id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
AdaptLLM/law-tasks | 2023-10-21T11:46:07.000Z | [
"arxiv:2309.09530",
"region:us"
] | AdaptLLM | null | null | 4 | 600 | 2023-09-19T07:44:48 | ---
configs:
- config_name: SCOTUS
data_files:
- split: test
path: "scotus/test.json"
- config_name: CaseHOLD
data_files:
- split: test
path: "case_hold/test.json"
- config_name: UNFAIR_ToS
data_files:
- split: test
path: "unfair_tos/test.json"
---
# Adapting Large Language Models via Reading Comprehension
This repo contains the evaluation datasets for our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530)
We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in **biomedicine, finance, and law domains**. Our 7B model competes with much larger domain-specific models like BloombergGPT-50B. Moreover, our domain-specific reading comprehension texts enhance model performance even on general benchmarks, indicating potential for developing a general LLM across more domains.
## GitHub repo:
https://github.com/microsoft/LMOps
## Domain-specific LLMs:
Our models of different domains are now available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
<p align='center'>
<img src="./comparison.png" width="700">
</p>
## Domain-specific Tasks:
To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
## Citation:
```bibtex
@inproceedings{AdaptLLM,
title={Adapting Large Language Models via Reading Comprehension},
author={Daixuan Cheng and Shaohan Huang and Furu Wei},
url={https://arxiv.org/abs/2309.09530},
year={2023},
}
```
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biomrc | 2023-04-05T09:41:42.000Z | [
"language:en",
"region:us"
] | null | We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard. | @inproceedings{pappas-etal-2020-biomrc,
title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension",
author = "Pappas, Dimitris and
Stavropoulos, Petros and
Androutsopoulos, Ion and
McDonald, Ryan",
booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.bionlp-1.15",
pages = "140--149",
abstract = "We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.",
} | 3 | 596 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: biomrc
pretty_name: BIOMRC
dataset_info:
- config_name: plain_text
features:
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dtype: string
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download_size: 19685
dataset_size: 59925
---
# Dataset Card for "biomrc"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [http://nlp.cs.aueb.gr/](http://nlp.cs.aueb.gr/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1.29 GB
- **Size of the generated dataset:** 5.81 GB
- **Total amount of disk used:** 7.09 GB
### Dataset Summary
We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.
### 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
### Data Instances
#### biomrc_large_A
- **Size of downloaded dataset files:** 408.08 MB
- **Size of the generated dataset:** 1.92 GB
- **Total amount of disk used:** 2.33 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"abstract": "\"OBJECTIVES: @entity9 is a @entity10 that may result from greater occipital nerve entrapment. Entrapped peripheral nerves typica...",
"answer": "@entity9 :: (MESH:D009437,Disease) :: ['unilateral occipital neuralgia']\n",
"entities_list": ["@entity1 :: ('9606', 'Species') :: ['patients']", "@entity10 :: ('MESH:D006261', 'Disease') :: ['headache', 'Headache']", "@entity9 :: ('MESH:D009437', 'Disease') :: ['Occipital neuralgia', 'unilateral occipital neuralgia']"],
"title": "Sonographic evaluation of the greater occipital nerve in XXXX .\n"
}
```
#### biomrc_large_B
- **Size of downloaded dataset files:** 343.06 MB
- **Size of the generated dataset:** 1.54 GB
