925086.76470588
num_examples: 81
- name: test
num_bytes: 2691917.6470588236
num_examples: 11
- name: validation
num_bytes: 2439834.588235294
num_examples: 10
download_size: 20649181
dataset_size: 25056838.999999996
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
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sagteam/author_profiling
|
sagteam
| 2022-08-09T12:33:07Z | 36 | 1 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ru",
"license:apache-2.0",
"region:us"
] |
[
"text-classification"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ru
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: The Corpus for the analysis of author profiling in Russian-language texts.
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
---
# Dataset Card for [author_profiling]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/sag111/Author-Profiling
- **Repository:** https://github.com/sag111/Author-Profiling
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Sboev Alexander](mailto:[email protected])
### Dataset Summary
The corpus for the author profiling analysis contains texts in Russian-language which labeled for 5 tasks:
1) gender -- 13448 texts with the labels, who wrote this: text female or male;
2) age -- 13448 texts with the labels, how old the person who wrote the text. This is a number from 12 to 80. In addition, for the classification task we added 5 age groups: 0-19; 20-29; 30-39; 40-49; 50+;
3) age imitation -- 8460 texts, where crowdsource authors is asked to write three texts:
a) in their natural manner,
b) imitating the style of someone younger,
c) imitating the style of someone older;
4) gender imitation -- 4988 texts, where the crowdsource authors is asked to write texts: in their origin gender and pretending to be the opposite gender;
5) style imitation -- 4988 texts, where crowdsource authors is asked to write a text on behalf of another person of your own gender, with a distortion of the authors usual style.
Dataset is collected sing the Yandex.Toloka service [link](https://toloka.yandex.ru/en).
You can read the data using the following python code:
```
def load_jsonl(input_path: str) -> list:
"""
Read list of objects from a JSON lines file.
"""
data = []
with open(input_path, 'r', encoding='utf-8') as f:
for line in f:
data.append(json.loads(line.rstrip('\n|\r')))
print('Loaded {} records from {}/n'.format(len(data), input_path))
return data
path_to_file = "./data/train.jsonl"
data = load_jsonl(path_to_file)
```
or you can use HuggingFace style:
```
from datasets import load_dataset
train_df = load_dataset('sagteam/author_profiling', split='train')
valid_df = load_dataset('sagteam/author_profiling', split='validation')
test_df = load_dataset('sagteam/author_profiling', split='test')
```
#### Here are some statistics:
1. For Train file:
- No. of documents -- 9564;
- No. of unique texts -- 9553;
- Text length in characters -- min: 197, max: 2984, mean: 500.5;
- No. of documents written -- by men: 4704, by women: 4860;
- No. of unique authors -- 2344; men: 1172, women: 1172;
- Age of the authors -- min: 13, max: 80, mean: 31.2;
- No. of documents by age group -- 0-19: 813, 20-29: 4188, 30-39: 2697, 40-49: 1194, 50+: 672;
- No. of documents with gender imitation: 1215; without gender imitation: 2430; not applicable: 5919;
- No. of documents with age imitation -- younger: 1973; older: 1973; without age imitation: 1973; not applicable: 3645;
- No. of documents with style imitation: 1215; without style imitation: 2430; not applicable: 5919.
2. For Valid file:
- No. of documents -- 1320;
- No. of unique texts -- 1316;
- Text length in characters -- min: 200, max: 2809, mean: 520.8;
- No. of documents written -- by men: 633, by women: 687;
- No. of unique authors -- 336; men: 168, women: 168;
- Age of the authors -- min: 15, max: 79, mean: 32.2;
- No. of documents by age group -- 1-19: 117, 20-29: 570, 30-39: 339, 40-49: 362, 50+: 132;
- No. of documents with gender imitation: 156; without gender imitation: 312; not applicable: 852;
- No. of documents with age imitation -- younger: 284; older: 284; without age imitation: 284; not applicable: 468;
- No. of documents with style imitation: 156; without style imitation: 312; not applicable: 852.
3. For Test file:
- No. of documents -- 2564;
- No. of unique texts -- 2561;
- Text length in characters -- min: 199, max: 3981, mean: 515.6;
- No. of documents written -- by men: 1290, by women: 1274;
- No. of unique authors -- 672; men: 336, women: 336;
- Age of the authors -- min: 12, max: 67, mean: 31.8;
- No. of documents by age group -- 1-19: 195, 20-29: 1131, 30-39: 683, 40-49: 351, 50+: 204;
- No. of documents with gender imitation: 292; without gender imitation: 583; not applicable: 1689;
- No. of documents with age imitation -- younger: 563; older: 563; without age imitation: 563; not applicable: 875;
- No. of documents with style imitation: 292; without style imitation: 583; not applicable: 1689.
### Supported Tasks and Leaderboards
This dataset is intended for multi-class and multi-label text classification.
The baseline models currently achieve the following F1-weighted metrics scores (table):
| Model name | gender | age_group | gender_imitation | age_imitation | style_imitation | no_imitation | average |
| ------------------- | ------ | --------- | ---------------- | ------------- | --------------- | ------------ | ------- |
| Dummy-stratified | 0.49 | 0.29 | 0.56 | 0.32 | 0.57 | 0.55 | 0.46 |
| Dummy-uniform | 0.49 | 0.23 | 0.51 | 0.32 | 0.51 | 0.51 | 0.43 |
| Dummy-most_frequent | 0.34 | 0.27 | 0.53 | 0.17 | 0.53 | 0.53 | 0.40 |
| LinearSVC + TF-IDF | 0.67 | 0.37 | 0.62 | 0.72 | 0.71 | 0.71 | 0.63 |
### Languages
The text in the dataset is in Russian.
## Dataset Structure
### Data Instances
Each instance is a text in Russian with some author profiling annotations.
An example for an instance from the dataset is shown below:
```
{
'id': 'crowdsource_4916',
'text': 'Ты очень симпатичный, Я давно не с кем не встречалась. Ты мне сильно понравился, ты умный интересный и удивительный, приходи ко мне в гости , у меня есть вкусное вино , и приготовлю вкусный ужин, посидим пообщаемся, узнаем друг друга поближе.',
'account_id': 'account_#1239',
'author_id': 411,
'age': 22,
'age_group': '20-29',
'gender': 'male',
'no_imitation': 'with_any_imitation',
'age_imitation': 'None',
'gender_imitation': 'with_gender_imitation',
'style_imitation': 'no_style_imitation'
}
```
### Data Fields
Data Fields includes:
- id -- unique identifier of the sample;
- text -- authors text written by a crowdsourcing user;
- author_id -- unique identifier of the user;
- account_id -- unique identifier of the crowdsource account;
- age -- age annotations;
- age_group -- age group annotations;
- no_imitation -- imitation annotations.
Label codes:
- 'with_any_imitation' -- there is some imitation in the text;
- 'no_any_imitation' -- the text is written without any imitation
- age_imitation -- age imitation annotations.
Label codes:
- 'younger' -- someone younger than the author is imitated in the text;
- 'older' -- someone older than the author is imitated in the text;
- 'no_age_imitation' -- the text is written without age imitation;
- 'None' -- not supported (the text was not written for this task)
- gender_imitation -- gender imitation annotations.
Label codes:
- 'no_gender_imitation' -- the text is written without gender imitation;
- 'with_gender_imitation' -- the text is written with a gender imitation;
- 'None' -- not supported (the text was not written for this task)
- style_imitation -- style imitation annotations.
Label codes:
- 'no_style_imitation' -- the text is written without style imitation;
- 'with_style_imitation' -- the text is written with a style imitation;
- 'None' -- not supported (the text was not written for this task).
### Data Splits
The dataset includes a set of train/valid/test splits with 9564, 1320 and 2564 texts respectively.
The unique authors do not overlap between the splits.
## Dataset Creation
### Curation Rationale
The formed dataset of examples consists of texts in Russian using a crowdsourcing platform. The created dataset can be used to improve the accuracy of supervised classifiers in author profiling tasks.
### Source Data
#### Initial Data Collection and Normalization
Data was collected from crowdsource platform. Each text was written by the author specifically for the task provided.
#### Who are the source language producers?
Russian-speaking Yandex.Toloka users.
### Annotations
#### Annotation process
We used a crowdsourcing platform to collect texts. Each respondent is asked to fill a questionnaire including their gender, age and native language.
For age imitation task the respondents are to choose a
topic out of a few suggested, and write three texts on it:
1) Text in their natural manner;
2) Text imitating the style of someone younger;
3) Text imitating the style of someone older.
For gender and style imitation task each author wrote three texts in certain different styles:
1) Text in the authors natural style;
2) Text imitating other gender style;
3) Text in a different style but without gender imitation.
The topics to choose from are the following.
- An attempt to persuade some arbitrary listener to meet the respondent at their place;
- A story about some memorable event/acquisition/rumour or whatever else the imaginary listener is supposed to enjoy;
- A story about oneself or about someone else, aiming to please the listener and win their favour;
- A description of oneself and one’s potential partner for a dating site;
- An attempt to persuade an unfamiliar person to come;
- A negative tour review.
The task does not pass checking and is considered improper work if it contains:
- Irrelevant answers to the questionnaire;
- Incoherent jumble of words;
- Chunks of text borrowed from somewhere else;
- Texts not conforming to the above list of topics.
Texts checking is performed firstly by automated search for borrowings (by an anti-plagiarism website), and then by manual review of compliance to the task.
#### Who are the annotators?
Russian-speaking Yandex.Toloka users.
### Personal and Sensitive Information
All personal data was anonymized. Each author has been assigned an impersonal, unique identifier.
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Researchers at AI technology lab at NRC "Kurchatov Institute". See the [website](https://sagteam.ru/).
### Licensing Information
Apache License 2.0.
### Citation Information
If you have found our results helpful in your work, feel free to cite our publication.
```
@article{сбоев2022сравнение,
title={СРАВНЕНИЕ ТОЧНОСТЕЙ МЕТОДОВ НА ОСНОВЕ ЯЗЫКОВЫХ И ГРАФОВЫХ НЕЙРОСЕТЕВЫХ МОДЕЛЕЙ ДЛЯ ОПРЕДЕЛЕНИЯ ПРИЗНАКОВ АВТОРСКОГО ПРОФИЛЯ ПО ТЕКСТАМ НА РУССКОМ ЯЗЫКЕ},
author={Сбоев, АГ and Молошников, ИА and Рыбка, РБ and Наумов, АВ and Селиванов, АА},
journal={Вестник Национального исследовательского ядерного университета МИФИ},
volume={10},
number={6},
pages={529--539},
year={2021},
publisher={Общество с ограниченной ответственностью МАИК "Наука/Интерпериодика"}
}
```
### Contributions
Thanks to [@naumov-al](https://github.com/naumov-al) for adding this dataset.
|
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yuanchuan/annotated_reference_strings
|
yuanchuan
| 2022-10-26T14:53:23Z | 36 | 1 |
[
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:other",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"token-classification"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- other
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- parsing
pretty_name: Annotated Reference Strings
---
# Dataset Card for annotated_reference_strings
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.github.com/kylase](https://www.github.com/kylase)
- **Repository:** [https://www.github.com/kylase](https://www.github.com/kylase)
- **Point of Contact:** [Yuan Chuan Kee](https://www.github.com/kylase)
### Dataset Summary
The `annotated_reference_strings` dataset comprises millions of the annotated reference strings, i.e. each token of the strings have an associated label such as author, title, year, etc.
These strings are synthesized using citation processor on millions of citations obtained from various sources, spanning different scientific domains.
### Supported Tasks
This dataset can be used for structure prediction.
### Languages
The dataset is composed of reference strings that are in English.
## Dataset Structure
### Data Instances
```json
{
"source": "pubmed",
"lang": "en",
"entry_type": "article",
"doi_prefix": "pubmed19n0001",
"csl_style": "annual-reviews",
"content": "<citation-number>8.</citation-number> <author>Mohr W.</author> <year>1977.</year> <title>[Morphology of bone tumors. 2. Morphology of benign bone tumors].</title> <container-title>Aktuelle Probleme in Chirurgie und Orthopadie.</container-title> <volume>5:</volume> <page>29–42</page>"
}
```
#### Important Note
1. Each citation is rendered to _at most_ **17** CSL styles. Therefore, there will be near duplicates.
2. All characters (including punctuations) of a segment (**a segment consists of 1 or more token**) are enclosed by tag(s).
1. Only tokens that act as "conjunctions" are not enclosed in tags. These tokens will be labelled as `other`.
3. There will be instances which a segment can be enclosed by more than one tag e.g. `<issued><year>2021</year></issued>`. This depends on how the styles' author(s).
### Data Fields
- `source`: Describe the source of the citation. `{pubmed, jstor, crossref}`
- `lang`: Describe the language of the citation. `{en}`
- `entry_type`: Describe the BibTeX entry type. `{article, book, inbook, misc, techreport, phdthesis, incollection, inproceedings}`
- `doi_prefix`: For JSTOR and CrossRef, it is the prefix of the DOI. For PubMed, it is the directory (e.g. `pubmed19nXXXX` where `XXXX` is 4 digits) of which the citation is generated from.
- `csl_style`: The CSL style which the citation is rendered as.
- `content`: The rendered citation of a specific style with each segment enclosed by tags named after the CSL variables
### Data Splits
Data splits are not available yet.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The citations that are used to generate these reference strings are obtained from 3 main sources:
- [PubMed](https://www.nlm.nih.gov/databases/download/pubmed_medline.html) (2019 Baseline)
- CrossRef via [Open Academic Graph v2](https://www.microsoft.com/en-us/research/project/open-academic-graph/)
- JSTOR Sample Datasets (not available online as of publication date)
If the citation is not in BibTeX format, [bibutils](https://sourceforge.net/p/bibutils/home/Bibutils/) is used to convert it to BibTeX.
#### Who are the source language producers?
The manner which the citations are rendered as reference strings are based on rules/specifications dictated by the publisher.
[Citation Style Language](https://citationstyles.org/) (CSL) is an established standard which such specifications are prescribed.
Thousands of citation styles are available.
### Annotations
#### Annotation process
The annotation process involves 2 main interventions:
1. Modification of the styles' CSL specification to inject the CSL variable names as part of the render process
2. Sanitization of the rendered strings using regular expressions to ensure all tokens and characters are enclosed in the tags
#### Who are the annotators?
The original CSL specification are available on [GitHub](https://github.com/citation-style-language/styles).
The modification of the styles and the sanitization process are done by the author of this work.
## Additional Information
### Licensing Information
This dataset is licensed under [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
This dataset is a product of a Master Project done in the National University of Singapore.
If you are using it, please cite the following:
```bibtex
@techreport{kee2021,
author = {Yuan Chuan Kee},
title = {Synthesis of a large dataset of annotated reference strings for developing citation parsers},
institution = {National University of Singapore},
year = {2021}
}
```
### Contributions
Thanks to [@kylase](https://github.com/kylase) for adding this dataset.
|
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biglam/atypical_animacy
|
biglam
| 2022-07-22T17:29:12Z | 36 | 3 |
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"arxiv:2005.11140",
"region:us"
] |
[
"text-classification"
] | 2022-07-11T21:33:07Z |
---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- machine-generated
license:
- cc0-1.0
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: Atypical Animacy
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- intent-classification
---
# Dataset Card for atypical_animacy
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://bl.iro.bl.uk/concern/datasets/323177af-6081-4e93-8aaf-7932ca4a390a?locale=en
- **Repository:** https://github.com/Living-with-machines/AtypicalAnimacy
- **Paper:** https://arxiv.org/abs/2005.11140
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Mariona Coll Ardanuy](mailto:[email protected]), [Daniel CS Wilson](mailto:[email protected])
### Dataset Summary
Atypical animacy detection dataset, based on nineteenth-century sentences in English extracted from an open dataset of nineteenth-century books digitized by the British Library. This dataset contains 598 sentences containing mentions of machines. Each sentence has been annotated according to the animacy and humanness of the machine in the sentence.
### Supported Tasks and Leaderboards
- `text-classification` - This dataset can be used to determine if a mention of an entity in a document was humanlike or not
- `entity-recognition` - The dataset can be used to fine tune large models for NER, albeit for a very specific use case
### Languages
The text in the dataset is in English, as written by authors of books digitized by the British Library. The associated BCP-47 code in `en`
## Dataset Structure
The dataset has a single configuration
### Data Instances
An example data point
```
{'id': '002757962_01_184_16',
'sentence': '100 shows a Cornish boiler improperly seated with one small side flue and a bottom flue.',
'context': 'Fig. 100 shows a Cornish boiler improperly seated with one small side flue and a bottom flue. The effect of this on a long boiler is to cause springing and leakage of the seams from the heat being applied to one side of the boiler only.',
'target': 'boiler',
'animacy': 0.0,
'humanness': 1.0,
'offsets': [20, 26],
'date': '1893'}
```
### Data Fields
- id: sentence identifier according to internal Living with Machines BL books indexing.
- sentence: sentence where target expression occurs.
- context: sentence where target expression occurs, plus one sentence to the left and one sentence to the right.
- target: target expression
- animacy: animacy of the target expression
- humanness: humanness of the target expression
### Data Splits
Train | 598
## Dataset Creation
The dataset was created by manually annotating books that had been digitized by the British Library. According to the paper's authors,
> "we provide a basis for examining how machines were imagined during the nineteenth century as everything from lifeless mechanical objects to living beings, or even human-like agents that feel, think, and love. We focus on texts from nineteenth-century Britain, a society being transformed by industrialization, as a good candidate for studying the broader issue"
### Curation Rationale
From the paper:
> The Stories dataset is largely composed of target expressions that correspond to either typically animate or typically inanimate entities. Even though some cases of unconventional animacy can be found(folktales, in particular, are richer in typically inanimate entities that become animate), these accountfor a very small proportion of the data.6 We decided to create our own dataset (henceforth 19thC Machines dataset) to gain a better sense of the suitability of our method to the problem of atypical animacy detection, with particular attention to the case of animacy of machines in nineteenth-century texts.
