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Divyanshu/IE_SemParse
|
Divyanshu
| 2023-07-13T18:35:10Z | 33 | 0 |
[
"task_categories:text2text-generation",
"task_ids:parsing",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:as",
"language:bn",
"language:gu",
"language:hi",
"language:kn",
"language:ml",
"language:mr",
"language:or",
"language:pa",
"language:ta",
"language:te",
"license:cc0-1.0",
"arxiv:2304.13005",
"region:us"
] |
[
"text2text-generation"
] | 2022-10-01T10:51:54Z |
---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: IE-SemParse
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- parsing
---
# Dataset Card for "IE-SemParse"
## Table of Contents
- [Dataset Card for "IE-SemParse"](#dataset-card-for-ie-semparse)
- [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 usage](#dataset-usage)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Human Verification Process](#human-verification-process)
- [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)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** <https://github.com/divyanshuaggarwal/IE-SemParse>
- **Paper:** [Evaluating Inter-Bilingual Semantic Parsing for Indian Languages](https://arxiv.org/abs/2304.13005)
- **Point of Contact:** [Divyanshu Aggarwal](mailto:[email protected])
### Dataset Summary
IE-SemParse is an InterBilingual Semantic Parsing Dataset for eleven major Indic languages that includes
Assamese (‘as’), Gujarat (‘gu’), Kannada (‘kn’),
Malayalam (‘ml’), Marathi (‘mr’), Odia (‘or’),
Punjabi (‘pa’), Tamil (‘ta’), Telugu (‘te’), Hindi
(‘hi’), and Bengali (‘bn’).
### Supported Tasks and Leaderboards
**Tasks:** Inter-Bilingual Semantic Parsing
**Leaderboards:** Currently there is no Leaderboard for this dataset.
### Languages
- `Assamese (as)`
- `Bengali (bn)`
- `Gujarati (gu)`
- `Kannada (kn)`
- `Hindi (hi)`
- `Malayalam (ml)`
- `Marathi (mr)`
- `Oriya (or)`
- `Punjabi (pa)`
- `Tamil (ta)`
- `Telugu (te)`
...
<!-- Below is the dataset split given for `hi` dataset.
```python
DatasetDict({
train: Dataset({
features: ['utterance', 'logical form', 'intent'],
num_rows: 36000
})
test: Dataset({
features: ['utterance', 'logical form', 'intent'],
num_rows: 3000
})
validation: Dataset({
features: ['utterance', 'logical form', 'intent'],
num_rows: 1500
})
})
``` -->
## Dataset usage
Code snippet for using the dataset using datasets library.
```python
from datasets import load_dataset
dataset = load_dataset("Divyanshu/IE_SemParse")
```
## Dataset Creation
Machine translation of 3 multilingual semantic Parsing datasets english dataset to 11 listed Indic Languages.
### Curation Rationale
[More information needed]
### Source Data
[mTOP dataset](https://aclanthology.org/2021.eacl-main.257/)
[multilingualTOP dataset](https://github.com/awslabs/multilingual-top)
[multi-ATIS++ dataset](https://paperswithcode.com/paper/end-to-end-slot-alignment-and-recognition-for)
#### Initial Data Collection and Normalization
[Detailed in the paper](https://arxiv.org/abs/2304.13005)
#### Who are the source language producers?
[Detailed in the paper](https://arxiv.org/abs/2304.13005)
#### Human Verification Process
[Detailed in the paper](https://arxiv.org/abs/2304.13005)
## Considerations for Using the Data
### Social Impact of Dataset
[Detailed in the paper](https://arxiv.org/abs/2304.13005)
### Discussion of Biases
[Detailed in the paper](https://arxiv.org/abs/2304.13005)
### Other Known Limitations
[Detailed in the paper](https://arxiv.org/abs/2304.13005)
### Dataset Curators
Divyanshu Aggarwal, Vivek Gupta, Anoop Kunchukuttan
### Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
If you use any of the datasets, models or code modules, please cite the following paper:
```
@misc{aggarwal2023evaluating,
title={Evaluating Inter-Bilingual Semantic Parsing for Indian Languages},
author={Divyanshu Aggarwal and Vivek Gupta and Anoop Kunchukuttan},
year={2023},
eprint={2304.13005},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!-- ### Contributions -->
|
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giulio98/xlcost-single-prompt
|
giulio98
| 2022-11-02T19:42:44Z | 33 | 3 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:cc-by-sa-4.0",
"arxiv:2206.08474",
"region:us"
] |
[
"text-generation"
] | 2022-10-19T12:06:36Z |
---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: xlcost-single-prompt
---
# XLCost for text-to-code synthesis
## Dataset Description
This is a subset of [XLCoST benchmark](https://github.com/reddy-lab-code-research/XLCoST), for text-to-code generation at program level for **2** programming languages: `Python, C++`. This dataset is based on [codeparrot/xlcost-text-to-code](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code) with the following improvements:
* NEWLINE, INDENT and DEDENT were replaced with the corresponding ASCII codes.
* the code text has been reformatted using autopep8 for Python and clang-format for cpp.
* new columns have been introduced to allow evaluation using pass@k metric.
* programs containing more than one function call in the driver code were removed
## Languages
The dataset contains text in English and its corresponding code translation. The text contains a set of concatenated code comments that allow to synthesize the program.
## Dataset Structure
To load the dataset you need to specify the language(Python or C++).
```python
from datasets import load_dataset
load_dataset("giulio98/xlcost-single-prompt", "Python")
DatasetDict({
train: Dataset({
features: ['text', 'context', 'code', 'test', 'output', 'fn_call'],
num_rows: 8306
})
test: Dataset({
features: ['text', 'context', 'code', 'test', 'output', 'fn_call'],
num_rows: 812
})
validation: Dataset({
features: ['text', 'context', 'code', 'test', 'output', 'fn_call'],
num_rows: 427
})
})
```
## Data Fields
* text: natural language description.
* context: import libraries/global variables.
* code: code at program level.
* test: test function call.
* output: expected output of the function call.
* fn_call: name of the function to call.
## Data Splits
Each subset has three splits: train, test and validation.
## Citation Information
```
@misc{zhu2022xlcost,
title = {XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence},
url = {https://arxiv.org/abs/2206.08474},
author = {Zhu, Ming and Jain, Aneesh and Suresh, Karthik and Ravindran, Roshan and Tipirneni, Sindhu and Reddy, Chandan K.},
year = {2022},
eprint={2206.08474},
archivePrefix={arXiv}
}
```
|
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matchbench/dbp15k-fr-en
|
matchbench
| 2023-01-23T12:28:45Z | 33 | 0 |
[
"language:fr",
"language:en",
"region:us"
] | null | 2022-10-31T07:08:08Z |
---
language:
- fr
- en
---
|
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] |
W4nkel/turkish-sentiment-dataset
|
W4nkel
| 2023-01-01T18:07:08Z | 33 | 0 |
[
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-12-31T22:37:06Z |
---
license: cc-by-sa-4.0
---
THIS DATASET BASED ON THIS SOURCE: [winvoker/turkish-sentiment-analysis-dataset](https://huggingface.co/datasets/winvoker/turkish-sentiment-analysis-dataset)
|
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mehul7/captioned_military_aircraft
|
mehul7
| 2023-01-11T23:35:22Z | 33 | 3 |
[
"license:mit",
"region:us"
] | null | 2023-01-11T22:54:08Z |
---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 5806592710.697
num_examples: 8341
download_size: 6709513141
dataset_size: 5806592710.697
---
|
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nthngdy/bert_dataset_202203
|
nthngdy
| 2023-01-17T10:10:06Z | 33 | 0 |
[
"task_categories:text-generation",
"task_categories:fill-mask",
"language:en",
"license:apache-2.0",
"language-modeling",
"masked-language-modeling",
"region:us"
] |
[
"text-generation",
"fill-mask"
] | 2023-01-16T14:40:52Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 24635440616
num_examples: 146707688
download_size: 14651841592
dataset_size: 24635440616
license: apache-2.0
task_categories:
- text-generation
- fill-mask
language:
- en
tags:
- language-modeling
- masked-language-modeling
pretty_name: BERT Dataset (BookCorpus + Wikipedia 03/2022)
---
# Dataset Card for "bert_dataset_202203"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
metaeval/mega
|
metaeval
| 2023-03-24T13:55:03Z | 33 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-01-18T12:20:22Z |
---
license: apache-2.0
---
|
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] |
GBaker/MedQA-USMLE-4-options-hf
|
GBaker
| 2023-01-30T22:57:33Z | 33 | 3 |
[
"license:cc-by-sa-4.0",
"region:us"
] | null | 2023-01-24T20:32:54Z |
---
license: cc-by-sa-4.0
---
Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large)
<h4>Citation information:</h4>
@article{jin2020disease,
title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={arXiv preprint arXiv:2009.13081},
year={2020}
}
|
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Cohere/miracl-ja-queries-22-12
|
Cohere
| 2023-02-06T11:57:00Z | 33 | 1 |
[
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:expert-generated",
"multilinguality:multilingual",
"language:ja",
"license:apache-2.0",
"region:us"
] |
[
"text-retrieval"
] | 2023-01-31T09:20:40Z |
---
annotations_creators:
- expert-generated
language:
- ja
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# MIRACL (ja) embedded with cohere.ai `multilingual-22-12` encoder
We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
The query embeddings can be found in [Cohere/miracl-ja-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-ja-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-ja-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-ja-corpus-22-12).
For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).
Dataset info:
> MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
>
> The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Loading the dataset
In [miracl-ja-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-ja-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large.
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-ja-corpus-22-12", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-ja-corpus-22-12", split="train", streaming=True)
for doc in docs:
docid = doc['docid']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
Have a look at [miracl-ja-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-ja-queries-22-12) where we provide the query embeddings for the MIRACL dataset.
To search in the documents, you must use **dot-product**.
And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product.
A full search example:
```python
# Attention! For large datasets, this requires a lot of memory to store
# all document embeddings and to compute the dot product scores.
# Only use this for smaller datasets. For large datasets, use a vector DB
from datasets import load_dataset
import torch
#Load documents + embeddings
docs = load_dataset(f"Cohere/miracl-ja-corpus-22-12", split="train")
doc_embeddings = torch.tensor(docs['emb'])
# Load queries
queries = load_dataset(f"Cohere/miracl-ja-queries-22-12", split="dev")
# Select the first query as example
qid = 0
query = queries[qid]
query_embedding = torch.tensor(queries['emb'])
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query['query'])
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'])
```
You can get embeddings for new queries using our API:
```python
#Run: pip install cohere
import cohere
co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :))
texts = ['my search query']
response = co.embed(texts=texts, model='multilingual-22-12')
query_embedding = response.embeddings[0] # Get the embedding for the first text
```
## Performance
In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset.
We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results.
Note: MIRACL only annotated a small fraction of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted.
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | ES 8.6.0 nDCG@10 | ES 8.6.0 acc@3 |
|---|---|---|---|---|
| miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 |
| miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 |
| miracl-de | 44.4 | 60.7 | 19.6 | 29.8 |
| miracl-en | 44.6 | 62.2 | 30.2 | 43.2 |
| miracl-es | 47.0 | 74.1 | 27.0 | 47.2 |
| miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 |
| miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 |
| miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 |
| miracl-id | 44.8 | 63.8 | 39.2 | 54.7 |
| miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 |
| **Avg** | 51.7 | 67.5 | 34.7 | 46.0 |
Further languages (not supported by Elasticsearch):
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 |
|---|---|---|
| miracl-fa | 44.8 | 53.6 |
| miracl-ja | 49.0 | 61.0 |
| miracl-ko | 50.9 | 64.8 |
| miracl-sw | 61.4 | 74.5 |
| miracl-te | 67.8 | 72.3 |
| miracl-th | 60.2 | 71.9 |
| miracl-yo | 56.4 | 62.2 |
| miracl-zh | 43.8 | 56.5 |
| **Avg** | 54.3 | 64.6 |
|
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] |
biu-nlp/qa_adj
|
biu-nlp
| 2023-02-06T21:23:15Z | 33 | 0 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-02-06T12:05:59Z |
---
license: cc-by-4.0
---
|
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] |
maximedb/sick_nl
|
maximedb
| 2023-04-25T10:19:43Z | 33 | 0 |
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:nl",
"license:mit",
"region:us"
] |
[
"text-classification"
] | 2023-02-16T07:44:25Z |
---
dataset_info:
features:
- name: pair_ID
dtype: int64
- name: sentence_A
dtype: string
- name: sentence_B
dtype: string
- name: entailment_label
dtype: string
- name: relatedness_score
dtype: float64
- name: entailment_AB
dtype: string
- name: entailment_BA
dtype: string
- name: sentence_A_original
dtype: string
- name: sentence_B_original
dtype: string
- name: sentence_A_dataset
dtype: string
- name: sentence_B_dataset
dtype: string
- name: SemEval_set
dtype: string
- name: label
dtype: int64
- name: label_seq2seq
dtype: string
splits:
- name: train
num_bytes: 1359887
num_examples: 4439
- name: validation
num_bytes: 153417
num_examples: 495
- name: test
num_bytes: 1496660
num_examples: 4906
download_size: 822658
dataset_size: 3009964
license: mit
task_categories:
- text-classification
language:
- nl
pretty_name: SICK-NL
size_categories:
- 1K<n<10K
---
## Dataset Description
- **Homepage:** https://github.com/gijswijnholds/sick_nl
- **Repository:** https://github.com/gijswijnholds/sick_nl
- **Paper:** https://aclanthology.org/2021.eacl-main.126/
- **Point of Contact:** [Gijs Wijnholds](mailto:[email protected])
### Dataset Summary
An automatically translated, manually corrected translation of the SICK dataset of [Marelli et al. 2014](https://www.aclweb.org/anthology/L14-1314), intended to boost research in Dutch NLP.
### Languages
The dataset is in Dutch.
## Dataset Structure
### Data Fields
- pair_ID: sentence pair ID
- sentence_A: sentence A
- sentence_B: sentence B
- label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)
- relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)
- entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B)
- entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A)
- sentence_A_original: original sentence from which sentence A is derived
- sentence_B_original: original sentence from which sentence B is derived
- sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL)
- sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL)
### Data Splits
Train Trial Test
4439 495 4906
## Dataset Creation
The dataset was created by first automatically translating all sentences, then by manually correcting any translation errors. This guarantees naturality of the examples while aligning the relatedness scores and entailment labels. Since the data IDs are preserved the dataset is fully aligned on the sentence level.
## Additional Information
### Licensing Information
This dataset falls under an MIT License.
### Citation Information
```
@inproceedings{wijnholds-etal-2021-sicknl,
title = "SICK-NL: A Dataset for Dutch Natural Language Inference",
author = "Wijnholds, Gijs and Moortgat, Michael",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.eacl-main.126/",
}
```
### Contributions
Thanks to [@maximedb](https://huggingface.co/maximedb) for adding this dataset.
|
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] |
Ddream-ai/InsuranceCorpus
|
Ddream-ai
| 2023-03-04T02:07:47Z | 33 | 4 |
[
"license:mit",
"region:us"
] | null | 2023-03-04T02:03:46Z |
---
license: mit
dataset_info:
features:
- name: 咨询
dtype: string
- name: 回复
dtype: string
splits:
- name: train
num_bytes: 3612350
num_examples: 3599
- name: validation
num_bytes: 186138
num_examples: 189
download_size: 2267366
dataset_size: 3798488
---
|
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] |
mstz/speeddating
|
mstz
| 2023-04-07T14:54:21Z | 33 | 0 |
[
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"speeddating",
"tabular_classification",
"binary_classification",
"region:us"
] |
[
"tabular-classification"
] | 2023-03-23T23:41:42Z |
---
language:
- en
tags:
- speeddating
- tabular_classification
- binary_classification
pretty_name: Speed dating
size_categories:
- 1K<n<10K
task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
- tabular-classification
configs:
- dating
---
# Speed dating
The [Speed dating dataset](https://www.openml.org/search?type=data&sort=nr_of_likes&status=active&id=40536) from OpenML.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|---------------------------------------------------------------|
| dating | Binary classification | Will the two date? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/speeddating")["train"]
```
# Features
|**Features** |**Type** |
|---------------------------------------------------|---------|
|`is_dater_male` |`int8` |
|`dater_age` |`int8` |
|`dated_age` |`int8` |
|`age_difference` |`int8` |
|`dater_race` |`string` |
|`dated_race` |`string` |
|`are_same_race` |`int8` |
|`same_race_importance_for_dater` |`float64`|
|`same_religion_importance_for_dater` |`float64`|
|`attractiveness_importance_for_dated` |`float64`|
|`sincerity_importance_for_dated` |`float64`|
|`intelligence_importance_for_dated` |`float64`|
|`humor_importance_for_dated` |`float64`|
|`ambition_importance_for_dated` |`float64`|
|`shared_interests_importance_for_dated` |`float64`|
|`attractiveness_score_of_dater_from_dated` |`float64`|
|`sincerity_score_of_dater_from_dated` |`float64`|
|`intelligence_score_of_dater_from_dated` |`float64`|
|`humor_score_of_dater_from_dated` |`float64`|
|`ambition_score_of_dater_from_dated` |`float64`|
|`shared_interests_score_of_dater_from_dated` |`float64`|
|`attractiveness_importance_for_dater` |`float64`|
|`sincerity_importance_for_dater` |`float64`|
|`intelligence_importance_for_dater` |`float64`|
|`humor_importance_for_dater` |`float64`|
|`ambition_importance_for_dater` |`float64`|
|`shared_interests_importance_for_dater` |`float64`|
|`self_reported_attractiveness_of_dater` |`float64`|
|`self_reported_sincerity_of_dater` |`float64`|
|`self_reported_intelligence_of_dater` |`float64`|
|`self_reported_humor_of_dater` |`float64`|
|`self_reported_ambition_of_dater` |`float64`|
|`reported_attractiveness_of_dated_from_dater` |`float64`|
|`reported_sincerity_of_dated_from_dater` |`float64`|
|`reported_intelligence_of_dated_from_dater` |`float64`|
|`reported_humor_of_dated_from_dater` |`float64`|
|`reported_ambition_of_dated_from_dater` |`float64`|
|`reported_shared_interests_of_dated_from_dater` |`float64`|
|`dater_interest_in_sports` |`float64`|
|`dater_interest_in_tvsports` |`float64`|
|`dater_interest_in_exercise` |`float64`|
|`dater_interest_in_dining` |`float64`|
|`dater_interest_in_museums` |`float64`|
|`dater_interest_in_art` |`float64`|
|`dater_interest_in_hiking` |`float64`|
|`dater_interest_in_gaming` |`float64`|
|`dater_interest_in_clubbing` |`float64`|
|`dater_interest_in_reading` |`float64`|
|`dater_interest_in_tv` |`float64`|
|`dater_interest_in_theater` |`float64`|
|`dater_interest_in_movies` |`float64`|
|`dater_interest_in_concerts` |`float64`|
|`dater_interest_in_music` |`float64`|
|`dater_interest_in_shopping` |`float64`|
|`dater_interest_in_yoga` |`float64`|
|`interests_correlation` |`float64`|
|`expected_satisfaction_of_dater` |`float64`|
|`expected_number_of_likes_of_dater_from_20_people` |`int8` |
|`expected_number_of_dates_for_dater` |`int8` |
|`dater_liked_dated` |`float64`|
|`probability_dated_wants_to_date` |`float64`|
|`already_met_before` |`int8` |
|`dater_wants_to_date` |`int8` |
|`dated_wants_to_date` |`int8` |
|
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mstz/abalone
|
mstz
| 2023-04-15T11:04:08Z | 33 | 0 |
[
"task_categories:tabular-regression",
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"abalone",
"tabular_regression",
"regression",
"binary_classification",
"region:us"
] |
[
"tabular-regression",
"tabular-classification"
] | 2023-04-05T10:59:09Z |
---
language:
- en
tags:
- abalone
- tabular_regression
- regression
- binary_classification
pretty_name: Abalone
size_categories:
- 1K<n<10K
task_categories:
- tabular-regression
- tabular-classification
configs:
- abalone
- binary
license: cc
---
# Abalone
The [Abalone dataset](https://archive-beta.ics.uci.edu/dataset/1/abalone) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Predict the age of the given abalone.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------|
| abalone | Regression | Predict the age of the abalone. |
| binary | Binary classification | Does the abalone have more than 9 rings?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/abalone")["train"]
```
# Features
Target feature in bold.
|**Feature** |**Type** |
|-----------------------|---------------|
| sex | `[string]` |
| length | `[float64]` |
| diameter | `[float64]` |
| height | `[float64]` |
| whole_weight | `[float64]` |
| shucked_weight | `[float64]` |
| viscera_weight | `[float64]` |
| shell_weight | `[float64]` |
| **number_of_rings** | `[int8]` |
|
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alexwww94/SimCLUE
|
alexwww94
| 2023-04-14T06:40:03Z | 33 | 0 |
[
"license:other",
"region:us"
] | null | 2023-04-13T09:56:06Z |
---
license: other
---
|
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camel-ai/biology
|
camel-ai
| 2023-05-23T21:11:56Z | 33 | 19 |
[
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"instruction-finetuning",
"arxiv:2303.17760",
"region:us"
] |
[
"text-generation"
] | 2023-04-16T01:30:03Z |
---
license: cc-by-nc-4.0
language:
- en
tags:
- instruction-finetuning
pretty_name: CAMEL Biology
task_categories:
- text-generation
arxiv: 2303.17760
extra_gated_prompt: "By using this data, you acknowledge and agree to utilize it solely for research purposes, recognizing that the dataset may contain inaccuracies due to its artificial generation through ChatGPT."
extra_gated_fields:
Name: text
Email: text
I will adhere to the terms and conditions of this dataset: checkbox
---
# **CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society**
- **Github:** https://github.com/lightaime/camel
- **Website:** https://www.camel-ai.org/
- **Arxiv Paper:** https://arxiv.org/abs/2303.17760
## Dataset Summary
Biology dataset is composed of 20K problem-solution pairs obtained using gpt-4. The dataset problem-solutions pairs generating from 25 biology topics, 25 subtopics for each topic and 32 problems for each "topic,subtopic" pairs.
We provide the data in `biology.zip`.
## Data Fields
**The data fields for files in `biology.zip` are as follows:**
* `role_1`: assistant role
* `topic`: biology topic
* `sub_topic`: biology subtopic belonging to topic
* `message_1`: refers to the problem the assistant is asked to solve.
* `message_2`: refers to the solution provided by the assistant.
**Download in python**
```
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="camel-ai/biology", repo_type="dataset", filename="biology.zip",
local_dir="datasets/", local_dir_use_symlinks=False)
```
### Citation
```
@misc{li2023camel,
title={CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society},
author={Guohao Li and Hasan Abed Al Kader Hammoud and Hani Itani and Dmitrii Khizbullin and Bernard Ghanem},
year={2023},
eprint={2303.17760},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
## Disclaimer:
This data was synthetically generated by GPT4 and might contain incorrect information. The dataset is there only for research purposes.