- **Total amount of disk used:** 1.88 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"abstract": "\"BACKGROUND: Adults with physical disabilities are less likely than others to receive @entity2 screening. It is not known, howev...",
"answer": "@entity2",
"entities_list": ["@entity2", "@entity1", "@entity0", "@entity3"],
"title": "Does a standard measure of self-reported physical disability correlate with clinician perception of impairment related to XXXX screening?\n"
}
```
#### biomrc_small_A
- **Size of downloaded dataset files:** 68.88 MB
- **Size of the generated dataset:** 236.32 MB
- **Total amount of disk used:** 305.20 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"abstract": "\"PURPOSE: @entity120 ( @entity120 ) is a life-limiting @entity102 that presents as an elevated blood pressure in the pulmonary a...",
"answer": "@entity148 :: (MESH:D001008,Disease) :: ['anxiety']\n",
"entities_list": "[\"@entity1 :: ('9606', 'Species') :: ['patients']\", \"@entity308 :: ('MESH:D003866', 'Disease') :: ['depression']\", \"@entity146 :...",
"title": "A predictive model of the effects of @entity308 , XXXX , stress, 6-minute-walk distance, and social support on health-related quality of life in an adult pulmonary hypertension population.\n"
}
```
#### biomrc_small_B
- **Size of downloaded dataset files:** 57.70 MB
- **Size of the generated dataset:** 189.62 MB
- **Total amount of disk used:** 247.33 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"abstract": "\"Single-agent activity for @entity12 reflected by response rates of 10%-30% has been reported in @entity0 with @entity3 ( @entit...",
"answer": "@entity10",
"entities_list": ["@entity0", "@entity6", "@entity2", "@entity5", "@entity12", "@entity11", "@entity1", "@entity7", "@entity9", "@entity10", "@entity3", "@entity4", "@entity8"],
"title": "No synergistic activity of @entity7 and XXXX in the treatment of @entity3 .\n"
}
```
#### biomrc_tiny_A
- **Size of downloaded dataset files:** 0.02 MB
- **Size of the generated dataset:** 0.07 MB
- **Total amount of disk used:** 0.09 MB
An example of 'test' looks as follows.
```
This example was too long and was cropped:
{
"abstract": "\"OBJECTIVE: Decompressive craniectomy (DC) requires later cranioplasty (CP) in survivors. However, if additional ventriculoperit...",
"answer": "@entity260 :: (MESH:D011183,Disease) :: ['Postoperative Complications']\n",
"entities_list": ["@entity1 :: ('9606', 'Species') :: ['Patients', 'patients', 'Patient']", "@entity260 :: ('MESH:D011183', 'Disease') :: ['VPS regarding postoperative complications']", "@entity1276 :: ('MESH:D006849', 'Disease') :: ['hydrocephalus']"],
"title": "Cranioplasty and Ventriculoperitoneal Shunt Placement after Decompressive Craniectomy: Staged Surgery Is Associated with Fewer XXXX .\n"
}
```
### Data Fields
The data fields are the same among all splits.
#### biomrc_large_A
- `abstract`: a `string` feature.
- `title`: a `string` feature.
- `entities_list`: a `list` of `string` features.
- `answer`: a `string` feature.
#### biomrc_large_B
- `abstract`: a `string` feature.
- `title`: a `string` feature.
- `entities_list`: a `list` of `string` features.
- `answer`: a `string` feature.
#### biomrc_small_A
- `abstract`: a `string` feature.
- `title`: a `string` feature.
- `entities_list`: a `list` of `string` features.
- `answer`: a `string` feature.
#### biomrc_small_B
- `abstract`: a `string` feature.
- `title`: a `string` feature.
- `entities_list`: a `list` of `string` features.
- `answer`: a `string` feature.
#### biomrc_tiny_A
- `abstract`: a `string` feature.
- `title`: a `string` feature.
- `entities_list`: a `list` of `string` features.
- `answer`: a `string` feature.