### Source Data
#### Initial Data Collection and Normalization
The dataset was generated by manually annotating books that have been digitized by the British Library
#### Who are the source language producers?
The data was originally produced by British authors in the 19th century. The books were then digitzed whcih produces some noise due to the OCR method. The annotators are from The Alan Turing Institute, The British Library, University of Cambridge, University of Exeter and Queen Mary University of London
### Annotations
#### Annotation process
Annotation was carried out in two parts.
For the intial annotation process, from the paper:
> "For human annotators, even history and literature experts, language subtleties made this task extremely subjective. In the first task, we masked the target word (i.e. the machine) in each sentence and asked the annotator to fill the slot with the most likely entity between ‘human’, ‘horse’, and ‘machine’, representing three levels in the animacy hierarchy: human, animal, and object (Comrie, 1989, 185). We asked annotators to stick to the most literal meaning and avoid metaphorical interpretations when possible. The second task was more straightforwardly related to determining the animacy of the target entity, given the same 100 sentences. We asked annotators to provide a score between -2 and 2, with -2 being definitely inanimate, -1 possibly inanimate, 1 possibly animate, and 2 definitely animate. Neutral judgements were not allowed. "
For the final annotations, from the paper:
> A subgroup of five annotators collaboratively wrote the guidelines based on their experience annotating the first batch of sentences, taking into account common discrepancies. After discussion, it was decided that a machine would be tagged as animate if it is described as having traits distinctive of biologically animate beings or human-specific skills, or portrayed as having feelings, emotions, or a soul. Sentences like the ones in example 2 would be considered animate, but an additional annotation layer would be provided to capture the notion of humanness, which would be true if the machine is portrayed as sentient and capable of specifically human emotions, and false if it used to suggest some degree of dehumanization.
#### Who are the annotators?
Annotations were carried out by the following people
- Giorgia Tolfo
- Ruth Ahnert
- Kaspar Beelen
- Mariona Coll Ardanuy
- Jon Lawrence
- Katherine McDonough
- Federico Nanni
- Daniel CS Wilson
### Personal and Sensitive Information
This dataset does not have any personal information since they are digitizations of books from the 19th century. Some passages might be sensitive, but it is not explicitly mentioned in the paper.
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The curators for this dataset are:
- Kaspar Beelen
- Mariona Coll Ardanuy
- Federico Nanni
- Giorgia Tolfo
### Licensing Information
CC0 1.0 Universal Public Domain
### Citation Information
```
@article{DBLP:journals/corr/abs-2005-11140,
author = {Mariona Coll Ardanuy and
Federico Nanni and
Kaspar Beelen and
Kasra Hosseini and
Ruth Ahnert and
Jon Lawrence and
Katherine McDonough and
Giorgia Tolfo and
Daniel C. S. Wilson and
Barbara McGillivray},
title = {Living Machines: {A} study of atypical animacy},
journal = {CoRR},
volume = {abs/2005.11140},
year = {2020},
url = {https://arxiv.org/abs/2005.11140},
eprinttype = {arXiv},
eprint = {2005.11140},
timestamp = {Sat, 23 Jan 2021 01:12:25 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2005-11140.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
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demelin/wino_x
|
demelin
| 2022-07-15T22:28:18Z | 36 | 1 |
[
"task_categories:translation",
"task_ids:multiple-choice-qa",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:de",
"language:fr",
"language:ru",
"license:mit",
"region:us"
] |
[
"translation",
"coreference resolution",
"commonsense reasoning"
] | 2022-07-14T11:21:23Z |
---
annotations_creators:
- no-annotation
language:
- en
- de
- fr
- ru
language_creators:
- machine-generated
- expert-generated
license:
- mit
multilinguality:
- multilingual
- translation
pretty_name: Wino-X
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
- coreference resolution
- commonsense reasoning
task_ids:
- multiple-choice-qa
- language-modeling
---
# Dataset Card for Wino-X
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Wino-X repository](https://github.com/demelin/Wino-X)
- **Repository:** [Wino-X repository](https://github.com/demelin/Wino-X)
- **Paper:** [Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution](https://aclanthology.org/2021.emnlp-main.670/)
- **Leaderboard:** [N/A]
- **Point of Contact:** [Denis Emelin](demelin.github.io)
### Dataset Summary
Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English
counterparts, used to examine whether neural machine translation models can perform coreference resolution that
requires commonsense knowledge, and whether multilingual language models are capable of commonsense reasoning across
multiple languages.
### Supported Tasks and Leaderboards
- translation: The dataset can be used to evaluate translations of ambiguous source sentences, as produced by translation models . A [pretrained transformer-based NMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) can be used for this purpose.
- coreference-resolution: The dataset can be used to rank alternative translations of an ambiguous source sentence that differ in the chosen referent of an ambiguous source pronoun. A [pretrained transformer-based NMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) can be used for this purpose.
- commonsense-reasoning: The dataset can also be used evaluate whether pretrained multilingual language models can perform commonsense reasoning in (or across) multiple languages by identifying the correct filler in a cloze completion task. An [XLM-based model](https://huggingface.co/xlm-roberta-base) can be used for this purpose.
### Languages
The dataset (both its MT and LM portions) is available in the following translation pairs: English-German, English-French, English-Russian. All English sentences included in *Wino-X* were extracted from publicly available parallel corpora, as detailed in the accompanying paper, and represent the dataset-specific language varieties. All non-English sentences were obtained through machine translation and may, as such, exhibit features of translationese.
## Dataset Structure
### Data Instances
The following represents a typical *MT-Wino-X* instance (for the English-German translation pair):
{"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",
"sentence": "The woman looked for a different vase for the bouquet because it was too small.",
"translation1": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil sie zu klein war.",
"translation2": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil er zu klein war.",
"answer": 1,
"pronoun1": "sie",
"pronoun2": "er",
"referent1_en": "vase",
"referent2_en": "bouquet",
"true_translation_referent_of_pronoun1_de": "Vase",
"true_translation_referent_of_pronoun2_de": "Blumenstrauß",
"false_translation_referent_of_pronoun1_de": "Vase",
"false_translation_referent_of_pronoun2_de": "Blumenstrauß"}
The following represents a typical *LM-Wino-X* instance (for the English-French translation pair):
{"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",
"sentence": "The woman looked for a different vase for the bouquet because it was too small.",
"context_en": "The woman looked for a different vase for the bouquet because _ was too small.",
"context_fr": "La femme a cherché un vase différent pour le bouquet car _ était trop petit.",
"option1_en": "the bouquet",
"option2_en": "the vase",
"option1_fr": "le bouquet",
"option2_fr": "le vase",
"answer": 2,
"context_referent_of_option1_fr": "bouquet",
"context_referent_of_option2_fr": "vase"}
### Data Fields
For *MT-Wino-X*:
- "qID": Unique identifier ID for this dataset instance.
- "sentence": English sentence containing the ambiguous pronoun 'it'.
- "translation1": First translation candidate.
- "translation2": Second translation candidate.
- "answer": ID of the correct translation.
- "pronoun1": Translation of the ambiguous source pronoun in translation1.
- "pronoun2": Translation of the ambiguous source pronoun in translation2.
- "referent1_en": English referent of the translation of the ambiguous source pronoun in translation1.
- "referent2_en": English referent of the translation of the ambiguous source pronoun in translation2.
- "true_translation_referent_of_pronoun1_[TGT-LANG]": Target language referent of pronoun1 in the correct translation.
- "true_translation_referent_of_pronoun2_[TGT-LANG]": Target language referent of pronoun2 in the correct translation.
- "false_translation_referent_of_pronoun1_[TGT-LANG]": Target language referent of pronoun1 in the incorrect translation.
- "false_translation_referent_of_pronoun2_[TGT-LANG]": Target language referent of pronoun2 in the incorrect translation.
For *LM-Wino-X*:
- "qID": Unique identifier ID for this dataset instance.
- "sentence": English sentence containing the ambiguous pronoun 'it'.
- "context_en": Same English sentence, where 'it' is replaced by a gap.
- "context_fr": Target language translation of the English sentence, where the translation of 'it' is replaced by a gap.
- "option1_en": First filler option for the English sentence.
- "option2_en": Second filler option for the English sentence.
- "option1_[TGT-LANG]": First filler option for the target language sentence.
- "option2_[TGT-LANG]": Second filler option for the target language sentence.
- "answer": ID of the correct gap filler.
- "context_referent_of_option1_[TGT-LANG]": English translation of option1_[TGT-LANG].
- "context_referent_of_option2_[TGT-LANG]": English translation of option2_[TGT-LANG]
### Data Splits
*Wno-X* was designed as an evaluation-only benchmark and therefore is intended to be used for zero-shot testing only. However, users are very welcome to split the data as they wish :) .
## Dataset Creation
### Curation Rationale
Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
### Source Data
#### Initial Data Collection and Normalization
Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
#### Who are the source language producers?
Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
### Annotations
#### Annotation process
Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
#### Who are the annotators?
Annotations were generated automatically and verified by the dataset author / curator for correctness.
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
### Discussion of Biases
Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
### Other Known Limitations
Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
## Additional Information
### Dataset Curators
[Denis Emelin](demelin.github.io)
### Licensing Information
MIT
### Citation Information
@inproceedings{Emelin2021WinoXMW,
title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},
author={Denis Emelin and Rico Sennrich},
booktitle={EMNLP},
year={2021}
}
|
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] |
tner/fin
|
tner
| 2022-08-15T17:50:31Z | 36 | 4 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] |
[
"token-classification"
] | 2022-07-16T11:08:45Z |
---
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: FIN
---
# Dataset Card for "tner/fin"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/U15-1010.pdf](https://aclanthology.org/U15-1010.pdf)
- **Dataset:** FIN
- **Domain:** Financial News
- **Number of Entity:** 4
### Dataset Summary
FIN NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
FIN dataset contains training (FIN5) and test (FIN3) only, so we randomly sample a half size of test instances from the training set to create validation set.
- Entity Types: `ORG`, `LOC`, `PER`, `MISC`
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
"tags": [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"tokens": ["1", ".", "1", ".", "4", "Borrower", "engages", "in", "criminal", "conduct", "or", "is", "involved", "in", "criminal", "activities", ";"]
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/fin/raw/main/dataset/label.json).
```python
{
"O": 0,
"B-PER": 1,
"B-LOC": 2,
"B-ORG": 3,
"B-MISC": 4,
"I-PER": 5,
"I-LOC": 6,
"I-ORG": 7,
"I-MISC": 8
}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|fin |1014 | 303| 150|
### Citation Information
```
@inproceedings{salinas-alvarado-etal-2015-domain,
title = "Domain Adaption of Named Entity Recognition to Support Credit Risk Assessment",
author = "Salinas Alvarado, Julio Cesar and
Verspoor, Karin and
Baldwin, Timothy",
booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
month = dec,
year = "2015",
address = "Parramatta, Australia",
url = "https://aclanthology.org/U15-1010",
pages = "84--90",
}
```
|
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imodels/diabetes-readmission
|
imodels
| 2022-08-14T15:38:59Z | 36 | 1 |
[
"task_categories:tabular-classification",
"size_categories:100K<n<1M",
"interpretability",
"fairness",
"medicine",
"region:us"
] |
[
"tabular-classification"
] | 2022-08-14T15:19:27Z |
---
annotations_creators: []
language: []
language_creators: []
license: []
multilinguality: []
pretty_name: diabetes-readmission
size_categories:
- 100K<n<1M
source_datasets: []
tags:
- interpretability
- fairness
- medicine
task_categories:
- tabular-classification
task_ids: []
---
Port of the diabetes-readmission dataset from UCI (link [here](https://archive.ics.uci.edu/ml/datasets/diabetes+130-us+hospitals+for+years+1999-2008)). See details there and use carefully.
Basic preprocessing done by the [imodels team](https://github.com/csinva/imodels) in [this notebook](https://github.com/csinva/imodels-data/blob/master/notebooks_fetch_data/00_get_datasets_custom.ipynb).
The target is the binary outcome `readmitted`.
### Sample usage
Load the data:
```
from datasets import load_dataset
dataset = load_dataset("imodels/diabetes-readmission")
df = pd.DataFrame(dataset['train'])
X = df.drop(columns=['readmitted'])
y = df['readmitted'].values
```
Fit a model:
```
import imodels
import numpy as np
m = imodels.FIGSClassifier(max_rules=5)
m.fit(X, y)
print(m)
```
Evaluate:
```
df_test = pd.DataFrame(dataset['test'])
X_test = df.drop(columns=['readmitted'])
y_test = df['readmitted'].values
print('accuracy', np.mean(m.predict(X_test) == y_test))
```
|
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BDas/ArabicNLPDataset
|
BDas
| 2022-09-26T18:52:01Z | 36 | 1 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ar",
"license:other",
"region:us"
] |
[
"text-classification"
] | 2022-08-26T21:33:24Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ar
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
pretty_name: 'ArabicNLPDataset'
---
# Dataset Card for "ArabicNLPDataset"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/BihterDass/ArabicTextClassificationDataset]
- **Repository:** [https://github.com/BihterDass/ArabicTextClassificationDataset]
- **Size of downloaded dataset files:** 23.5 MB
- **Size of the generated dataset:** 23.5 MB
### Dataset Summary
The dataset was compiled from user comments from e-commerce sites. It consists of 10,000 validations, 10,000 tests and 80000 train data. Data were classified into 3 classes (positive(pos), negative(neg) and natural(nor). The data is available to you on github.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
#### arabic-dataset-v1
- **Size of downloaded dataset files:** 23.5 MB
- **Size of the generated dataset:** 23.5 MB
### Data Fields
The data fields are the same among all splits.
#### arabic-dataset-v-v1
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `positive` (2), `natural` (1), `negative` (0).
### Data Splits
| |train |validation|test |
|----|--------:|---------:|---------:|
|Data| 80000 | 10000 | 10000 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@PnrSvc](https://github.com/PnrSvc) for adding this dataset.
|
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zishuod/pokemon-icons
|
zishuod
| 2022-09-24T15:35:39Z | 36 | 2 |
[
"task_categories:image-classification",
"license:mit",
"pokemon",
"region:us"
] |
[
"image-classification"
] | 2022-09-24T15:12:08Z |
---
annotations_creators: []
language: []
language_creators: []
license:
- mit
multilinguality: []
pretty_name: pokemon-icons
size_categories: []
source_datasets: []
tags:
- pokemon
task_categories:
- image-classification
task_ids: []
---
# Dataset Card for pokemon-icons
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Pokemon Icons. Most of them are collected and cropped from screenshots captured in Pokémon Sword and Shield.
### Supported Tasks and Leaderboards
Image classification
|
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tner/wikineural
|
tner
| 2022-09-27T19:46:37Z | 36 | 4 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:multilingual",
"size_categories:10K<100k",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:nl",
"language:pl",
"language:pt",
"language:ru",
"region:us"
] |
[
"token-classification"
] | 2022-09-27T17:56:40Z |
---
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
multilinguality:
- multilingual
size_categories:
- 10K<100k
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: WikiNeural
---
# Dataset Card for "tner/wikineural"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/2021.findings-emnlp.215/](https://aclanthology.org/2021.findings-emnlp.215/)
- **Dataset:** WikiNeural
- **Domain:** Wikipedia
- **Number of Entity:** 16
### Dataset Summary
WikiAnn NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `PER`, `LOC`, `ORG`, `ANIM`, `BIO`, `CEL`, `DIS`, `EVE`, `FOOD`, `INST`, `MEDIA`, `PLANT`, `MYTH`, `TIME`, `VEHI`, `MISC`
## Dataset Structure
### Data Instances
An example of `train` of `de` looks as follows.
```
{
'tokens': [ "Dieses", "wiederum", "basierte", "auf", "dem", "gleichnamigen", "Roman", "von", "Noël", "Calef", "." ],
'tags': [ 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0 ]
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wikineural/raw/main/dataset/label.json).
```python
{
"O": 0,
"B-PER": 1,
"I-PER": 2,
"B-LOC": 3,
"I-LOC": 4,
"B-ORG": 5,
"I-ORG": 6,
"B-ANIM": 7,
"I-ANIM": 8,
"B-BIO": 9,
"I-BIO": 10,
"B-CEL": 11,
"I-CEL": 12,
"B-DIS": 13,
"I-DIS": 14,
"B-EVE": 15,
"I-EVE": 16,
"B-FOOD": 17,
"I-FOOD": 18,
"B-INST": 19,
"I-INST": 20,
"B-MEDIA": 21,
"I-MEDIA": 22,
"B-PLANT": 23,
"I-PLANT": 24,
"B-MYTH": 25,
"I-MYTH": 26,
"B-TIME": 27,
"I-TIME": 28,
"B-VEHI": 29,
"I-VEHI": 30,
"B-MISC": 31,
"I-MISC": 32
}
```
### Data Splits
| language | train | validation | test |
|:-----------|--------:|-------------:|-------:|
| de | 98640 | 12330 | 12372 |
| en | 92720 | 11590 | 11597 |
| es | 76320 | 9540 | 9618 |
| fr | 100800 | 12600 | 12678 |
| it | 88400 | 11050 | 11069 |
| nl | 83680 | 10460 | 10547 |
| pl | 108160 | 13520 | 13585 |
| pt | 80560 | 10070 | 10160 |
| ru | 92320 | 11540 | 11580 |
### Citation Information
```
@inproceedings{tedeschi-etal-2021-wikineural-combined,
title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}",
author = "Tedeschi, Simone and
Maiorca, Valentino and
Campolungo, Niccol{\`o} and
Cecconi, Francesco and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.215",
doi = "10.18653/v1/2021.findings-emnlp.215",
pages = "2521--2533",
abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.",
}
```
|
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drt/complex_web_questions
|
drt
| 2023-04-27T21:04:50Z | 36 | 4 |
[
"license:apache-2.0",
"arxiv:1803.06643",
"arxiv:1807.09623",
"region:us"
] | null | 2022-10-22T22:14:27Z |
---
license: apache-2.0
source: https://github.com/KGQA/KGQA-datasets
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** https://www.tau-nlp.sites.tau.ac.il/compwebq
- **Repository:** https://github.com/alontalmor/WebAsKB
- **Paper:** https://arxiv.org/abs/1803.06643
- **Leaderboard:** https://www.tau-nlp.sites.tau.ac.il/compwebq-leaderboard
- **Point of Contact:** [email protected].