---
license: cc-by-nc-4.0
---
|
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EdwardLin2023/MELD-Audio
|
EdwardLin2023
| 2023-04-24T04:04:52Z | 33 | 0 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-04-21T02:47:11Z |
---
license: cc-by-4.0
---
|
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mhhmm/leetcode-solutions-python
|
mhhmm
| 2023-04-27T06:40:41Z | 33 | 16 |
[
"license:lgpl",
"region:us"
] | null | 2023-04-25T10:48:36Z |
---
license: lgpl
---
All credits belong to https://www.kaggle.com/datasets/erichartford/leetcode-solutions
I collected only python solutions:
```
id: <number>
code_with_data:
<
# Slug
# Title
# Difficulty
# Content
Code Answer in Python
# Explanation
>
code_only: < Code Answer in Python >
code_with_problem: <
# Content
Code
>
explanation_only: < Explanation >
```
I'm using this for code generation and code summarization so the data will have the format like above
|
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] |
fptudsc/face-celeb-vietnamese
|
fptudsc
| 2023-05-10T15:13:18Z | 33 | 1 |
[
"task_categories:image-classification",
"task_categories:zero-shot-classification",
"size_categories:10M<n<100M",
"language:vi",
"license:apache-2.0",
"region:us"
] |
[
"image-classification",
"zero-shot-classification"
] | 2023-05-03T17:56:54Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: string
splits:
- name: train
num_bytes: 82233752.864
num_examples: 8557
download_size: 80630170
dataset_size: 82233752.864
license: apache-2.0
task_categories:
- image-classification
- zero-shot-classification
language:
- vi
size_categories:
- 10M<n<100M
---
# Dataset Card for "face-celeb-vietnamese"
## Dataset Summary
This dataset contains information on over 8,000 samples of well-known Vietnamese individuals, categorized into three professions: singers, actors, and beauty queens. The dataset includes data on more than 100 celebrities in each of the three job categories.
## Languages
- Vietnamese: The label is used to indicate the name of celebrities in Vietnamese.
## Dataset Structure
- The image and Vietnamese sequences are
## Source Data - Initial Data Collection and Normalization
[Website người nổi tiếng](https://nguoinoitieng.tv)
### Licensing Information
Apache License 2.0
### Contributions
Thanks to [@github-duongttr](https://github.com/duongttr) and [@github-pphuc25](https://github.com/pphuc25) for adding this dataset.
|
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cmu-lti/cobracorpus
|
cmu-lti
| 2023-06-26T17:20:21Z | 33 | 1 |
[
"task_categories:text-generation",
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:openrail",
"arxiv:2306.01985",
"arxiv:2203.09509",
"region:us"
] |
[
"text-generation",
"text-classification"
] | 2023-06-08T02:12:47Z |
---
license: openrail
task_categories:
- text-generation
- text-classification
language:
- en
pretty_name: COBRA🐍
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage: https://cobra.xuhuiz.com/**
- **Paper: https://arxiv.org/abs/2306.01985**
### Dataset Summary
This dataset contains COBRACOPURS and COBRACORPUS-counterfactual in this [paper](https://arxiv.org/abs/2306.01985)
### Data Splits
* `advContexts_explanations.csv` is `COBRACorpus-CF`
* `toxigen_explanations.csv` is the full `COBRACorpus`
* `toxigen_explanations_train.csv` is the training split of `COBRACorpus`
* `toxigen_explanations_val.csv` is the validation split of `COBRACorpus`
### Data Entries
For `COBRACorpus`, the relevant entries in the `csv` files are
*`situationalContext (string)`, `speakerIdentity (string)`, `listenerIdentity (string)`, `statement (string)`,
`intent (string)`, `targetGroup (string)`, `relevantPowerDynamics (string)`, `implication (string)`,
`targetGroupEmotionalReaction (string)`, `targetGroupCognitiveReaction (string)`, `offensiveness (string)`*
Please refer to the [paper](https://arxiv.org/abs/2306.01985) for the specific explanations of these entries.
The *`examples`* entry is the few-shot prompt that we used to generate explanations.
All other entries are from the [Toxicgen](https://arxiv.org/abs/2203.09509) dataset, which is not directly relevant to this
work but we leave them there as the metadata in case it's useful for the future works.
### Citation Information
If you find this dataset useful, please cite:
```
@inproceedings{zhou2023cobra,
title = {COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements},
author = {Zhou, Xuhui and Zhu, Hao and Yerukola, Akhila and Davidson, Thomas and D. Hwang, Jena and Swayamdipta, Swabha and Sap, Maarten},
year = {2023},
booktitle = {Findings of ACL}
}
```
|
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agkphysics/AudioSet
|
agkphysics
| 2023-07-13T12:25:32Z | 33 | 2 |
[
"task_categories:audio-classification",
"license:cc-by-4.0",
"audio",
"region:us"
] |
[
"audio-classification"
] | 2023-06-14T08:17:23Z |
---
license: cc-by-4.0
tags:
- audio
task_categories:
- audio-classification
---
# AudioSet data
This repository contains the balanced training set and evaluation set
of the [AudioSet data](
https://research.google.com/audioset/dataset/index.html). The YouTube
videos were downloaded in March 2023, and so not all of the original
audios are available.
Extracting the `*.tar` files will place audio clips into the `audio/`
directory. The distribuion of audio clips is as follows:
- `audio/bal_train`: 18685 audio clips out of 22160 originally.
- `audio/eval`: 17142 audio clips out of 20371 originally.
Most audio is sampled at 48 kHz 24 bit, but about 10% is sampled at
44.1 kHz 24 bit. Audio files are stored in the FLAC format.
## Citation
```bibtex
@inproceedings{45857,
title = {Audio Set: An ontology and human-labeled dataset for audio events},
author = {Jort F. Gemmeke and Daniel P. W. Ellis and Dylan Freedman and Aren Jansen and Wade Lawrence and R. Channing Moore and Manoj Plakal and Marvin Ritter},
year = {2017},
booktitle = {Proc. IEEE ICASSP 2017},
address = {New Orleans, LA}
}
```
|
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mikex86/stackoverflow-posts
|
mikex86
| 2023-08-01T01:31:12Z | 33 | 15 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10M<n<100M",
"language:code",
"language:en",
"license:other",
"code",
"region:us"
] |
[
"question-answering",
"text-generation",
"text2text-generation"
] | 2023-06-14T18:48:00Z |
---
license: other
language:
- code
- en
task_categories:
- question-answering
- text-generation
- text2text-generation
tags:
- code
viewer: true
pretty_name: StackOverflow Posts Markdown
size_categories:
- 10M<n<100M
---
# StackOverflow Posts Markdown
## Dataset Summary
This dataset contains all posts submitted to StackOverflow before the 14th of June 2023 formatted as **Markdown text**.<br>
The dataset contains ~60 Million posts, totaling ~35GB in size and ~65 billion characters of text.<br>
The data is sourced from [Internet Archive StackExchange Data Dump](https://archive.org/download/stackexchange).
## Dataset Structure
Each record corresponds to one post of a particular type.
Original ordering from the data dump is not exactly preserved due to parallelism in the script used to process the data dump.
The markdown content of each post is contained in the `Body` field. The license for a particular post is contained in the `ContentLicense` field.
### Data Fields
```typescript
{
Id: long,
PostTypeId: long, // 1=Question, 2=Answer, 3=Orphaned tag wiki, 4=Tag wiki excerpt, 5=Tag wiki, 6=Moderator nomination, 7=Wiki Placeholder, 8=Privilige Wiki
AcceptedAnswerId: long | null, // only present if PostTypeId=1
ParentId: long | null, // only present if PostTypeId=2
Score: long,
ViewCount: long | null,
Body: string | null,
Title: string | null,
ContentLicense: string | null,
FavoriteCount: long | null,
CreationDate: string | null,
LastActivityDate: string | null,
LastEditDate: string | null,
LastEditorUserId: long | null,
OwnerUserId: long | null,
Tags: array<string> | null
}
```
Also consider the [StackExchange Datadump Schema Documentation](https://meta.stackexchange.com/questions/2677/database-schema-documentation-for-the-public-data-dump-and-sede), as all fields
have analogs in the original dump format.
## How to use?
```python
from datasets import load_dataset
# predownload full dataset
ds = load_dataset('mikex86/stackoverflow-posts', split='train')
# dataset streaming (will only download the data as needed)
ds = load_dataset('mikex86/stackoverflow-posts', split='train', streaming=True)
for sample in iter(ds): print(sample["Body"])
```
## How is the text stored?
The original Data Dump formats the "Body" field as HTML, using tags such as `<code>`, `<h1>`, `<ul>`, etc.
This HTML format has been converted to Markdown.
### Markdown format
For reference, [this post on StackOverflow](https://stackoverflow.com/questions/53253940/make-react-useeffect-hook-not-run-on-initial-render) is formatted as follows:
#### Title: Make React useEffect hook not run on initial render
```markdown
According to the docs:
> `componentDidUpdate()` is invoked immediately after updating occurs. This method is not called for the initial render.
We can use the new `useEffect()` hook to simulate `componentDidUpdate()`, but it seems like `useEffect()` is being ran after every render, even the first time. How do I get it to not run on initial render?
As you can see in the example below, `componentDidUpdateFunction` is printed during the initial render but `componentDidUpdateClass` was not printed during the initial render.
```
function ComponentDidUpdateFunction() {
const [count, setCount] = React.useState(0);
React.useEffect(() => {
console.log(""componentDidUpdateFunction"");
});
return (
<div>
<p>componentDidUpdateFunction: {count} times</p>
<button
onClick={() => {
setCount(count + 1);
}}
>
Click Me
</button>
</div>
);
}
```
rest of the post omitted for brevity
```
## Details on the HTML to Markdown conversion
Using Jsoup, the original Body field was converted into a Jsoup Document. The child **nodes** (has special meaning in context of Jsoup) of this document were recursively traversed in a depth-first order.
Jsoup defines `.text()` as follows:
> ... the normalized, combined text of this element and all its children. Whitespace is normalized and trimmed. For example, given HTML <code><p>Hello <b>there</b> now! </p><code>, p.text() returns "Hello there now!"
Jsoup defines a `Node` as follows:
> The base, abstract Node model. Elements, Documents, Comments etc are all Node instances.
Additionally the existence of the `TextNode` should be noted, which represents floating text inside an HTML document that is not itself an HTML element.
Thus this text tag `<p>Hello<code>World</code></p>` would have two Jsoup child nodes `TextNode(value="Hello")` and `Element(tag="code", value="World")`.
The value `field` of a `TextNode` contains the free standing text without any further treatment (no whitespace stripping, etc.)
### Traversing Rules
- When ecountering a html tag for which a rule exists, children are not further traversed, **unless explicitly stated otherwise**.
- When encountering an `<a>` tag, `[${element.text()}](${element.attr("href")})` is emitted.
- When encountering an `<h1>` tag, `\n# ${element.text()}\n\n` is emitted.
- When encountering an `<h2>` tag, `\n## ${element.text()}\n\n` is emitted.
- When encountering an `<h3>` tag, `\n### ${element.text()}\n\n` is emitted.
- When encountering an `<h4>` tag, `\n#### ${element.text()}\n\n` is emitted.
- When encountering an `<h5>` tag, `\n##### ${element.text()}\n\n` is emitted.
- When encountering an `<h6>` tag, `\n###### ${element.text()}\n\n` is emitted.
- When encountering a `<code>` tag, `` `${element.text()}` ``is emitted
- When encountering a `<pre>` tag and said element **has** a `<code>` child tag, `` ```\n${element.text()}`\n```\n`` is emitted.
- When encountering a `<pre>` tag and said element **does not** have a `<code>` child tag, **children are traversed further**.
- When encountering an `<li>` tag, `- ` is emitted and **children are traversed further**.
- When encountering a `<blockquote>` tag, `> ` is emitted and **children are traversed further**.
- When encountering an `<hr>` tag, `\n---\n\n` is emitted
- When encountering an `<img>` tag, `![${element.attr("alt")}](${element.attr("src")})` is emitted.
- When encountering a `<table>` tag
- `\n| ` is emitted
- For each element of `element.select("th")`
- `${element.text()} | ` is emitted
- After the loop `\n| ` is emitted
- For each element of `element.select("th")`
- For each character of the `th.text()`
- `-` is emitted
- After the loop over each character of th ` | ` is emitted
- `\n` is emitted
- For each element of `element.select("tr")` with more than one children of tag type `td`
- `| ` is emitted
- For each element of `element.select("td")`
- `${td.text()} | ` is emitted
- After the loop over `<td>` elements, `\n` is emitted
- After the loop over `<tr>` elements, `\n` is emitted
- When encountering a jsoup `TextNode`, `${node.attr(node.nodeName())}` (which is equivalent to accessing the private field `node.value`) is emitted.
|
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] |
KaiLv/UDR_Java
|
KaiLv
| 2023-06-21T12:40:15Z | 33 | 0 |
[
"region:us"
] | null | 2023-06-21T12:39:27Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: question
dtype: string
- name: target
dtype: string
- name: len_question
dtype: int64
- name: len_target
dtype: int64
splits:
- name: train
num_bytes: 105539111
num_examples: 164514
- name: validation
num_bytes: 3088869
num_examples: 5172
- name: test
num_bytes: 6865702
num_examples: 10928
- name: debug
num_bytes: 64147056
num_examples: 100000
download_size: 77259976
dataset_size: 179640738
---
# Dataset Card for "UDR_Java"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
DataHammer/scimrc
|
DataHammer
| 2023-06-28T12:00:41Z | 33 | 6 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"question-answering",
"text-generation"
] | 2023-06-28T06:15:50Z |
---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- en
size_categories:
- 10K<n<100K
---
# Scientific Emotional Dialogue
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a dataset for question answering on scientific research papers. It consists of 21.297 questions-answer-evidence pairs.
### Supported Tasks and Leaderboards
- question-answering: The dataset can be used to train a model for Scientific Question Answering. Success on this task is typically measured by achieving a high F1 score.
### Languages
English
## Dataset Structure
### Data Instances
A typical instance in the dataset:
```
{
"question": "What aim do the authors have by improving Wiki(GOLD) results?",
"answer": "The aim is not to tune their model specifically on this class hierarchy. They instead aim to present a framework which can be modified easily to any domain hierarchy and has acceptable out-of-the-box performances to any fine-grained dataset.",
"evidence": "The results for each class type are shown in Table TABREF19 , with some specific examples shown in Figure FIGREF18 . For the Wiki(gold) we quote the micro-averaged F-1 scores for the entire top level entity category. The total F-1 score on the OntoNotes dataset is 88%, and the total F-1 cross-validation score on the 112 class Wiki(gold) dataset is 53%. It is worth noting that one could improve Wiki(gold) results by training directly using this dataset. However, the aim is not to tune our model specifically on this class hierarchy. We instead aim to present a framework which can be modified easily to any domain hierarchy and has acceptable out-of-the-box performances to any fine-grained dataset. The results in Table TABREF19 (OntoNotes) only show the main 7 categories in OntoNotes which map to Wiki(gold) for clarity. The other categories (date, time, norp, language, ordinal, cardinal, quantity, percent, money, law) have F-1 scores between 80-90%, with the exception of time (65%)\nIt is worth noting that one could improve Wiki(GOLD) results by training directly using this dataset. However, the aim is not to tune our model specifically on this class hierarchy. We instead aim to present a framework which can be modified easily to any domain hierarchy and has acceptable out-of-the-box performances to any fine-grained dataset.",
"yes_no": false
}
```
|
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] |
santoshtyss/billsum
|
santoshtyss
| 2023-08-06T11:45:22Z | 33 | 1 |
[
"region:us"
] | null | 2023-08-06T11:45:00Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 186689203
num_examples: 16107
- name: test
num_bytes: 37866257
num_examples: 3269
- name: ca_test
num_bytes: 14945291
num_examples: 1237
- name: validation
num_bytes: 32906887
num_examples: 2842
download_size: 113748846
dataset_size: 272407638
---
# Dataset Card for "billsum"
[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/polemo2_out
|
PL-MTEB
| 2023-08-11T12:42:58Z | 33 | 0 |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-08-11T12:42:01Z |
---
license: cc-by-nc-sa-4.0
---
|
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Fsoft-AIC/the-vault-class
|
Fsoft-AIC
| 2023-10-11T16:42:43Z | 33 | 1 |
[
"task_categories:text-generation",
"multilinguality:multiprogramming languages",
"language:code",
"language:en",
"license:mit",
"arxiv:2305.06156",
"region:us"
] |
[
"text-generation"
] | 2023-08-22T07:11:11Z |
---
language:
- code
- en
multilinguality:
- multiprogramming languages
task_categories:
- text-generation
license: mit
dataset_info:
features:
- name: identifier
dtype: string
- name: repo
dtype: string
- name: path
dtype: string
- name: language
dtype: string
- name: code
dtype: string
- name: code_tokens
dtype: string
- name: original_docstring
dtype: string
- name: comment
dtype: string
- name: docstring_tokens
dtype: string
- name: docstring
dtype: string
- name: original_string
dtype: string
pretty_name: The Vault Function
viewer: true
---
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Statistics](#dataset-statistics)
- [Usage](#usage)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [FSoft-AI4Code/TheVault](https://github.com/FSoft-AI4Code/TheVault)
- **Paper:** [The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation](https://arxiv.org/abs/2305.06156)
- **Contact:** [email protected]
- **Website:** https://www.fpt-aicenter.com/ai-residency/
<p align="center">
<img src="https://raw.githubusercontent.com/FSoft-AI4Code/TheVault/main/assets/the-vault-4-logo-png.png" width="300px" alt="logo">
</p>
<div align="center">
# The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
</div>
## Dataset Summary
The Vault dataset is a comprehensive, large-scale, multilingual parallel dataset that features high-quality code-text pairs derived from The Stack, the largest permissively-licensed source code dataset.
We provide The Vault which contains code snippets from 10 popular programming languages such as Java, JavaScript, Python, Ruby, Rust, Golang, C#, C++, C, and PHP. This dataset provides multiple code-snippet levels, metadata, and 11 docstring styles for enhanced usability and versatility.
## Supported Tasks
The Vault can be used for pretraining LLMs or downstream code-text interaction tasks. A number of tasks related to code understanding and geneartion can be constructed using The Vault such as *code summarization*, *text-to-code generation* and *code search*.
## Languages
The natural language text (docstring) is in English.
10 programming languages are supported in The Vault: `Python`, `Java`, `JavaScript`, `PHP`, `C`, `C#`, `C++`, `Go`, `Ruby`, `Rust`
*Note: C and Go are not contained in this repo due to the nonexistence of traditional classes in these languages.*
## Dataset Structure
### Data Instances
```
{
"hexsha": "78b961a6673ec1e12f8d95c33ef081f75561a87c",
"repo": "AIS-Bonn/sl-cutscenes",
"path": "sl_cutscenes/object_models.py",
"license": [
"MIT"
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"language": "Python",
"identifier": "MeshLoader",
"original_docstring": "\n Class to load the meshes for the objects in a scene.\n ",
"docstring": "Class to load the meshes for the objects in a scene.",
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"code": "class MeshLoader:\n \"\"\"\n Class to load the meshes for the objects in a scene.\n \"\"\"\n\n def __init__(self):\n \"\"\"Module initializer\"\"\"\n self.base_dir = CONSTANTS.MESH_BASE_DIR\n self.text_dir = CONSTANTS.TEXT_BASE_DIR\n self.reset()\n\n def reset(self):\n self.loaded_meshes = []\n\n def get_meshes(self):\n \"\"\" \"\"\"\n extract_singular = lambda x: x[0] if len(x) == 1 else x\n return [extract_singular(item) for item in self.loaded_meshes]\n\n def load_meshes(self, obj_info: List[object_info.ObjectInfo], **kwargs):\n \"\"\"\n Loads the meshes whose information is given in parameter 'obj_info.\n Each call of this method APPENDS a list to the loaded_meshes attribute.\n :param obj_info: The object information of the meshes to be loaded.\n :param kwargs: additional mesh modifiers such as scale, specified with a leading 'mod_'\n \"\"\"\n paths = []\n for obj in obj_info:\n path = self.text_dir if obj.name.endswith(\"_floor\") or obj.name.endswith(\"_wall\") else self.base_dir\n paths.append((path / obj.mesh_fp).resolve())\n scales = [obj.scale for obj in obj_info]\n class_ids = [obj.class_id for obj in obj_info]\n mod_scales = kwargs.get(\"mod_scale\", [1.0] * len(scales))\n scales = [s * ms for (s, ms) in zip(scales, mod_scales)]\n flags = [mesh_flags(obj) for obj in obj_info]\n meshes = sl.Mesh.load_threaded(filenames=paths, flags=flags)\n\n # Setup class IDs\n for _, (mesh, scale, class_id) in enumerate(zip(meshes, scales, class_ids)):\n pt = torch.eye(4)\n pt[:3, :3] *= scale\n mesh.pretransform = pt\n mesh.class_index = class_id\n\n info_mesh_tuples = list(zip(obj_info, meshes))\n self.loaded_meshes.append(info_mesh_tuples)",
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"short_docstring": "Class to load the meshes for the objects in a scene.",
"short_docstring_tokens": [
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"comment": [
"\"\"\"\n Class to load the meshes for the objects in a scene.\n \"\"\"",
"\"\"\"Module initializer\"\"\"",
"\"\"\" \"\"\"",
"\"\"\"\n Loads the meshes whose information is given in parameter 'obj_info.\n Each call of this method APPENDS a list to the loaded_meshes attribute.\n :param obj_info: The object information of the meshes to be loaded.\n :param kwargs: additional mesh modifiers such as scale, specified with a leading 'mod_'\n \"\"\"",
"# Setup class IDs"
],
"parameters": [],
"docstring_params": {
"returns": [],
"raises": [],
"params": [],
"outlier_params": [],
"others": []
}
}
```
### Data Fields
Data fields for function level:
- **hexsha** (string): the unique git hash of file
- **repo** (string): the owner/repo
- **path** (string): the full path to the original file
- **license** (list): licenses in the repo
- **language** (string): the programming language
- **identifier** (string): the function or method name
- **original_string** (string): original version of function/class node
- **original_docstring** (string): the raw string before tokenization or parsing
- **code** (string): the part of the original that is code
- **code_tokens** (list): tokenized version of `code`
- **short_docstring** (string): short, brief summarization (first line of the docstring)
- **short_docstring_tokens** (list): tokenized version of `short_docstring
- **docstring** (string): the top-level comment or docstring (docstring version without param’s doc, return, exception fields, etc)
- **docstring_tokens** (list): tokenized version of docstring
- **comment** (list): list of comments (line) inside the function/class
- **parameters** (list): List of parameters and its type (type can be None)
- **docstring_params** (dict): Dictionary of the parsed information from docstring
See [here](https://github.com/FSoft-AI4Code/TheVault/blob/main/data/README.md) for more details and examples.
### Data Splits
In this repo, the class level data is not split, and contained in only train set.