### Data Splits
#### biomrc_large_A
| |train |validation|test |
|--------------|-----:|---------:|----:|
|biomrc_large_A|700000| 50000|62707|
#### biomrc_large_B
| |train |validation|test |
|--------------|-----:|---------:|----:|
|biomrc_large_B|700000| 50000|62707|
#### biomrc_small_A
| |train|validation|test|
|--------------|----:|---------:|---:|
|biomrc_small_A|87500| 6250|6250|
#### biomrc_small_B
| |train|validation|test|
|--------------|----:|---------:|---:|
|biomrc_small_B|87500| 6250|6250|
#### biomrc_tiny_A
| |test|
|-------------|---:|
|biomrc_tiny_A| 30|
## 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
```
@inproceedings{pappas-etal-2020-biomrc,
title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension",
author = "Pappas, Dimitris and
Stavropoulos, Petros and
Androutsopoulos, Ion and
McDonald, Ryan",
booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.bionlp-1.15",
pages = "140--149",
abstract = "We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.",
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@PetrosStav](https://github.com/PetrosStav), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | 15,183 | [
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kuanhuggingface/promptTTS_encodec_v2_small | 2023-06-12T05:45:16.000Z | [
"region:us"
] | kuanhuggingface | null | null | 0 | 596 | 2023-06-12T05:36:48 | ---
dataset_info:
features:
- name: file_id
dtype: string
- name: instruction
dtype: string
- name: transcription
dtype: string
- name: src_encodec_0
sequence: int64
- name: src_encodec_1
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- name: src_encodec_2
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- name: src_encodec_3
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- name: src_encodec_4
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- name: src_encodec_5
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- name: src_encodec_6
sequence: int64
- name: src_encodec_7
sequence: int64
- name: tgt_encodec_0
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- name: tgt_encodec_1
sequence: int64
- name: tgt_encodec_2
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- name: tgt_encodec_3
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- name: tgt_encodec_4
sequence: int64
- name: tgt_encodec_5
sequence: int64
- name: tgt_encodec_6
sequence: int64
- name: tgt_encodec_7
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splits:
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num_bytes: 2975164369
num_examples: 47270
- name: validation
num_bytes: 97855975
num_examples: 1349
- name: test
num_bytes: 80754157
num_examples: 1350
download_size: 437609990
dataset_size: 3153774501
---
# Dataset Card for "promptTTS_encodec_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,290 | [
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] |
jxie/stl10 | 2023-08-10T07:13:23.000Z | [
"region:us"
] | jxie | null | null | 0 | 596 | 2023-08-10T07:08:50 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '1'
'1': '10'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9': '9'
splits:
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num_bytes: 76300500.0
num_examples: 5000
- name: test
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num_examples: 8000
- name: unlabeled
num_bytes: 1764141081.0
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- name: train_0
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- name: train_1
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- name: train_2
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- name: train_3
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- name: train_4
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- name: train_5
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- name: train_6
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- name: train_8
num_bytes: 17773354.0
num_examples: 1000
- name: train_9
num_bytes: 17778944.0
num_examples: 1000
download_size: 2180539841
dataset_size: 2136030395.0
---
# Dataset Card for "stl10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,418 | [
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shibing624/nli-zh-all | 2023-06-22T06:39:46.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:shibing624",
"language_creators:shibing624",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:https://github.com/shibing624/text2vec",
"language:zh",
"license:cc-by-4.0",
"region:us"
] | shibing624 | The SNLI corpus (version 1.0) is a merged chinese sentence similarity dataset, supporting the task of natural language
inference (NLI), also known as recognizing textual entailment (RTE). | https://github.com/shibing624/text2vec | 18 | 595 | 2023-06-14T05:12:45 | ---
annotations_creators:
- shibing624
language_creators:
- shibing624
language:
- zh
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- https://github.com/shibing624/text2vec
task_categories:
- text-classification
task_ids:
- natural-language-inference
- semantic-similarity-scoring
- text-scoring
paperswithcode_id: nli
pretty_name: Chinese Natural Language Inference
---
# Dataset Card for nli-zh-all
## Dataset Description
- **Repository:** [Chinese NLI dataset](https://github.com/shibing624/text2vec)
- **Dataset:** [zh NLI](https://huggingface.co/datasets/shibing624/nli-zh-all)
- **Size of downloaded dataset files:** 4.7 GB
- **Total amount of disk used:** 4.7 GB
### Dataset Summary
中文自然语言推理（NLI）数据合集（nli-zh-all）
整合了文本推理，相似，摘要，问答，指令微调等任务的820万高质量数据，并转化为匹配格式数据集。
### Supported Tasks and Leaderboards
Supported Tasks: 支持中文文本匹配任务，文本相似度计算等相关任务。
中文匹配任务的结果目前在顶会paper上出现较少，我罗列一个我自己训练的结果：
**Leaderboard:** [NLI_zh leaderboard](https://github.com/shibing624/text2vec)
### Languages
数据集均是简体中文文本。
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{"text1":"借款后多长时间给打电话","text2":"借款后多久打电话啊","label":1}
{"text1":"没看到微粒贷","text2":"我借那么久也没有提升啊","label":0}
```
- label 有2个标签，1表示相似，0表示不相似。
### Data Fields
The data fields are the same among all splits.