### Dataset Summary
**A dataset for answering complex questions that require reasoning over multiple web snippets**
ComplexWebQuestions is a new dataset that contains a large set of complex questions in natural language, and can be used in multiple ways:
- By interacting with a search engine, which is the focus of our paper (Talmor and Berant, 2018);
- As a reading comprehension task: we release 12,725,989 web snippets that are relevant for the questions, and were collected during the development of our model;
- As a semantic parsing task: each question is paired with a SPARQL query that can be executed against Freebase to retrieve the answer.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
- English
## Dataset Structure
QUESTION FILES
The dataset contains 34,689 examples divided into 27,734 train, 3,480 dev, 3,475 test.
each containing:
```
"ID”: The unique ID of the example;
"webqsp_ID": The original WebQuestionsSP ID from which the question was constructed;
"webqsp_question": The WebQuestionsSP Question from which the question was constructed;
"machine_question": The artificial complex question, before paraphrasing;
"question": The natural language complex question;
"sparql": Freebase SPARQL query for the question. Note that the SPARQL was constructed for the machine question, the actual question after paraphrasing
may differ from the SPARQL.
"compositionality_type": An estimation of the type of compositionally. {composition, conjunction, comparative, superlative}. The estimation has not been manually verified,
the question after paraphrasing may differ from this estimation.
"answers": a list of answers each containing answer: the actual answer; answer_id: the Freebase answer id; aliases: freebase extracted aliases for the answer.
"created": creation time
```
NOTE: test set does not contain “answer” field. For test evaluation please send email to
[email protected].
WEB SNIPPET FILES
The snippets files consist of 12,725,989 snippets each containing
PLEASE DON”T USE CHROME WHEN DOWNLOADING THESE FROM DROPBOX (THE UNZIP COULD FAIL)
"question_ID”: the ID of related question, containing at least 3 instances of the same ID (full question, split1, split2);
"question": The natural language complex question;
"web_query": Query sent to the search engine.
“split_source”: 'noisy supervision split' or ‘ptrnet split’, please train on examples containing “ptrnet split” when comparing to Split+Decomp from https://arxiv.org/abs/1807.09623
“split_type”: 'full_question' or ‘split_part1' or ‘split_part2’ please use ‘composition_answer’ in question of type composition and split_type: “split_part1” when training a reading comprehension model on splits as in Split+Decomp from https://arxiv.org/abs/1807.09623 (in the rest of the cases use the original answer).
"web_snippets": ~100 web snippets per query. Each snippet includes Title,Snippet. They are ordered according to Google results.
With a total of
10,035,571 training set snippets
1,350,950 dev set snippets
1,339,468 test set snippets
### Source Data
The original files can be found at this [dropbox link](https://www.dropbox.com/sh/7pkwkrfnwqhsnpo/AACuu4v3YNkhirzBOeeaHYala)
### Licensing Information
Not specified
### Citation Information
```
@inproceedings{talmor2018web,
title={The Web as a Knowledge-Base for Answering Complex Questions},
author={Talmor, Alon and Berant, Jonathan},
booktitle={Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
pages={641--651},
year={2018}
}
```
### Contributions
Thanks for [happen2me](https://github.com/happen2me) for contributing this dataset.
|
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] |
tonytan48/Re-DocRED
|
tonytan48
| 2022-11-25T02:48:32Z | 36 | 0 |
[
"license:mit",
"arxiv:2205.12696",
"region:us"
] | null | 2022-11-25T02:42:48Z |
---
license: mit
---
# Re-DocRED Dataset
This repository contains the dataset of our EMNLP 2022 research paper [Revisiting DocRED – Addressing the False Negative Problem
in Relation Extraction](https://arxiv.org/pdf/2205.12696.pdf).
DocRED is a widely used benchmark for document-level relation extraction. However, the DocRED dataset contains a significant percentage of false negative examples (incomplete annotation). We revised 4,053 documents in the DocRED dataset and resolved its problems. We released this dataset as: Re-DocRED dataset.
The Re-DocRED Dataset resolved the following problems of DocRED:
1. Resolved the incompleteness problem by supplementing large amounts of relation triples.
2. Addressed the logical inconsistencies in DocRED.
3. Corrected the coreferential errors within DocRED.
# Statistics of Re-DocRED
The Re-DocRED dataset is located as ./data directory, the statistics of the dataset are shown below:
| | Train | Dev | Test |
| :---: | :-: | :-: |:-: |
| # Documents | 3,053 | 500 | 500 |
| Avg. # Triples | 28.1 | 34.6 | 34.9 |
| Avg. # Entities | 19.4 | 19.4 | 19.6 |
| Avg. # Sents | 7.9 | 8.2 | 7.9 |
# Citation
If you find our work useful, please cite our work as:
```bibtex
@inproceedings{tan2022revisiting,
title={Revisiting DocRED – Addressing the False Negative Problem in Relation Extraction},
author={Tan, Qingyu and Xu, Lu and Bing, Lidong and Ng, Hwee Tou and Aljunied, Sharifah Mahani},
booktitle={Proceedings of EMNLP},
url={https://arxiv.org/abs/2205.12696},
year={2022}
}
```
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autoevaluate/autoeval-eval-bazzhangz__sumdataset-bazzhangz__sumdataset-18687b-2355774138
|
autoevaluate
| 2022-12-06T15:57:43Z | 36 | 0 |
[
"autotrain",
"evaluation",
"region:us"
] | null | 2022-12-06T15:26:09Z |
---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- bazzhangz/sumdataset
eval_info:
task: summarization
model: knkarthick/MEETING_SUMMARY
metrics: []
dataset_name: bazzhangz/sumdataset
dataset_config: bazzhangz--sumdataset
dataset_split: train
col_mapping:
text: dialogue
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: knkarthick/MEETING_SUMMARY
* Dataset: bazzhangz/sumdataset
* Config: bazzhangz--sumdataset
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@bazzhangz](https://huggingface.co/bazzhangz) for evaluating this model.
|
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Zombely/diachronia-ocr-test-A
|
Zombely
| 2022-12-14T17:21:46Z | 36 | 0 |
[
"region:us"
] | null | 2022-12-14T17:21:21Z |
---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 62457501.0
num_examples: 81
download_size: 62461147
dataset_size: 62457501.0
---
# Dataset Card for "diachronia-ocr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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DFKI-SLT/fabner
|
DFKI-SLT
| 2023-04-05T23:20:21Z | 36 | 0 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"manufacturing",
"2000-2020",
"region:us"
] |
[
"token-classification"
] | 2023-01-13T13:01:38Z |
---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: FabNER is a manufacturing text dataset for Named Entity Recognition.
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- manufacturing
- 2000-2020
task_categories:
- token-classification
task_ids:
- named-entity-recognition
dataset_info:
- config_name: fabner
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-MATE
'2': I-MATE
'3': O-MATE
'4': E-MATE
'5': S-MATE
'6': B-MANP
'7': I-MANP
'8': O-MANP
'9': E-MANP
'10': S-MANP
'11': B-MACEQ
'12': I-MACEQ
'13': O-MACEQ
'14': E-MACEQ
'15': S-MACEQ
'16': B-APPL
'17': I-APPL
'18': O-APPL
'19': E-APPL
'20': S-APPL
'21': B-FEAT
'22': I-FEAT
'23': O-FEAT
'24': E-FEAT
'25': S-FEAT
'26': B-PRO
'27': I-PRO
'28': O-PRO
'29': E-PRO
'30': S-PRO
'31': B-CHAR
'32': I-CHAR
'33': O-CHAR
'34': E-CHAR
'35': S-CHAR
'36': B-PARA
'37': I-PARA
'38': O-PARA
'39': E-PARA
'40': S-PARA
'41': B-ENAT
'42': I-ENAT
'43': O-ENAT
'44': E-ENAT
'45': S-ENAT
'46': B-CONPRI
'47': I-CONPRI
'48': O-CONPRI
'49': E-CONPRI
'50': S-CONPRI
'51': B-MANS
'52': I-MANS
'53': O-MANS
'54': E-MANS
'55': S-MANS
'56': B-BIOP
'57': I-BIOP
'58': O-BIOP
'59': E-BIOP
'60': S-BIOP
splits:
- name: train
num_bytes: 4394010
num_examples: 9435
- name: validation
num_bytes: 934347
num_examples: 2183
- name: test
num_bytes: 940136
num_examples: 2064
download_size: 3793613
dataset_size: 6268493
- config_name: fabner_bio
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-MATE
'2': I-MATE
'3': B-MANP
'4': I-MANP
'5': B-MACEQ
'6': I-MACEQ
'7': B-APPL
'8': I-APPL
'9': B-FEAT
'10': I-FEAT
'11': B-PRO
'12': I-PRO
'13': B-CHAR
'14': I-CHAR
'15': B-PARA
'16': I-PARA
'17': B-ENAT
'18': I-ENAT
'19': B-CONPRI
'20': I-CONPRI
'21': B-MANS
'22': I-MANS
'23': B-BIOP
'24': I-BIOP
splits:
- name: train
num_bytes: 4394010
num_examples: 9435
- name: validation
num_bytes: 934347
num_examples: 2183
- name: test
num_bytes: 940136
num_examples: 2064
download_size: 3793613
dataset_size: 6268493
- config_name: fabner_simple
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': MATE
'2': MANP
'3': MACEQ
'4': APPL
'5': FEAT
'6': PRO
'7': CHAR
'8': PARA
'9': ENAT
'10': CONPRI
'11': MANS
'12': BIOP
splits:
- name: train
num_bytes: 4394010
num_examples: 9435
- name: validation
num_bytes: 934347
num_examples: 2183
- name: test
num_bytes: 940136
num_examples: 2064
download_size: 3793613
dataset_size: 6268493
- config_name: text2tech
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': Technological System
'2': Method
'3': Material
'4': Technical Field
splits:
- name: train
num_bytes: 4394010
num_examples: 9435
- name: validation
num_bytes: 934347
num_examples: 2183
- name: test
num_bytes: 940136
num_examples: 2064
download_size: 3793613
dataset_size: 6268493
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://figshare.com/articles/dataset/Dataset_NER_Manufacturing_-_FabNER_Information_Extraction_from_Manufacturing_Process_Science_Domain_Literature_Using_Named_Entity_Recognition/14782407](https://figshare.com/articles/dataset/Dataset_NER_Manufacturing_-_FabNER_Information_Extraction_from_Manufacturing_Process_Science_Domain_Literature_Using_Named_Entity_Recognition/14782407)
- **Paper:** ["FabNER": information extraction from manufacturing process science domain literature using named entity recognition](https://par.nsf.gov/servlets/purl/10290810)
- **Size of downloaded dataset files:** 3.79 MB
- **Size of the generated dataset:** 6.27 MB
### Dataset Summary
FabNER is a manufacturing text corpus of 350,000+ words for Named Entity Recognition.
It is a collection of abstracts obtained from Web of Science through known journals available in manufacturing process
science research.
For every word, there were categories/entity labels defined namely Material (MATE), Manufacturing Process (MANP),
Machine/Equipment (MACEQ), Application (APPL), Features (FEAT), Mechanical Properties (PRO), Characterization (CHAR),
Parameters (PARA), Enabling Technology (ENAT), Concept/Principles (CONPRI), Manufacturing Standards (MANS) and
BioMedical (BIOP). Annotation was performed in all categories along with the output tag in 'BIOES' format:
B=Beginning, I-Intermediate, O=Outside, E=End, S=Single.
For details about the dataset, please refer to the paper: ["FabNER": information extraction from manufacturing process science domain literature using named entity recognition](https://par.nsf.gov/servlets/purl/10290810)
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The language in the dataset is English.
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 3.79 MB
- **Size of the generated dataset:** 6.27 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"tokens": ["Revealed", "the", "location-specific", "flow", "patterns", "and", "quantified", "the", "speeds", "of", "various", "types", "of", "flow", "."],
"ner_tags": [0, 0, 0, 46, 49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
```
### Data Fields
#### fabner
- `id`: the instance id of this sentence, a `string` feature.
- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
- `ner_tags`: the list of entity tags, a `list` of classification labels.
```json
{"O": 0, "B-MATE": 1, "I-MATE": 2, "O-MATE": 3, "E-MATE": 4, "S-MATE": 5, "B-MANP": 6, "I-MANP": 7, "O-MANP": 8, "E-MANP": 9, "S-MANP": 10, "B-MACEQ": 11, "I-MACEQ": 12, "O-MACEQ": 13, "E-MACEQ": 14, "S-MACEQ": 15, "B-APPL": 16, "I-APPL": 17, "O-APPL": 18, "E-APPL": 19, "S-APPL": 20, "B-FEAT": 21, "I-FEAT": 22, "O-FEAT": 23, "E-FEAT": 24, "S-FEAT": 25, "B-PRO": 26, "I-PRO": 27, "O-PRO": 28, "E-PRO": 29, "S-PRO": 30, "B-CHAR": 31, "I-CHAR": 32, "O-CHAR": 33, "E-CHAR": 34, "S-CHAR": 35, "B-PARA": 36, "I-PARA": 37, "O-PARA": 38, "E-PARA": 39, "S-PARA": 40, "B-ENAT": 41, "I-ENAT": 42, "O-ENAT": 43, "E-ENAT": 44, "S-ENAT": 45, "B-CONPRI": 46, "I-CONPRI": 47, "O-CONPRI": 48, "E-CONPRI": 49, "S-CONPRI": 50, "B-MANS": 51, "I-MANS": 52, "O-MANS": 53, "E-MANS": 54, "S-MANS": 55, "B-BIOP": 56, "I-BIOP": 57, "O-BIOP": 58, "E-BIOP": 59, "S-BIOP": 60}
```
#### fabner_bio
- `id`: the instance id of this sentence, a `string` feature.
- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
- `ner_tags`: the list of entity tags, a `list` of classification labels.
```json
{"O": 0, "B-MATE": 1, "I-MATE": 2, "B-MANP": 3, "I-MANP": 4, "B-MACEQ": 5, "I-MACEQ": 6, "B-APPL": 7, "I-APPL": 8, "B-FEAT": 9, "I-FEAT": 10, "B-PRO": 11, "I-PRO": 12, "B-CHAR": 13, "I-CHAR": 14, "B-PARA": 15, "I-PARA": 16, "B-ENAT": 17, "I-ENAT": 18, "B-CONPRI": 19, "I-CONPRI": 20, "B-MANS": 21, "I-MANS": 22, "B-BIOP": 23, "I-BIOP": 24}
```
#### fabner_simple
- `id`: the instance id of this sentence, a `string` feature.
- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
- `ner_tags`: the list of entity tags, a `list` of classification labels.
```json
{"O": 0, "MATE": 1, "MANP": 2, "MACEQ": 3, "APPL": 4, "FEAT": 5, "PRO": 6, "CHAR": 7, "PARA": 8, "ENAT": 9, "CONPRI": 10, "MANS": 11, "BIOP": 12}
```
#### text2tech
- `id`: the instance id of this sentence, a `string` feature.
- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
- `ner_tags`: the list of entity tags, a `list` of classification labels.
```json
{"O": 0, "Technological System": 1, "Method": 2, "Material": 3, "Technical Field": 4}
```
### Data Splits
| | Train | Dev | Test |
|--------|-------|------|------|
| fabner | 9435 | 2183 | 2064 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{DBLP:journals/jim/KumarS22,
author = {Aman Kumar and
Binil Starly},
title = {"FabNER": information extraction from manufacturing process science
domain literature using named entity recognition},
journal = {J. Intell. Manuf.},
volume = {33},
number = {8},
pages = {2393--2407},
year = {2022},
url = {https://doi.org/10.1007/s10845-021-01807-x},
doi = {10.1007/s10845-021-01807-x},
timestamp = {Sun, 13 Nov 2022 17:52:57 +0100},
biburl = {https://dblp.org/rec/journals/jim/KumarS22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.
|
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] |
jbrazzy/baby_names
|
jbrazzy
| 2023-03-06T00:45:44Z | 36 | 1 |
[
"region:us"
] | null | 2023-03-06T00:45:34Z |
---
dataset_info:
features:
- name: Names
dtype: string
- name: Sex
dtype: string
- name: Count
dtype: int64
- name: Year
dtype: int64
splits:
- name: train
num_bytes: 33860482
num_examples: 1084385
- name: test
num_bytes: 8482889
num_examples: 271663
download_size: 13301020
dataset_size: 42343371
---
# Dataset Card for "baby_names"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Bingsu/ko_alpaca_data
|
Bingsu
| 2023-03-30T23:21:40Z | 36 | 12 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:ko",
"license:cc-by-nc-4.0",
"region:us"
] |
[
"text-generation"
] | 2023-03-20T05:36:21Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 13791136
num_examples: 49620
download_size: 8491044
dataset_size: 13791136
license: cc-by-nc-4.0
language:
- ko
pretty_name: ko-alpaca-data
size_categories:
- 10K<n<100K
task_categories:
- text-generation
---
# Dataset Card for "ko_alpaca_data"
## Dataset Description
- **Repository:** [Beomi/KoAlpaca](https://github.com/Beomi/KoAlpaca)
- **Huggingface:** [beomi/KoAlpaca](https://huggingface.co/beomi/KoAlpaca)
- **Size of downloaded dataset files:** 8.10 MB
- **Size of the generated dataset:** 13.15 MB
### Dataset Summary
Korean translation of [alpaca data](https://huggingface.co/datasets/tatsu-lab/alpaca).
repository: [Beomi/KoAlpaca](https://github.com/Beomi/KoAlpaca)<br>
huggingface: [beomi/KoAlpaca](https://huggingface.co/beomi/KoAlpaca)
1. Translate dataset
Translated 'instruction' and 'input' in the dataset via the DeepL API, except for 'output', which we did not translate because it is the output of OpenAI's `text-davinci-003` model.