## Dataset Statistics
|Language | Number of samples |
|:-----------|------------------------:|
|Python | 422,187 |
|Java | 4,872,485 |
|JavaScript | 291,479 |
|PHP | 1,173,916 |
|C# | 1,437,800 |
|C++ | 174,370 |
|Ruby | 353,859 |
|Rust | 93,311 |
|C | - |
|Go | - |
|TOTAL | **9,121,300** |
## Usage
You can load The Vault dataset using datasets library: ```pip install datasets```
```python
from datasets import load_dataset
# Load full class level dataset
dataset = load_dataset("Fsoft-AIC/the-vault-class")
# specific language (e.g. Python)
dataset = load_dataset("Fsoft-AIC/the-vault-class", languages=['Python'])
# dataset streaming
data = load_dataset("Fsoft-AIC/the-vault-class", streaming= True)
for sample in iter(data['train']):
print(sample)
```
A back up dataset can be downloaded in azure storage. See [Download The Vault from Azure blob storage](https://github.com/FSoft-AI4Code/TheVault#download-via-link).
## Additional information
### Licensing Information
MIT License
### Citation Information
```
@article{manh2023vault,
title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation},
author={Manh, Dung Nguyen and Hai, Nam Le and Dau, Anh TV and Nguyen, Anh Minh and Nghiem, Khanh and Guo, Jin and Bui, Nghi DQ},
journal={arXiv preprint arXiv:2305.06156},
year={2023}
}
```
### Contributions
This dataset is developed by [FSOFT AI4Code team](https://github.com/FSoft-AI4Code).
|
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mickume/alt_potterverse
|
mickume
| 2023-11-03T06:34:35Z | 33 | 0 |
[
"region:us"
] | null | 2023-09-01T08:15:27Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 633374028
num_examples: 3509338
download_size: 392101893
dataset_size: 633374028
---
# Dataset Card for "alt_potterverse"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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daje/ko_wiki
|
daje
| 2023-09-21T05:38:01Z | 33 | 2 |
[
"region:us"
] | null | 2023-09-21T01:42:49Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 986780351
num_examples: 311237
download_size: 550489937
dataset_size: 986780351
---
# 한국어 위키 데이터셋(Ko_wiki)
* 개요
- 이 데이터셋은 한국어 위키 데이터를 기반으로 만들어졌습니다. 원본 위키 데이터를 처리하기 위해 wikiextractor.py를 사용하여 텍스트 형식으로 변환하였습니다.
- 이 데이터셋을 제작한 주요 취지는 한국어 자연어 처리 연구와 애플리케이션 개발에 사용할 수 있는 광범위한 텍스트 데이터를 제공하기 위함입니다.
* 데이터 구조
- text: 위키 문서의 본문을 포함하는 문자열입니다.
* 사용 방법
1. huggingface dataset과 map을 활용하는 방법
```python3
from datasets import load_dataset
ko_dataset = load_dataset("text",
"daje/ko_wiki",
split="train",
streaming=True)
ko_wiki_tokenized = ko_dataset.map(lambda x : tokenizer(x["text"],
max_length=256,
padding="max_length",
truncation=True),
remove_columns=["text"])
```
2. 파이썬 스크립트를 사용하는 방법
```
import os
from tqdm import tqdm
from transformers import AutoTokenizer
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--input_path', type=str)
parser.add_argument('--output_path', type=str)
parser.add_argument('--model_name_or_path', type=str)
parser.add_argument('--max_seq_length', type=int, default=256)
parser.add_argument('--add_sep', default=True, action='store_true')
args = parser.parse_args()
def get_num_lines(fname):
res = os.popen(f'wc -l {fname}').read()
lines = res.strip().split()[0]
return int(lines)
def main(args):
seq_length = args.max_seq_length - 3 # room for [BOS], [EOS], [UNK]
input_fs = open(args.input_path, 'r')
output_fs = open(args.output_path, 'a')
total_line = get_num_lines(args.input_path)
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
buffer = []
for doc in tqdm(input_fs, total=total_line):
tokens = tokenizer.tokenize(doc)
buffer += tokens
if args.add_sep:
buffer += [tokenizer.eos_token] # 자신이 사용하는 tokenizer에 맞추어서 eos, sep을 넣으시면 됩니다.
while len(buffer) > seq_length:
text = ' '.join(buffer[:seq_length])
output_fs.write(text)
output_fs.write('\n')
buffer = buffer[seq_length:]
input_fs.close()
output_fs.close()
if __name__ == '__main__':
main(args)
```
|
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HumanCompatibleAI/ppo-Pendulum-v1
|
HumanCompatibleAI
| 2023-10-04T16:52:12Z | 33 | 0 |
[
"region:us"
] | null | 2023-10-04T16:52:08Z |
---
dataset_info:
features:
- name: obs
sequence:
sequence: float32
- name: acts
sequence:
sequence: float32
- name: infos
sequence: string
- name: terminal
dtype: bool
- name: rews
sequence: float32
splits:
- name: train
num_bytes: 2575710
num_examples: 200
download_size: 940375
dataset_size: 2575710
---
# Dataset Card for "ppo-Pendulum-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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cointegrated/nli-rus-translated-v2021
|
cointegrated
| 2023-10-06T14:51:23Z | 33 | 0 |
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"size_categories:1M<n<10M",
"language:ru",
"region:us"
] |
[
"text-classification"
] | 2023-10-06T11:47:22Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: string
- name: source
dtype: string
- name: split
dtype: string
- name: premise_ru
dtype: string
- name: hypothesis_ru
dtype: string
- name: reverse_entailment_score
dtype: float64
- name: len_ratio
dtype: float64
- name: idx
dtype: int64
splits:
- name: train
num_bytes: 1156491691
num_examples: 1756548
- name: dev
num_bytes: 78632908
num_examples: 106557
- name: test
num_bytes: 30464486
num_examples: 34615
download_size: 504709758
dataset_size: 1265589085
task_categories:
- text-classification
task_ids:
- natural-language-inference
language:
- ru
size_categories:
- 1M<n<10M
---
# Dataset Card for "nli-rus-translated-v2021"
This dataset was introduced in the Habr post
["Нейросети для Natural Language Inference (NLI): логические умозаключения на русском языке"](https://habr.com/ru/articles/582620/).
It is composed from various English NLI datasets automatically translated into Russian.
Here are the sizes of the source datasets included into different splits:
| source | train | dev | test |
|:------------|--------:|------:|-------:|
| add_one_rte | 4991 | 387 | 0 |
| anli_r1 | 16946 | 1000 | 1000 |
| anli_r2 | 45460 | 1000 | 1000 |
| anli_r3 | 100459 | 1200 | 1200 |
| copa | 800 | 200 | 0 |
| fever | 162330 | 20478 | 20343 |
| help | 29347 | 3355 | 3189 |
| iie | 281643 | 31232 | 0 |
| imppres | 10179 | 7661 | 7660 |
| joci | 8412 | 939 | 0 |
| mnli | 392662 | 19647 | 0 |
| monli | 2186 | 269 | 223 |
| mpe | 9000 | 1000 | 0 |
| qnli | 108436 | 5732 | 0 |
| scitail | 24900 | 2126 | 0 |
| sick | 9500 | 500 | 0 |
| snli | 549297 | 9831 | 0 |
Most of the original data were taken from the repository [felipessalvatore/NLI_datasets](https://github.com/felipessalvatore/NLI_datasets).
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
amphora/lmsys-finance
|
amphora
| 2023-10-10T12:25:26Z | 33 | 1 |
[
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"finance",
"region:us"
] |
[
"conversational"
] | 2023-10-10T12:16:02Z |
---
dataset_info:
features:
- name: conversation_id
dtype: string
- name: model
dtype: string
- name: conversation
dtype: string
- name: turn
dtype: int64
- name: language
dtype: string
- name: openai_moderation
dtype: string
- name: redacted
dtype: bool
- name: count
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 10328855
num_examples: 735
download_size: 3912614
dataset_size: 10328855
task_categories:
- conversational
language:
- en
tags:
- finance
size_categories:
- n<1K
---
# Dataset Card for "lmsys-finance"
This dataset is a curated version of the [lmsys-chat-1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) dataset,
focusing solely on finance-related conversations. The refinement process encompassed:
1. Removing non-English conversations.
2. Selecting conversations from models: "vicuna-33b", "wizardlm-13b", "gpt-4", "gpt-3.5-turbo", "claude-2", "palm-2", and "claude-instant-1".
3. Excluding conversations with responses under 30 characters.
4. Using 100 financial keywords, choosing conversations with at least 10 keywords.
|
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stepkurniawan/qa-rag-llama
|
stepkurniawan
| 2023-10-17T14:26:55Z | 33 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-10-16T16:37:09Z |
---
license: mit
dataset_info:
- config_name: Llama-2-13b-chat-hf
features:
- name: question
dtype: string
- name: ground_truths
sequence: string
- name: answer
dtype: string
- name: contexts
sequence: string
splits:
- name: train
num_bytes: 188631
num_examples: 50
download_size: 99989
dataset_size: 188631
- config_name: Llama-2-7b-chat-hf
features:
- name: question
dtype: string
- name: ground_truths
sequence: string
- name: answer
dtype: string
- name: contexts
sequence: string
splits:
- name: train
num_bytes: 168301
num_examples: 50
download_size: 89924
dataset_size: 168301
- config_name: default
features:
- name: question
dtype: string
- name: ground_truths
sequence: string
- name: answer
dtype: string
- name: contexts
sequence: string
splits:
- name: train
num_bytes: 10068
num_examples: 3
download_size: 0
dataset_size: 10068
configs:
- config_name: Llama-2-13b-chat-hf
data_files:
- split: train
path: Llama-2-13b-chat-hf/train-*
- config_name: Llama-2-7b-chat-hf
data_files:
- split: train
path: Llama-2-7b-chat-hf/train-*
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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AdapterOcean/med_alpaca_standardized_cluster_84_alpaca
|
AdapterOcean
| 2023-10-24T02:17:37Z | 33 | 0 |
[
"region:us"
] | null | 2023-10-24T02:17:35Z |
---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 11744974
num_examples: 6087
download_size: 6180689
dataset_size: 11744974
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_84_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
AlFrauch/im2latex
|
AlFrauch
| 2023-10-25T16:21:16Z | 33 | 2 |
[
"task_categories:image-to-text",
"size_categories:1M<n<10M",
"code",
"region:us"
] |
[
"image-to-text"
] | 2023-10-25T14:53:53Z |
---
task_categories:
- image-to-text
tags:
- code
size_categories:
- 1M<n<10M
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset is a set of pairs: an image and its corresponding latex code for expression. This set of pairs was generated by analyzing more than 100,000 articles on natural sciences and mathematics and further generating a corresponding set of latex expressions. The set has been cleared of duplicates. There are about 1 500 000 images in the set.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Latex
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
```python
Dataset({
features: ['image', 'text'],
num_rows: 1586584
})
```
### 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
@misc{alexfrauch_VSU_2023,
title = {Recognition of mathematical formulas in the Latex: Image-Text Pair Dataset},
author = {Aleksandr Frauch (Proshunin)},
year = {2023},
howpublished = {\url{https://huggingface.co/datasets/AlFrauch/im2latex}},
}
### Contributions
[More Information Needed]
|
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] |
HoangHa/Vie_alpaca
|
HoangHa
| 2023-10-26T09:44:26Z | 33 | 0 |
[
"region:us"
] | null | 2023-10-26T09:44:22Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 51907952
num_examples: 49999
download_size: 24606528
dataset_size: 51907952
---
# Dataset Card for "Vie_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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aimsks/ts-aims-reefscapes-marine-features
|
aimsks
| 2023-10-28T02:15:24Z | 33 | 0 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-10-28T01:52:19Z |
---
license: cc-by-4.0
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 313066076.0
num_examples: 772
- name: test
num_bytes: 174907531.0
num_examples: 423
- name: validation
num_bytes: 79156495.0
num_examples: 194
download_size: 565979839
dataset_size: 567130102.0
---
|
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Bluebomber182/AI-Emotions
|
Bluebomber182
| 2023-10-29T21:18:45Z | 33 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-10-28T06:30:19Z |
---
license: unknown
---
|
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] |
kosta-naumenko/medflex
|
kosta-naumenko
| 2023-11-17T12:38:41Z | 33 | 0 |
[
"region:us"
] | null | 2023-10-31T13:14:13Z |
---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence: int64
splits:
- name: train
num_bytes: 2574069
num_examples: 1934
download_size: 314783
dataset_size: 2574069
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "medflex"
dataset = load_dataset("kosta-naumenko/medflex", split='train', download_mode='force_redownload', verification_mode='no_checks')
'tokens' - список списков слов предложений (is_split_into_words=True при токенизации)
'ner_tags' - список списков классов слов
- 0 - не симптом
- 1 - начало симптома
- 2 - продолжение симптома
Пример дальнейшей обработки - https://huggingface.co/learn/nlp-course/chapter7/2
|
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] |
ESGBERT/environmental_2k
|
ESGBERT
| 2023-11-03T16:12:00Z | 33 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-02T13:51:02Z |
---
license: apache-2.0
---
|
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] |
hajili/azerbaijani_review_sentiment_classification
|
hajili
| 2023-11-06T03:03:43Z | 33 | 4 |
[
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:az",
"license:mit",
"doi:10.57967/hf/1363",
"region:us"
] |
[
"text-classification"
] | 2023-11-06T02:52:46Z |
---
license: mit
task_categories:
- text-classification
language:
- az
size_categories:
- 100K<n<1M
---
Azerbaijani Sentiment Classification Dataset with ~160K reviews.
Dataset contains 3 columns: Content, Score, Upvotes
|
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] |
crylake/fill50k_vi
|
crylake
| 2023-11-10T07:34:25Z | 33 | 0 |
[
"region:us"
] | null | 2023-11-10T07:06:06Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: text
dtype: string
- name: vi_text
dtype: string
splits:
- name: train
num_bytes: 456972354.0
num_examples: 50000
download_size: 326272883
dataset_size: 456972354.0
---
# Dataset Card for "fill50k_vi"
[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_alice_hard_4_grader_first_1.0e
|
atmallen
| 2023-11-16T18:27:19Z | 33 | 0 |
[
"region:us"
] | null | 2023-11-16T03:19:33Z |
---
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: 1063722
dataset_size: 4191094.0
---
# Dataset Card for "qm_alice_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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] |
jlbaker361/small_multiplication_decimal
|
jlbaker361
| 2023-11-17T05:53:55Z | 33 | 0 |
[
"region:us"
] | null | 2023-11-17T04:47:41Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1934.2222222222222
num_examples: 40
- name: test
num_bytes: 241.77777777777777
num_examples: 5
download_size: 4575
dataset_size: 2176.0
---
# Dataset Card for "small_multiplication_decimal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
HamdanXI/paradetox-1Token-Split
|
HamdanXI
| 2023-11-17T09:47:19Z | 33 | 0 |
[
"region:us"
] | null | 2023-11-17T09:47:13Z |
---
dataset_info:
features:
- name: en_toxic_comment
dtype: string
- name: en_neutral_comment
dtype: string
- name: edit_ops
sequence:
sequence: string
- name: masked_comment
dtype: string
splits:
- name: train
num_bytes: 770809.759526452
num_examples: 3784
- name: validation
num_bytes: 165202.62023677395
num_examples: 811
- name: test
num_bytes: 165202.62023677395
num_examples: 811
download_size: 632132
dataset_size: 1101215.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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darrel999/business-java-code
|
darrel999
| 2023-11-17T12:48:42Z | 33 | 1 |
[
"region:us"
] | null | 2023-11-17T12:48:37Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 30579823
num_examples: 53183
download_size: 15957467
dataset_size: 30579823
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
pnadel/iliad_odyssey_aligned
|
pnadel
| 2023-11-20T18:38:44Z | 33 | 0 |
[
"region:us"
] | null | 2023-11-20T18:38:42Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: sentid
dtype: string
- name: cit
dtype: string
- name: Eng
dtype: string
- name: Gk
dtype: string
- name: Lems
dtype: string
splits:
- name: train
num_bytes: 5475867.200602134
num_examples: 12223
- name: test
num_bytes: 1369078.7993978662
num_examples: 3056
download_size: 3953094
dataset_size: 6844946.0
---
# Dataset Card for "iliad_odyssey_aligned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
TakanashiShiya/FamilyPlusLlama
|
TakanashiShiya
| 2023-11-24T01:50:59Z | 33 | 0 |
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"question-answering"
] | 2023-11-22T21:57:26Z |
---
license: apache-2.0
task_categories:
- question-answering
language:
- en
size_categories:
- 10K<n<100K
---
# 🤗 Dataset Card: TakanashiShiya/PlusLlama
|
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] |
DianaJin/miniproject_test
|
DianaJin
| 2023-11-28T12:19:06Z | 33 | 0 |
[
"region:us"
] | null | 2023-11-28T05:16:45Z |
---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 13448688
num_examples: 14
download_size: 5196734
dataset_size: 13448688
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
AshtonIsNotHere/biosift
|
AshtonIsNotHere
| 2023-11-30T18:00:28Z | 33 | 0 |
[
"region:us"
] | null | 2023-11-30T17:59:53Z |
---
dataset_info:
features:
- name: PMID
dtype: int64
- name: Title
dtype: string
- name: Abstract
dtype: string
- name: Split
dtype: string
- name: Number of Annotators
dtype: int64
- name: Aggregate
dtype: int64
- name: Has Human Subjects
dtype: float64
- name: Has Target Disease
dtype: float64
- name: Cohort Study or Clinical Trial
dtype: float64
- name: Has Quantitative Outcome Measure
dtype: float64
- name: Has Study Drug(s)
dtype: float64
- name: Has Population Size
dtype: float64
- name: Has Comparator Group
dtype: float64
- name: label
sequence: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 15286088
num_examples: 8005
- name: validation
num_bytes: 1931610
num_examples: 997
- name: test
num_bytes: 1923714
num_examples: 998
download_size: 9802250
dataset_size: 19141412
---
# Dataset Card for "biosift"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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witchling22/tokenized_T5_base
|
witchling22
| 2023-12-03T04:39:25Z | 33 | 0 |
[
"region:us"
] | null | 2023-12-01T20:42:42Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: source
dtype: string
- name: source_labels
dtype: string
- name: rouge_scores
dtype: string
- name: paper_id
dtype: string
- name: target
dtype: string
- name: full_source_text
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 17340567
num_examples: 1992
- name: test
num_bytes: 5620222
num_examples: 618
- name: validation
num_bytes: 5534448
num_examples: 619
download_size: 6371599
dataset_size: 28495237
---
# Dataset Card for "tokenized_T5_base"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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w11wo/imdb-javanese
|
w11wo
| 2022-10-25T10:01:48Z | 32 | 0 |
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:jv",
"license:odbl",
"region:us"
] |
[
"text-classification"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- found
language_creators:
- machine-generated
language:
- jv
license:
- odbl
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
extended:
- original
---
# Dataset Card for "imdb-javanese"
## 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 Sample Size](#data-instances-sample-size)
- [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:** [Github](https://github.com/w11wo/nlp-datasets#javanese-imdb)
- **Repository:** [Github](https://github.com/w11wo/nlp-datasets#javanese-imdb)
- **Paper:** [Aclweb](http://www.aclweb.org/anthology/P11-1015)
- **Point of Contact:** [Wilson Wongso](https://github.com/w11wo)
- **Size of downloaded dataset files:** 17.0 MB
- **Size of the generated dataset:** 47.5 MB
- **Total amount of disk used:** 64.5 MB
### Dataset Summary
Large Movie Review Dataset translated to Javanese. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. We translated the [original IMDB Dataset](https://huggingface.co/datasets/imdb) to Javanese using the multi-lingual MarianMT Transformer model from [`Helsinki-NLP/opus-mt-en-mul`](https://huggingface.co/Helsinki-NLP/opus-mt-en-mul).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
### Data Instances
An example of `javanese_imdb_train.csv` looks as follows.
| label | text |
| ----- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1 | "Drama romantik sing digawé karo direktur Martin Ritt kuwi ora dingertèni, nanging ana momen-momen sing marahi karisma lintang Jane Fonda lan Robert De Niro (kelompok sing luar biasa). Dhèwèké dadi randha sing ora isa mlaku, iso anu anyar lan anyar-inventor-- kowé isa nganggep isiné. Adapsi novel Pat Barker ""Union Street"" (yak titel sing apik!) arep dinggo-back-back it on bland, lan pendidikan film kuwi gampang, nanging isih nyenengké; a rosy-hued-inventor-fantasi. Ora ana sing ngganggu gambar sing sejati ding kok iso dinggo nggawe gambar sing paling nyeneng." |
| 0 | "Pengalaman wong lanang sing nduwé perasaan sing ora lumrah kanggo babi. Mulai nganggo tuladha sing luar biasa yaiku komedia. Wong orkestra termel digawé dadi wong gila, sing kasar merga nyanyian nyanyi. Sayangé, kuwi tetep absurd wektu WHOLE tanpa ceramah umum sing mung digawé. Malah, sing ana ing jaman kuwi kudu ditinggalké. Diyalog kryptik sing nggawé Shakespeare marah gampang kanggo kelas telu. Pak teknis kuwi luwih apik timbang kowe mikir nganggo cinematografi sing apik sing jenengé Vilmos Zsmond. Masa depan bintang Saly Kirkland lan Frederic Forrest isa ndelok." |
### Data Fields
- `text`: The movie review translated into Javanese.
- `label`: The sentiment exhibited in the review, either `1` (positive) or `0` (negative).
### Data Splits Sample Size
| train | unsupervised | test |
| ----: | -----------: | ----: |
| 25000 | 50000 | 25000 |
## 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
If you use this dataset in your research, please cite:
```
@inproceedings{wongso2021causal,
title={Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures},
author={Wongso, Wilson and Setiawan, David Samuel and Suhartono, Derwin},
booktitle={2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)},
pages={1--7},
year={2021},
organization={IEEE}
}
```
```
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
}
```
|
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ruanchaves/snap
|
ruanchaves
| 2022-10-20T19:12:47Z | 32 | 1 |
[
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:unknown",
"word-segmentation",
"region:us"
] |
[
"structure-prediction"
] | 2022-03-06T00:17:23Z |
---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: SNAP
tags:
- word-segmentation
---
# Dataset Card for SNAP
## Dataset Description
- **Repository:** [ardax/hashtag-segmentor](https://github.com/ardax/hashtag-segmentor)
- **Paper:** [Segmenting hashtags using automatically created training data](http://www.lrec-conf.org/proceedings/lrec2016/pdf/708_Paper.pdf)
### Dataset Summary
Automatically segmented 803K SNAP Twitter Data Set hashtags with the heuristic described in the paper "Segmenting hashtags using automatically created training data".
### Languages
English
## Dataset Structure
### Data Instances
```
{
"index": 0,
"hashtag": "BrandThunder",
"segmentation": "Brand Thunder"
}
```
### Data Fields
- `index`: a numerical index.
- `hashtag`: the original hashtag.
- `segmentation`: the gold segmentation for the hashtag.
## Dataset Creation
- All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields: `hashtag` and `segmentation` or `identifier` and `segmentation`.
- The only difference between `hashtag` and `segmentation` or between `identifier` and `segmentation` are the whitespace characters. Spell checking, expanding abbreviations or correcting characters to uppercase go into other fields.
- There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as `_` , `:`, `~` ).
- If there are any annotations for named entity recognition and other token classification tasks, they are given in a `spans` field.