- `text1`: a `string` feature.
- `text2`: a `string` feature.
- `label`: a classification label, with possible values including entailment(1), contradiction(0)。
### Data Splits
after remove None and len(text) < 1 data:
```shell
$ wc -l nli-zh-all/*
48818 nli-zh-all/alpaca_gpt4-train.jsonl
5000 nli-zh-all/amazon_reviews-train.jsonl
519255 nli-zh-all/belle-train.jsonl
16000 nli-zh-all/cblue_chip_sts-train.jsonl
549326 nli-zh-all/chatmed_consult-train.jsonl
10142 nli-zh-all/cmrc2018-train.jsonl
395927 nli-zh-all/csl-train.jsonl
50000 nli-zh-all/dureader_robust-train.jsonl
709761 nli-zh-all/firefly-train.jsonl
9568 nli-zh-all/mlqa-train.jsonl
455875 nli-zh-all/nli_zh-train.jsonl
50486 nli-zh-all/ocnli-train.jsonl
2678694 nli-zh-all/simclue-train.jsonl
419402 nli-zh-all/snli_zh-train.jsonl
3024 nli-zh-all/webqa-train.jsonl
1213780 nli-zh-all/wiki_atomic_edits-train.jsonl
93404 nli-zh-all/xlsum-train.jsonl
1006218 nli-zh-all/zhihu_kol-train.jsonl
8234680 total
```
### Data Length
count text length script: https://github.com/shibing624/text2vec/blob/master/examples/data/count_text_length.py
## Dataset Creation
### Curation Rationale
受[m3e-base](https://huggingface.co/moka-ai/m3e-base#M3E%E6%95%B0%E6%8D%AE%E9%9B%86)启发，合并了中文高质量NLI(natural langauge inference)数据集，
这里把这个数据集上传到huggingface的datasets，方便大家使用。
### Source Data
#### Initial Data Collection and Normalization
如果您想要查看数据集的构建方法，你可以在 [https://github.com/shibing624/text2vec/blob/master/examples/data/build_zh_nli_dataset.py](https://github.com/shibing624/text2vec/blob/master/examples/data/build_zh_nli_dataset.py) 中找到生成 nli-zh-all 数据集的脚本，所有数据均上传到 huggingface datasets。
| 数据集名称 | 领域 | 数量 | 任务类型 | Prompt | 质量 | 数据提供者 | 说明 | 是否开源/研究使用 | 是否商用 | 脚本 | Done | URL | 是否同质 |
|:---------------------| :---- |:-----------|:---------------- |:------ |:----|:-----------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------- |:------|:---- |:---- |:---------------------------------------------------------------------------------------------|:------|
| cmrc2018 | 百科 | 14,363 | 问答 | 问答 | 优 | Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu | https://github.com/ymcui/cmrc2018/blob/master/README_CN.md 专家标注的基于维基百科的中文阅读理解数据集，将问题和上下文视为正例 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/cmrc2018 | 否 |
| belle_0.5m | 百科 | 500,000 | 指令微调 | 无 | 优 | LianjiaTech/BELLE | belle 的指令微调数据集，使用 self instruct 方法基于 gpt3.5 生成 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/BelleGroup/ | 否 |
| firefily | 百科 | 1,649,399 | 指令微调 | 无 | 优 | YeungNLP | Firefly（流萤） 是一个开源的中文对话式大语言模型，使用指令微调（Instruction Tuning）在中文数据集上进行调优。使用了词表裁剪、ZeRO等技术，有效降低显存消耗和提高训练效率。 在训练中，我们使用了更小的模型参数量，以及更少的计算资源。 | 未说明 | 未说明 | 是 | 是 | https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M | 否 |