2. Generate output data
Then, using the instruction and input, generate output data via the OpenAI ChatGPT API (gpt-3.5-turbo).
Below is the prompt we used to generate the answer.
```python
PROMPT = """\
다양한 작업에 대한 답변을 생성해주세요. 이러한 작업 지침은 ChatGPT 모델에 주어지며, ChatGPT 모델이 지침을 완료하는지 평가합니다.
요구 사항은 다음과 같습니다:
1. 다양성을 극대화하기 위해 각 지시에 대해 동사를 반복하지 않도록 하세요.
2. 지시에 사용되는 언어도 다양해야 합니다. 예를 들어, 질문과 명령형 지시를 결합해야 합니다.
3. 지시 사항의 유형이 다양해야 합니다. 목록에는 개방형 생성, 분류, 편집 등과 같은 다양한 유형의 작업이 포함되어야 합니다.
2. GPT 언어 모델은 지시를 완료할 수 있어야 합니다. 예를 들어 어시스턴트에게 시각적 또는 오디오 출력을 생성하도록 요청하지 마세요. 또 다른 예로, 어시스턴트가 어떤 작업도 수행할 수 없으므로 오후 5시에 깨우거나 미리 알림을 설정하도록 요청하지 마세요.
3. 답변은 한국어로 작성해야 합니다.
4. 답변을 1~2문장으로 작성하세요. 명령문이나 질문도 허용됩니다.
5. 지시 사항에 대한 적절한 입력을 생성해야 합니다. 입력 필드에는 지시에 대한 구체적인 예가 포함되어야 합니다. 실제 데이터를 포함해야 하며 단순한 자리 표시자를 포함해서는 안 됩니다. 입력은 지시 사항을 어렵게 만들 수 있는 상당한 내용을 제공해야 하지만 100단어를 넘지 않는 것이 이상적입니다.
6. 일부 지시사항은 추가 입력이 있고, 일부 지시에는 입력 필드가 비어있습니다. 예를 들어 "세계에서 가장 높은 봉우리는 무엇인가?"라는 일반적인 정보를 묻는 지시의 경우 구체적인 맥락을 제공할 필요가 없어, 입력 필드가 비어있을 수 있습니다.
7. 출력은 명령어와 입력에 대한 적절한 응답이어야 합니다.
아래에 10개의 명령어와 입력(옵션)에 따라 적절한 응답을 생성하세요.
응답은 아래와 같은 형식으로 10가지를 0번 부터 9번 까지, 번호에 따라 해당 번호의 명령어와 입력에 알맞게 작성하세요.
각 응답 사이는 ### 으로 내용을 분리해주세요.
응답0: 첫 번째 응답내용###
응답1: 두 번째 응답내용###
...
응답9: 마지막 응답내용"""
```
### Lisence
CC-BY-NC-4.0
### Data Splits
| | train |
| --------- | -------- |
| # of data | 49620 |
\# Note that the number is not the same as the original data(52002)
```python
>>> from datasets import load_dataset
>>> ds = load_dataset("Bingsu/ko_alpaca_data", split="train")
>>> ds
Dataset({
features: ['instruction', 'input', 'output'],
num_rows: 49620
})
```
```python
>>> ds[0]
{'instruction': '건강을 유지하기 위한 세 가지 팁을 알려주세요.',
'input': '',
'output': '세 가지 팁은 아침식사를 꼭 챙기며, 충분한 수면을 취하고, 적극적으로 운동을 하는 것입니다.'}
```
|
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mstz/student_performance
|
mstz
| 2023-04-07T14:54:45Z | 36 | 0 |
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"student performance",
"tabular_classification",
"binary_classification",
"region:us"
] |
[
"tabular-classification"
] | 2023-03-24T13:53:31Z |
---
language:
- en
tags:
- student performance
- tabular_classification
- binary_classification
pretty_name: Student Performance
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- encoding
- math
- writing
- reading
license: cc
---
# Student performance
The [Student performance dataset](https://www.kaggle.com/datasets/ulrikthygepedersen/student_performances) from Kaggle.
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| encoding | | Encoding dictionary showing original values of encoded features.|
| math | Binary classification | Has the student passed the math exam? |
| writing | Binary classification | Has the student passed the writing exam? |
| reading | Binary classification | Has the student passed the reading exam? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/student_performance", "math")["train"]
```
# Features
|**Feature** |**Type** |
|-----------------------------------|-----------|
|`is_male` |`bool` |
|`ethnicity` |`string` |
|`parental_level_of_education` |`int8` |
|`has_standard_lunch` |`bool` |
|`has_completed_preparation_test` |`bool` |
|`reading_score` |`int64` |
|`writing_score` |`int64` |
|`math_score` |`int64` |
|
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] |
suolyer/pile_pile-cc
|
suolyer
| 2023-03-27T03:04:43Z | 36 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-03-26T16:38:55Z |
---
license: apache-2.0
---
|
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] |
tasksource/ScienceQA_text_only
|
tasksource
| 2023-07-13T11:50:29Z | 36 | 19 |
[
"language:en",
"region:us"
] | null | 2023-04-11T11:45:03Z |
---
language: en
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype: int8
- name: hint
dtype: string
- name: task
dtype: string
- name: grade
dtype: string
- name: subject
dtype: string
- name: topic
dtype: string
- name: category
dtype: string
- name: skill
dtype: string
- name: lecture
dtype: string
- name: solution
dtype: string
splits:
- name: train
num_bytes: 8105771.787521609
num_examples: 6508
- name: validation
num_bytes: 2638142.7097382694
num_examples: 2144
- name: test
num_bytes: 2757852.295213393
num_examples: 2224
download_size: 2925662
dataset_size: 13501766.792473271
---
# Dataset Card for "scienceQA_text_only"
ScienceQA text-only examples (examples where no image was initially present, which means they should be doable with text-only models.)
```
@article{10.1007/s00799-022-00329-y,
author = {Saikh, Tanik and Ghosal, Tirthankar and Mittal, Amish and Ekbal, Asif and Bhattacharyya, Pushpak},
title = {ScienceQA: A Novel Resource for Question Answering on Scholarly Articles},
year = {2022},
journal = {Int. J. Digit. Libr.},
month = {sep}
}
```
|
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] |
siddharthtumre/jnlpba-split
|
siddharthtumre
| 2023-04-25T04:49:37Z | 36 | 0 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:unknown",
"region:us"
] |
[
"token-classification"
] | 2023-04-25T04:35:39Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: IASL-BNER Revised JNLPBA
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-DNA
'2': I-DNA
'3': B-RNA
'4': I-RNA
'5': B-cell_line
'6': I-cell_line
'7': B-cell_type
'8': I-cell_type
'9': B-protein
'10': I-protein
config_name: revised-jnlpba
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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] |
ChanceFocus/flare-sm-acl
|
ChanceFocus
| 2023-06-25T18:16:24Z | 36 | 1 |
[
"region:us"
] | null | 2023-06-25T17:56:25Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
- name: choices
sequence: string
- name: gold
dtype: int64
splits:
- name: train
num_bytes: 70385369
num_examples: 20781
- name: valid
num_bytes: 9049127
num_examples: 2555
- name: test
num_bytes: 13359338
num_examples: 3720
download_size: 46311736
dataset_size: 92793834
---
# Dataset Card for "flare-sm-acl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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sngsfydy/aptos_train
|
sngsfydy
| 2023-07-19T19:52:54Z | 36 | 0 |
[
"region:us"
] | null | 2023-07-19T18:43:34Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
splits:
- name: train
num_bytes: 7204351279.337521
num_examples: 2929
download_size: 7192333107
dataset_size: 7204351279.337521
---
# Dataset Card for "aptos_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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jonathancui/oxford-pets
|
jonathancui
| 2023-08-01T02:55:57Z | 36 | 0 |
[
"license:cc-by-sa-4.0",
"region:us"
] | null | 2023-08-01T02:49:22Z |
---
license: cc-by-sa-4.0
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Abyssinian
'1': Bengal
'2': Birman
'3': Bombay
'4': British_Shorthair
'5': Egyptian_Mau
'6': Maine_Coon
'7': Persian
'8': Ragdoll
'9': Russian_Blue
'10': Siamese
'11': Sphynx
'12': american_bulldog
'13': american_pit_bull_terrier
'14': basset_hound
'15': beagle
'16': boxer
'17': chihuahua
'18': english_cocker_spaniel
'19': english_setter
'20': german_shorthaired
'21': great_pyrenees
'22': havanese
'23': japanese_chin
'24': keeshond
'25': leonberger
'26': miniature_pinscher
'27': newfoundland
'28': pomeranian
'29': pug
'30': saint_bernard
'31': samoyed
'32': scottish_terrier
'33': shiba_inu
'34': staffordshire_bull_terrier
'35': wheaten_terrier
'36': yorkshire_terrier
splits:
- name: train
num_bytes: 378015144.64
num_examples: 3680
- name: test
num_bytes: 412951221.999
num_examples: 3669
download_size: 790031129
dataset_size: 790966366.6389999
---
|
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] |
adityarra07/sub_ATC
|
adityarra07
| 2023-08-06T05:38:09Z | 36 | 0 |
[
"region:us"
] | null | 2023-08-04T19:13:17Z |
---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 136737944.06422067
num_examples: 1000
- name: test
num_bytes: 13673794.406422066
num_examples: 100
download_size: 12473551
dataset_size: 150411738.47064275
---
# Dataset Card for "sub_ATC"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
jinaai/negation-dataset-v2
|
jinaai
| 2023-11-08T15:49:17Z | 36 | 5 |
[
"multilinguality:monolingual",
"size_categories:10K<n<50k",
"language:en",
"finetuner",
"arxiv:2307.11224",
"region:us"
] | null | 2023-08-11T11:28:31Z |
---
tags:
- finetuner
language: en
dataset_info:
features:
- name: anchor
dtype: string
- name: entailment
dtype: string
- name: negative
dtype: string
splits:
- name: train
num_examples: 50000
- name: test
num_examples: 1000
multilinguality:
- monolingual
size_categories:
- 10K<n<50k
---
<br><br>
<p align="center">
<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
</p>
<p align="center">
<b>The data offered by Jina AI, Finetuner team.</b>
</p>
## Summary
This dataset is an English-language dataset containing negation triplets. It is based on five datasets:
[SNLI](https://huggingface.co/datasets/snli), [Multi-NLI](https://huggingface.co/datasets/multi_nli),
[sentence-compression](https://huggingface.co/datasets/sent_comp), [Simple Wikipedia](https://www.loc.gov/item/2019205402/)
and [COCO Captions](https://cocodataset.org/#home).
## Instances
Each data point consists of a triplet ('anchor', 'entailment', 'negative') of strings, where ('anchor', 'entailment') are positive pairs
taken from SNLI, and 'negative' contradicts both 'anchor' and 'entailment'.
## Fields
- 'anchor': string, some statement
- 'entailment': string, a statement which follows from 'anchor', but is usually syntactically dissimilar
- 'negative': string, a statement contradicting 'anchor' and 'entailment'. Syntactically very similar to 'entailment'
## Splits
| | train | test |
|------------|-------|------|
| # of items | 50000 | 1000 |
## Source
Positive pairs were sampled from the five source datasets and negative samples were created using GPT-3.5
and GPT-4.
## Example Usage
```python
from datasets import load_dataset
from pprint import pprint
dataset = load_dataset('jinaai/negation-dataset-v2')
```
Example data:
```python
'anchor': ['Pedestrians walking down a sidewalk next to a small street.',
"A car stopped at a traffic light with it's brake lights on.",
'A couple on a motorcycle in front of a bus and a metermaid car'],
'entailment': ['People walking on a city street with light traffic.',
'A car stopped at the front of the stop light. ',
'A busy city street with a bus, taxi, and motorcycle.'],
'negative': ['People standing still on a city street with heavy traffic.',
'A car speeding away from the front of the stop light. ',
'A calm city street with no bus, taxi, and motorcycle.']
```
## Evaluate Models on the Test Set
For evaluation, you can use the evaluation script provided together with this dataset:
https://huggingface.co/datasets/jinaai/negation-dataset-v2/blob/main/evaluate_on_negations.py
## Licensing Information
Please respect the licenses of the original data sources for the achor and entailment data.
The additional negatives are licensed under the [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) license.
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
## Citation
If you find this dataset useful in your research, please cite the following paper:
``` latex
@misc{günther2023jina,
title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models},
author={Michael Günther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},
year={2023},
eprint={2307.11224},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
|
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] |
luisroque/instruct-python-llama2-20k
|
luisroque
| 2023-08-18T09:44:00Z | 36 | 0 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] |
[
"text-generation"
] | 2023-08-17T17:59:03Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 34661192.7
num_examples: 19000
- name: test
num_bytes: 1824273.3
num_examples: 1000
download_size: 19060329
dataset_size: 36485466
license: cc-by-sa-3.0
task_categories:
- text-generation
language:
- en
pretty_name: Instruct Python 500k
size_categories:
- 10K<n<100K
---
# Fine-tuning Instruct Llama2 Stack Overflow Python Q&A
## Transformed Dataset
### Objective
The transformed dataset is designed for fine-tuning LLMs to improve Python coding assistance by focusing on high-quality content from Stack Overflow. It has around 20k instructions.
### Structure
- **Question-Answer Pairing**: Questions and answers are paired using the `ParentId` linkage.
- **Quality Focus**: Only top-rated answers for each question are retained.
- **HTML Tag Removal**: All HTML tags in the content are removed.
- **Combined Question Field**: Each question's title and body are merged.
- **Filtering**: Entries with negative scores or those not containing Python code structures are excluded.
Final columns:
- `score_question`
- `score_answer`
- `question`
- `answer`
### Llama2 Transformation
The dataset has been transformed to match the Llama2 prompt structure, which is relevant for the model's fine-tuning. The format is the following:
`<s>[INST] <<SYS>> {{ system_prompt }} <</SYS>> {{ user_message }} [/INST]`
Where:
- `system_prompt` gives context or instructions to the model.
- `user_message` is the user's query following the system prompt, expecting a particular response from the model.
This structure ensures the training aligns with Llama2's expectations, optimizing the fine-tuning quality.
## Original Dataset
The dataset contains questions and answers from Stack Overflow with the `python` tag, covering the period from August 2, 2008, to October 19, 2016.
## License
All contributions are under the [CC-BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). Attribution is required. The original dataset was posted [here](https://www.kaggle.com/datasets/stackoverflow/pythonquestions).
Keep in touch: [LinkedIn](https://www.linkedin.com/in/luisbrasroque/)
|
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] |
mlabonne/Evol-Instruct-Python-26k
|
mlabonne
| 2023-08-25T16:29:36Z | 36 | 5 |
[
"region:us"
] | null | 2023-08-25T13:25:34Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 39448413.53337422
num_examples: 26588
download_size: 22381182
dataset_size: 39448413.53337422
---
# Evol-Instruct-Python-26k
Filtered version of the [`nickrosh/Evol-Instruct-Code-80k-v1`](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) dataset that only keeps Python code (26,588 samples). You can find a smaller version of it here [`mlabonne/Evol-Instruct-Python-1k`](https://huggingface.co/datasets/mlabonne/Evol-Instruct-Python-1k).