## Additional Information
### Citation Information
```
@inproceedings{celebi2016segmenting,
title={Segmenting hashtags using automatically created training data},
author={Celebi, Arda and {\"O}zg{\"u}r, Arzucan},
booktitle={Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)},
pages={2981--2985},
year={2016}
}
```
### Contributions
This dataset was added by [@ruanchaves](https://github.com/ruanchaves) while developing the [hashformers](https://github.com/ruanchaves/hashformers) library.
|
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wanyu/IteraTeR_human_sent
|
wanyu
| 2022-10-24T18:58:22Z | 32 | 0 |
[
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"conditional-text-generation",
"text-editing",
"arxiv:2203.03802",
"region:us"
] |
[
"text2text-generation"
] | 2022-03-13T20:46:23Z |
---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: IteraTeR_human_sent
language_bcp47:
- en-US
tags:
- conditional-text-generation
- text-editing
---
Paper: [Understanding Iterative Revision from Human-Written Text](https://arxiv.org/abs/2203.03802)
Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang
Github repo: https://github.com/vipulraheja/IteraTeR
|
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] |
h4iku/coconut_java2006
|
h4iku
| 2023-09-28T22:53:23Z | 32 | 0 |
[
"code",
"region:us"
] | null | 2022-03-29T23:30:34Z |
---
tags:
- code
pretty_name: CoCoNuT-Java(2006)
---
# Dataset Card for CoCoNuT-Java(2006)
## Dataset Description
- **Homepage:** [CoCoNuT training data](https://github.com/lin-tan/CoCoNut-Artifact/releases/tag/training_data_1.0.0)
- **Repository:** [CoCoNuT repository](https://github.com/lin-tan/CoCoNut-Artifact)
- **Paper:** [CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair](https://dl.acm.org/doi/abs/10.1145/3395363.3397369)
### Dataset Summary
Part of the data used to train the models in the "CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair" paper.
These datasets contain raw data extracted from GitHub, GitLab, and Bitbucket, and have neither been shuffled nor tokenized.
The year in the dataset’s name is the cutting year that shows the year of the newest commit in the dataset.
### Languages
- Java
## Dataset Structure
### Data Fields
The dataset consists of 4 columns: `add`, `rem`, `context`, and `meta`.
These match the original dataset files: `add.txt`, `rem.txt`, `context.txt`, and `meta.txt`.
### Data Instances
There is a mapping between the 4 columns for each instance.
For example:
5 first rows of `rem` (i.e., the buggy line/hunk):
```
1 public synchronized StringBuffer append(char ch)
2 ensureCapacity_unsynchronized(count + 1); value[count++] = ch; return this;
3 public String substring(int beginIndex, int endIndex)
4 if (beginIndex < 0 || endIndex > count || beginIndex > endIndex) throw new StringIndexOutOfBoundsException(); if (beginIndex == 0 && endIndex == count) return this; int len = endIndex - beginIndex; return new String(value, beginIndex + offset, len, (len << 2) >= value.length);
5 public Object next() {
```
5 first rows of add (i.e., the fixed line/hunk):
```
1 public StringBuffer append(Object obj)
2 return append(obj == null ? "null" : obj.toString());
3 public String substring(int begin)
4 return substring(begin, count);
5 public FSEntry next() {
```
These map to the 5 instances:
```diff
- public synchronized StringBuffer append(char ch)
+ public StringBuffer append(Object obj)
```
```diff
- ensureCapacity_unsynchronized(count + 1); value[count++] = ch; return this;
+ return append(obj == null ? "null" : obj.toString());
```
```diff
- public String substring(int beginIndex, int endIndex)
+ public String substring(int begin)
```
```diff
- if (beginIndex < 0 || endIndex > count || beginIndex > endIndex) throw new StringIndexOutOfBoundsException(); if (beginIndex == 0 && endIndex == count) return this; int len = endIndex - beginIndex; return new String(value, beginIndex + offset, len, (len << 2) >= value.length);
+ return substring(begin, count);
```
```diff
- public Object next() {
+ public FSEntry next() {
```
`context` contains the associated "context". Context is the (in-lined) buggy function (including the buggy lines and comments).
For example, the context of
```
public synchronized StringBuffer append(char ch)
```
is its associated function:
```java
public synchronized StringBuffer append(char ch) { ensureCapacity_unsynchronized(count + 1); value[count++] = ch; return this; }
```
`meta` contains some metadata about the project:
```
1056 /local/tlutelli/issta_data/temp/all_java0context/java/2006_temp/2006/1056/68a6301301378680519f2b146daec37812a1bc22/StringBuffer.java/buggy/core/src/classpath/java/java/lang/StringBuffer.java
```
`1056` is the project id. `/local/...` is the absolute path to the buggy file. This can be parsed to extract the commit id: `68a6301301378680519f2b146daec37812a1bc22`, the file name: `StringBuffer.java` and the original path within the project
`core/src/classpath/java/java/lang/StringBuffer.java`
| Number of projects | Number of Instances |
| ------------------ |-------------------- |
| 45,180 | 3,241,966 |
## Dataset Creation
### Curation Rationale
Data is collected to train automated program repair (APR) models.
### Citation Information
```bib
@inproceedings{lutellierCoCoNuTCombiningContextaware2020,
title = {{{CoCoNuT}}: Combining Context-Aware Neural Translation Models Using Ensemble for Program Repair},
shorttitle = {{{CoCoNuT}}},
booktitle = {Proceedings of the 29th {{ACM SIGSOFT International Symposium}} on {{Software Testing}} and {{Analysis}}},
author = {Lutellier, Thibaud and Pham, Hung Viet and Pang, Lawrence and Li, Yitong and Wei, Moshi and Tan, Lin},
year = {2020},
month = jul,
series = {{{ISSTA}} 2020},
pages = {101--114},
publisher = {{Association for Computing Machinery}},
address = {{New York, NY, USA}},
doi = {10.1145/3395363.3397369},
url = {https://doi.org/10.1145/3395363.3397369},
urldate = {2022-12-06},
isbn = {978-1-4503-8008-9},
keywords = {AI and Software Engineering,Automated program repair,Deep Learning,Neural Machine Translation}
}
```
|
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westphal-jan/mnli_matched
|
westphal-jan
| 2022-04-16T12:02:51Z | 32 | 0 |
[
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"source_datasets:multi_nli",
"region:us"
] |
[
"text-classification"
] | 2022-04-11T10:06:59Z |
---
source_datasets:
- multi_nli
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
---
## Dataset Description
This dataset provides easier accessibility to the original [MNLI dataset](https://huggingface.co/datasets/multi_nli).
We randomly choose 10% of the original `validation_matched` split and use it as the validation split.
The remaining 90% are used for the test split.
The train split remains unchanged.
|
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] |
Goud/Goud-sum
|
Goud
| 2022-07-04T16:02:36Z | 32 | 2 |
[
"task_categories:summarization",
"task_ids:news-articles-headline-generation",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"size_categories:100K<n<1M",
"source_datasets:original",
"region:us"
] |
[
"summarization"
] | 2022-04-21T15:25:00Z |
---
annotations_creators:
- no-annotation
language_creators:
- machine-generated
language: []
license: []
multilinguality: []
pretty_name: Goud-sum
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-headline-generation
---
# Dataset Card for Goud summarization 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:**[Needs More Information]
- **Repository:**[Needs More Information]
- **Paper:**[Goud.ma: a News Article Dataset for Summarization in Moroccan Darija](https://openreview.net/forum?id=BMVq5MELb9)
- **Leaderboard:**[Needs More Information]
- **Point of Contact:**[Needs More Information]
### Dataset Summary
Goud-sum contains 158k articles and their headlines extracted from [Goud.ma](https://www.goud.ma/) news website. The articles are written in the Arabic script. All headlines are in Moroccan Darija, while articles may be in Moroccan Darija, in Modern Standard Arabic, or a mix of both (code-switched Moroccan Darija).
### Supported Tasks and Leaderboards
Text Summarization
### Languages
* Moroccan Arabic (Darija)
* Modern Standard Arabic
## Dataset Structure
### Data Instances
The dataset consists of article-headline pairs in string format.
### Data Fields
* article: a string containing the body of the news article
* headline: a string containing the article's headline
* categories: a list of string of article categories
### Data Splits
Goud-sum dataset has 3 splits: _train_, _validation_, and _test_. Below are the number of instances in each split.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 139,288 |
| Validation | 9,497 |
| Test | 9,497 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The text was written by journalists at [Goud](https://www.goud.ma/).
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### 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
```
@inproceedings{issam2022goudma,
title={Goud.ma: a News Article Dataset for Summarization in Moroccan Darija},
author={Abderrahmane Issam and Khalil Mrini},
booktitle={3rd Workshop on African Natural Language Processing},
year={2022},
url={https://openreview.net/forum?id=BMVq5MELb9}
}
```
### Contributions
Thanks to [@issam9](https://github.com/issam9) and [@KhalilMrini](https://github.com/KhalilMrini) for adding this dataset.
|
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strombergnlp/rustance
|
strombergnlp
| 2022-10-25T21:46:32Z | 32 | 1 |
[
"task_categories:text-classification",
"task_ids:fact-checking",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:ru",
"license:cc-by-4.0",
"stance-detection",
"arxiv:1809.01574",
"region:us"
] |
[
"text-classification"
] | 2022-05-09T08:53:27Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ru
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
- sentiment-classification
paperswithcode_id: rustance
pretty_name: RuStance
tags:
- stance-detection
---
# Dataset Card for "rustance"
## 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_csv/7151906](https://figshare.com/articles/dataset/dataset_csv/7151906)
- **Repository:** [https://github.com/StrombergNLP/rustance](https://github.com/StrombergNLP/rustance)
- **Paper:** [https://link.springer.com/chapter/10.1007/978-3-030-14687-0_16](https://link.springer.com/chapter/10.1007/978-3-030-14687-0_16), [https://arxiv.org/abs/1809.01574](https://arxiv.org/abs/1809.01574)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
- **Size of downloaded dataset files:** 212.54 KiB
- **Size of the generated dataset:** 186.76 KiB
- **Total amount of disk used:** 399.30KiB
### Dataset Summary
This is a stance prediction dataset in Russian. The dataset contains comments on news articles,
and rows are a comment, the title of the news article it responds to, and the stance of the comment
towards the article.
Stance detection is a critical component of rumour and fake news identification. It involves the extraction of the stance a particular author takes related to a given claim, both expressed in text. This paper investigates stance classification for Russian. It introduces a new dataset, RuStance, of Russian tweets and news comments from multiple sources, covering multiple stories, as well as text classification approaches to stance detection as benchmarks over this data in this language. As well as presenting this openly-available dataset, the first of its kind for Russian, the paper presents a baseline for stance prediction in the language.
### Supported Tasks and Leaderboards
* Stance Detection: [Stance Detection on RuStance](https://paperswithcode.com/sota/stance-detection-on-rustance)
### Languages
Russian, as spoken on the Meduza website (i.e. from multiple countries) (`bcp47:ru`)
## Dataset Structure
### Data Instances
#### rustance
- **Size of downloaded dataset files:** 349.79 KiB
- **Size of the generated dataset:** 366.11 KiB
- **Total amount of disk used:** 715.90 KiB
An example of 'train' looks as follows.
```
{
'id': '0',
'text': 'Волки, волки!!',
'title': 'Минобороны обвинило «гражданского сотрудника» в публикации скриншота из игры вместо фото террористов. И показало новое «неоспоримое подтверждение»',
'stance': 3
}
```
### Data Fields
- `id`: a `string` feature.
- `text`: a `string` expressing a stance.
- `title`: a `string` of the target/topic annotated here.
- `stance`: a class label representing the stance the text expresses towards the target. Full tagset with indices:
```
0: "support",
1: "deny",
2: "query",
3: "comment",
```
### Data Splits
| name |train|
|---------|----:|
|rustance|958 sentences|
## Dataset Creation
### Curation Rationale
Toy data for training and especially evaluating stance prediction in Russian
### Source Data
#### Initial Data Collection and Normalization
The data is comments scraped from a Russian news site not situated in Russia, [Meduza](https://meduza.io/), in 2018.
#### Who are the source language producers?
Russian speakers including from the Russian diaspora, especially Latvia
### Annotations
#### Annotation process
Annotators labelled comments for supporting, denying, querying or just commenting on a news article.
#### Who are the annotators?
Russian native speakers, IT education, male, 20s.
### Personal and Sensitive Information
The data was public at the time of collection. No PII removal has been performed.
## Considerations for Using the Data
### Social Impact of Dataset
There's a risk of misinformative content being in this data. The data has NOT been vetted for any content.
### Discussion of Biases
### Other Known Limitations
The above limitations apply.
## Additional Information
### Dataset Curators
The dataset is curated by the paper's authors.
### Licensing Information
The authors distribute this data under Creative Commons attribution license, CC-BY 4.0.
### Citation Information
```
@inproceedings{lozhnikov2018stance,
title={Stance prediction for russian: data and analysis},
author={Lozhnikov, Nikita and Derczynski, Leon and Mazzara, Manuel},
booktitle={International Conference in Software Engineering for Defence Applications},
pages={176--186},
year={2018},
organization={Springer}
}
```
### Contributions
Author-added dataset [@leondz](https://github.com/leondz)
|
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] |
bigscience-data/roots_indic-mr_indic_nlp_corpus
|
bigscience-data
| 2022-12-12T11:11:02Z | 32 | 0 |
[
"language:mr",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-05-18T09:16:54Z |
---
language: mr
license: cc-by-nc-4.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_indic-mr_indic_nlp_corpus
# Indic NLP Corpus
- Dataset uid: `indic_nlp_corpus`
### Description
The IndicNLP corpus is a largescale, general-domain corpus containing 2.7 billion words for 10 Indian languages from two language families. s (IndoAryan branch and Dravidian). Each language has at least 100 million words (except Oriya).
### Homepage
https://github.com/AI4Bharat/indicnlp_corpus#publicly-available-classification-datasets
### Licensing
- non-commercial use
- cc-by-nc-sa-4.0: Creative Commons Attribution Non Commercial Share Alike 4.0 International
### Speaker Locations
- Southern Asia
- India
### Sizes
- 3.4019 % of total
- 44.4368 % of indic-hi
- 64.2943 % of indic-ta
- 70.5374 % of indic-ml
- 54.2394 % of indic-te
- 55.9105 % of indic-kn
- 61.6111 % of indic-mr
- 67.2242 % of indic-pa
- 68.1470 % of indic-or
- 64.3879 % of indic-gu
- 4.1495 % of indic-bn
### BigScience processing steps
#### Filters applied to: indic-hi
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ta
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-kn
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-mr
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-pa
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-or
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
#### Filters applied to: indic-gu
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-bn
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
|
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BeIR/cqadupstack-generated-queries
|
BeIR
| 2022-10-23T06:15:48Z | 32 | 0 |
[
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] |
[
"text-retrieval",
"zero-shot-retrieval",
"information-retrieval",
"zero-shot-information-retrieval"
] | 2022-06-17T13:20:44Z |
---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## 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://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## 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
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
|
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] |
sileod/wikimedqa
|
sileod
| 2023-05-16T07:47:46Z | 32 | 6 |
[
"task_categories:text-classification",
"task_categories:multiple-choice",
"language:en",
"license:apache-2.0",
"medical",
"region:us"
] |
[
"text-classification",
"multiple-choice"
] | 2022-07-14T15:09:22Z |
---
license: apache-2.0
task_categories:
- text-classification
- multiple-choice
language:
- en
tags:
- medical
---
```bib
@article{sileo2023wikimedqa,
title={Generating multiple-choice questions for medical question answering with distractors and cue-masking},
author={Sileo, Damien and Uma, Kanimozhi and Moens, Marie-Francine},
journal={arXiv preprint arXiv:2303.07069 },
year={2023}
}
```
|
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affahrizain/jigsaw-toxic-comment
|
affahrizain
| 2023-02-19T11:51:27Z | 32 | 1 |
[
"region:us"
] | null | 2022-09-06T19:36:24Z |
---
dataset_info:
features:
- name: labels
dtype: int64
- name: comment_clean
dtype: string
splits:
- name: train
num_bytes: 57080609
num_examples: 159100
- name: dev
num_bytes: 7809213
num_examples: 22393
- name: test
num_bytes: 22245686
num_examples: 63978
download_size: 13050863
dataset_size: 87135508
---
# Dataset Card for "jigsaw-toxic-comment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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StonyBrookNLP/tellmewhy
|
StonyBrookNLP
| 2022-09-29T13:05:59Z | 32 | 1 |
[
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] |
[
"text2text-generation"
] | 2022-09-21T16:11:29Z |
---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: null
pretty_name: TellMeWhy
---
# Dataset Card for NewsCommentary
## 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://stonybrooknlp.github.io/tellmewhy/
- **Repository:** https://github.com/StonyBrookNLP/tellmewhy
- **Paper:** https://aclanthology.org/2021.findings-acl.53/
- **Leaderboard:** None
- **Point of Contact:** [Yash Kumar Lal](mailto:[email protected])
### Dataset Summary
TellMeWhy is a large-scale crowdsourced dataset made up of more than 30k questions and free-form answers concerning why characters in short narratives perform the actions described.
### Supported Tasks and Leaderboards
The dataset is designed to test why-question answering abilities of models when bound by local context.
### Languages
English
## Dataset Structure
### Data Instances
A typical data point consists of a story, a question and a crowdsourced answer to that question. Additionally, the instance also indicates whether the question's answer would be implicit or if it is explicitly stated in text. If applicable, it also contains Likert scores (-2 to 2) about the answer's grammaticality and validity in the given context.
```
{
"narrative":"Cam ordered a pizza and took it home. He opened the box to take out a slice. Cam discovered that the store did not cut the pizza for him. He looked for his pizza cutter but did not find it. He had to use his chef knife to cut a slice.",
"question":"Why did Cam order a pizza?",
"original_sentence_for_question":"Cam ordered a pizza and took it home.",
"narrative_lexical_overlap":0.3333333333,
"is_ques_answerable":"Not Answerable",
"answer":"Cam was hungry.",
"is_ques_answerable_annotator":"Not Answerable",
"original_narrative_form":[
"Cam ordered a pizza and took it home.",
"He opened the box to take out a slice.",
"Cam discovered that the store did not cut the pizza for him.",
"He looked for his pizza cutter but did not find it.",
"He had to use his chef knife to cut a slice."
],
"question_meta":"rocstories_narrative_41270_sentence_0_question_0",
"helpful_sentences":[
],
"human_eval":false,
"val_ann":[
],
"gram_ann":[
]
}
```
### Data Fields
- `question_meta` - Unique meta for each question in the corpus
- `narrative` - Full narrative from ROCStories. Used as the context with which the question and answer are associated
- `question` - Why question about an action or event in the narrative
- `answer` - Crowdsourced answer to the question
- `original_sentence_for_question` - Sentence in narrative from which question was generated
- `narrative_lexical_overlap` - Unigram overlap of answer with the narrative
- `is_ques_answerable` - Majority judgment by annotators on whether an answer to this question is explicitly stated in the narrative. If "Not Answerable", it is part of the Implicit-Answer questions subset, which is harder for models.
- `is_ques_answerable_annotator` - Individual annotator judgment on whether an answer to this question is explicitly stated in the narrative.
- `original_narrative_form` - ROCStories narrative as an array of its sentences
- `human_eval` - Indicates whether a question is a specific part of the test set. Models should be evaluated for their answers on these questions using the human evaluation suite released by the authors. They advocate for this human evaluation to be the correct way to track progress on this dataset.
- `val_ann` - Array of Likert scores (possible sizes are 0 and 3) about whether an answer is valid given the question and context. Empty arrays exist for cases where the human_eval flag is False.
- `gram_ann` - Array of Likert scores (possible sizes are 0 and 3) about whether an answer is grammatical. Empty arrays exist for cases where the human_eval flag is False.
### Data Splits
The data is split into training, valiudation, and test sets.
| Train | Valid | Test |
| ------ | ----- | ----- |
| 23964 | 2992 | 3563 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
ROCStories corpus (Mostafazadeh et al, 2016)
#### Initial Data Collection and Normalization
ROCStories was used to create why-questions related to actions and events in the stories.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
Amazon Mechanical Turk workers were provided a story and an associated why-question, and asked to answer. Three answers were collected for each question. For a small subset of questions, the quality of answers was also validated in a second round of annotation. This smaller subset should be used to perform human evaluation of any new models built for this dataset.
#### Who are the annotators?
Amazon Mechanical Turk workers
### Personal and Sensitive Information
None
## 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
### Evaluation
To evaluate progress on this dataset, the authors advocate for human evaluation and release a suite with the required settings [here](https://github.com/StonyBrookNLP/tellmewhy). Once inference on the test set has been completed, please filter out the answers on which human evaluation needs to be performed by selecting the questions (one answer per question, deduplication might be needed) in the test set where the `human_eval` flag is set to `True`. This subset can then be used to complete the requisite evaluation on TellMeWhy.
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{lal-etal-2021-tellmewhy,
title = "{T}ell{M}e{W}hy: A Dataset for Answering Why-Questions in Narratives",
author = "Lal, Yash Kumar and
Chambers, Nathanael and
Mooney, Raymond and
Balasubramanian, Niranjan",
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.53",
doi = "10.18653/v1/2021.findings-acl.53",
pages = "596--610",
}
```
### Contributions
Thanks to [@yklal95](https://github.com/ykl7) for adding this dataset.
|
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] |
arbml/ArSAS
|
arbml
| 2022-10-15T22:51:35Z | 32 | 1 |
[
"region:us"
] | null | 2022-10-15T22:51:23Z |
---
dataset_info:
features:
- name: '#Tweet_ID'
dtype: string
- name: Tweet_text
dtype: string
- name: Topic
dtype: string
- name: Sentiment_label_confidence
dtype: string
- name: Speech_act_label
dtype: string
- name: Speech_act_label_confidence
dtype: string
- name: label
dtype:
class_label:
names:
0: Negative
1: Neutral
2: Positive
3: Mixed
splits:
- name: train
num_bytes: 6147723
num_examples: 19897
download_size: 2998319
dataset_size: 6147723
---
# Dataset Card for "ArSAS"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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darrow-ai/USClassActionOutcomes_ExpertsAnnotations
|
darrow-ai
| 2022-11-06T12:35:30Z | 32 | 0 |
[
"license:gpl-3.0",
"arxiv:2211.00582",
"region:us"
] | null | 2022-10-25T12:43:36Z |
---
license: gpl-3.0
---
## Dataset Description
- **Homepage:** https://www.darrow.ai/
- **Repository:** https://github.com/darrow-labs/ClassActionPrediction
- **Paper:** https://arxiv.org/abs/2211.00582
- **Leaderboard:** N/A
- **Point of Contact:** [Gila Hayat](mailto:[email protected])
### Dataset Summary
USClassActions is an English dataset of 200 complaints from the US Federal Court with the respective binarized judgment outcome (Win/Lose). The dataset poses a challenging text classification task. We are happy to share this dataset in order to promote robustness and fairness studies on the critical area of legal NLP. The data was annotated using Darrow.ai proprietary tool.