| alpaca_gpt4 | 百科 | 48,818 | 指令微调 | 无 | 优 | Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao | 本数据集是参考Alpaca方法基于GPT4得到的self-instruct数据，约5万条。 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/shibing624/alpaca-zh | 否 |
| zhihu_kol | 百科 | 1,006,218 | 问答 | 问答 | 优 | wangrui6 | 知乎问答 | 未说明 | 未说明 | 是 | 是 | https://huggingface.co/datasets/wangrui6/Zhihu-KOL | 否 |
| amazon_reviews_multi | 电商 | 210,000 | 问答 文本分类 | 摘要 | 优 | 亚马逊 | 亚马逊产品评论数据集 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/amazon_reviews_multi/viewer/zh/train?row=8 | 否 |
| mlqa | 百科 | 85,853 | 问答 | 问答 | 良 | patrickvonplaten | 一个用于评估跨语言问答性能的基准数据集 | 是 | 未说明 | 是 | 是 | https://huggingface.co/datasets/mlqa/viewer/mlqa-translate-train.zh/train?p=2 | 否 |
| xlsum | 新闻 | 93,404 | 摘要 | 摘要 | 良 | BUET CSE NLP Group | BBC的专业注释文章摘要对 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/csebuetnlp/xlsum/viewer/chinese_simplified/train?row=259 | 否 |
| ocnli | 口语 | 17,726 | 自然语言推理 | 推理 | 良 | Thomas Wolf | 自然语言推理数据集 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/clue/viewer/ocnli | 是 |
| BQ | 金融 | 60,000 | 文本分类 | 相似 | 优 | Intelligent Computing Research Center, Harbin Institute of Technology(Shenzhen) | http://icrc.hitsz.edu.cn/info/1037/1162.htm BQ 语料库包含来自网上银行自定义服务日志的 120，000 个问题对。它分为三部分：100，000 对用于训练，10，000 对用于验证，10，000 对用于测试。 数据提供者： 哈尔滨工业大学（深圳）智能计算研究中心 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/shibing624/nli_zh/viewer/BQ | 是 |
| lcqmc | 口语 | 149,226 | 文本分类 | 相似 | 优 | Ming Xu | 哈工大文本匹配数据集，LCQMC 是哈尔滨工业大学在自然语言处理国际顶会 COLING2018 构建的问题语义匹配数据集，其目标是判断两个问题的语义是否相同 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/shibing624/nli_zh/viewer/LCQMC/train | 是 |
| paws-x | 百科 | 23,576 | 文本分类 | 相似 | 优 | Bhavitvya Malik | PAWS Wiki中的示例 | 是 | 是 | 是 | 是 | https://huggingface.co/datasets/paws-x/viewer/zh/train | 是 |
| wiki_atomic_edit | 百科 | 1,213,780 | 平行语义 | 相似 | 优 | abhishek thakur | 基于中文维基百科的编辑记录收集的数据集 | 未说明 | 未说明 | 是 | 是 | https://huggingface.co/datasets/wiki_atomic_edits | 是 |
| chatmed_consult | 医药 | 549,326 | 问答 | 问答 | 优 | Wei Zhu | 真实世界的医学相关的问题，使用 gpt3.5 进行回答 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/michaelwzhu/ChatMed_Consult_Dataset | 否 |
| webqa | 百科 | 42,216 | 问答 | 问答 | 优 | suolyer | 百度于2016年开源的数据集，数据来自于百度知道；格式为一个问题多篇意思基本一致的文章，分为人为标注以及浏览器检索；数据整体质量中，因为混合了很多检索而来的文章 | 是 | 未说明 | 是 | 是 | https://huggingface.co/datasets/suolyer/webqa/viewer/suolyer--webqa/train?p=3 | 否 |
| dureader_robust | 百科 | 65,937 | 机器阅读理解 问答 | 问答 | 优 | 百度 | DuReader robust旨在利用真实应用中的数据样本来衡量阅读理解模型的鲁棒性，评测模型的过敏感性、过稳定性以及泛化能力，是首个中文阅读理解鲁棒性数据集。 | 是 | 是 | 是 | 是 | https://huggingface.co/datasets/PaddlePaddle/dureader_robust/viewer/plain_text/train?row=96 | 否 |