Here is the distribution of the number of tokens in each row (instruction + output) using Llama's tokenizer:
|
[
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yangwang825/audioset
|
yangwang825
| 2023-09-18T11:19:55Z | 36 | 0 |
[
"task_categories:audio-classification",
"size_categories:100M<n<1B",
"audioset",
"region:us"
] |
[
"audio-classification"
] | 2023-09-02T12:56:33Z |
---
configs:
- config_name: audioset500k
data_files:
- split: train
path: audioset500k.json
- config_name: balanced_train
data_files:
- split: train
path: balanced_train.json
- config_name: eval
data_files:
- split: test
path: eval.json
- config_name: unbalanced_train_part00
data_files: unbalanced_train_part00.json
# dataset_size: 46940
- config_name: unbalanced_train_part01
data_files: unbalanced_train_part01.json
# dataset_size: 47052
- config_name: unbalanced_train_part02
data_files: unbalanced_train_part02.json
# dataset_size: 46923
- config_name: unbalanced_train_part03
data_files: unbalanced_train_part03.json
# dataset_size: 46952
- config_name: unbalanced_train_part04
data_files: unbalanced_train_part04.json
# dataset_size: 46916
- config_name: unbalanced_train_part05
data_files: unbalanced_train_part05.json
# dataset_size: 47011
- config_name: unbalanced_train_part06
data_files: unbalanced_train_part06.json
# dataset_size: 46964
- config_name: unbalanced_train_part07
data_files: unbalanced_train_part07.json
# dataset_size: 46915
- config_name: unbalanced_train_part08
data_files: unbalanced_train_part08.json
# dataset_size: 46927
- config_name: unbalanced_train_part09
data_files: unbalanced_train_part09.json
# dataset_size: 46839
- config_name: unbalanced_train_part10
data_files: unbalanced_train_part10.json
# dataset_size: 46862
- config_name: unbalanced_train_part11
data_files: unbalanced_train_part11.json
# dataset_size: 46836
- config_name: unbalanced_train_part12
data_files: unbalanced_train_part12.json
# dataset_size: 46865
- config_name: unbalanced_train_part13
data_files: unbalanced_train_part13.json
# dataset_size: 46800
- config_name: unbalanced_train_part14
data_files: unbalanced_train_part14.json
# dataset_size: 46837
- config_name: unbalanced_train_part15
data_files: unbalanced_train_part15.json
# dataset_size: 46824
- config_name: unbalanced_train_part16
data_files: unbalanced_train_part16.json
# dataset_size: 46813
- config_name: unbalanced_train_part17
data_files: unbalanced_train_part17.json
# dataset_size: 46771
- config_name: unbalanced_train_part18
data_files: unbalanced_train_part18.json
# dataset_size: 46875
- config_name: unbalanced_train_part19
data_files: unbalanced_train_part19.json
# dataset_size: 46885
- config_name: unbalanced_train_part20
data_files: unbalanced_train_part20.json
# dataset_size: 46884
- config_name: unbalanced_train_part21
data_files: unbalanced_train_part21.json
# dataset_size: 46736
- config_name: unbalanced_train_part22
data_files: unbalanced_train_part22.json
# dataset_size: 46832
- config_name: unbalanced_train_part23
data_files: unbalanced_train_part23.json
# dataset_size: 46823
- config_name: unbalanced_train_part24
data_files: unbalanced_train_part24.json
# dataset_size: 46795
- config_name: unbalanced_train_part25
data_files: unbalanced_train_part25.json
# dataset_size: 46740
- config_name: unbalanced_train_part26
data_files: unbalanced_train_part26.json
# dataset_size: 46765
- config_name: unbalanced_train_part27
data_files: unbalanced_train_part27.json
# dataset_size: 46708
- config_name: unbalanced_train_part28
data_files: unbalanced_train_part28.json
# dataset_size: 46736
- config_name: unbalanced_train_part29
data_files: unbalanced_train_part29.json
# dataset_size: 46819
- config_name: unbalanced_train_part30
data_files: unbalanced_train_part30.json
# dataset_size: 46694
- config_name: unbalanced_train_part31
data_files: unbalanced_train_part31.json
# dataset_size: 46735
- config_name: unbalanced_train_part32
data_files: unbalanced_train_part32.json
# dataset_size: 46731
- config_name: unbalanced_train_part33
data_files: unbalanced_train_part33.json
# dataset_size: 46627
- config_name: unbalanced_train_part34
data_files: unbalanced_train_part34.json
# dataset_size: 46740
- config_name: unbalanced_train_part35
data_files: unbalanced_train_part35.json
# dataset_size: 46866
- config_name: unbalanced_train_part36
data_files: unbalanced_train_part36.json
# dataset_size: 46758
- config_name: unbalanced_train_part37
data_files: unbalanced_train_part37.json
# dataset_size: 46751
- config_name: unbalanced_train_part38
data_files: unbalanced_train_part38.json
# dataset_size: 46750
- config_name: unbalanced_train_part39
data_files: unbalanced_train_part39.json
# dataset_size: 46700
- config_name: unbalanced_train_part40
data_files: unbalanced_train_part40.json
# dataset_size: 39137
task_categories:
- audio-classification
tags:
- audioset
size_categories:
- 100M<n<1B
---
# AudioSet
AudioSet<sup>[1]</sup> consists of an expanding ontology of 527 audio event classes and a collection of 2M human-labelled 10-second sound clips drawn from YouTube.
Some clips are missing on YouTube, so the number of files downloaded is different from time to time.
This repository contains 20550 / 22160 of the balanced train set, 1913637 / 2041789 of the unbalanced train set (separated into 41 parts), and 18887 / 20371 of the evaluation set.
The pre-process script can be found at qiuqiangkong's [github](https://github.com/qiuqiangkong/audioset_tagging_cnn)<sup>[2]</sup>.
To improve training efficiency, we add a slightly more balanced subset AudioSet500K<sup>[3]</sup>.
## References
1. Gemmeke, Jort F., et al., Audio set: An ontology and human-labeled dataset for audio events, 2017
2. Kong, Qiuqiang, et al., Panns: Large-scale pretrained audio neural networks for audio pattern recognition, 2020
3. Nagrani, Arsha, et al., Attention bottlenecks for multimodal fusion, 2021
|
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] |
ZhongshengWang/Alpaca-cnn-dailymail
|
ZhongshengWang
| 2023-09-19T15:23:01Z | 36 | 0 |
[
"task_categories:summarization",
"task_categories:text-generation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"conditional-text-generation",
"region:us"
] |
[
"summarization",
"text-generation"
] | 2023-09-19T15:16:44Z |
---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
- text-generation
task_ids: []
paperswithcode_id: cnn-daily-mail-1
pretty_name: CNN / Daily Mail
tags:
- conditional-text-generation
---
## Data Summary
Data set Alpaca-cnn-dailymail is a data set version format changed by [ccdv/cnn_dailymail](https://huggingface.co/datasets/ccdv/cnn_dailymail) to meet Alpaca fine-tuning Llama2. Only versions 3.0.0 and 2.0.0 were used for merging and as a key data set for the summary extraction task.
## Licensing Information
The Alpaca-cnn-dailymail dataset version 1.0.0 is released under the Apache-2.0 License.
## Citation Information
```
@inproceedings{see-etal-2017-get,
title = "Get To The Point: Summarization with Pointer-Generator Networks",
author = "See, Abigail and
Liu, Peter J. and
Manning, Christopher D.",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P17-1099",
doi = "10.18653/v1/P17-1099",
pages = "1073--1083",
abstract = "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.",
}
```
```
@inproceedings{DBLP:conf/nips/HermannKGEKSB15,
author={Karl Moritz Hermann and Tomás Kociský and Edward Grefenstette and Lasse Espeholt and Will Kay and Mustafa Suleyman and Phil Blunsom},
title={Teaching Machines to Read and Comprehend},
year={2015},
cdate={1420070400000},
pages={1693-1701},
url={http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend},
booktitle={NIPS},
crossref={conf/nips/2015}
}
```
|
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bzantium/LongBench
|
bzantium
| 2023-09-25T04:03:43Z | 36 | 0 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:summarization",
"task_categories:conversational",
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"language:zh",
"Long Context",
"arxiv:2308.14508",
"arxiv:2108.00573",
"arxiv:1712.07040",
"arxiv:2105.03011",
"arxiv:2104.02112",
"arxiv:2104.05938",
"arxiv:2305.05280",
"arxiv:2303.09752",
"arxiv:1910.10683",
"arxiv:2306.14893",
"arxiv:2306.03091",
"region:us"
] |
[
"question-answering",
"text-generation",
"summarization",
"conversational",
"text-classification"
] | 2023-09-21T06:13:03Z |
---
task_categories:
- question-answering
- text-generation
- summarization
- conversational
- text-classification
language:
- en
- zh
tags:
- Long Context
size_categories:
- 1K<n<10K
---
# Introduction
**LongBench** is the first benchmark for bilingual, multitask, and comprehensive assessment of **long context understanding** capabilities of large language models. LongBench includes different languages (Chinese and English) to provide a more comprehensive evaluation of the large models' multilingual capabilities on long contexts. In addition, LongBench is composed of six major categories and twenty one different tasks, covering key long-text application scenarios such as single-document QA, multi-document QA, summarization, few-shot learning, synthetic tasks and code completion.
We are fully aware of the potentially high costs involved in the model evaluation process, especially in the context of long context scenarios (such as manual annotation costs or API call costs). Therefore, we adopt a fully automated evaluation method, aimed at measuring and evaluating the model's ability to understand long contexts at the lowest cost.
LongBench includes 14 English tasks, 5 Chinese tasks, and 2 code tasks, with the average length of most tasks ranging from 5k to 15k, and a total of 4,750 test data. For detailed statistics and construction methods of LongBench tasks, please refer [here](task.md). In addition, we provide LongBench-E, a test set with a more uniform length distribution constructed by uniform sampling, with comparable amounts of data in the 0-4k, 4k-8k, and 8k+ length intervals to provide an analysis of the model's performance variations at different input lengths.
Github Repo for LongBench: https://github.com/THUDM/LongBench
Arxiv Paper for LongBench: https://arxiv.org/pdf/2308.14508.pdf
# How to use it?
#### Loading Data
```python
from datasets import load_dataset
datasets = ["narrativeqa", "qasper", "multifieldqa_en", "multifieldqa_zh", "hotpotqa", "2wikimqa", "musique", \
"dureader", "gov_report", "qmsum", "multi_news", "vcsum", "trec", "triviaqa", "samsum", "lsht", \
"passage_count", "passage_retrieval_en", "passage_retrieval_zh", "lcc", "repobench-p"]
for dataset in datasets:
data = load_dataset('THUDM/LongBench', dataset, split='test')
```
Similarly, you can load the **LongBench-E** data
```python
from datasets import load_dataset
datasets = ["qasper", "multifieldqa_en", "hotpotqa", "2wikimqa", "gov_report", "multi_news", "trec", \
"triviaqa", "samsum", "passage_count", "passage_retrieval_en", "lcc", "repobench-p"]
for dataset in datasets:
data = load_dataset('THUDM/LongBench', f"{dataset}_e", split='test')
```
Alternatively, you can download the folder from [this link](https://huggingface.co/datasets/THUDM/LongBench/resolve/main/data.zip) to load the data.
#### Data Format
All data in **LongBench** (LongBench-E) are standardized to the following format:
```json
{
"input": "The input/command for the task, usually short, such as questions in QA, queries in Few-shot tasks, etc",
"context": "The long context required for the task, such as documents, cross-file code, few-shot examples in Few-shot tasks",
"answers": "A List of all true answers",
"length": "Total length of the first three items (counted in characters for Chinese and words for English)",
"dataset": "The name of the dataset to which this piece of data belongs",
"language": "The language of this piece of data",
"all_classes": "All categories in classification tasks, null for non-classification tasks",
"_id": "Random id for each piece of data"
}
```
#### Evaluation
This repository provides data download for LongBench. If you wish to use this dataset for automated evaluation, please refer to our [github](https://github.com/THUDM/LongBench).
# Task statistics
| Task | Task Type | Eval metric | Avg len |Language | \#Sample |
| :-------- | :-----------:| :-----------: |:-------: | :-----------: |:--------: |
| HotpotQA | Multi-doc QA | F1 |9,151 |EN |200 |
| 2WikiMultihopQA| Multi-doc QA | F1 |4,887 |EN |200 |
| MuSiQue| Multi-doc QA | F1 |11,214 |EN |200 |
| DuReader| Multi-doc QA | Rouge-L |15,768 |ZH |200 |
| MultiFieldQA-en| Single-doc QA | F1 |4,559 |EN |150 |
| MultiFieldQA-zh| Single-doc QA | F1 |6,701 |ZH |200 |
| NarrativeQA| Single-doc QA | F1 |18,409 |EN |200 |
| Qasper| Single-doc QA | F1 |3,619 |EN |200 |
| GovReport| Summarization | Rouge-L |8,734 |EN |200 |
| QMSum| Summarization | Rouge-L |10,614 |EN |200 |
| MultiNews| Summarization | Rouge-L |2,113 |EN |200 |
| VCSUM| Summarization | Rouge-L |15,380 |ZH |200 |
| TriviaQA| Few shot | F1 |8,209 |EN |200 |
| SAMSum| Few shot | Rouge-L |6,258 |EN |200 |
| TREC| Few shot | Accuracy |5,177 |EN |200 |
| LSHT| Few shot | Accuracy |22,337 |ZH |200 |
| PassageRetrieval-en| Synthetic | Accuracy |9,289 |EN |200 |
| PassageCount| Synthetic | Accuracy |11,141 |EN |200 |
| PassageRetrieval-zh | Synthetic | Accuracy |6,745 |ZH |200 |
| LCC| Code | Edit Sim |1,235 |Python/C#/Java |500 |
| RepoBench-P| Code | Edit Sim |4,206 |Python/Java |500 |
> Note: In order to avoid discrepancies caused by different tokenizers, we use the word count (using Python's split function) to calculate the average length of English datasets and code datasets, and use the character count to calculate the average length of Chinese datasets.
# Task description
| Task | Task Description |
| :---------------- | :----------------------------------------------------------- |
| HotpotQA | Answer related questions based on multiple given documents |
| 2WikiMultihopQA | Answer related questions based on multiple given documents |
| MuSiQue | Answer related questions based on multiple given documents |
| DuReader | Answer related Chinese questions based on multiple retrieved documents |
| MultiFieldQA-en | Answer English questions based on a long article, which comes from a relatively diverse field |
| MultiFieldQA-zh | Answer Chinese questions based on a long article, which comes from a relatively diverse field |
| NarrativeQA | Answer questions based on stories or scripts, including understanding of important elements such as characters, plots, themes, etc. |
| Qasper | Answer questions based on a NLP research paper, questions proposed and answered by NLP practitioners |
| GovReport | A summarization task that requires summarizing government work reports |
| MultiNews | A multi-doc summarization that requires summarizing over multiple news |
| QMSum | A summarization task that requires summarizing meeting records based on user queries |
| VCSUM | A summarization task that requires summarizing Chinese meeting records |
| SAMSum | A dialogue summarization task, providing several few-shot examples |
| TriviaQA | Single document question answering task, providing several few-shot examples |
| NQ | Single document question answering task, providing several few-shot examples |
| TREC | A classification task that requires categorizing questions, includes 50 categories in total |
| LSHT | A Chinese classification task that requires categorizing news, includes 24 categories in total |
| PassageRetrieval-en | Given 30 English Wikipedia paragraphs, determine which paragraph the given summary corresponds to |
| PassageCount | Determine the total number of different paragraphs in a given repetitive article |
| PassageRetrieval-zh | Given several Chinese paragraphs from the C4 data set, determine which paragraph the given abstract corresponds to |
| LCC | Given a long piece of code, predict the next line of code |
| RepoBench-P | Given code in multiple files within a GitHub repository (including cross-file dependencies), predict the next line of code |
# Task construction
> Note: For all tasks constructed from existing datasets, we use data from the validation or test set of the existing dataset (except for VCSUM).
- The tasks of [HotpotQA](https://hotpotqa.github.io/), [2WikiMultihopQA](https://aclanthology.org/2020.coling-main.580/), [MuSiQue](https://arxiv.org/abs/2108.00573), and [DuReader](https://github.com/baidu/DuReader) are built based on the original datasets and processed to be suitable for long context evaluation. Specifically, for questions in the validation set, we select the evidence passage that contains the answer and several distracting articles. These articles together with the original question constitute the input of the tasks.
- The tasks of MultiFiedQA-zh and MultiFieldQA-en consist of long artical data from about 10 sources, including Latex papers, judicial documents, government work reports, and PDF documents indexed by Google. For each long artical, we invite several PhD and master students to annotate, i.e., to ask questions based on the long artical and give the correct answers. To better automate evaluation, we ask the annotators to propose questions with definitive answers as much as possible.
- The tasks of [NarrativeQA](https://arxiv.org/pdf/1712.07040.pdf), [Qasper](https://arxiv.org/pdf/2105.03011.pdf), [GovReport](https://arxiv.org/pdf/2104.02112.pdf), [QMSum](https://arxiv.org/pdf/2104.05938.pdf) and [MultiNews](https://aclanthology.org/P19-1102.pdf) directly use the data provided by the original papers. In the specific construction, we use the template provided by [ZeroSCROLLS](https://www.zero.scrolls-benchmark.com/) to convert the corresponding data into pure text input.
- The [VCSUM](https://arxiv.org/abs/2305.05280) task is built based on the original dataset, and we design a corresponding template to convert the corresponding data into pure text input.
- The [TriviaQA](https://nlp.cs.washington.edu/triviaqa/) task is constructed in the manner of [CoLT5](https://arxiv.org/abs/2303.09752), which provides several examples of question and answering based on documents, and requires the language model to answer related questions based on new documents.
- The tasks of [SAMSum](https://aclanthology.org/D19-5409.pdf), [TREC](https://aclanthology.org/C02-1150.pdf) and [LSHT](http://tcci.ccf.org.cn/conference/2014/dldoc/evatask6.pdf) are built based on the original datasets. For each question in the validation set, we sample several data from the training set to form few-shot examples. These examples together with the questions in the validation set constitute the input for this task.
- The PassageRetrieval-en task is constructed based on English Wikipedia. For each piece of data, we randomly sample 30 paragraphs from English Wikipedia and select one for summarization (using GPT-3.5-Turbo). This task requires the model to give the original paragraph name to which the summary corresponds.
- The PassageCount task is constructed based on the English wiki. For each piece of data, we randomly sample several passages from English Wikipedia, repeat each paragraph at random several times, and finally shuffle the paragraphs. This task requires the model to determine the total number of different paragraphs in the given context.
- The PasskeyRetrieval-zh task is constructed based on [C4](https://arxiv.org/abs/1910.10683). For each piece of data, we randomly sample several Chinese paragraphs from C4 and select one of them for summarization (using GPT-3.5-Turbo). This task requires the model to give the original paragraph name to which the summary corresponds.
- For the [LCC](https://arxiv.org/abs/2306.14893) task, we sample from the original code completion dataset. In the [RepoBench-P](https://arxiv.org/abs/2306.03091) task, we select the most challenging XF-F (Cross-File-First) setting from the original dataset and refer to the Oracle-Filled scenario in the paper. For each original piece of data, we randomly extract multiple cross-file code snippets, including the gold cross-file code snippet, and concatenate them as input, requiring the model to effectively use cross-file code for completion.