### Data Instances
```python
from datasets import load_dataset
dataset = load_dataset('darrow-ai/USClassActionOutcomes_ExpertsAnnotations')
```
### Data Fields
`id`: (**int**) a unique identifier of the document \
`origin_label `: (**str**) the outcome of the case \
`target_text`: (**str**) the facts of the case \
`annotator_prediction `: (**str**) annotators predictions of the case outcome based on the target_text \
`annotator_confidence `: (**str**) the annotator's level of confidence in his outcome prediction \
### Curation Rationale
The dataset was curated by Darrow.ai (2022).
### Citation Information
*Gil Semo, Dor Bernsohn, Ben Hagag, Gila Hayat, and Joel Niklaus*
*ClassActionPrediction: A Challenging Benchmark for Legal Judgment Prediction of Class Action Cases in the US*
*Proceedings of the 2022 Natural Legal Language Processing Workshop. Abu Dhabi. 2022*
```
@InProceedings{darrow-niklaus-2022-uscp,
author = {Semo, Gil
and Bernsohn, Dor
and Hagag, Ben
and Hayat, Gila
and Niklaus, Joel},
title = {ClassActionPrediction: A Challenging Benchmark for Legal Judgment Prediction of Class Action Cases in the US},
booktitle = {Proceedings of the 2022 Natural Legal Language Processing Workshop},
year = {2022},
location = {Abu Dhabi},
}
```
|
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] |
bigbio/umnsrs
|
bigbio
| 2022-12-22T15:47:36Z | 32 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:cc0-1.0",
"region:us"
] | null | 2022-11-13T22:12:42Z |
---
language:
- en
bigbio_language:
- English
license: cc0-1.0
multilinguality: monolingual
bigbio_license_shortname: CC0_1p0
pretty_name: UMNSRS
homepage: https://conservancy.umn.edu/handle/11299/196265/
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- SEMANTIC_SIMILARITY
---
# Dataset Card for UMNSRS
## Dataset Description
- **Homepage:** https://conservancy.umn.edu/handle/11299/196265/
- **Pubmed:** False
- **Public:** True
- **Tasks:** STS
UMNSRS, developed by Pakhomov, et al., consists of 725 clinical term pairs whose semantic similarity and relatedness.
The similarity and relatedness of each term pair was annotated based on a continuous scale by having the resident touch
a bar on a touch sensitive computer screen to indicate the degree of similarity or relatedness.
The following subsets are available:
- similarity: A set of 566 UMLS concept pairs manually rated for semantic similarity (e.g. whale-dolphin) using a
continuous response scale.
- relatedness: A set of 588 UMLS concept pairs manually rated for semantic relatedness (e.g. needle-thread) using a
continuous response scale.
- similarity_mod: Modification of the UMNSRS-Similarity dataset to exclude control samples and those pairs that did not
match text in clinical, biomedical and general English corpora. Exact modifications are detailed in the paper (Corpus
Domain Effects on Distributional Semantic Modeling of Medical Terms. Serguei V.S. Pakhomov, Greg Finley, Reed McEwan,
Yan Wang, and Genevieve B. Melton. Bioinformatics. 2016; 32(23):3635-3644). The resulting dataset contains 449 pairs.
- relatedness_mod: Modification of the UMNSRS-Relatedness dataset to exclude control samples and those pairs that did
not match text in clinical, biomedical and general English corpora. Exact modifications are detailed in the paper
(Corpus Domain Effects on Distributional Semantic Modeling of Medical Terms. Serguei V.S. Pakhomov, Greg Finley,
Reed McEwan, Yan Wang, and Genevieve B. Melton. Bioinformatics. 2016; 32(23):3635-3644).
The resulting dataset contains 458 pairs.
## Citation Information
```
@inproceedings{pakhomov2010semantic,
title={Semantic similarity and relatedness between clinical terms: an experimental study},
author={Pakhomov, Serguei and McInnes, Bridget and Adam, Terrence and Liu, Ying and Pedersen, Ted and Melton, Genevieve B},
booktitle={AMIA annual symposium proceedings},
volume={2010},
pages={572},
year={2010},
organization={American Medical Informatics Association}
}
```
|
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] |
dkagramanyan/horoscopes_ru
|
dkagramanyan
| 2022-11-20T22:05:14Z | 32 | 3 |
[
"task_categories:text-generation",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:ru",
"region:us"
] |
[
"text-generation"
] | 2022-11-20T20:54:21Z |
---
annotations_creators: []
language:
- ru
language_creators: []
license: []
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets: []
tags: []
task_categories:
- text-generation
task_ids: []
dataset_info:
features:
- name: date
dtype: string
- name: sign
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 6532293
num_examples: 6976
- name: train
num_bytes: 62194608
num_examples: 66501
download_size: 31753326
dataset_size: 68726901
---
Horoscopes from website Rumbler.ru from 2004 to 2020.
73477 records.
Train dataset size - 66481
Test dataset size - 6996
Split - 10%
|
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] |
ai4bharat/kathbath
|
ai4bharat
| 2022-12-09T09:59:48Z | 32 | 2 |
[
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"license:mit",
"arxiv:2208.11761",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2022-12-04T13:28:53Z |
---
annotations_creators:
- expert-generated
language_bcp47:
- bn,gu,kn,hi,ml,mr,or,pa,sn,ta,te,ur
language_creators:
- machine-generated
license:
- mit
multilinguality:
- multilingual
pretty_name: Kathbath
size_categories:
- 100K<n<1M
source_datasets:
- original
tags: []
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for Kathbath
## 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://ai4bharat.org/indic-superb**
- **Repository:https://github.com/AI4Bharat/IndicSUPERB**
- **Paper:https://arxiv.org/pdf/2208.11761.pdf**
- **Point of Contact:[email protected]**
### Dataset Summary
Kathbath is an human-labeled ASR dataset containing 1,684 hours of labelled speech data across 12 Indian languages from 1,218 contributors located in 203 districts in India
### Languages
- Bengali
- Gujarati
- Kannada
- Hindi
- Malayalam
- Marathi
- Odia
- Punjabi
- Sanskrit
- Tamil
- Telugu
- Urdu
## Dataset Structure
```
Audio Data
data
├── bengali
│ ├── <split_name>
│ │ ├── 844424931537866-594-f.m4a
│ │ ├── 844424931029859-973-f.m4a
│ │ ├── ...
├── gujarati
├── ...
Transcripts
data
├── bengali
│ ├── <split_name>
│ │ ├── transcription_n2w.txt
├── gujarati
├── ...
```
### Licensing Information
The IndicSUPERB dataset is released under this licensing scheme:
- We do not own any of the raw text used in creating this dataset.
- The text data comes from the IndicCorp dataset which is a crawl of publicly available websites.
- The audio transcriptions of the raw text and labelled annotations of the datasets have been created by us.
- We license the actual packaging of all this data under the Creative Commons CC0 license (“no rights reserved”).
- To the extent possible under law, AI4Bharat has waived all copyright and related or neighboring rights to the IndicSUPERB dataset.
- This work is published from: India.
### Citation Information
```
@misc{https://doi.org/10.48550/arxiv.2208.11761,
doi = {10.48550/ARXIV.2208.11761},
url = {https://arxiv.org/abs/2208.11761},
author = {Javed, Tahir and Bhogale, Kaushal Santosh and Raman, Abhigyan and Kunchukuttan, Anoop and Kumar, Pratyush and Khapra, Mitesh M.},
title = {IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
### Contributions
We would like to thank the Ministry of Electronics and Information Technology (MeitY) of the Government of India and the Centre for Development of Advanced Computing (C-DAC), Pune for generously supporting this work and providing us access to multiple GPU nodes on the Param Siddhi Supercomputer. We would like to thank the EkStep Foundation and Nilekani Philanthropies for their generous grant which went into hiring human resources as well as cloud resources needed for this work. We would like to thank DesiCrew for connecting us to native speakers for collecting data. We would like to thank Vivek Seshadri from Karya Inc. for helping setup the data collection infrastructure on the Karya platform. We would like to thank all the members of AI4Bharat team in helping create the Query by Example dataset.
|
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Jzuluaga/atco2_corpus_1h
|
Jzuluaga
| 2022-12-05T11:15:31Z | 32 | 1 |
[
"task_categories:automatic-speech-recognition",
"multilinguality:monolingual",
"language:en",
"audio",
"automatic-speech-recognition",
"en-atc",
"en",
"noisy-speech-recognition",
"speech-recognition",
"arxiv:2211.04054",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2022-12-05T10:37:25Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: segment_start_time
dtype: float32
- name: segment_end_time
dtype: float32
- name: duration
dtype: float32
splits:
- name: test
num_bytes: 113872168.0
num_examples: 871
download_size: 113467762
dataset_size: 113872168.0
tags:
- audio
- automatic-speech-recognition
- en-atc
- en
- noisy-speech-recognition
- speech-recognition
task_categories:
- automatic-speech-recognition
language:
- en
multilinguality:
- monolingual
---
# Dataset Card for ATCO2 test set corpus (1hr set)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages and Other Details](#languages-and-other-details)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [ATCO2 project homepage](https://www.atco2.org/)
- **Repository:** [ATCO2 corpus](https://github.com/idiap/atco2-corpus)
- **Paper:** [ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications](https://arxiv.org/abs/2211.04054)
### Dataset Summary
ATCO2 project aims at developing a unique platform allowing to collect, organize and pre-process air-traffic control (voice communication) data from air space. This project has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 864702. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union.
The project collected the real-time voice communication between air-traffic controllers and pilots available either directly through publicly accessible radio frequency channels or indirectly from air-navigation service providers (ANSPs). In addition to the voice communication data, contextual information is available in a form of metadata (i.e. surveillance data). The dataset consists of two distinct packages:
- A corpus of 5000+ hours (pseudo-transcribed) of air-traffic control speech collected across different airports (Sion, Bern, Zurich, etc.) in .wav format for speech recognition. Speaker distribution is 90/10% between males and females and the group contains native and non-native speakers of English.
- A corpus of 4 hours (transcribed) of air-traffic control speech collected across different airports (Sion, Bern, Zurich, etc.) in .wav format for speech recognition. Speaker distribution is 90/10% between males and females and the group contains native and non-native speakers of English. This corpus has been transcribed with orthographic information in XML format with speaker noise information, SNR values and others. Read Less
- A free sample of the 4 hours transcribed data is in [ATCO2 project homepage](https://www.atco2.org/data)
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`. Already adapted/fine-tuned models are available here --> [Wav2Vec 2.0 LARGE mdel](https://huggingface.co/Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc-and-atcosim).
### Languages and other details
The text and the recordings are in English. For more information see Table 3 and Table 4 of [ATCO2 corpus paper](https://arxiv.org/abs/2211.04054)
## Dataset Structure
### Data Fields
- `id (string)`: a string of recording identifier for each example, corresponding to its.
- `audio (audio)`: audio data for the given ID
- `text (string)`: transcript of the file already normalized. Follow these repositories for more details [w2v2-air-traffic](https://github.com/idiap/w2v2-air-traffic) and [bert-text-diarization-atc](https://github.com/idiap/bert-text-diarization-atc)
- `segment_start_time (float32)`: segment start time (normally 0)
- `segment_end_time (float32): segment end time
- `duration (float32)`: duration of the recording, compute as segment_end_time - segment_start_time
## Additional Information
### Licensing Information
The licensing status of the ATCO2-test-set-1h corpus is in the file **ATCO2-ASRdataset-v1_beta - End-User Data Agreement** in the data folder. Download the data in [ATCO2 project homepage](https://www.atco2.org/data)
### Citation Information
Contributors who prepared, processed, normalized and uploaded the dataset in HuggingFace:
```
@article{zuluaga2022how,
title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications},
author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others},
journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
year={2022}
}
@article{zuluaga2022bertraffic,
title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications},
author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others},
journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
year={2022}
}
@article{zuluaga2022atco2,
title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications},
author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others},
journal={arXiv preprint arXiv:2211.04054},
year={2022}
}
```
|
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] |
sasha/birdsnap
|
sasha
| 2022-12-17T21:29:07Z | 32 | 0 |
[
"region:us"
] | null | 2022-12-17T20:35:55Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: string
splits:
- name: train
num_bytes: 63184668691.7
num_examples: 39860
download_size: 69093722465
dataset_size: 63184668691.7
---
# Dataset Card for Birdsnap Dataset v 1.1.
Welcome to the Birdsnap dataset, consisting of 49,829 images of 500 species of North American birds, collected from Flickr, and corresponding species, bounding box, and part labels.
The dataset distribution also consists of the following files:
1. species.txt
This file lists the species in the dataset. The first line is a header. Each subsequent line represents a species. Lines are tab-delimited, and the fields
are:
- id: An integer id for the species. These ids run from 1 to 500 for the 500 species.
- common: The common English name of the species, for example "Blue Jay."
- scientific: The scientific (Latin) name of the species, for example "Cyanocitta cristata."
- dir: The name of the a directory in which to store the images of this species. This is just the common name with spaces and other dangerous-in-file-path characters replaced or removed.
2. images.txt
This file lists the images in the dataset, with the coresponding bounding boxes, part locations, and species labels. Like species.txt, it is tab-delimited with the first line giving field names. The fields are:
- url: The URL from which the image was downloaded.
- md5: An MD5 sum of the image file constants.
- path: The local path of the image.
- species_id: The id of the species of the labeled bird in the image.
- bb_x1, bb_y1, bb_x2, bb_y2: The coordinates of the top-left (bb_x1, bb_y1) and bottom-right (bb_x2, bb_y2) corners of the bounding box of the labeled
bird.
- ${part}_x, ${part}_y: The coordinates of part ${part}. Parts are back, beak, belly, breast, crown, forehead, left_cheek, left_eye, left_leg, left_wing, nape, right_cheek, right_eye, right_leg, right_wing, tail, throat.
3. test_images.txt
This file lists the 2443 test images used in the species identification experiments in the paper. It has a header line, then the "path" (from images.txt) of each test image, one per line.
### Citation
```
@inproceedings{berg2014birdsnap,
title={Birdsnap: Large-scale fine-grained visual categorization of birds},
author={Berg, Thomas and Liu, Jiongxin and Woo Lee, Seung and Alexander, Michelle L and Jacobs, David W and Belhumeur, Peter N},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={2011--2018},
year={2014}
}
```
|
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lmqg/qag_itquad
|
lmqg
| 2022-12-18T08:21:31Z | 32 | 0 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:1k<n<10K",
"source_datasets:lmqg/qg_itquad",
"language:it",
"license:cc-by-sa-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] |
[
"text-generation"
] | 2022-12-18T08:05:18Z |
---
license: cc-by-sa-4.0
pretty_name: SQuAD for question generation
language: it
multilinguality: monolingual
size_categories: 1k<n<10K
source_datasets: lmqg/qg_itquad
task_categories:
- text-generation
task_ids:
- language-modeling
tags:
- question-generation
---
# Dataset Card for "lmqg/qag_itquad"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is the question & answer generation dataset based on the ITQuAD.
### Supported Tasks and Leaderboards
* `question-answer-generation`: The dataset is assumed to be used to train a model for question & answer generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
Itallian (it)
## Dataset Structure
An example of 'train' looks as follows.
```
{
"paragraph": ""4 Minuti" è uscito come primo singolo dell' album e ha raggiunto il terzo posto sulla Billboard Hot 100. E' stato il 37° top-ten di Madonna che ha spinto Madonna oltre Elvis Presley come l' artista con i più top-ten hit. Nel Regno Unito ha mantenuto il suo record per il più numero uno single per una artista femminile;"4 Minuti" diventando il suo tredicesimo. Al 23° Japan Gold Disc Awards, Madonna ha ricevuto il suo quinto trofeo Artista dell' anno dalla Recording Industry Association of Japan, la più importante per qualsiasi artista. Per promuovere ulteriormente l' album, Madonna ha intrapreso il Sticky & Sweet Tour, la sua prima grande avventura con Live Nation. Con un lordo di 280 milioni di dollari, è diventato il tour più incassato di un artista solista, superando il precedente record di Madonna stabilito con il Confessions Tour; è stato poi superato da The Wall Live di Roger Waters. E' stato esteso al prossimo anno, aggiungendo nuove date europee, e dopo la fine, il totale lordo totale era di 408 milioni di dollari.",
"questions": [ "Qual è il nome del primo tour con Live Nation?", "4 minuti è diventato Madonna's che numero uno nel Regno Unito?", "Quanto ha incassato Stick e Sweet Tour?", "Madonna ha superato l' artista con i più alti dieci colpi?" ],
"answers": [ "Sticky & Sweet Tour", "tredicesimo", "280 milioni di dollari,", "Elvis Presley" ],
"questions_answers": "question: Qual è il nome del primo tour con Live Nation?, answer: Sticky & Sweet Tour | question: 4 minuti è diventato Madonna's che numero uno nel Regno Unito?, answer: tredicesimo | question: Quanto ha incassato Stick e Sweet Tour?, answer: 280 milioni di dollari, | question: Madonna ha superato l' artista con i più alti dieci colpi?, answer: Elvis Presley"
}
```
The data fields are the same among all splits.
- `questions`: a `list` of `string` features.
- `answers`: a `list` of `string` features.
- `paragraph`: a `string` feature.
- `questions_answers`: a `string` feature.
## Data Splits
|train|validation|test |
|----:|---------:|----:|
|16918 | 6280 | 1988|
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
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silatus/1k_Website_Screenshots_and_Metadata
|
silatus
| 2023-01-19T05:20:33Z | 32 | 12 |
[
"task_categories:text-to-image",
"task_categories:image-classification",
"task_categories:image-segmentation",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-sa-4.0",
"screenshots",
"metadata",
"websites",
"webpages",
"region:us"
] |
[
"text-to-image",
"image-classification",
"image-segmentation"
] | 2023-01-19T04:33:07Z |
---
license: cc-by-nc-sa-4.0
task_categories:
- text-to-image
- image-classification
- image-segmentation
language:
- en
tags:
- screenshots
- metadata
- websites
- webpages
pretty_name: 1000 Website Screenshots with Metadata
size_categories:
- 1K<n<10K
---
# Dataset Card for 1000 Website Screenshots with Metadata
## Dataset Description
- **Homepage:** [silatus.com](https://silatus.com/datasets)
- **Point of Contact:** [[email protected]](mailto:[email protected])
### Dataset Summary
Silatus is sharing, for free, a segment of a dataset that we are using to train a generative AI model for text-to-mockup conversions. This dataset was collected in December 2022 and early January 2023, so it contains very recent data from 1,000 of the world's most popular websites. You can get our larger 10,000 website dataset for free at: [https://silatus.com/datasets](https://silatus.com/datasets)
This dataset includes:
**High-res screenshots**
- 1024x1024px
- Loaded Javascript
- Loaded Images
**Text metadata**
- Site title
- Navbar content
- Full page text data
- Page description
**Visual metadata**
- Content (images, videos, inputs, buttons) absolute & relative positions
- Color profile
- Base font
|
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s-nlp/en_paradetox_content
|
s-nlp
| 2023-09-08T08:38:03Z | 32 | 0 |
[
"task_categories:text-classification",
"language:en",
"license:openrail++",
"region:us"
] |
[
"text-classification"
] | 2023-03-24T11:07:04Z |
---
license: openrail++
task_categories:
- text-classification
language:
- en
---
# ParaDetox: Detoxification with Parallel Data (English). Content Task Results
This repository contains information about **Content Task** markup from [English Paradetox dataset](https://huggingface.co/datasets/s-nlp/paradetox) collection pipeline.
The original paper ["ParaDetox: Detoxification with Parallel Data"](https://aclanthology.org/2022.acl-long.469/) was presented at ACL 2022 main conference.
## ParaDetox Collection Pipeline
The ParaDetox Dataset collection was done via [Yandex.Toloka](https://toloka.yandex.com/) crowdsource platform. The collection was done in three steps:
* *Task 1:* **Generation of Paraphrases**: The first crowdsourcing task asks users to eliminate toxicity in a given sentence while keeping the content.
* *Task 2:* **Content Preservation Check**: We show users the generated paraphrases along with their original variants and ask them to indicate if they have close meanings.
* *Task 3:* **Toxicity Check**: Finally, we check if the workers succeeded in removing toxicity.
Specifically this repo contains the results of **Task 2: Content Preservation Check**. Here, the samples with markup confidence >= 90 are present. One text in the pair is toxic, another -- its non-toxic paraphrase (should be).
Totally, datasets contains 32,317 pairs. Among them, the minor part is negative examples (4,562 pairs).