# LongBench-E statistics
| Task | Task Type | \#data in 0-4k | \#data in 4-8k | \#data in 8k+|
| :--------- | :-----------:| :-----------: |:---------: | :-------------: |
| HotpotQA | Multi-doc QA | 100 |100 |100 |
| 2WikiMultihopQA| Multi-doc QA | 100 |100 |100 |
| MultiFieldQA-en| Single-doc QA | 67 |70 |13 |
| Qasper| Single-doc QA | 100 |100 |24 |
| GovReport| Summarization | 100 |100 |100 |
| MultiNews| Summarization | 100 |100 |94 |
| TriviaQA| Few shot | 100 |100 |100 |
| SAMSum| Few shot | 100 |100 |100 |
| TREC| Few shot | 100 |100 |100 |
| PassageRetrieval-en| Synthetic | 100 |100 |100 |
| PassageCount| Synthetic | 100 |100 |100 |
| LCC| Code | 100 |100 |100 |
| RepoBench-P| Code | 100 |100 |100 |
# Citation
```
@misc{bai2023longbench,
title={LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding},
author={Yushi Bai and Xin Lv and Jiajie Zhang and Hongchang Lyu and Jiankai Tang and Zhidian Huang and Zhengxiao Du and Xiao Liu and Aohan Zeng and Lei Hou and Yuxiao Dong and Jie Tang and Juanzi Li},
year={2023},
eprint={2308.14508},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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goendalf666/sales-conversations
|
goendalf666
| 2023-10-04T20:39:04Z | 36 | 6 |
[
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:en",
"sales",
"arxiv:2306.11644",
"region:us"
] |
[
"conversational"
] | 2023-09-21T21:37:30Z |
---
language:
- en
size_categories:
- 1K<n<10K
task_categories:
- conversational
dataset_info:
features:
- name: '0'
dtype: string
- name: '1'
dtype: string
- name: '2'
dtype: string
- name: '3'
dtype: string
- name: '4'
dtype: string
- name: '5'
dtype: string
- name: '6'
dtype: string
- name: '7'
dtype: string
- name: '8'
dtype: string
- name: '9'
dtype: string
- name: '10'
dtype: string
- name: '11'
dtype: string
- name: '12'
dtype: string
- name: '13'
dtype: string
- name: '14'
dtype: string
- name: '15'
dtype: string
- name: '16'
dtype: string
- name: '17'
dtype: string
- name: '18'
dtype: string
- name: '19'
dtype: string
splits:
- name: train
num_bytes: 6821725
num_examples: 3412
download_size: 2644154
dataset_size: 6821725
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- sales
---
# Dataset Card for "sales-conversations"
This dataset was created for the purpose of training a sales agent chatbot that can convince people.
The initial idea came from: textbooks is all you need https://arxiv.org/abs/2306.11644
gpt-3.5-turbo was used for the generation
# Structure
The conversations have a customer and a salesman which appear always in changing order. customer, salesman, customer, salesman, etc.
The customer always starts the conversation
Who ends the conversation is not defined.
# Generation
Note that a textbook dataset is mandatory for this conversation generation. This examples rely on the following textbook dataset:
https://huggingface.co/datasets/goendalf666/sales-textbook_for_convincing_and_selling
The data generation code can be found here: https://github.com/tom813/salesGPT_foundation/blob/main/data_generation/textbook_and_conversation_gen.py
The following prompt was used to create a conversation
```
def create_random_prompt(chapter, roles=["Customer", "Salesman"], range_vals=(3, 7), industries=None):
if industries is None:
industries = ["tech", "health", "finance"] # default industries; replace with your default list if different
x = random.randint(*range_vals)
y = 0
for i in reversed(range(3, 9)): # Generalized loop for range of values
if i * x < 27:
y = i
break
conversation_structure = ""
for i in range(1, x+1):
conversation_structure += f"""
{roles[0]}: #{i}. sentence of {roles[0].lower()}
{roles[1]}: #{i}. sentence of {roles[1].lower()}"""
prompt = f"""Here is a chapter from a textbook about convincing people.
The purpose of this data is to use it to fine tune a llm.
Generate conversation examples that are based on the chapter that is provided and would help an ai to learn the topic by examples.
Focus only on the topic that is given in the chapter when generating the examples.
Let the example be in the {random.choice(industries)} industry.
Follow this structure and put each conversation in a list of objects in json format. Only return the json nothing more:
{conversation_structure}
Generate {y} lists of those conversations
Chapter:{chapter}"""
return prompt
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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GIZ/policy_qa_v2
|
GIZ
| 2023-09-27T00:17:49Z | 36 | 1 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-09-26T23:45:55Z |
---
license: apache-2.0
---
|
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ahhany/engd_researches
|
ahhany
| 2023-11-06T11:46:18Z | 36 | 0 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:afl-3.0",
"region:us"
] |
[
"text-generation"
] | 2023-10-15T08:07:15Z |
---
license: afl-3.0
task_categories:
- text-generation
language:
- en
pretty_name: engd-researches
size_categories:
- 10K<n<100K
---
|
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shossain/merged-no-pad-text-16384
|
shossain
| 2023-11-06T21:54:52Z | 36 | 0 |
[
"region:us"
] | null | 2023-10-21T06:10:01Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 372439533
num_examples: 6401
download_size: 184155020
dataset_size: 372439533
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "merged-no-pad-text-16384"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Taj-Mahal/magic-the-gathering
|
Taj-Mahal
| 2023-10-31T20:58:35Z | 36 | 1 |
[
"region:us"
] | null | 2023-10-26T19:43:32Z |
---
dataset_info:
features:
- name: name
dtype: string
- name: firstPrinting
dtype: string
- name: manaCost
dtype: string
- name: convertedManaCost
dtype: float64
- name: type
dtype: string
- name: text
dtype: string
- name: power
dtype: string
- name: toughness
dtype: string
- name: loyalty
dtype: string
- name: layout
dtype: string
splits:
- name: train
num_bytes: 6999997
num_examples: 27703
- name: train_clean
num_bytes: 6813519.081146446
num_examples: 26965
download_size: 2539289
dataset_size: 13813516.081146445
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: train_clean
path: data/train_clean-*
---
# Dataset Card for "magic-the-gathering"
This is a HuggingFace adaptation of the [MTGJSON Atomic Card Database](https://mtgjson.com/data-models/card/card-atomic/) from the Taj-Mahal Data Science & Machine Learning Group.
## Usage
```
from datasets import load_dataset
dataset = load_dataset("Taj-Mahal/magic-the-gathering")
```
|
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] |
Morevorot/Dog_breed_Japanese_Spitz
|
Morevorot
| 2023-10-30T15:49:24Z | 36 | 0 |
[
"size_categories:1K<n<10K",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2023-10-27T14:23:53Z |
---
license: cc-by-sa-4.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 4336374796.1
num_examples: 3497
download_size: 3682840051
dataset_size: 4336374796.1
size_categories:
- 1K<n<10K
---
Japanese Spitz, resolution above 256x256, no 1 to 1 copies (but there are processed versions of one photo), serial names.
mistaken breed at 632, 639, 640, 644, 1003, 1810, 2000 numbers
|
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] |
SoAp9035/turkish_instructions
|
SoAp9035
| 2023-11-03T13:45:02Z | 36 | 2 |
[
"language:tr",
"license:apache-2.0",
"region:us"
] | null | 2023-11-03T12:11:25Z |
---
license: apache-2.0
language:
- tr
---
# Turkish Instructions
## Apache 2.0
This dataset is a cleaned and organized version (for Mistral) of [afkfatih/turkishdataset](https://huggingface.co/datasets/afkfatih/turkishdataset)
|
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btt-mining-coalation/open_web_random_5000
|
btt-mining-coalation
| 2023-11-09T14:45:32Z | 36 | 0 |
[
"region:us"
] | null | 2023-11-05T01:42:24Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: reward_dpo
dtype: float64
splits:
- name: train
num_bytes: 30649367
num_examples: 5000
download_size: 18002442
dataset_size: 30649367
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "open_web_random_5000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
tierdesafinante/fatima_noya
|
tierdesafinante
| 2023-11-06T18:45:11Z | 36 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-06T18:44:40Z |
---
license: openrail
---
|
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kpriyanshu256/semeval-task-8-a-mono-gltr-ppl
|
kpriyanshu256
| 2023-11-10T04:41:46Z | 36 | 0 |
[
"region:us"
] | null | 2023-11-10T04:41:27Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: model
dtype: string
- name: source
dtype: string
- name: id
dtype: int64
- name: gltr
sequence: int64
- name: ppl
sequence: float64
splits:
- name: train
num_bytes: 245302117
num_examples: 83829
- name: val
num_bytes: 105434420
num_examples: 35928
- name: test
num_bytes: 11023757
num_examples: 5000
download_size: 209455821
dataset_size: 361760294
---
# Dataset Card for "semeval-task-8-a-mono-gltr-ppl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ineoApp/factures_dataset
|
ineoApp
| 2023-11-22T14:53:14Z | 36 | 0 |
[
"region:us"
] | null | 2023-11-10T13:45:51Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: bboxes
sequence:
sequence: int64
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': reference
'2': numero facture
'3': fournisseur
'4': date facture
'5': date limite
'6': montant ht
'7': tva
'8': montant ttc
'9': unitP
'10': prix tva
'11': addresse
'12': art1 prix unit
'13': art1 designation
'14': art1 quantite
'15': art1 tva
'16': art1 montant ht
'17': art2 designation
'18': art2 quantite
'19': art2 prix unit
'20': art2 tva
'21': art2 montant ht
- name: tokens
sequence: string
splits:
- name: train
num_bytes: 3917957.6
num_examples: 4
- name: test
num_bytes: 674646.0
num_examples: 1
download_size: 4892196
dataset_size: 4592603.6
---
# Dataset Card for "factures_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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peterbeamish/environment-multi-labels-even
|
peterbeamish
| 2023-11-23T20:29:54Z | 36 | 0 |
[
"region:us"
] | null | 2023-11-12T07:51:31Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: filename
dtype: string
- name: content
dtype: string
- name: environment
sequence: string
- name: variablearg
sequence: 'null'
- name: constarg
sequence: string
- name: variableargjson
dtype: string
- name: constargjson
dtype: string
- name: lang
dtype: string
- name: constargcount
dtype: float64
- name: variableargcount
dtype: float64
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 260777154
num_examples: 19503
- name: test
num_bytes: 258313170
num_examples: 19504
download_size: 191420386
dataset_size: 519090324
---
# Dataset Card for "environment-multi-labels-even"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Lollitor/CASF
|
Lollitor
| 2023-11-13T18:28:12Z | 36 | 0 |
[
"region:us"
] | null | 2023-11-13T18:28:09Z |
---
dataset_info:
features:
- name: '#code'
dtype: string
- name: inputs
dtype: string
splits:
- name: train
num_bytes: 310419
num_examples: 285
download_size: 110166
dataset_size: 310419
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "CASF"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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frenchtext/banque-fr-2311
|
frenchtext
| 2023-11-13T23:23:22Z | 36 | 0 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:fr",
"license:apache-2.0",
"wordslab-webscraper",
"region:us"
] |
[
"text-generation"
] | 2023-11-13T23:22:23Z |
---
pretty_name: "banque fr websites - 2311"
tags:
- wordslab-webscraper
task_categories:
- text-generation
task_ids:
- language-modeling
size_categories: 10K<n<100K
language: fr
multilinguality: monolingual
license: apache-2.0
source_datasets: original
language_creators: found
annotations_creators: no-annotation
configs:
- config_name: default
data_files:
- split: train
path: "banque_fr_2311_train_*.parquet"
- split: valid
path: "banque_fr_2311_valid_*.parquet"
- split: test
path: "banque_fr_2311_test_*.parquet"
dataset_info:
features:
- name: Uri
dtype: string
- name: Timestamp
dtype: string
- name: Lang
dtype: string
- name: Title
dtype: string
- name: Text
dtype: string
- name: Words
dtype: int32
- name: AvgWordsLength
dtype: int32
- name: Chars
dtype: int32
- name: LetterChars
dtype: int32
- name: NumberChars
dtype: int32
- name: OtherChars
dtype: int32
- name: Website
dtype: string
- name: PDF
dtype: bool
config_name: default
splits:
- name: train
num_examples: 68166
- name: valid
num_examples: 8522
- name: test
num_examples: 8541
download_size: 247147772
---
# Dataset Card for "banque fr websites - 2311"
Dataset extracted from public websites by [wordslab-webscraper](https://github.com/wordslab-org/wordslab-webscraper) in 2311:
- domain: banque
- language: fr
- license: Apache 2.0
## Dataset Sources
wordslab-webscraper follows the industry best practices for **polite web scraping**:
- clearly identifies itself as a known text indexing bot: "bingbot"
- doesn't try to hide the user IP address behind proxies
- doesn't try to circumvent bots protection solutions
- waits for a minimum delay between two pages to avoid generating too much load
- respects the website "robots.txt" indexing directives
- respects the web page Meta Robots HTML tag
- respects the web page X-Robots-Tag HTTP header
- respects the web page links rel=nofollow HTML attributes
The text data was extracted from the following websites:
| Website | HTML pages | PDF docs | Words |
|:---|:---:|:---:|:---:|
| banque.meilleurtaux.com | 5055 | 1 | 3209252 |
| banquefrancaisemutualiste.fr | 196 | 0 | 205197 |
| groupebpce.com | 1137 | 3 | 456194 |
| mabanque.bnpparibas | 388 | 184 | 1411792 |
| particuliers.sg.fr | 385 | 168 | 1317226 |
| selectra.info_finance_banques | 231 | 0 | 490518 |
| www.20minutes.fr_economie_banque_ | 412 | 0 | 220504 |
| www.afb.fr | 121 | 44 | 159912 |
| www.allianz.fr | 854 | 1 | 785651 |
| www.axa.fr | 1568 | 0 | 731906 |
| www.banque-france.fr | 212 | 306 | 1169549 |
| www.banquebcp.fr | 232 | 0 | 94114 |
| www.banquedesterritoires.fr | 1899 | 169 | 2243316 |
| www.banquemondiale.org | 216 | 0 | 299993 |
| www.banquepopulaire.fr | 1530 | 0 | 778258 |
| www.bforbank.com | 71 | 0 | 38220 |
| www.boursobank.com | 97 | 0 | 89211 |
| www.boursorama.com | 24581 | 0 | 17269453 |
| www.bpifrance.fr | 523 | 1 | 489908 |
| www.bred.fr | 1531 | 209 | 1511004 |
| www.caisse-epargne.fr | 1393 | 0 | 574874 |
| www.cic.fr | 3353 | 567 | 6086714 |
| www.credit-agricole.fr | 1672 | 133 | 2070741 |
| www.creditmutuel.fr | 2111 | 526 | 3876220 |
| www.ecb.europa.eu | 79 | 1 | 71446 |
| www.esbanque.fr | 269 | 0 | 135072 |
| www.fbf.fr | 683 | 571 | 1939478 |
| www.fortuneo.fr | 898 | 8 | 573493 |
| www.hellobank.fr | 648 | 31 | 474719 |
| www.hsbc.fr | 295 | 144 | 1527838 |
| www.impots.gouv.fr | 122 | 10 | 63166 |
| www.labanquepostale.fr | 1677 | 0 | 1224971 |
| www.lafinancepourtous.com | 7849 | 357 | 6011655 |
| www.lcl.fr | 1268 | 0 | 834973 |
| www.lesclesdelabanque.com | 1478 | 135 | 769241 |
| www.lesechos.fr_finance-marches_ | 1493 | 0 | 1412840 |
| www.mafrenchbank.fr | 361 | 10 | 238469 |
| www.moneyvox.fr | 1860 | 0 | 1764097 |
| www.orangebank.fr | 682 | 92 | 1062804 |
| www.palatine.fr | 228 | 0 | 79346 |
| www.revue-banque.fr | 11171 | 6 | 3055775 |
| www.service-public.fr | 721 | 2 | 242446 |
## Uses
**WARNING**
- **the text included in this dataset belongs to its original authors** and is protected by copyright laws
- you are not allowed to use this dataset for anything else than **training a large language model**
- when using a large language model trained on this dataset, you will need to ensure that you comply with the law
- if you benefit from this large language model, you should try to share the value with the original text authors
wordslab-webscraper uses an advanced Html to text conversion algorithm optimized for **long context language modeling**:
- tries to recover the logical structure of the document from the Html or PDF layout
- preserves document / section / list / table grouping and nesting information
- **deduplicates text at the website level while preserving the document structure**
Each example in this dataset is a **markdown text conversion of a full HTML page or PDF document**:
- the document structure is preserved by markdown syntax: headers, lists, tables, paragraphs
- all duplicate paragraphs are removed
## Dataset Structure
The dataset is divided in 3 splits:
- train: 80% of the data
- valid: 10% of the data
- test: 10% of the data
wordslab-webscraper generates **one parquet file per website and per split**.
The parquet files are named with the following pattern:
- banque_fr_2311_[split]_[website].parquet
Note than you can load individual splits or websites with HuggingFace datasets using the following commands:
```python
from datasets import load_dataset
# Load a single plit
dataset = load_dataset("namespace/banque-fr-2311", split="train")
# Load a single website
data_files = { "train": "banque_fr_2311_train_[website].parquet", "valid": "banque_fr_2311_valid_[website].parquet", "test": "banque_fr_2311_test_[website].parquet" }
dataset = load_dataset("namespace/banque-fr-2311", data_files=data_files)
```
Each example in the dataset contains the text of a full web page or PDF document, with the following features:
- Uri: string
- Timestamp: string
- Lang: string
- Title: string
- Text: string
- Words: int32
- AvgWordsLength: int32
- Chars: int32
- LetterChars: int32
- NumberChars: int32
- OtherChars: int32
- Website: string
- PDF: bool
Note that beause each example is a full page or document, the "Text" feature can be a pretty long string containing thousands of words (as measured by the "Words" feature): you will typically need to chunk it down to the context size of your large language model before using it.
## Bias, Risks, and Limitations
This dataset is a direct extraction from the source websites.
It was not manually curated to remove misleading, offensive, or harmful content.
**Please add a filtering step before using it to train a large language model** if the source websites can't be trusted.
## Dataset Card Contact
Please add a comment in the community section of this repository if you want the maintainer to add or remove websites from this dataset.
|
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zxvix/amazon_review_automotive_subset
|
zxvix
| 2023-11-14T06:09:02Z | 36 | 0 |
[
"region:us"
] | null | 2023-11-14T06:03:17Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 172169682.4682526
num_examples: 1000000
- name: test
num_bytes: 172169.68246825258
num_examples: 1000
download_size: 287498929
dataset_size: 172341852.15072086
---
# Dataset Card for "amazon_review_automotive_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Liberty-L/race_val
|
Liberty-L
| 2023-11-15T05:06:11Z | 36 | 0 |
[
"region:us"
] | null | 2023-11-15T05:05:30Z |
---
dataset_info:
features:
- name: data_index_by_user
dtype: int64
- name: article
dtype: string
- name: answer
dtype: string
- name: question
dtype: string
- name: options
sequence: string
- name: input_ids
sequence:
sequence: int32
- name: token_type_ids
sequence:
sequence: int8
- name: attention_mask
sequence:
sequence: int8
- name: label
dtype: int64
splits:
- name: train
num_bytes: 38434030
num_examples: 3547
download_size: 8065978
dataset_size: 38434030
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "race_val"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Liberty-L/race_test
|
Liberty-L
| 2023-11-15T05:06:14Z | 36 | 0 |
[
"region:us"
] | null | 2023-11-15T05:05:52Z |
---
dataset_info:
features:
- name: data_index_by_user
dtype: int64
- name: article
dtype: string
- name: answer
dtype: string
- name: question
dtype: string
- name: options
sequence: string
- name: input_ids
sequence:
sequence: int32
- name: token_type_ids
sequence:
sequence: int8
- name: attention_mask
sequence:
sequence: int8
- name: label
dtype: int64
splits:
- name: train
num_bytes: 39853553
num_examples: 3638
download_size: 8418028
dataset_size: 39853553
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "race_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
lhallee/dl_binary_reg
|
lhallee
| 2023-11-15T18:33:01Z | 36 | 0 |
[
"region:us"
] | null | 2023-11-15T18:32:54Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: seqs
dtype: string
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 2692075
num_examples: 5473
- name: valid
num_bytes: 653234
num_examples: 1335
- name: test
num_bytes: 905979
num_examples: 1729
download_size: 4189564
dataset_size: 4251288
---
# Dataset Card for "dl_binary_reg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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manu/gallica_ocr_cleaned
|
manu
| 2023-11-18T22:34:35Z | 36 | 0 |
[
"region:us"
] | null | 2023-11-18T21:21:57Z |
---
dataset_info:
features:
- name: file
dtype: string
- name: clean_text
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 44570227
num_examples: 7687
download_size: 25073743
dataset_size: 44570227
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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ayan1988/diffusion.8.instruct_pix2pix
|
ayan1988
| 2023-11-19T12:42:31Z | 36 | 0 |
[
"region:us"
] | null | 2023-11-19T11:27:29Z |
---
dataset_info:
features:
- name: input
dtype: image
- name: text
dtype: string
- name: output
dtype: image
splits:
- name: train
num_bytes: 416880509.0
num_examples: 1000
download_size: 416911651
dataset_size: 416880509.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "diffusion.8.instruct_pix2pix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Ricktlw/ThomazCosta
|
Ricktlw
| 2023-11-19T20:33:35Z | 36 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-19T20:32:17Z |
---
license: openrail
---
|
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] |
lmg-anon/VNTL-2k
|
lmg-anon
| 2023-11-20T20:41:54Z | 36 | 0 |
[
"region:us"
] | null | 2023-11-20T17:56:51Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 87890178
num_examples: 16887
download_size: 0
dataset_size: 87890178
---
# Dataset Card for "VNTL-2k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
seonjeongh/parallel_en.am
|
seonjeongh
| 2023-11-22T04:11:31Z | 36 | 0 |
[
"region:us"
] | null | 2023-11-22T02:21:43Z |
---
dataset_info:
features:
- name: en
dtype: string
- name: am
dtype: string
splits:
- name: train
num_bytes: 34707491
num_examples: 140000
download_size: 21200415
dataset_size: 34707491
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "parallel_en.am"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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shrop/multinerd_en_filtered
|
shrop
| 2023-11-28T00:27:58Z | 36 | 0 |
[
"language:en",
"license:unknown",
"region:us"
] | null | 2023-11-28T00:22:23Z |
---
license: unknown
language:
- en
---
A filtered version of the [original English subset from the MultiNERD dataset by Babelscape](https://huggingface.co/datasets/Babelscape/multinerd). The version contains only 5 of the original 15 NER categories: PERSON(PER), ORGANIZATION(ORG), LOCATION(LOC), DISEASES(DIS),
ANIMAL(ANIM). Dataset filtered as part of a test.
See https://huggingface.co/datasets/Babelscape/multinerd for information on the dataset structure.
|
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] |
iohadrubin/gibberish-47-15-08
|
iohadrubin
| 2023-12-01T00:20:19Z | 36 | 0 |
[
"region:us"
] | null | 2023-12-01T00:20:14Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: int32
- name: file_loc
dtype: int64
splits:
- name: train
num_bytes: 70234967
num_examples: 10475
download_size: 39895849
dataset_size: 70234967
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "gibberish-47-15-08"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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projecte-aina/xquad-ca
|
projecte-aina
| 2023-11-25T05:37:46Z | 35 | 1 |
[
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-sa-4.0",
"arxiv:2107.07903",
"arxiv:1606.05250",
"arxiv:1910.11856",
"region:us"
] |
[
"question-answering"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ca
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: xquad-ca
size_categories:
- unknown
source_datasets: []
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for XQuAD-Ca
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://zenodo.org/record/6669801
- **Paper:** [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://arxiv.org/abs/2107.07903)
- **Point of Contact:** [Carlos Rodríguez-Penagos]([email protected]) and [Carme Armentano-Oller]([email protected])
### Dataset Summary
Professional translation into Catalan of [XQuAD dataset](https://github.com/deepmind/xquad).
XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 ([Rajpurkar, Pranav et al., 2016](http://arxiv.org/abs/1606.05250)) together with their professional translations into ten language: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Rumanian was added later. We added the 13th language to the corpus using also professional native Catalan translators.
XQuAD and XQuAD-Ca datasets are released under [CC-by-sa](https://creativecommons.org/licenses/by-sa/3.0/legalcode) licence.
### Supported Tasks and Leaderboards
Cross-lingual-QA, Extractive-QA, Language Model
### Languages
The dataset is in Catalan (`ca-ES`)
## Dataset Structure
### Data Instances
One json file.
1189 examples.
<pre>
{
"data": [
{
"context": "Al llarg de la seva existència, Varsòvia ha estat una ciutat multicultural. Segons el cens del 1901, de 711.988 habitants, el 56,2 % eren catòlics, el 35,7 % jueus, el 5 % cristians ortodoxos grecs i el 2,8 % protestants. Vuit anys després, el 1909, hi havia 281.754 jueus (36,9 %), 18.189 protestants (2,4 %) i 2.818 mariavites (0,4 %). Això va provocar que es construïssin centenars de llocs de culte religiós a totes les parts de la ciutat. La majoria d’ells es van destruir després de la insurrecció de Varsòvia del 1944. Després de la guerra, les noves autoritats comunistes de Polònia van apocar la construcció d’esglésies i només se’n va construir un petit nombre.",
"qas": [
{
"answers": [
{
"text": "711.988",
"answer_start": 104
}
],
"id": "57338007d058e614000b5bdb",
"question": "Quina era la població de Varsòvia l’any 1901?"
},
{
"answers": [
{
"text": "56,2 %",
"answer_start": 126
}
],
"id": "57338007d058e614000b5bdc",
"question": "Dels habitants de Varsòvia l’any 1901, quin percentatge era catòlic?"
},
...
]
}
]
},
...
]
}
</pre>
### Data Fields
Follows [Rajpurkar, Pranav et al., 2016](http://arxiv.org/abs/1606.05250) for SQuAD v1 datasets.
- `id` (str): Unique ID assigned to the question.
- `title` (str): Title of the Wikipedia article.
- `context` (str): Wikipedia section text.
- `question` (str): Question.
- `answers` (list): List of answers to the question, each containing:
- `text` (str): Span text answering to the question.
- `answer_start` Starting offset of the span text answering to the question.
### Data Splits
- test.json: 1189 examples.
## Dataset Creation
### Curation RationaleCA
We created this dataset to contribute to the development of language models in Catalan, a low-resource language, and for compatibility with similar datasets in other languages, and to allow inter-lingual comparisons.
### Source Data
- [XQuAD's webpage](https://github.com/deepmind/xquad).
#### Initial Data Collection and Normalization
This dataset is a professional translation of [XQuAD](https://github.com/deepmind/xquad) into Catalan, commissioned by [BSC TeMU](https://temu.bsc.es/) within [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/).
For more information on how XQuAD was created, refer to the paper, On the [Cross-lingual Transferability of Monolingual Representations](https://arxiv.org/abs/1910.11856), or visit the [XQuAD's webpage](https://github.com/deepmind/xquad).
#### Who are the source language producers?
For more information on how XQuAD was created, refer to the paper, [On the Cross-lingual Transferability of Monolingual Representations ](https://arxiv.org/abs/1910.11856), or visit the [XQuAD's webpage](https://github.com/deepmind/xquad).
### Annotations
This is a professional translation of the XQuAD corpus and its annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
Translation was commissioned to a professional translation company.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
This work is licensed under a [CC-by-sa](https://creativecommons.org/licenses/by-sa/3.0/legalcode) licence.
### Citation Information
```
@inproceedings{armengol-estape-etal-2021-multilingual,
title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
author = "Armengol-Estap{\'e}, Jordi and
Carrino, Casimiro Pio and
Rodriguez-Penagos, Carlos and
de Gibert Bonet, Ona and
Armentano-Oller, Carme and
Gonzalez-Agirre, Aitor and
Melero, Maite and
Villegas, Marta",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.437",
doi = "10.18653/v1/2021.findings-acl.437",
pages = "4933--4946",
}
```
[DOI](https://doi.org/10.5281/zenodo.4526223)
### Contributions
[N/A]
|
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] |
sagnikrayc/quasar
|
sagnikrayc
| 2022-10-25T09:54:36Z | 35 | 0 |
[
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"license:bsd-3-clause",
"arxiv:1707.03904",
"region:us"
] |
[
"question-answering"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en-US
license:
- bsd-3-clause
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
-
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
paperswithcode_id: quasar-1
---
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** N/A
- **Repository:** [GitHub](https://github.com/bdhingra/quasar)
- **Paper:** [Quasar: Datasets for Question Answering by Search and Reading](https://arxiv.org/abs/1707.03904)
- **Leaderboard:** N/A
- **Point of Contact:** -
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
|
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SocialGrep/the-antiwork-subreddit-dataset
|
SocialGrep
| 2022-07-01T17:57:34Z | 35 | 1 |
[
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-08T21:09:51Z |
---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for the-antiwork-subreddit-dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://socialgrep.com/datasets](https://socialgrep.com/datasets/the-antiwork-subreddit-dataset?utm_source=huggingface&utm_medium=link&utm_campaign=theantiworksubredditdataset)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=theantiworksubredditdataset)
### Dataset Summary
This corpus contains the complete data for the activity of the /r/Antiwork subreddit until 2022-02-18.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
CC-BY v4.0
### Contributions
[Needs More Information]
|
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] |
IIC/ms_marco_es
|
IIC
| 2022-10-23T05:26:06Z | 35 | 1 |
[
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:ms_marco",
"language:es",
"region:us"
] |
[
"sequence-modeling"
] | 2022-03-27T20:40:24Z |
---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- es
multilinguality:
- monolingual
pretty_name: MSMARCO_ES
size_categories:
- 100K<n<1M
source_datasets:
- ms_marco
task_categories:
- sequence-modeling
task_ids:
- language-modeling
---
# MSMARCO_ES
This is an automatically translated version of the [msmarco v1 dataset](https://huggingface.co/datasets/ms_marco) , a dataset used for text similarity tasks.
The translation was performed for the queries and passages using the [marianMT english-spanish](https://huggingface.co/Helsinki-NLP/opus-mt-en-es) . A posterior processing was required to sample the querys because there was some of them with more or less positive and negative labels than the recommended (4 neg and 1 pos).
License, distribution and usage conditions of the original dataset apply.
### Contributions
Thanks to [@avacaondata](https://huggingface.co/avacaondata), [@alborotis](https://huggingface.co/alborotis), [@albarji](https://huggingface.co/albarji), [@Dabs](https://huggingface.co/Dabs), [@GuillemGSubies](https://huggingface.co/GuillemGSubies) for adding this dataset.
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MicPie/unpredictable_mmo-champion-com
|
MicPie
| 2022-08-04T20:09:49Z | 35 | 0 |
[
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-classification",
"task_ids:multiple-choice-qa",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"task_ids:closed-domain-qa",
"task_ids:closed-book-qa",
"task_ids:open-book-qa",
"task_ids:language-modeling",
"task_ids:multi-class-classification",
"task_ids:natural-language-inference",
"task_ids:topic-classification",
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"task_ids:tabular-multi-class-classification",
"task_ids:tabular-multi-label-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"arxiv:2208.01009",
"region:us"
] |
[
"multiple-choice",
"question-answering",
"zero-shot-classification",
"text2text-generation",
"table-question-answering",
"text-generation",
"text-classification",
"tabular-classification"
] | 2022-07-03T08:15:38Z |
---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-mmo-champion-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-mmo-champion-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** [email protected], [email protected]
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
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] |
pnr-svc/Turkish-Multiclass-Dataset
|
pnr-svc
| 2022-07-20T21:40:17Z | 35 | 2 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:tr",
"license:other",
"region:us"
] |
[
"text-classification"
] | 2022-07-16T16:01:20Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- tr
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
pretty_name: 'Turkish-Multiclass-Dataset'
train-eval-index:
- config: TurkishMulticlassDataset
task: text-classification
task_id: multi_class_classification
splits:
eval_split: test
col_mapping:
text: text
label: target
---
# Dataset Card for "Turkish-Multiclass-Dataset"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/PnrSvc/Turkish-Multiclass-Dataset]
- **Repository:**[https://github.com/PnrSvc/Turkish-Multiclass-Dataset]
- **Size of downloaded dataset files:**
- **Size of the generated dataset:**
### Dataset Summary
The dataset was compiled from user comments from e-commerce sites. It consists of 53,000 validations, 53,000 tests and 160600 train data. Data were classified into 3 classes (positive(pos), negative(neg) and natural(nor). The data is available to you on github.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
#### turkish-dataset-v1
- **Size of downloaded dataset files:**
- **Size of the generated dataset:**
### Data Fields
The data fields are the same among all splits.
#### turkish-dataset-v-v1
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `positive` (2), `natural` (1), `negative` (0).
### Data Splits
| |train |validation|test |
|----|--------:|---------:|---------:|
|Data| 15000 | 5000| 5000|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to for adding this dataset.
|
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] |
jakartaresearch/indoqa
|
jakartaresearch
| 2022-12-17T06:07:27Z | 35 | 2 |
[
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:id",
"license:cc-by-nd-4.0",
"indoqa",
"qa",
"question-answering",
"indonesian",
"region:us"
] |
[
"question-answering"
] | 2022-08-13T10:54:08Z |
---
annotations_creators:
- expert-generated
language:
- id
language_creators:
- found
license:
- cc-by-nd-4.0
multilinguality:
- monolingual
pretty_name: Indonesian Question Answering Dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- indoqa
- qa
- question-answering
- indonesian
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for Indonesian Question Answering Dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@fhrzn](https://github.com/fhrzn)[@Kalzaik](https://github.com/Kalzaik) [@ibamibrahim](https://github.com/ibamibrahim) [@andreaschandra](https://github.com/andreaschandra) for adding this dataset.
|
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ThankGod/celeb-identities
|
ThankGod
| 2023-04-25T12:00:42Z | 35 | 0 |
[
"region:us"
] | null | 2022-10-09T18:37:35Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Andrew_Ng
'1': Elon_Musk
'2': Jay_Z
'3': Kanye
'4': Obama
'5': Queen
splits:
- name: train
num_bytes: 624532.0
num_examples: 16
download_size: 626669
dataset_size: 624532.0
---
# Dataset Card for "celeb-identities"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Kavindu99/celeb-identities
|
Kavindu99
| 2022-10-13T20:27:44Z | 35 | 0 |
[
"region:us"
] | null | 2022-10-13T20:27:31Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
0: Emilia_Clarke
1: Henry_Cavil
2: Jason_Mamoa
3: Sadie_Sink
4: Sangakkara
5: Zendaya
splits:
- name: train
num_bytes: 160371.0
num_examples: 18
download_size: 160832
dataset_size: 160371.0
---
# Dataset Card for "celeb-identities"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
bigbio/bionlp_st_2013_gro
|
bigbio
| 2022-12-22T15:44:01Z | 35 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | null | 2022-11-13T22:07:10Z |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: GENIA_PROJECT_LICENSE
pretty_name: BioNLP 2013 GRO
homepage: https://github.com/openbiocorpora/bionlp-st-2013-gro
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- EVENT_EXTRACTION
- NAMED_ENTITY_RECOGNITION
- RELATION_EXTRACTION
---
# Dataset Card for BioNLP 2013 GRO
## Dataset Description
- **Homepage:** https://github.com/openbiocorpora/bionlp-st-2013-gro
- **Pubmed:** True
- **Public:** True
- **Tasks:** EE,NER,RE
GRO Task: Populating the Gene Regulation Ontology with events and
relations. A data set from the bio NLP shared tasks competition from 2013
## Citation Information
```
@inproceedings{kim-etal-2013-gro,
title = "{GRO} Task: Populating the Gene Regulation Ontology with events and relations",
author = "Kim, Jung-jae and
Han, Xu and
Lee, Vivian and
Rebholz-Schuhmann, Dietrich",
booktitle = "Proceedings of the {B}io{NLP} Shared Task 2013 Workshop",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-2007",
pages = "50--57",
}
```
|
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thennal/IMaSC
|
thennal
| 2022-12-08T17:21:02Z | 35 | 2 |
[
"task_categories:text-to-speech",
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ml",
"license:cc-by-sa-4.0",
"arxiv:2211.12796",
"region:us"
] |
[
"text-to-speech",
"automatic-speech-recognition"
] | 2022-11-17T05:16:00Z |
---
annotations_creators:
- expert-generated
language:
- ml
language_creators:
- found
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: ICFOSS Malayalam Speech Corpus
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- text-to-speech
- automatic-speech-recognition
task_ids: []
---
# IMaSC: ICFOSS Malayalam Speech Corpus
**IMaSC** is a Malayalam text and speech corpus made available by [ICFOSS](https://icfoss.in/) for the purpose of developing speech technology for Malayalam, particularly text-to-speech. The corpus contains 34,473 text-audio pairs of Malayalam sentences spoken by 8 speakers, totalling in approximately 50 hours of audio.