## Citation
```
@inproceedings{logacheva-etal-2022-paradetox,
title = "{P}ara{D}etox: Detoxification with Parallel Data",
author = "Logacheva, Varvara and
Dementieva, Daryna and
Ustyantsev, Sergey and
Moskovskiy, Daniil and
Dale, David and
Krotova, Irina and
Semenov, Nikita and
Panchenko, Alexander",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.469",
pages = "6804--6818",
abstract = "We present a novel pipeline for the collection of parallel data for the detoxification task. We collect non-toxic paraphrases for over 10,000 English toxic sentences. We also show that this pipeline can be used to distill a large existing corpus of paraphrases to get toxic-neutral sentence pairs. We release two parallel corpora which can be used for the training of detoxification models. To the best of our knowledge, these are the first parallel datasets for this task.We describe our pipeline in detail to make it fast to set up for a new language or domain, thus contributing to faster and easier development of new parallel resources.We train several detoxification models on the collected data and compare them with several baselines and state-of-the-art unsupervised approaches. We conduct both automatic and manual evaluations. All models trained on parallel data outperform the state-of-the-art unsupervised models by a large margin. This suggests that our novel datasets can boost the performance of detoxification systems.",
}
```
## Contacts
For any questions, please contact: Daryna Dementieva ([email protected])
|
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] |
mstz/ozone
|
mstz
| 2023-04-16T17:57:24Z | 32 | 0 |
[
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"ozone",
"tabular_classification",
"binary_classification",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-06T21:44:22Z |
---
language:
- en
tags:
- ozone
- tabular_classification
- binary_classification
pretty_name: Ozone
size_categories:
- 1K<n<10K
task_categories:
- tabular-classification
configs:
- 8hr
- 1hr
license: cc
---
# Ozone
The [Ozone dataset](https://archive.ics.uci.edu/ml/datasets/Ozone) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-------------------------|
| 8hr | Binary classification | Is there an ozone layer?|
| 1hr | Binary classification | Is there an ozone layer?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/ozone", "8hr")["train"]
```
|
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] |
claritylab/utcd
|
claritylab
| 2023-05-24T17:27:42Z | 32 | 4 |
[
"task_categories:text-classification",
"annotations_creators:no-annotation",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"arxiv:2005.00547",
"arxiv:2010.12421",
"arxiv:1509.01626",
"arxiv:1307.5336",
"arxiv:1909.05855",
"arxiv:1909.02027",
"arxiv:2011.13205",
"arxiv:2003.04807",
"arxiv:1805.10190",
"arxiv:1903.05566",
"region:us"
] |
[
"text-classification"
] | 2023-05-11T16:17:23Z |
---
license: mit
task_categories:
- text-classification
language:
- en
size_categories:
- 1M<n<10M
annotations_creators:
- no-annotation
multilinguality:
- monolingual
pretty_name: UTCD
dataset_info:
- config_name: in-domain
features:
- name: text
dtype: string
- name: labels
sequence:
class_label:
names:
'0': Add Alarm
'1': Album
'2': Animal
'3': Artist
'4': Athlete
'5': Book Appointment
'6': Book House
'7': Building
'8': Business
'9': Business & Finance
'10': Buy Bus Ticket
'11': Buy Event Tickets
'12': Buy Movie Tickets
'13': Check Balance
'14': Company
'15': Computers & Internet
'16': Education & Reference
'17': Educational Institution
'18': Entertainment & Music
'19': Family & Relationships
'20': Film
'21': Find Apartment
'22': Find Attractions
'23': Find Bus
'24': Find Events
'25': Find Home By Area
'26': Find Movies
'27': Find Provider
'28': Find Restaurants
'29': Find Trains
'30': Get Alarms
'31': Get Available Time
'32': Get Cars Available
'33': Get Event Dates
'34': Get Events
'35': Get Ride
'36': Get Times For Movie
'37': Get Weather
'38': Health
'39': Lookup Music
'40': Lookup Song
'41': Make Payment
'42': Mean Of Transportation
'43': Natural Place
'44': Office Holder
'45': Plant
'46': Play Media
'47': Play Movie
'48': Play Song
'49': Politics & Government
'50': Request Payment
'51': Reserve Car
'52': Reserve Hotel
'53': Reserve One way Flight
'54': Reserve Restaurant
'55': Reserve Round trip Flights
'56': Schedule Visit
'57': Science & Mathematics
'58': Science & Technology
'59': Search Hotel
'60': Search House
'61': Search One way Flight
'62': Search Round trip Flights
'63': Society & Culture
'64': Sports
'65': Transfer Money
'66': Village
'67': World News
'68': Written Work
'69': accept reservations
'70': account blocked
'71': add contact
'72': admiration
'73': alarm
'74': alarm query
'75': alarm remove
'76': alarm set
'77': amusement
'78': anger
'79': annoyance
'80': application status
'81': approval
'82': apr
'83': are you a bot
'84': audio volume down
'85': audio volume mute
'86': audio volume other
'87': audio volume up
'88': balance
'89': bill balance
'90': bill due
'91': book flight
'92': book hotel
'93': calculator
'94': calendar
'95': calendar query
'96': calendar remove
'97': calendar set
'98': calendar update
'99': calories
'100': cancel
'101': cancel reservation
'102': car rental
'103': card declined
'104': caring
'105': carry on
'106': change accent
'107': change ai name
'108': change language
'109': change speed
'110': change user name
'111': change volume
'112': cleaning
'113': coffee
'114': confirm reservation
'115': confusion
'116': convert
'117': cook time
'118': cooking query
'119': cooking recipe
'120': create or add
'121': credit limit
'122': credit limit change
'123': credit score
'124': curiosity
'125': currency
'126': current location
'127': damaged card
'128': date
'129': date time convert
'130': date time query
'131': definition
'132': desire
'133': direct deposit
'134': directions
'135': disappointment
'136': disapproval
'137': disgust
'138': distance
'139': do you have pets
'140': email add contact
'141': email query
'142': email query contact
'143': email send email
'144': embarrassment
'145': events
'146': exchange rate
'147': excitement
'148': expiration date
'149': factoid
'150': fear
'151': find phone
'152': flight status
'153': flip coin
'154': food last
'155': freeze account
'156': fun fact
'157': game
'158': gas
'159': gas type
'160': general greet
'161': general joke
'162': general quirky
'163': goodbye
'164': gratitude
'165': greet
'166': greeting
'167': grief
'168': how busy
'169': how old are you
'170': hue light dim
'171': hue light off
'172': hue light up
'173': improve credit score
'174': income
'175': ingredient substitution
'176': ingredients list
'177': insurance
'178': insurance change
'179': interest rate
'180': international fees
'181': international visa
'182': iot cleaning
'183': iot coffee
'184': iot hue light change
'185': iot hue light dim
'186': iot hue light off
'187': iot hue light on
'188': iot hue light up
'189': iot wemo on
'190': iot wemo plug off
'191': joke
'192': joy
'193': jump start
'194': last maintenance
'195': lists create or add
'196': lists query
'197': lists remove
'198': lost luggage
'199': love
'200': make call
'201': maybe
'202': meal suggestion
'203': meaning of life
'204': measurement conversion
'205': meeting schedule
'206': min payment
'207': mpg
'208': music
'209': music dislike ness
'210': music likeness
'211': music query
'212': music settings
'213': negative
'214': nervousness
'215': neutral
'216': new card
'217': news query
'218': next holiday
'219': next song
'220': 'no'
'221': nutrition info
'222': oil change how
'223': oil change when
'224': optimism
'225': order
'226': order checks
'227': order status
'228': paid time off request status
'229': paid time off used
'230': pay bill
'231': payday
'232': pin change
'233': play audiobook
'234': play game
'235': play music
'236': play podcasts
'237': play radio
'238': plug type
'239': podcasts
'240': positive
'241': post
'242': pride
'243': pto balance
'244': pto request
'245': qa currency
'246': qa definition
'247': qa factoid
'248': qa maths
'249': qa stock
'250': query
'251': query contact
'252': quirky
'253': radio
'254': realization
'255': recipe
'256': recommendation events
'257': recommendation locations
'258': recommendation movies
'259': redeem rewards
'260': relief
'261': reminder
'262': reminder update
'263': remorse
'264': remove
'265': repeat
'266': replacement card duration
'267': report fraud
'268': report lost card
'269': reset settings
'270': restaurant reservation
'271': restaurant reviews
'272': restaurant suggestion
'273': rewards balance
'274': roll dice
'275': rollover 401k
'276': routing
'277': sadness
'278': schedule maintenance
'279': schedule meeting
'280': send email
'281': set
'282': settings
'283': share location
'284': shopping list
'285': shopping list update
'286': smart home
'287': social post
'288': social query
'289': spelling
'290': spending history
'291': surprise
'292': sync device
'293': take away order
'294': take away query
'295': taxes
'296': tell joke
'297': text
'298': thank you
'299': ticket
'300': time
'301': timer
'302': timezone
'303': tire change
'304': tire pressure
'305': todo list
'306': todo list update
'307': traffic
'308': transactions
'309': transfer
'310': translate
'311': transport query
'312': transport taxi
'313': transport ticket
'314': transport traffic
'315': travel alert
'316': travel notification
'317': travel suggestion
'318': uber
'319': update playlist
'320': user name
'321': vaccines
'322': volume other
'323': w2 wage and tax statement
'324': weather
'325': weather query
'326': wemo off
'327': wemo plug on
'328': what are your hobbies
'329': what can i ask you
'330': what is your name
'331': what song
'332': where are you from
'333': whisper mode
'334': who do you work for
'335': who made you
'336': 'yes'
- name: dataset_name
dtype:
class_label:
names:
'0': go_emotion
'1': sentiment_tweets_2020
'2': emotion
'3': sgd
'4': clinc_150
'5': slurp
'6': ag_news
'7': dbpedia
'8': yahoo
splits:
- name: train
num_bytes: 347382307
num_examples: 2192703
- name: test
num_bytes: 36063588
num_examples: 168365
download_size: 1744258165
dataset_size: 383445895
- config_name: aspect-normalized-in-domain
features:
- name: text
dtype: string
- name: labels
sequence:
class_label:
names:
'0': Add Alarm
'1': Album
'2': Animal
'3': Artist
'4': Athlete
'5': Book Appointment
'6': Book House
'7': Building
'8': Business
'9': Business & Finance
'10': Buy Bus Ticket
'11': Buy Event Tickets
'12': Buy Movie Tickets
'13': Check Balance
'14': Company
'15': Computers & Internet
'16': Education & Reference
'17': Educational Institution
'18': Entertainment & Music
'19': Family & Relationships
'20': Film
'21': Find Apartment
'22': Find Attractions
'23': Find Bus
'24': Find Events
'25': Find Home By Area
'26': Find Movies
'27': Find Provider
'28': Find Restaurants
'29': Find Trains
'30': Get Alarms
'31': Get Available Time
'32': Get Cars Available
'33': Get Event Dates
'34': Get Events
'35': Get Ride
'36': Get Times For Movie
'37': Get Weather
'38': Health
'39': Lookup Music
'40': Lookup Song
'41': Make Payment
'42': Mean Of Transportation
'43': Natural Place
'44': Office Holder
'45': Plant
'46': Play Media
'47': Play Movie
'48': Play Song
'49': Politics & Government
'50': Request Payment
'51': Reserve Car
'52': Reserve Hotel
'53': Reserve One way Flight
'54': Reserve Restaurant
'55': Reserve Round trip Flights
'56': Schedule Visit
'57': Science & Mathematics
'58': Science & Technology
'59': Search Hotel
'60': Search House
'61': Search One way Flight
'62': Search Round trip Flights
'63': Society & Culture
'64': Sports
'65': Transfer Money
'66': Village
'67': World News
'68': Written Work
'69': accept reservations
'70': account blocked
'71': add contact
'72': admiration
'73': alarm
'74': alarm query
'75': alarm remove
'76': alarm set
'77': amusement
'78': anger
'79': annoyance
'80': application status
'81': approval
'82': apr
'83': are you a bot
'84': audio volume down
'85': audio volume mute
'86': audio volume other
'87': audio volume up
'88': balance
'89': bill balance
'90': bill due
'91': book flight
'92': book hotel
'93': calculator
'94': calendar
'95': calendar query
'96': calendar remove
'97': calendar set
'98': calendar update
'99': calories
'100': cancel
'101': cancel reservation
'102': car rental
'103': card declined
'104': caring
'105': carry on
'106': change accent
'107': change ai name
'108': change language
'109': change speed
'110': change user name
'111': change volume
'112': cleaning
'113': coffee
'114': confirm reservation
'115': confusion
'116': convert
'117': cook time
'118': cooking query
'119': cooking recipe
'120': create or add
'121': credit limit
'122': credit limit change
'123': credit score
'124': curiosity
'125': currency
'126': current location
'127': damaged card
'128': date
'129': date time convert
'130': date time query
'131': definition
'132': desire
'133': direct deposit
'134': directions
'135': disappointment
'136': disapproval
'137': disgust
'138': distance
'139': do you have pets
'140': email add contact
'141': email query
'142': email query contact
'143': email send email
'144': embarrassment
'145': events
'146': exchange rate
'147': excitement
'148': expiration date
'149': factoid
'150': fear
'151': find phone
'152': flight status
'153': flip coin
'154': food last
'155': freeze account
'156': fun fact
'157': game
'158': gas
'159': gas type
'160': general greet
'161': general joke
'162': general quirky
'163': goodbye
'164': gratitude
'165': greet
'166': greeting
'167': grief
'168': how busy
'169': how old are you
'170': hue light dim
'171': hue light off
'172': hue light up
'173': improve credit score
'174': income
'175': ingredient substitution
'176': ingredients list
'177': insurance
'178': insurance change
'179': interest rate
'180': international fees
'181': international visa
'182': iot cleaning
'183': iot coffee
'184': iot hue light change
'185': iot hue light dim
'186': iot hue light off
'187': iot hue light on
'188': iot hue light up
'189': iot wemo on
'190': iot wemo plug off
'191': joke
'192': joy
'193': jump start
'194': last maintenance
'195': lists create or add
'196': lists query
'197': lists remove
'198': lost luggage
'199': love
'200': make call
'201': maybe
'202': meal suggestion
'203': meaning of life
'204': measurement conversion
'205': meeting schedule
'206': min payment
'207': mpg
'208': music
'209': music dislike ness
'210': music likeness
'211': music query
'212': music settings
'213': negative
'214': nervousness
'215': neutral
'216': new card
'217': news query
'218': next holiday
'219': next song
'220': 'no'
'221': nutrition info
'222': oil change how
'223': oil change when
'224': optimism
'225': order
'226': order checks
'227': order status
'228': paid time off request status
'229': paid time off used
'230': pay bill
'231': payday
'232': pin change
'233': play audiobook
'234': play game
'235': play music
'236': play podcasts
'237': play radio
'238': plug type
'239': podcasts
'240': positive
'241': post
'242': pride
'243': pto balance
'244': pto request
'245': qa currency
'246': qa definition
'247': qa factoid
'248': qa maths
'249': qa stock
'250': query
'251': query contact
'252': quirky
'253': radio
'254': realization
'255': recipe
'256': recommendation events
'257': recommendation locations
'258': recommendation movies
'259': redeem rewards
'260': relief
'261': reminder
'262': reminder update
'263': remorse
'264': remove
'265': repeat
'266': replacement card duration
'267': report fraud
'268': report lost card
'269': reset settings
'270': restaurant reservation
'271': restaurant reviews
'272': restaurant suggestion
'273': rewards balance
'274': roll dice
'275': rollover 401k
'276': routing
'277': sadness
'278': schedule maintenance
'279': schedule meeting
'280': send email
'281': set
'282': settings
'283': share location
'284': shopping list
'285': shopping list update
'286': smart home
'287': social post
'288': social query
'289': spelling
'290': spending history
'291': surprise
'292': sync device
'293': take away order
'294': take away query
'295': taxes
'296': tell joke
'297': text
'298': thank you
'299': ticket
'300': time
'301': timer
'302': timezone
'303': tire change
'304': tire pressure
'305': todo list
'306': todo list update
'307': traffic
'308': transactions
'309': transfer
'310': translate
'311': transport query
'312': transport taxi
'313': transport ticket
'314': transport traffic
'315': travel alert
'316': travel notification
'317': travel suggestion
'318': uber
'319': update playlist
'320': user name
'321': vaccines
'322': volume other
'323': w2 wage and tax statement
'324': weather
'325': weather query
'326': wemo off
'327': wemo plug on
'328': what are your hobbies
'329': what can i ask you
'330': what is your name
'331': what song
'332': where are you from
'333': whisper mode
'334': who do you work for
'335': who made you
'336': 'yes'
- name: dataset_name
dtype:
class_label:
names:
'0': go_emotion
'1': sentiment_tweets_2020
'2': emotion
'3': sgd
'4': clinc_150
'5': slurp
'6': ag_news
'7': dbpedia
'8': yahoo
splits:
- name: train
num_bytes: 28974188
num_examples: 115127
- name: validation
num_bytes: 3213586
num_examples: 12806
- name: test
num_bytes: 36063590
num_examples: 168365
download_size: 1744258165
dataset_size: 68251364
- config_name: out-of-domain
features:
- name: text
dtype: string
- name: labels
sequence:
class_label:
names:
'0': Add To Playlist
'1': Bank account or service
'2': Book Restaurant
'3': Checking or savings account
'4': Chemistry; Metallurgy
'5': Consumer Loan
'6': Credit card
'7': Credit card or prepaid card
'8': Credit reporting
'9': Credit reporting, credit repair services, or other personal consumer
reports
'10': Debt collection
'11': EUROPEAN UNION
'12': Electricity
'13': Fixed Constructions
'14': General tagging of new or cross-sectional technology
'15': Get Weather
'16': Human Necessities
'17': Mechanical Engineering; Lightning; Heating; Weapons; Blasting
'18': Money transfer, virtual currency, or money service
'19': Money transfers
'20': Mortgage
'21': Other financial service
'22': Payday loan
'23': Payday loan, title loan, or personal loan
'24': Performing Operations; Transporting
'25': Physics
'26': Play Music
'27': Prepaid card
'28': Rate Book
'29': Refund not showing up
'30': Search Creative Work
'31': Search Screening Event
'32': Student loan
'33': Textiles; Paper
'34': Vehicle loan or lease
'35': Virtual currency
'36': activate my card
'37': age limit
'38': agri-foodstuffs
'39': agriculture, forestry and fisheries
'40': alarm query
'41': alarm remove
'42': alarm set
'43': apple pay or google pay
'44': atm support
'45': audio volume down
'46': audio volume mute
'47': audio volume other
'48': audio volume up
'49': automatic top up
'50': balance not updated after bank transfer
'51': balance not updated after cheque or cash deposit
'52': beneficiary not allowed
'53': business and competition
'54': calendar query
'55': calendar remove
'56': calendar set
'57': cancel transfer
'58': card about to expire
'59': card acceptance
'60': card arrival
'61': card delivery estimate
'62': card linking
'63': card not working
'64': card payment fee charged
'65': card payment not recognised
'66': card payment wrong exchange rate
'67': card swallowed
'68': cash withdrawal charge
'69': cash withdrawal not recognised
'70': change pin
'71': compromised card
'72': contactless not working
'73': cooking query
'74': cooking recipe
'75': country support
'76': datetime convert
'77': datetime query
'78': declined card payment
'79': declined cash withdrawal
'80': declined transfer
'81': direct debit payment not recognised
'82': disposable card limits
'83': economics
'84': edit personal details
'85': education and communications
'86': email addcontact
'87': email query
'88': email querycontact
'89': email sendemail
'90': employment and working conditions
'91': energy
'92': environment
'93': exchange charge
'94': exchange rate
'95': exchange via app
'96': extra charge on statement
'97': failed transfer
'98': fiat currency support
'99': finance
'100': general affirm
'101': general commandstop
'102': general confirm
'103': general dontcare
'104': general explain
'105': general greet
'106': general joke
'107': general negate
'108': general praise
'109': general quirky
'110': general repeat
'111': geography
'112': get disposable virtual card
'113': get physical card
'114': getting spare card
'115': getting virtual card
'116': industry
'117': international organisations
'118': international relations
'119': iot cleaning
'120': iot coffee
'121': iot hue lightchange
'122': iot hue lightdim
'123': iot hue lightoff
'124': iot hue lighton
'125': iot hue lightup
'126': iot wemo off
'127': iot wemo on
'128': law
'129': lists createoradd
'130': lists query
'131': lists remove
'132': lost or stolen card
'133': lost or stolen phone
'134': music dislikeness
'135': music likeness
'136': music query
'137': music settings
'138': negative
'139': neutral
'140': news query
'141': order physical card
'142': passcode forgotten
'143': pending card payment
'144': pending cash withdrawal
'145': pending top up
'146': pending transfer
'147': pin blocked
'148': play audiobook
'149': play game
'150': play music
'151': play podcasts
'152': play radio
'153': politics
'154': positive
'155': production, technology and research
'156': qa currency
'157': qa definition
'158': qa factoid
'159': qa maths
'160': qa stock
'161': receiving money
'162': recommendation events
'163': recommendation locations
'164': recommendation movies
'165': request refund
'166': reverted card payment?
'167': science
'168': social post
'169': social query
'170': social questions
'171': supported cards and currencies
'172': takeaway order
'173': takeaway query
'174': terminate account
'175': top up by bank transfer charge
'176': top up by card charge
'177': top up by cash or cheque
'178': top up failed
'179': top up limits
'180': top up reverted
'181': topping up by card
'182': trade
'183': transaction charged twice
'184': transfer fee charged
'185': transfer into account
'186': transfer not received by recipient
'187': transfer timing
'188': transport
'189': transport query
'190': transport taxi
'191': transport ticket
'192': transport traffic
'193': unable to verify identity
'194': verify my identity
'195': verify source of funds
'196': verify top up
'197': virtual card not working
'198': visa or mastercard
'199': weather query
'200': why verify identity
'201': wrong amount of cash received
'202': wrong exchange rate for cash withdrawal
- name: dataset_name
dtype:
class_label:
names:
'0': amazon_polarity
'1': finance_sentiment
'2': yelp
'3': banking77
'4': snips
'5': nlu_evaluation
'6': multi_eurlex
'7': patent
'8': consumer_finance
splits:
- name: train
num_bytes: 3608196895
num_examples: 4996673
- name: test
num_bytes: 541174753
num_examples: 625911
download_size: 1744258165
dataset_size: 4149371648
- config_name: aspect-normalized-out-of-domain
features:
- name: text
dtype: string
- name: labels
sequence:
class_label:
names:
'0': Add To Playlist
'1': Bank account or service
'2': Book Restaurant
'3': Checking or savings account
'4': Chemistry; Metallurgy
'5': Consumer Loan
'6': Credit card
'7': Credit card or prepaid card
'8': Credit reporting
'9': Credit reporting, credit repair services, or other personal consumer
reports
'10': Debt collection
'11': EUROPEAN UNION
'12': Electricity
'13': Fixed Constructions
'14': General tagging of new or cross-sectional technology
'15': Get Weather
'16': Human Necessities
'17': Mechanical Engineering; Lightning; Heating; Weapons; Blasting
'18': Money transfer, virtual currency, or money service
'19': Money transfers
'20': Mortgage
'21': Other financial service
'22': Payday loan
'23': Payday loan, title loan, or personal loan
'24': Performing Operations; Transporting
'25': Physics
'26': Play Music
'27': Prepaid card
'28': Rate Book
'29': Refund not showing up
'30': Search Creative Work
'31': Search Screening Event
'32': Student loan
'33': Textiles; Paper
'34': Vehicle loan or lease
'35': Virtual currency
'36': activate my card
'37': age limit
'38': agri-foodstuffs
'39': agriculture, forestry and fisheries
'40': alarm query
'41': alarm remove
'42': alarm set
'43': apple pay or google pay
'44': atm support
'45': audio volume down
'46': audio volume mute
'47': audio volume other
'48': audio volume up
'49': automatic top up
'50': balance not updated after bank transfer
'51': balance not updated after cheque or cash deposit
'52': beneficiary not allowed
'53': business and competition
'54': calendar query
'55': calendar remove
'56': calendar set
'57': cancel transfer
'58': card about to expire
'59': card acceptance
'60': card arrival
'61': card delivery estimate
'62': card linking
'63': card not working
'64': card payment fee charged
'65': card payment not recognised
'66': card payment wrong exchange rate
'67': card swallowed
'68': cash withdrawal charge
'69': cash withdrawal not recognised
'70': change pin
'71': compromised card
'72': contactless not working
'73': cooking query
'74': cooking recipe
'75': country support
'76': datetime convert
'77': datetime query
'78': declined card payment
'79': declined cash withdrawal
'80': declined transfer
'81': direct debit payment not recognised
'82': disposable card limits
'83': economics
'84': edit personal details
'85': education and communications
'86': email addcontact
'87': email query
'88': email querycontact
'89': email sendemail
'90': employment and working conditions
'91': energy
'92': environment
'93': exchange charge
'94': exchange rate
'95': exchange via app
'96': extra charge on statement
'97': failed transfer
'98': fiat currency support
'99': finance
'100': general affirm
'101': general commandstop
'102': general confirm
'103': general dontcare
'104': general explain
'105': general greet
'106': general joke
'107': general negate
'108': general praise
'109': general quirky
'110': general repeat
'111': geography
'112': get disposable virtual card
'113': get physical card
'114': getting spare card
'115': getting virtual card
'116': industry
'117': international organisations
'118': international relations
'119': iot cleaning
'120': iot coffee
'121': iot hue lightchange
'122': iot hue lightdim
'123': iot hue lightoff
'124': iot hue lighton
'125': iot hue lightup
'126': iot wemo off
'127': iot wemo on
'128': law
'129': lists createoradd
'130': lists query
'131': lists remove
'132': lost or stolen card
'133': lost or stolen phone
'134': music dislikeness
'135': music likeness
'136': music query
'137': music settings
'138': negative
'139': neutral
'140': news query
'141': order physical card
'142': passcode forgotten
'143': pending card payment
'144': pending cash withdrawal
'145': pending top up
'146': pending transfer
'147': pin blocked
'148': play audiobook
'149': play game
'150': play music
'151': play podcasts
'152': play radio
'153': politics
'154': positive
'155': production, technology and research
'156': qa currency
'157': qa definition
'158': qa factoid
'159': qa maths
'160': qa stock
'161': receiving money
'162': recommendation events
'163': recommendation locations
'164': recommendation movies
'165': request refund
'166': reverted card payment?