## Dataset Description
- **Paper:** [IMaSC — ICFOSS Malayalam Speech Corpus](https://arxiv.org/abs/2211.12796)
- **Point of Contact:** [Thennal D K](mailto:[email protected])
## Dataset Structure
The dataset consists of 34,473 instances with fields `text`, `speaker`, and `audio`. The audio is mono, sampled at 16kH. The transcription is normalized and only includes Malayalam characters and common punctuation. The table given below specifies how the 34,473 instances are split between the speakers, along with some basic speaker info:
| Speaker | Gender | Age | Time (HH:MM:SS) | Sentences |
| --- | --- | --- | --- | --- |
| Joji | Male | 28 | 06:08:55 | 4,332 |
| Sonia | Female | 43 | 05:22:39 | 4,294 |
| Jijo | Male | 26 | 05:34:05 | 4,093 |
| Greeshma | Female | 22 | 06:32:39 | 4,416 |
| Anil | Male | 48 | 05:58:34 | 4,239 |
| Vidhya | Female | 23 | 04:21:56 | 3,242 |
| Sonu | Male | 25 | 06:04:43 | 4,219 |
| Simla | Female | 24 | 09:34:21 | 5,638 |
| **Total** | | | **49:37:54** | **34,473** |
### Data Instances
An example instance is given below:
```json
{'text': 'സർവ്വകലാശാല വൈസ് ചാൻസലർ ഡോ. ചന്ദ്രബാബുവിനും സംഭവം തലവേദനയാവുകയാണ്',
'speaker': 'Sonia',
'audio': {'path': None,
'array': array([ 0.00921631, 0.00930786, 0.00939941, ..., -0.00497437,
-0.00497437, -0.00497437]),
'sampling_rate': 16000}}
```
### Data Fields
- **text** (str): Transcription of the audio file
- **speaker** (str): The name of the speaker
- **audio** (dict): Audio object including loaded audio array, sampling rate and path to audio (always None)
### Data Splits
We provide all the data in a single `train` split. The loaded dataset object thus looks like this:
```json
DatasetDict({
train: Dataset({
features: ['text', 'speaker', 'audio'],
num_rows: 34473
})
})
```
### Dataset Creation
The text is sourced from [Malayalam Wikipedia](https://ml.wikipedia.org), and read by our speakers in studio conditions. Extensive error correction was conducted to provide a clean, accurate database. Further details are given in our paper, accessible at [https://arxiv.org/abs/2211.12796](https://arxiv.org/abs/2211.12796).
## Additional Information
### Licensing
The corpus is made available under the [Creative Commons license (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation
```
@misc{gopinath2022imasc,
title={IMaSC -- ICFOSS Malayalam Speech Corpus},
author={Deepa P Gopinath and Thennal D K and Vrinda V Nair and Swaraj K S and Sachin G},
year={2022},
eprint={2211.12796},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
```
|
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heegyu/korean-petitions
|
heegyu
| 2023-01-15T09:46:48Z | 35 | 2 |
[
"license:mit",
"region:us"
] | null | 2022-11-22T07:56:58Z |
---
license: mit
---
# 청와대 국민청원
데이터 출처: https://github.com/lovit/petitions_archive<br/>
크기: 651.8MB
sample
```
{
"category": "반려동물",
"begin": "2017-08-25",
"end": "2017-11-23",
"content": "길고양이들 밥주고있는 사람입니다. 최근에 동네주민과 트러블이 생겨 싸움이 일어났습니다. 길고양이들이 모여든다고 밥주지마라고 윽박지르셨습니다. 쓰레기봉투를 뜯는다거나 사람에게 해끼치거나 하지 않았습니다. 단순히 고양이가 모여드는게 싫답니다. 그럼 애들은 굶어죽어야하나요? 길고양이들이 맘놓고 쉬고 밥먹을 수 있는 환경이 전혀 없는데 무작정 밥안주고 물 안주면 얘네는 어떻게 하나요? 안그래도 수명도 짧은데다가 길고양이를 상대로 학대하는 사람들도 많은데 너무 가엾습니다. 강동구청은 고양이 급식소라고 만들어주셨던데 동네마다 한개씩이라도 만들어 주셨으면좋겠어요.. 밥에다가 이상한짓 하는 사람 있을 수 있으니까 cctv도 설치도 해주셨으면 합니다.. (급식소에 쥐약을 뿌려 고양이가 죽은 사례가 있습니다) 지구가 사람껀 아니잖아요 동물과도 더불어 살줄 알아야죠 문대통령님께서 동물복지 관련 공략을 내셨지만 나아진게 전혀 없는거같아요. 공략 꼭 지켜주세요.. 믿고 뽑았는데 전혀 나아지고 바뀐게 없으면 너무 실망스럽잖아요.. 그리고 길고양이뿐만 아니라 다른 동물 학대하는 부분도 처벌 강화 부탁드립니다",
"num_agree": 5,
"petition_idx": "513",
"status": "청원종료",
"title": "길고양이를 도와주세요"
}
```
|
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] |
EdBianchi/SmokeFire
|
EdBianchi
| 2022-12-29T14:45:31Z | 35 | 0 |
[
"region:us"
] | null | 2022-12-28T09:21:45Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Fire
'1': Normal
'2': Smoke
splits:
- name: train
num_bytes: 166216842.46
num_examples: 6060
- name: test
num_bytes: 89193578.0
num_examples: 759
- name: validation
num_bytes: 75838884.0
num_examples: 756
download_size: 890673915
dataset_size: 331249304.46000004
---
# Dataset Card for "SmokeFire"
Wildfires or forest fires are unpredictable catastrophic and destructive events that affect rural areas. The impact of these events affects both vegetation and wildlife.
This dataset can be used to train networks able to detect smoke and/or fire in forest environments.
## Data Sources & Description
- **This dataset consist of sample from two datasets hosted on Kaggle:**
- [Forest Fire](https://www.kaggle.com/datasets/kutaykutlu/forest-fire?select=train_fire)
- [Forest Fire Images](https://www.kaggle.com/datasets/mohnishsaiprasad/forest-fire-images)
- **The datasets consist of:**
- 2525 **Fire** samples
- 2525 **Smoke** samples
- 2525 **Normal** samples
- **The dataset is splitted into:**
- Train Set -> 6060 samples
- Validation Set -> 756 samples
- Test Set -> 759 samples
|
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keremberke/plane-detection
|
keremberke
| 2023-01-27T13:46:18Z | 35 | 2 |
[
"task_categories:object-detection",
"roboflow",
"roboflow2huggingface",
"region:us"
] |
[
"object-detection"
] | 2023-01-18T09:43:30Z |
---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
---
<div align="center">
<img width="640" alt="keremberke/plane-detection" src="https://huggingface.co/datasets/keremberke/plane-detection/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['planes']
```
### Number of Images
```json
{'test': 25, 'valid': 50, 'train': 175}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("keremberke/plane-detection", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/skybot-cam/overhead-plane-detector/dataset/4](https://universe.roboflow.com/skybot-cam/overhead-plane-detector/dataset/4?ref=roboflow2huggingface)
### Citation
```
@misc{ overhead-plane-detector_dataset,
title = { Overhead Plane Detector Dataset },
type = { Open Source Dataset },
author = { SkyBot Cam },
howpublished = { \\url{ https://universe.roboflow.com/skybot-cam/overhead-plane-detector } },
url = { https://universe.roboflow.com/skybot-cam/overhead-plane-detector },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { jan },
note = { visited on 2023-01-27 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.ai on March 30, 2022 at 3:11 PM GMT
It includes 250 images.
Planes are annotated in COCO format.
The following pre-processing was applied to each image:
No image augmentation techniques were applied.
|
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] |
jonathan-roberts1/USTC_SmokeRS
|
jonathan-roberts1
| 2023-03-31T14:56:13Z | 35 | 0 |
[
"license:other",
"region:us"
] | null | 2023-01-26T10:45:45Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': cloud
'1': dust
'2': haze
'3': land
'4': seaside
'5': smoke
splits:
- name: train
num_bytes: 1229029078.725
num_examples: 6225
download_size: 1115042620
dataset_size: 1229029078.725
license: other
---
# Dataset Card for "USTC_SmokeRS"
## Dataset Description
- **Paper:** [SmokeNet: Satellite smoke scene detection using convolutional neural network with spatial and channel-wise attention](https://www.mdpi.com/2072-4292/11/14/1702/pdf)
### Licensing Information
For research/education purposes.
## Citation Information
[SmokeNet: Satellite smoke scene detection using convolutional neural network with spatial and channel-wise attention](https://www.mdpi.com/2072-4292/11/14/1702/pdf)
```
@article{ba2019smokenet,
title = {SmokeNet: Satellite smoke scene detection using convolutional neural network with spatial and channel-wise attention},
author = {Ba, Rui and Chen, Chen and Yuan, Jing and Song, Weiguo and Lo, Siuming},
year = 2019,
journal = {Remote Sensing},
publisher = {MDPI},
volume = 11,
number = 14,
pages = 1702
}
```
|
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] |
sagnikrayc/snli-bt
|
sagnikrayc
| 2023-02-20T23:11:17Z | 35 | 0 |
[
"license:afl-3.0",
"region:us"
] | null | 2023-02-20T23:03:02Z |
---
license: afl-3.0
---
### Dataset Card for SNLI Back Translation
back translation of SNLI dataset: only use the test version
|
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totuta/youtube_subs_howto100M
|
totuta
| 2023-03-04T01:38:37Z | 35 | 3 |
[
"task_categories:conversational",
"size_categories:10M<n<100M",
"language:en",
"license:apache-2.0",
"arxiv:1906.03327",
"region:us"
] |
[
"conversational"
] | 2023-03-03T04:56:10Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: response
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1260882571
num_examples: 309136
download_size: 668637627
dataset_size: 1260882571
license: apache-2.0
task_categories:
- conversational
language:
- en
pretty_name: 'YouTube Subtitles of Instructions: HowTo100M'
size_categories:
- 10M<n<100M
---
# Dataset Card for youtube_subs_howto100M
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [HowTo100M homepage](https://www.di.ens.fr/willow/research/howto100m/)
- **Repository:** [HowTo100M repository](https://github.com/antoine77340/howto100m)
- **Paper:** [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://arxiv.org/abs/1906.03327)
### Dataset Summary
The `youtube_subs_howto100M` dataset is an English-language dataset of instruction-response pairs extracted from 309136 YouTube videos. The dataset was orignally inspired by and sourced from the HowTo100M dataset, which was developed for natural language search for video clips.
### Supported Tasks and Leaderboards
- `conversational`: The dataset can be used to train a model for instruction(request) and a long form of response generation. This dataset is originally prepared for the [Open Assistant](https://github.com/LAION-AI/Open-Assistant), which is an open-source chat-based large language model.
### Languages
Currently, all text in the dataset is in English.
## Dataset Structure
### Data Instances
A typical data point comprises an `instruction`, `response`, and a `source`
An example from the youtube_subs_howto100M looks as follows:
```
{"instruction": "Please explain how to remove plaque without going to the dentist 2016", "response": "mineral deposit on teeth is known as tartar or plaque as time passes by the amount of tartar increases and if you don't take care it can cause periodontitis of course the best way to remove tartar is paying a visit to your dentist but another way is to remove plaque at your home in this video you will learn how to remove plaque at home to do so you will need baking soda toothbrush salt you hydrogen peroxide cup you gentle pick you water anti septic mouthwash you step one first mix one tablespoon of bacon soda with TSP of salt into the cup after you at the toothbrush with warm water dip it into the mixture scrub teeth with an in spit continue the same process for five minutes step to mix a cup full with hydrogen peroxide with cup of warm water and rinse your mouth for one minute then spit and rinse with cup of cool water step 3 rub the yellow tartar from teeth with a dental pick be careful not to scrape the gums it may irritate and damage them step 4 rinse mouth with an antiseptic mouthwash and repeat every second day here are some other advice is to help you keep your beautiful smile tomatoes and strawberries tomatoes and strawberries are rich in vitamin C which is excellent for oral health you can rub these fruits directly onto your teeth and let it sit for five minutes this way the tartar buildup will soften cheese being a Swiss or cheddar before meals helps neutralize the acids that involve black creation an ingredient in a cheese works as a barrier agent guava both guava fruit and leaves are considered excellent anti black agents to help remove plaque accumulated on the teeth and gums gloss they have anti-inflammatory and analgesic properties that help reduce swelling and pain in the gums brush your teeth regularly with a soft brush and make vertical movements pay attention on the space between gums and teeth floss regularly consuming spicy food stimulates syllabary glands that way saliva cleans mouth in a natural way five bacteria with an orange peel before going to bed and don't rinse mouth", "source": "YouTube"}
```
### Data Fields
- `instruction`: a request for an explanation.
- `response`: a long text of response sentences, currently not punctuated.
- `source`: the source of the datapoint, currently all `YouTube`.
### Data Splits
The dataset does not have train/valid/eval splits now.
## Dataset Creation
### Curation Rationale
The original HowTo100M dataset was developed for natural language search for video clips, not necessarily for conversational or chat based training. However, the long monologue response can be regarded as a sequence of answers for a question, which can be induced from the video title. Therefore, a good amount of high-quality request-response(long) pairs can be extracted from HowTo100M youtube videos.
Concretely, this dataset is curated like below:
```
for each video in YouTube100M dataset
if video_title starts with `how to`
add `Please explain` to the title to make an `instruction`
extract subtitles from the video to make a `response`
```
### Source Data
#### Initial Data Collection and Normalization
Refer to the [Curation Rationale](#curation-rationale)
#### Who are the source language producers?
The language producers are YouTube users of the videos in HowTo100M dataset.
### Annotations
#### Annotation process
Refer to the [Curation Rationale](#curation-rationale)
#### 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
Apache license 2.0
## Additional Information
### Dataset Curators
The youtube_subs_howto100M dataset was created by [@totuta](https://github.com/totuta). The original HowTo100M dataset was created by Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic.
### Licensing Information
[N/A]
### Citation Information
@inproceedings{miech19howto100m,
title={How{T}o100{M}: {L}earning a {T}ext-{V}ideo {E}mbedding by {W}atching {H}undred {M}illion {N}arrated {V}ideo {C}lips},
author={Miech, Antoine and Zhukov, Dimitri and Alayrac, Jean-Baptiste and Tapaswi, Makarand and Laptev, Ivan and Sivic, Josef},
booktitle={ICCV},
year={2019},
}
### Contributions
Thanks to [@totuta](https://github.com/totuta) for adding this dataset.
|
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Den4ikAI/russian_dialogues
|
Den4ikAI
| 2023-03-12T07:58:54Z | 35 | 8 |
[
"task_categories:conversational",
"size_categories:1M<n<10M",
"language:ru",
"license:mit",
"region:us"
] |
[
"conversational"
] | 2023-03-12T06:54:22Z |
---
license: mit
task_categories:
- conversational
language:
- ru
size_categories:
- 1M<n<10M
---
Датасет русских диалогов собранных с Telegram чатов.
Диалоги имеют разметку по релевантности.
Также были сгенерированы негативные примеры с помощью перемешивания похожих ответов.
Количество диалогов - 2 миллиона
Формат датасета:
```
{
'question': 'Привет',
'answer': 'Привет, как дела?'
'relevance': 1
}
```
Программа парсинга: https://github.com/Den4ikAI/telegram_chat_parser
### Citation:
```
@MISC{russian_instructions,
author = {Denis Petrov},
title = {Russian dialogues dataset for conversational agents},
url = {https://huggingface.co/datasets/Den4ikAI/russian_dialogues},
year = 2023
}
```
|
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ArmelR/stack-exchange-sample10000
|
ArmelR
| 2023-04-06T13:19:45Z | 35 | 2 |
[
"region:us"
] | null | 2023-04-06T13:19:39Z |
---
dataset_info:
features:
- name: qid
dtype: int64
- name: question
dtype: string
- name: date
dtype: string
- name: metadata
sequence: string
- name: response_j
dtype: string
- name: response_k
dtype: string
splits:
- name: train
num_bytes: 27983797.447734267
num_examples: 10000
download_size: 15522939
dataset_size: 27983797.447734267
---
# Dataset Card for "stack-exchange-sample10000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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