'167': science
'168': social post
'169': social query
'170': social questions
'171': supported cards and currencies
'172': takeaway order
'173': takeaway query
'174': terminate account
'175': top up by bank transfer charge
'176': top up by card charge
'177': top up by cash or cheque
'178': top up failed
'179': top up limits
'180': top up reverted
'181': topping up by card
'182': trade
'183': transaction charged twice
'184': transfer fee charged
'185': transfer into account
'186': transfer not received by recipient
'187': transfer timing
'188': transport
'189': transport query
'190': transport taxi
'191': transport ticket
'192': transport traffic
'193': unable to verify identity
'194': verify my identity
'195': verify source of funds
'196': verify top up
'197': virtual card not working
'198': visa or mastercard
'199': weather query
'200': why verify identity
'201': wrong amount of cash received
'202': wrong exchange rate for cash withdrawal
- name: dataset_name
dtype:
class_label:
names:
'0': amazon_polarity
'1': finance_sentiment
'2': yelp
'3': banking77
'4': snips
'5': nlu_evaluation
'6': multi_eurlex
'7': patent
'8': consumer_finance
splits:
- name: train
num_bytes: 109566474
num_examples: 119167
- name: validation
num_bytes: 12432497
num_examples: 13263
- name: test
num_bytes: 541174753
num_examples: 625911
download_size: 1744258165
dataset_size: 663173724
---
# Universal Text Classification Dataset (UTCD)
## Load dataset
```python
from datasets import load_dataset
dataset = load_dataset('claritylab/utcd', name='in-domain')
```
## Description
UTCD is a curated compilation of 18 datasets revised for Zero-shot Text Classification spanning 3 aspect categories of Sentiment, Intent/Dialogue, and Topic classification. UTCD focuses on the task of zero-shot text classification where the candidate labels are descriptive of the text being classified. TUTCD consists of ~ 6M/800K train/test examples.
UTCD was introduced in the Findings of ACL'23 Paper **Label Agnostic Pre-training for Zero-shot Text Classification** by ***Christopher Clarke, Yuzhao Heng, Yiping Kang, Krisztian Flautner, Lingjia Tang and Jason Mars***. [Project Homepage](https://github.com/ChrisIsKing/zero-shot-text-classification/tree/master).
UTCD Datasets & Principles:
In order to make NLP models more broadly useful, zero-shot techniques need to be capable of label, domain \& aspect transfer. As such, in the construction of UTCD we enforce the following principles:
- **Textual labels**: In UTCD, we mandate the use of textual labels. While numerical label values are often used in classification tasks, descriptive textual labels such as those present in the datasets across UTCD enable the development of techniques that can leverage the class name which is instrumental in providing zero-shot support. As such, for each of the compiled datasets, labels are standardized such that the labels are descriptive of the text in natural language.
- **Diverse domains and Sequence lengths**: In addition to broad coverage of aspects, UTCD compiles diverse data across several domains such as Banking, Finance, Legal, etc each comprising varied length sequences (long and short). The datasets are listed above.
- Sentiment
- GoEmotions introduced in [GoEmotions: A Dataset of Fine-Grained Emotions](https://arxiv.org/pdf/2005.00547v2.pdf)
- TweetEval introduced in [TWEETEVAL: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://arxiv.org/pdf/2010.12421v2.pdf) (Sentiment subset)
- Emotion introduced in [CARER: Contextualized Affect Representations for Emotion Recognition](https://aclanthology.org/D18-1404.pdf)
- Amazon Polarity introduced in [Character-level Convolutional Networks for Text Classification](https://arxiv.org/pdf/1509.01626.pdf)
- Finance Phrasebank introduced in [Good debt or bad debt: Detecting semantic orientations in economic texts](https://arxiv.org/pdf/1307.5336.pdf)
- Yelp introduced in [Character-level Convolutional Networks for Text Classification](https://arxiv.org/pdf/1509.01626.pdf)
- Intent/Dialogue
- Schema-Guided Dialogue introduced in [Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://arxiv.org/pdf/1909.05855v2.pdf)
- Clinc-150 introduced in [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://arxiv.org/pdf/1909.02027v1.pdf)
- SLURP SLU introduced in [SLURP: A Spoken Language Understanding Resource Package](https://arxiv.org/pdf/2011.13205.pdf)
- Banking77 introduced in [Efficient Intent Detection with Dual Sentence Encoders](https://arxiv.org/abs/2003.04807](https://arxiv.org/pdf/2003.04807.pdf)
- Snips introduced in [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://arxiv.org/pdf/1805.10190.pdf)
- NLU Evaluation introduced in [Benchmarking Natural Language Understanding Services for building Conversational Agents](https://arxiv.org/pdf/1903.05566.pdf)
- Topic
- AG News introduced in [Character-level Convolutional Networks for Text Classification](https://arxiv.org/pdf/1509.01626.pdf)
- DBpedia 14 introduced in [DBpedia: A Nucleus for a Web of Open Data](https://link.springer.com/chapter/10.1007/978-3-540-76298-0_52)
- Yahoo Answer Topics introduced in [Character-level Convolutional Networks for Text Classification](https://arxiv.org/pdf/1509.01626.pdf)
- MultiEurlex introduced in [MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer](https://aclanthology.org/2021.emnlp-main.559v2.pdf)
- BigPatent introduced in [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://aclanthology.org/P19-1212.pdf)
- Consumer Finance introduced in [Consumer Complaint Database](https://www.consumerfinance.gov/data-research/consumer-complaints/)
## Structure
### Data Samples
Each dataset sample contains the text, the label encoded as an integer, and the dataset name encoded as an integer.
```python
{
'text': "My favourite food is anything I didn't have to cook myself.",
'labels': [215],
'dataset_name': 0
}
```
### Datasets Contained
The UTCD dataset contains 18 datasets, 9 `in-domain`, 9 `out-of-domain`, spanning 3 aspects: `sentiment`, `intent` and `topic`.
Below are statistics on the datasets.
**In-Domain Datasets**
| Dataset | Aspect | #Samples in Train/Test | #labels | average #token in text in Train/Test |
| ---------- | --------- | ---------------------- | ------- | ------------------------------------ |
| GoEmotions | sentiment | 43K/5.4K | 28 | 12/12 |
| TweetEval | sentiment | 45K/12K | 3 | 19/14 |
| Emotion | sentiment | 16K/2K | 6 | 17/17 |
| SGD | intent | 16K/4.2K | 26 | 8/9 |
| Clinc-150 | intent | 15K/4.5K | 150 | 8/8 |
| SLURP | intent | 12K/2.6K | 75 | 7/7 |
| AG News | topic | 120K7.6K | 4 | 38/37 |
| DBpedia | topic | 560K/70K | 14 | 45/45 |
| Yahoo | topic | 1.4M/60K | 10 | 10/10 |
**Out-of-Domain Datasets**
| Dataset | Aspect | #Samples in Train/Test | #labels | average #token in text |
| --------------------- | --------- | ---------------------- | ------- | ---------------------- |
| Amazon Polarity | sentiment | 3.6M/400K | 2 | 71/71 |
| Financial Phrase Bank | sentiment | 1.8K/453 | 3 | 19/19 |
| Yelp | sentiment | 650K/50K | 3 | 128/128 |
| Banking77 | intent | 10K/3.1K | 77 | 11/10 |
| SNIPS | intent | 14K/697 | 7 | 8/8 |
| NLU Eval | intent | 21K/5.2K | 68 | 7/7 |
| MultiEURLEX | topic | 55K/5K | 21 | 1198/1853 |
| Big Patent | topic | 25K/5K | 9 | 2872/2892 |
| Consumer Finance | topic | 630K/160K | 18 | 190/189 |
### Configurations
The `in-domain` and `out-of-domain` configurations has 2 splits: `train` and `test`.
The aspect-normalized configurations (`aspect-normalized-in-domain`, `aspect-normalized-out-of-domain`) has 3 splits: `train`, `validation` and `test`.
Below are statistics on the configuration splits.
**In-Domain Configuration**
| Split | #samples |
| ----- | --------- |
| Train | 2,192,703 |
| Test | 168,365 |
**Out-of-Domain Configuration**
| Split | #samples |
| ----- | --------- |
| Train | 4,996,673 |
| Test | 625,911 |
**Aspect-Normalized In-Domain Configuration**
| Split | #samples |
| ---------- | -------- |
| Train | 115,127 |
| Validation | 12,806 |
| Test | 168,365 |
**Aspect-Normalized Out-of-Domain Configuration**
| Split | #samples |
| ---------- | -------- |
| Train | 119,167 |
| Validation | 13,263 |
| Test | 625,911 |
|
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] |
Linly-AI/Chinese-pretraining-dataset
|
Linly-AI
| 2023-05-26T02:32:06Z | 32 | 24 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-05-25T08:31:43Z |
---
license: apache-2.0
---
Data source: https://github.com/CVI-SZU/Linly/wiki/Linly-OpenLLaMA
|
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] |
bbz662bbz/databricks-dolly-15k-ja-gozaru
|
bbz662bbz
| 2023-05-29T12:58:37Z | 32 | 1 |
[
"license:cc-by-sa-3.0",
"region:us"
] | null | 2023-05-28T00:51:18Z |
---
license: cc-by-sa-3.0
---
This dataset was using "kunishou/databricks-dolly-15k-ja"
This dataset is licensed under CC BY SA 3.0
Last Update : 2023-05-28
databricks-dolly-15k-ja-gozaru
kunishou/databricks-dolly-15k-ja
https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja
|
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TrainingDataPro/high_quality_webcam_video_attacks
|
TrainingDataPro
| 2023-09-14T16:47:53Z | 32 | 2 |
[
"task_categories:video-classification",
"language:en",
"license:cc-by-nc-nd-4.0",
"finance",
"legal",
"code",
"region:us"
] |
[
"video-classification"
] | 2023-05-30T08:52:17Z |
---
license: cc-by-nc-nd-4.0
task_categories:
- video-classification
language:
- en
tags:
- finance
- legal
- code
dataset_info:
features:
- name: video_file
dtype: string
- name: assignment_id
dtype: string
- name: worker_id
dtype: string
- name: gender
dtype: string
- name: age
dtype: uint8
- name: country
dtype: string
- name: resolution
dtype: string
splits:
- name: train
num_bytes: 1547
num_examples: 10
download_size: 623356178
dataset_size: 1547
---
# High Definition Live Attacks
The dataset includes live-recorded Anti-Spoofing videos from around the world, captured via **high-quality** webcams with Full HD resolution and above.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=high_quality_webcam_video_attacks) to discuss your requirements, learn about the price and buy the dataset.
.png?generation=1684702390091084&alt=media)
# Webcam Resolution
The collection of different video resolutions from Full HD (1080p) up to 4K (2160p) is provided, including several intermediate resolutions like QHD (1440p)
# Metadata
Each attack instance is accompanied by the following details:
- Unique attack identifier
- Identifier of the user recording the attack
- User's age
- User's gender
- User's country of origin
- Attack resolution
Additionally, the model of the webcam is also specified.
Metadata is represented in the `file_info.csv`.
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=high_quality_webcam_video_attacks) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
|
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] |
tasksource/com2sense
|
tasksource
| 2023-06-05T10:09:30Z | 32 | 1 |
[
"language:en",
"commonsense",
"region:us"
] | null | 2023-06-02T14:47:54Z |
---
language:
- en
tags:
- commonsense
---
https://github.com/PlusLabNLP/Com2Sense
```
@inproceedings{singh-etal-2021-com2sense,
title = "{COM}2{SENSE}: A Commonsense Reasoning Benchmark with Complementary Sentences",
author = "Singh, Shikhar and
Wen, Nuan and
Hou, Yu and
Alipoormolabashi, Pegah and
Wu, Te-lin and
Ma, Xuezhe and
Peng, Nanyun",
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.78",
doi = "10.18653/v1/2021.findings-acl.78",
pages = "883--898",
}
```
|
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kaist-ai/Multilingual-CoT-Collection
|
kaist-ai
| 2023-10-14T15:00:43Z | 32 | 15 |
[
"task_categories:text-generation",
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-4.0",
"arxiv:2305.14045",
"region:us"
] |
[
"text-generation",
"text-classification"
] | 2023-06-05T04:42:21Z |
---
license: cc-by-4.0
task_categories:
- text-generation
- text-classification
language:
- en
size_categories:
- 100K<n<1M
configs:
- config_name: fr
data_files: "./data/CoT_collection_fr.json"
- config_name: ja
data_files: "./data/CoT_collection_ja.json"
- config_name: ko
data_files: "./data/CoT_collection_ko.json"
- config_name: ru
data_files: "./data/CoT_collection_ru.json"
- config_name: zh
data_files: "./data/CoT_collection_zh.json"
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:https://github.com/kaistAI/CoT-Collection**
- **Repository:https://github.com/kaistAI/CoT-Collection**
- **Paper:https://arxiv.org/abs/2305.14045**
- **Point of Contact:[email protected]**
### Dataset Summary
The Multilingual CoT Collection is a dataset designed to induce Chain-of-Thought (CoT) capabilities into multilingual language models.
While proprietary LLMs excel at generating Chain-of-Thoughts based on prompting, smaller LMs do not have this capability. Thus, by fine-tuning to generate Chain-of-Thoughts, it could acquire such abilities.
The Multilingual CoT Collection provides 1.84 million Chain-of-Thoughts augmented across 1060 tasks from the Flan Collection.\\
Experimental results show that fine-tuning on the CoT Collection results in (1) better zero-shot performance and (2) a better base model for few-shot learning.
We also provide a multilingual version of CoT Collection at this [link](https://huggingface.co/datasets/kaist-ai/Multilingual-CoT-Collection).
### Supported Tasks and Leaderboards
1060 tasks chosen from the Flan Collection.
The list of categories within the CoT Collection are:
* Natural Language Inference
* Extractive Question Answering
* Closed Book Question Answering
* Science
* Toxic Classification
* Arithmetic
* Program Execution
* Dialogue
* Ethics
* Commonsense Reasoning
* Multiple Choice Question Answering
### Languages
English
## Dataset Structure
* source: The input that is given to the language model (LM).
* target: The ground truth answer to the source.
* rationale: The Chain of Thought (CoT) that explains how the target could be derived from the source.
* task: A category that shows which dataset the source and target was extracted from.
In our paper, we trained the underlying language model to generate in the following format:
```
\{rationale\}
[RESULT]
\{target\}
```
Then during evaluation, we parsed the prediction after the phrase ```[RESULT]```.
### Data Splits
| name | train |
|-------------------|------:|
|CoT-Collection|1837928|
### Citation Information
If you find the following model helpful, please considering citing our paper!
```
@article{kim2023cot,
title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning},
author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon},
journal={arXiv preprint arXiv:2305.14045},
year={2023}
}
```
|
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-0.13404130935668945,
0.32485470175743103,
0.3669933080673218,
-0.5583367347717285,
-0.45023876428604126,
-0.10751384496688843,
0.058092039078474045,
-0.27942946553230286
] |
notrichardren/easy_qa
|
notrichardren
| 2023-06-26T12:33:45Z | 32 | 0 |
[
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"question-answering"
] | 2023-06-11T21:29:56Z |
---
license: apache-2.0
task_categories:
- question-answering
language:
- en
pretty_name: Easy Question Answer
---
# EasyQA: A Kindergarten-Level QA Dataset for Investigating Truthfulness.
EasyQA is a GPT-3.5-turbo-generated dataset of easy kindergarten-level facts, meant to be used to prompt and evaluate large language models for "common-sense" truthful responses. This dataset was originally created to understand how different types of truthfulness may be represented in the intermediate activations of large language models. EasyQA compromises 2346 questions that span 50 categories, including art, technology, education, music, and animals. The questions are meant to be extremely simple and obvious, eliciting an obvious truth that would not be susceptible to misconceptions -- making it an excellent comparison compared to benchmarks related to other types of truth (e.g. TruthfulQA, which focuses on common misconceptions).
Credits to Kevin Wang, Richard Ren, and Phillip Guo.
## Dataset Creation
The dataset was created by prompting GPT-3.5-turbo with: "*Please generate 50 easy, obvious, common-knowledge questions that a kindergartener would learn in class about the topic prompted, as well as correct and incorrect responses. These questions should be less like trivia questions (i.e. Who is known as the Queen of Jazz?) and more like obvious facts (ie What color is the sky?). Your generations should be in the format: Question: {Your question here} Right: {Right answer} Wrong: {Wrong answer} where each question is a new line. Please follow this format verbatim (e.g. do not number the questions).*"
The following categories were used:
```
Animals
Plants
Food and drink
Music
Movies
Television shows
Literature
Sports
Geography
History
Science
Mathematics
Art
Technology
Politics
Business and Economy
Education
Health and Fitness
Environment and Climate
Space and Astronomy
Fashion and Style
Video Games
Travel and Tourism
Language and Literature
Religion and Spirituality
Famous Personalities
Cultural Events/Festivals
Cars and Automobiles
Photography
Architecture
Medicine and Health
Psychology
Philosophy
Law
Social Sciences
Human Rights
Current Events/News
Global Affairs
National Landmarks
Celebrities and Entertainment
Nature
Cooking and Baking
Gardening
DIY Projects
Dance
Comic Books and Graphic Novels
Mythology and Folklore
Internet and Social Media
Parenting and Family Life
Home Decor
```
|
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changpt/ko-lima-vicuna
|
changpt
| 2023-06-14T07:47:51Z | 32 | 16 |
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:ko",
"license:cc-by-2.0",
"KoLima",
"region:us"
] |
[
"text-generation"
] | 2023-06-14T03:58:58Z |
---
license: cc-by-2.0
task_categories:
- text-generation
language:
- ko
size_categories:
- n<1K
pretty_name: KoLima(vicuna)
tags:
- KoLima
---
# Ko Lima Vicuna Dataset
GPT4 API를 사용하여 [lima_vicuna_format 데이터](https://huggingface.co/datasets/64bits/lima_vicuna_format)를 한국어로 재생성한 데이터셋입니다.
GPT4 사용시 프롬프트는 "단순 번역이 아닌, 원문 내용을 참조하여 데이터를 재생성하라"는 내용으로 작성하였습니다.
원본 데이터셋 1,030건 중 866건의 데이터가 재생성되었습니다.
*데이터셋 제작은 웹 크롤링 전문 기업, **[해시스크래퍼(HASHSCRAPER)](https://www.hashscraper.com/)** 에서 진행되었습니다.*
*This is the lima_vicuna_format data regenerated into Korean using the GPT4 API.*
*When using GPT4, the prompt was written to read "Regenerate the data by referring to the original content, not just a simple translation".*
*Out of the 1,030 original datasets, 866 data points were regenerated.*
*The dataset was created by [HASHSCRAPER](https://www.hashscraper.com/), a company specializing in web scraping.*
---
# How to use
```python
>>> from datasets import load_dataset
>>> data = load_dataset("changpt/ko-lima-vicuna", data_files="ko_lima_vicuna.json", split="train")
>>> data
Dataset({
features: ['id', 'conversations'],
num_rows: 866
})
```
```python
>>> data[0]
{'id': '7-stackexchange',
'conversations': [{'from': 'human',
'value': '나는 ```ffmpeg```로 비디오를 GIF 파일로 변환하고 있어:\n```ffmpeg \\\n -i input.flv \\\n -ss 00:00:00.000 \\\n -pix_fmt rgb24 \\\n -r 10 \\\n -s 320x240 \\\n -t 00:00:10.000 \\\n output.gif\n```\n잘 작동하는데, 출력된 gif 파일의 품질이 너무 낮아. 변환된 gif의 품질을 어떻게 개선할 수 있을까?'},
{'from': 'gpt',
'value': '중간 이미지 파일을 피하고 싶다면, LordNeckBeard가 제공한 명령어를 ```ffmpeg```와 ImageMagick의 ```convert``` 사이에 파이프로 연결할 수 있습니다. 이렇게 하면 중간 파일이 필요하지 않습니다:\n```ffmpeg -i input.flv -vf scale=320:-1 -r 10 -f image2pipe -vcodec ppm - | convert -delay 10 -loop 0 - output.gif\n```\n```-f image2pipe```는 ffmpeg에게 비디오를 이미지로 분할하고 파이프 출력에 적합하게 만들라고 지시하며, ```-vcodec ppm```은 출력 형식을 ppm으로 지정합니다(png 형식인 경우, ```convert```가 파이프에서 모든 이미지를 읽지 않거나 ffmpeg가 모두 출력하지 않는 것 같습니다). 두 명령어의 ```-```는 각각 출력과 입력에 파이프를 사용하도록 지정합니다.\n파일을 저장하지 않고 결과를 최적화하려면, ```convert```의 출력을 두 번째 ```convert``` 명령어로 파이프할 수 있습니다:\n```ffmpeg -i input.flv -vf scale=320:-1 -r 10 -f image2pipe -vcodec ppm - | convert -delay 10 -loop 0 - gif:- | convert -layers Optimize - output.gif\n```\n```gif:-```는 ```convert```에게 출력을 gif 형식의 데이터로 파이프하도록 지시하고, ```-layers Optimize```는 두 번째 ```convert```에게 ```optimize-frame``` 및 ```optimize-transparancy``` 방법을 수행하도록 지시합니다(ImageMagick 애니메이션 최적화 소개 참조). ```-layers Optimize```의 출력이 항상 더 작은 파일 크기를 제공하지는 않으므로, 먼저 최적화 없이 gif로 변환해 보는 것이 좋습니다.\n이 과정에서 모든 것이 메모리에 있으므로 이미지가 매우 큰 경우 충분한 메모리가 필요할 수 있습니다.'}]}
```
---
# License
[CC BY 2.0 KR](https://creativecommons.org/licenses/by/2.0/kr/)
[Open AI](https://openai.com/policies/terms-of-use)
|
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dominguesm/wikipedia-ptbr-20230601
|
dominguesm
| 2023-07-13T12:31:13Z | 32 | 3 |
[
"language:pt",
"region:us"
] | null | 2023-06-17T18:45:12Z |
---
language: pt
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2424207600
num_examples: 993101
- name: test
num_bytes: 269529120
num_examples: 110345
download_size: 1626930291
dataset_size: 2693736720
---
# Dataset Card for "wikipedia-ptbr-20230601"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
BramVanroy/dolly-15k-dutch
|
BramVanroy
| 2023-10-11T08:24:55Z | 32 | 1 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:nl",
"license:cc-by-nc-sa-3.0",
"dolly",
"instruct",
"instruction",
"doi:10.57967/hf/0785",
"region:us"
] |
[
"question-answering",
"text-generation"
] | 2023-06-19T06:51:47Z |
---
license: cc-by-nc-sa-3.0
task_categories:
- question-answering
- text-generation
language:
- nl
tags:
- dolly
- instruct
- instruction
pretty_name: Dolly 15k Dutch
size_categories:
- 10K<n<100K
---
# Dataset Card for Dolly 15k Dutch
## Dataset Description
- **Homepage:** N/A
- **Repository:** N/A
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** Bram Vanroy
### Dataset Summary
This dataset contains 14,934 instructions, contexts and responses, in several natural language categories such as classification, closed QA, generation, etc. The English [original dataset](https://huggingface.co/datasets/databricks/databricks-dolly-15k) was created by @databricks, who crowd-sourced the data creation via its employees. The current dataset is a translation of that dataset through ChatGPT (`gpt-3.5-turbo`).
☕ [**Want to help me out?**](https://www.buymeacoffee.com/bramvanroy) Translating the data with the OpenAI API, and prompt testing, cost me 💸$19.38💸. If you like this dataset, please consider [buying me a coffee](https://www.buymeacoffee.com/bramvanroy) to offset a portion of this cost, I appreciate it a lot! ☕
### Languages
- Dutch
## Dataset Structure
### Data Instances
```python
{
"id": 14963,
"instruction": "Wat zijn de duurste steden ter wereld?",
"context": "",
"response": "Dit is een uitgebreide lijst van de duurste steden: Singapore, Tel Aviv, New York, Hong Kong, Los Angeles, Zurich, Genève, San Francisco, Parijs en Sydney.",
"category": "brainstorming"
}
```
### Data Fields
- **id**: the ID of the item. The following 77 IDs are not included because they could not be translated (or were too long): `[1502, 1812, 1868, 4179, 4541, 6347, 8851, 9321, 10588, 10835, 11257, 12082, 12319, 12471, 12701, 12988, 13066, 13074, 13076, 13181, 13253, 13279, 13313, 13346, 13369, 13446, 13475, 13528, 13546, 13548, 13549, 13558, 13566, 13600, 13603, 13657, 13668, 13733, 13765, 13775, 13801, 13831, 13906, 13922, 13923, 13957, 13967, 13976, 14028, 14031, 14045, 14050, 14082, 14083, 14089, 14110, 14155, 14162, 14181, 14187, 14200, 14221, 14222, 14281, 14473, 14475, 14476, 14587, 14590, 14667, 14685, 14764, 14780, 14808, 14836, 14891, 1
4966]`
- **instruction**: the instruction (question)
- **context**: additional context that the AI can use to answer the question
- **response**: the AI's expected response
- **category**: the category of this type of question (see [Dolly](https://huggingface.co/datasets/databricks/databricks-dolly-15k#annotator-guidelines) for more info)
## Dataset Creation
Both the translations and the topics were translated with OpenAI's API for `gpt-3.5-turbo`. `max_tokens=1024, temperature=0` as parameters.
The prompt template to translate the input is (where `src_lang` was English and `tgt_lang` Dutch):
```python
CONVERSATION_TRANSLATION_PROMPT = """You are asked to translate a task's instruction, optional context to the task, and the response to the task, from {src_lang} to {tgt_lang}.
Here are the requirements that you should adhere to:
1. maintain the format: the task consists of a task instruction (marked `instruction: `), optional context to the task (marked `context: `) and response for the task marked with `response: `;
2. do not translate the identifiers `instruction: `, `context: `, and `response: ` but instead copy them to your output;
3. make sure that text is fluent to read and does not contain grammatical errors. Use standard {tgt_lang} without regional bias;
4. translate the instruction and context text using informal, but standard, language;
5. make sure to avoid biases (such as gender bias, grammatical bias, social bias);
6. if the instruction is to correct grammar mistakes or spelling mistakes then you have to generate a similar mistake in the context in {tgt_lang}, and then also generate a corrected output version in the output in {tgt_lang};
7. if the instruction is to translate text from one language to another, then you do not translate the text that needs to be translated in the instruction or the context, nor the translation in the response (just copy them as-is);
8. do not translate code fragments but copy them to your output. If there are English examples, variable names or definitions in code fragments, keep them in English.
Now translate the following task with the requirements set out above. Do not provide an explanation and do not add anything else.\n\n"""
```
The system message was:
```
You are a helpful assistant that translates English to Dutch according to the requirements that are given to you.
```
Note that 77 items (0.5%) were not successfully translated. This can either mean that the prompt was too long for the given limit (`max_tokens=1024`) or that the generated translation could not be parsed into `instruction`, `context` and `response` fields. The missing IDs are `[1502, 1812, 1868, 4179, 4541, 6347, 8851, 9321, 10588, 10835, 11257, 12082, 12319, 12471, 12701, 12988, 13066, 13074, 13076, 13181, 13253, 13279, 13313, 13346, 13369, 13446, 13475, 13528, 13546, 13548, 13549, 13558, 13566, 13600, 13603, 13657, 13668, 13733, 13765, 13775, 13801, 13831, 13906, 13922, 13923, 13957, 13967, 13976, 14028, 14031, 14045, 14050, 14082, 14083, 14089, 14110, 14155, 14162, 14181, 14187, 14200, 14221, 14222, 14281, 14473, 14475, 14476, 14587, 14590, 14667, 14685, 14764, 14780, 14808, 14836, 14891, 1
4966]`.
### Source Data
#### Initial Data Collection and Normalization
Initial data collection by [databricks](https://huggingface.co/datasets/databricks/databricks-dolly-15k). See their repository for more information about this dataset.
## Considerations for Using the Data
Note that the translations in this new dataset have not been verified by humans! Use at your own risk, both in terms of quality and biases.
### Discussion of Biases
As with any machine-generated texts, users should be aware of potential biases that are included in this dataset. Although the prompt specifically includes `make sure to avoid biases (such as gender bias, grammatical bias, social bias)`, of course the impact of such command is not known. It is likely that biases remain in the dataset so use with caution.
### Other Known Limitations
The translation quality has not been verified. Use at your own risk!
### Licensing Information
This repository follows the original databricks license, which is CC BY-SA 3.0 but see below for a specific restriction.
This text was generated (either in part or in full) with GPT-3 (`gpt-3.5-turbo`), OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.
If you use this dataset, you must also follow the [Sharing](https://openai.com/policies/sharing-publication-policy) and [Usage](https://openai.com/policies/usage-policies) policies.
As clearly stated in their [Terms of Use](https://openai.com/policies/terms-of-use), specifically 2c.iii, "[you may not] use output from the Services to develop models that compete with OpenAI". That means that you cannot use this dataset to build models that are intended to commercially compete with OpenAI. [As far as I am aware](https://law.stackexchange.com/questions/93308/licensing-material-generated-with-chatgpt), that is a specific restriction that should serve as an addendum to the current license.
### Citation Information
If you use this data set, please cite :
Vanroy, B. (2023). Dolly 15k Dutch [Data set]. Hugging Face. https://doi.org/10.57967/hf/0785
```bibtex
@misc {https://doi.org/10.57967/hf/0785,
author = {Vanroy, Bram },
title = { {D}olly 15k {D}utch },
year = 2023,
url = { https://huggingface.co/datasets/BramVanroy/dolly-15k-dutch },
doi = { 10.57967/hf/0785 },
publisher = { Hugging Face }
}
```
### Contributions
Thanks to [databricks](https://huggingface.co/datasets/databricks/databricks-dolly-15k) for the initial, high-quality dataset.
|
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] |
KaiLv/UDR_BREAK
|
KaiLv
| 2023-06-21T12:23:29Z | 32 | 0 |
[
"region:us"
] | null | 2023-06-21T12:23:18Z |
---
dataset_info:
features:
- name: question_id
dtype: string
- name: question_text
dtype: string
- name: decomposition
dtype: string
- name: operators
dtype: string
- name: split
dtype: string
splits:
- name: train
num_bytes: 12757200
num_examples: 44321
- name: validation
num_bytes: 2231632
num_examples: 7760
- name: test
num_bytes: 894558
num_examples: 8069
download_size: 5175505
dataset_size: 15883390
---
# Dataset Card for "UDR_BREAK"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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CVdatasets/food101_50
|
CVdatasets
| 2023-06-29T19:58:54Z | 32 | 0 |
[
"region:us"
] | null | 2023-06-29T14:08:05Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': apple_pie
'1': baby_back_ribs
'2': beef_tartare
'3': beignets
'4': bruschetta
'5': cannoli
'6': carrot_cake
'7': ceviche
'8': cheesecake
'9': cheese_plate
'10': chicken_curry
'11': chicken_wings
'12': chocolate_cake
'13': chocolate_mousse
'14': cup_cakes
'15': donuts
'16': dumplings
'17': edamame
'18': filet_mignon
'19': fish_and_chips
'20': french_onion_soup
'21': french_toast
'22': fried_calamari
'23': garlic_bread
'24': guacamole
'25': gyoza
'26': hamburger
'27': hot_and_sour_soup
'28': hot_dog
'29': huevos_rancheros
'30': ice_cream
'31': macarons
'32': miso_soup
'33': mussels
'34': nachos
'35': omelette
'36': onion_rings
'37': oysters
'38': pizza
'39': poutine
'40': prime_rib
'41': ravioli
'42': red_velvet_cake
'43': samosa
'44': scallops
'45': spring_rolls
'46': steak
'47': strawberry_shortcake
'48': tiramisu
'49': waffles
splits:
- name: train
num_bytes: 1892100970.0
num_examples: 37500
- name: validation
num_bytes: 628838834.0
num_examples: 12500
download_size: 1091112117
dataset_size: 2520939804.0
---
# Dataset Card for "food101_50"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Rhasan97/test_data
|
Rhasan97
| 2023-07-12T18:39:15Z | 32 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-07-12T18:38:03Z |
---
license: apache-2.0
---
|
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] |
eren23/tr-snli-small
|
eren23
| 2023-07-15T17:51:53Z | 32 | 0 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-15T17:46:33Z |
---
license: cc-by-4.0
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 68011
num_examples: 441
download_size: 36196
dataset_size: 68011
---
|
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] |
lionelchg/dolly_closed_qa
|
lionelchg
| 2023-08-09T18:27:56Z | 32 | 1 |
[
"region:us"
] | null | 2023-08-07T23:30:03Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: instruction
dtype: string
- name: context
dtype: string
- name: response
dtype: string
- name: category
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 4661240.764805415
num_examples: 1684
- name: test
num_bytes: 246348.23519458546
num_examples: 89
download_size: 2899694
dataset_size: 4907589.0
---
# Dataset Card for "dolly_closed_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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imvladikon/QAmeleon
|
imvladikon
| 2023-08-13T19:36:48Z | 32 | 0 |
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:ar",
"language:bn",
"language:fi",
"language:id",
"language:ko",
"language:ru",
"language:sw",
"language:te",
"license:cc-by-4.0",
"arxiv:2211.08264",
"region:us"
] |
[
"question-answering"
] | 2023-08-13T19:29:03Z |
---
language:
- ar
- bn
- fi
- id
- ko
- ru
- sw
- te
license: cc-by-4.0
size_categories:
- 10K<n<100K
task_categories:
- question-answering
dataset_info:
- config_name: ar
features:
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dtype: string
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dtype: string
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dtype: string
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dtype: string
splits:
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download_size: 0
dataset_size: 4773335
- config_name: bn
features:
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splits:
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num_examples: 6084
download_size: 0
dataset_size: 6458441
- config_name: default
features:
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splits:
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dataset_size: 32190633
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dataset_size: 2158030
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download_size: 0
dataset_size: 2635540
- config_name: ko
features:
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features:
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splits:
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num_examples: 4673
download_size: 0
dataset_size: 5024122
configs:
- config_name: ar
data_files:
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path: ar/train-*
- config_name: bn
data_files:
- split: train
path: bn/train-*
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: fi
data_files:
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path: fi/train-*
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data_files:
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path: id/train-*
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data_files:
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path: ko/train-*
- config_name: ru
data_files:
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path: ru/train-*
- config_name: sw
data_files:
- split: train
path: sw/train-*
- config_name: te
data_files:
- split: train
path: te/train-*
---
# Dataset Card for "QAmeleon"
QAmeleon introduces synthetic multilingual QA data contaning in 8 langauges using PaLM-540B, a large language model. This dataset was generated by prompt tuning PaLM with only five examples per language. We use the synthetic data to finetune downstream QA models leading to improved accuracy in comparison to English-only and translation-based baselines.
Data available at https://storage.googleapis.com/qameleon/qamelon_pt_accepted.csv
More details can be found in the [QAmeleon: Multilingual QA with Only 5 Examples](https://arxiv.org/abs/2211.08264) which can be cited as follows:
```
@misc{agrawal2022qameleon,
title={QAmeleon: Multilingual QA with Only 5 Examples},
author={Priyanka Agrawal and Chris Alberti and Fantine Huot and Joshua Maynez and Ji Ma and Sebastian Ruder and Kuzman Ganchev and Dipanjan Das and Mirella Lapata},
year={2022},
eprint={2211.08264},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
This dataset contains a total of 47173 Question Answer instances across 8 langauges, following is the count per language.
|Language | Count |
|---------|------:|
|ar |6966 |
|bn |6084 |
|fi |5028 |
|id |6797 |
|ko |6471 |
|ru |5557 |
|sw |5597 |
|te |4673 |
|**Total** |**47173**|
The QAmeleon dataset is released under the [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
eaglewatch/Korean_Wikipedia_Dataset_for_GPT2_August_2022
|
eaglewatch
| 2023-08-25T05:35:38Z | 32 | 2 |
[
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_categories:translation",
"task_categories:conversational",
"task_categories:visual-question-answering",
"task_ids:open-domain-qa",
"task_ids:closed-domain-qa",
"task_ids:dialogue-generation",
"task_ids:visual-question-answering",
"annotations_creators:other",
"language_creators:other",
"multilinguality:multilingual",
"size_categories:100M<n<1B",
"language:ko",
"license:apache-2.0",
"gpt2",
"korean",
"wikipedia",
"pertained",
"region:us"
] |
[
"question-answering",
"text2text-generation",
"translation",
"conversational",
"visual-question-answering"
] | 2023-08-25T05:30:30Z |
---
annotations_creators:
- other
language:
- ko
language_creators:
- other
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: Korean wikipedia dataset for GPT-2 training
size_categories:
- 100M<n<1B
source_datasets: []
tags:
- gpt2
- korean
- wikipedia
- pertained
task_categories:
- question-answering
- text2text-generation
- translation
- conversational
- visual-question-answering
task_ids:
- open-domain-qa
- closed-domain-qa
- closed-domain-qa
- dialogue-generation
- visual-question-answering
viewer: true
---
# Dataset Card for korean_wikipedia_dataset_for_GPT2
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [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)
- [Contributions](#contributions)
## Dataset Description
Entire Korean language Wikipedia data for GPT-2 training as of August 1st, 2022.
email: [email protected]
### Dataset Summary
This is to make a pre-trained GPT-2 Korean model
### Languages
Korean
## Dataset Structure
### Data Instances
Train wikipedia article count: 334420
validation wikipedia article count: 83605
### Data Fields
'text'
### Data Splits
80% vs. 20%, randomly, according to the Pareto Principle.
## Dataset Creation
### Source Data
Wikipedia
https://dumps.wikimedia.org/kowiki/latest/kowiki-latest-pages-articles.xml.bz2
## Considerations for Using the Data
### Social Impact of Dataset
None
### Discussion of Biases
None
### Other Known Limitations
None
## Additional Information
### Dataset Curators
Yongwoo Jeong
|
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EgilKarlsen/AA_GPT2_FT
|
EgilKarlsen
| 2023-09-04T07:38:06Z | 32 | 0 |
[
"region:us"
] | null | 2023-09-04T07:37:40Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
- name: train
num_bytes: 80318765
num_examples: 26057
- name: test
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num_examples: 8686
download_size: 147157938
dataset_size: 107092821
---
# Dataset Card for "AA_GPT2_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
rb05751/reuters_articles
|
rb05751
| 2023-09-09T17:03:54Z | 32 | 1 |
[
"license:cc",
"region:us"
] | null | 2023-09-09T17:03:24Z |
---
license: cc
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: title
dtype: string
- name: body
dtype: string
splits:
- name: train
num_bytes: 12503434
num_examples: 15000
- name: validation
num_bytes: 4272675
num_examples: 5000
- name: test
num_bytes: 1709070
num_examples: 2000
download_size: 10790292
dataset_size: 18485179
---
|
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] |
garcianacho/human_genome_csv
|
garcianacho
| 2023-10-04T12:41:28Z | 32 | 1 |
[
"task_categories:token-classification",
"license:apache-2.0",
"biology",
"genome",
"human genome",
"bioinformatics",
"region:us"
] |
[
"token-classification"
] | 2023-09-20T08:52:07Z |
---
license: apache-2.0
task_categories:
- token-classification
tags:
- biology
- genome
- human genome
- bioinformatics
---
## Human Genome Dataset
Here is a human genome ready to be used to train LLM.
|
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] |
AntoineBlanot/alpaca-llama2-chat
|
AntoineBlanot
| 2023-10-09T07:30:40Z | 32 | 2 |
[
"region:us"
] | null | 2023-10-05T08:57:49Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 46095859
num_examples: 52002
download_size: 0
dataset_size: 46095859
---
# Dataset Card for "alpaca-llama2-chat"
This dataset is the [alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) dataset formatted for [llama2-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf).
The default system prompt, as well as special tokens has all been added for a ready-to-train dataset.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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hackaprompt/hackaprompt-dataset
|
hackaprompt
| 2023-11-29T01:56:14Z | 32 | 8 |
[
"size_categories:100K<n<1M",
"language:en",
"code",
"arxiv:2311.16119",
"region:us"
] | null | 2023-10-19T03:01:52Z |
---
language:
- en
tags:
- code
pretty_name: HackAPrompt Dataset
size_categories:
- 100K<n<1M
---
# Dataset Card for HackAPrompt 💻🔍
This dataset contains submissions from a prompt hacking competition. An in-depth analysis of the dataset has been accepted at the EMNLP 2023 conference. 📊👾
Submissions were sourced from two environments: a playground for experimentation and an official submissions platform.
The playground itself can be accessed [here](https://huggingface.co/spaces/hackaprompt/playground) 🎮
More details about the competition itself [here](http://paper.hackaprompt.com) 🏆
## Dataset Details 📋
### Dataset Description 📄
We conducted a prompt hacking competition where users were competing to "hack" different large language models (LLMs). Different levels were proposed, with varying degrees of difficulty, and for each level, 3 LLMs were evaluated: GPT-3 (`text-davinci-003`), FlanT5-XXL (`philschmid/flan-t5-xxl-sharded-fp16`), and ChatGPT (`gpt-3.5-turbo`).
We anonymously collected user submissions throughout the competition and also had users submit their best attempts via an online platform for a chance to win the competition. Users submitted their prompts, and our servers automatically evaluated their attempts. To delineate between ties, token counts were used where lower counts gave better scores.
This dataset releases all submissions sent to both our playground and submission servers. 📤📥
### Columns Description 🧾
- **level**: A numerical value indicating the difficulty or complexity of the prompt.
- **user_input**: The input provided by the user or participant in response to the given challenge.
- **prompt**: The full prompt that was used to query the model, this includes the user's input.
- **completion**: The output or completion generated by the model based on the user's input.
- **model**: The type or version of the model that generated the completion. For example, "gpt-3.5-turbo" or "FlanT5-XXL".
- **expected_completion**: The expected or ideal output that should have been generated by the model for the given user input.
- **token_count**: The number of tokens present in the user's input. This serves as a measure of the input's length.
- **correct**: A boolean value indicating whether the model's completion was correct or not, based on the expected output.
- **error**: A boolean value indicating if there was an error during the model's processing of the user input. Note: we did not include submissions that triggered errors in this dataset.
- **score**: A numerical value representing the score assigned to the model's completion based on its accuracy, correctness, and other evaluation metrics. (Only available for prompts on the submissions platform)
- **dataset**: A categorical variable indicating the source of the submission. The two categories are "playground_data" (for submissions from the playground environment) and "submission_data" (for official submissions).
- **timestamp**: The date and time when the submission was made. (Only available for playground dataset)
<!-- - **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed] -->
## Uses 🧑🔬
This dataset is meant to be used in a research context to better understand the different types of attacks "in the wild" on LLMs. 📚🔬
<!-- Address questions around how the dataset is intended to be used. -->
#### Personal and Sensitive Information 🔒
We did not release directly any personal or sensitive information explicitly. On the playground, users could submit anonymously, and we did not collect information about the users directly.
For the submissions data, teams did submit in their names, but that information has not been made available in this version of the dataset to preserve participants' privacy.
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
## Bias, Risks, and Limitations ⚠️
The data was submitted via a public portal hosted on huggingface.
We did not curate the data before publishing it.
The data may contain offensive material.
Please use at your own risk.
### Recommendations 🚀
Users should be made aware of the risks, biases, and limitations of the dataset and use at their own risk.
Please use at your own risk.
## Citation 📝
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
https://arxiv.org/abs/2311.16119
```
@inproceedings{Schulhoff:Pinto:Khan:Bouchard:Si:Boyd-Graber:Anati:Tagliabue:Kost:Carnahan-2023,
Title = {Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs Through a Global Prompt Hacking Competition},
Author = {Sander V Schulhoff and Jeremy Pinto and Anaum Khan and Louis-François Bouchard and Chenglei Si and Jordan Lee Boyd-Graber and Svetlina Anati and Valen Tagliabue and Anson Liu Kost and Christopher R Carnahan},
Booktitle = {Empirical Methods in Natural Language Processing},
Year = {2023},
Location = {Singapore}
}
```
|
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] |
antareepdey/Medical_chat_Llama-chat-50k
|
antareepdey
| 2023-10-22T03:16:54Z | 32 | 0 |
[
"region:us"
] | null | 2023-10-22T03:15:55Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: Text
dtype: string
splits:
- name: train
num_bytes: 50561249
num_examples: 50000
download_size: 31132221
dataset_size: 50561249
---
# Dataset Card for "Medical_chat_Llama-chat-50k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
kardosdrur/hestenet-qa
|
kardosdrur
| 2023-10-23T14:16:16Z | 32 | 1 |
[
"license:mit",
"region:us"
] | null | 2023-10-23T13:37:15Z |
---
license: mit
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1144206.5903728174
num_examples: 1695
- name: test
num_bytes: 286220.40962718264
num_examples: 424
download_size: 936129
dataset_size: 1430427.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Hestenet Question-Answer
The dataset is based on data from Hestenettet in the Danish Gigaword corpus.
Question-answer pairs are purely extracted on the basis of heuristics, and have not been manually evaluated.
The dataset was created for aiding the training of sentence transformer models in the Danish Foundation Models project.
The dataset is currently not production-ready.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
kardosdrur/opensubtitles-da-sv
|
kardosdrur
| 2023-10-26T07:12:17Z | 32 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-10-25T13:45:35Z |
---
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: 201404638
dataset_size: 338124697.0
---
# OpenSubtitles Danish-Swedish
Aligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Swedish.
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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] |
Otter-AI/MagnifierBench
|
Otter-AI
| 2023-11-07T03:07:33Z | 32 | 5 |
[
"license:mit",
"region:us"
] | null | 2023-10-29T05:17:28Z |
---
license: mit
---
|
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