| csl | 学术 | 395,927 | 语料 | 摘要 | 优 | Yudong Li, Yuqing Zhang, Zhe Zhao, Linlin Shen, Weijie Liu, Weiquan Mao and Hui Zhang | 提供首个中文科学文献数据集（CSL），包含 396,209 篇中文核心期刊论文元信息 （标题、摘要、关键词、学科、门类）。CSL 数据集可以作为预训练语料，也可以构建许多NLP任务，例如文本摘要（标题预测）、 关键词生成和文本分类等。 | 是 | 是 | 是 | 是 | https://huggingface.co/datasets/neuclir/csl | 否 |
| snli-zh | 口语 | 419,402 | 文本分类 | 推理 | 优 | liuhuanyong | 中文SNLI数据集，翻译自英文SNLI | 是 | 否 | 是 | 是 | https://github.com/liuhuanyong/ChineseTextualInference/ | 是 |
| SimCLUE | 百科 | 2,678,694 | 平行语义 | 相似 | 优 | 数据集合，请在 simCLUE 中查看 | 整合了中文领域绝大多数可用的开源的语义相似度和自然语言推理的数据集，并重新做了数据拆分和整理。 | 是 | 否 | 否 | 是 | https://github.com/CLUEbenchmark/SimCLUE | 是 |
#### Who are the source language producers?
数据集的版权归原作者所有，使用各数据集时请尊重原数据集的版权。
SNLI:
@inproceedings{snli:emnlp2015,
Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
Publisher = {Association for Computational Linguistics},
Title = {A large annotated corpus for learning natural language inference},
Year = {2015}
}
#### Who are the annotators?
原作者。
### Social Impact of Dataset
This dataset was developed as a benchmark for evaluating representational systems for text, especially including those induced by representation learning methods, in the task of predicting truth conditions in a given context.
Systems that are successful at such a task may be more successful in modeling semantic representations.
### Licensing Information
for reasearch
用于学术研究
### Contributions
[shibing624](https://github.com/shibing624) add this dataset.
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OxAISH-AL-LLM/wiki_toxic | 2022-09-19T15:53:19.000Z | [
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other",
"language:en",
"license:cc0-1.0",
"wikipedia",
"toxicity",
"toxic comments",
"region:us"
] | OxAISH-AL-LLM | Jigsaw Toxic Comment Challenge dataset. This dataset was the basis of a Kaggle competition run by Jigsaw | """
_DESCRIPTION = | 9 | 594 | 2022-08-25T12:59:12 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: Toxic Wikipedia Comments
size_categories:
- 100K<n<1M
source_datasets:
- extended|other
tags:
- wikipedia
- toxicity
- toxic comments
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Wiki Toxic
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Wiki Toxic dataset is a modified, cleaned version of the dataset used in the [Kaggle Toxic Comment Classification challenge](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/overview) from 2017/18. The dataset contains comments collected from Wikipedia forums and classifies them into two categories, `toxic` and `non-toxic`.
The Kaggle dataset was cleaned using the included `clean.py` file.
### Supported Tasks and Leaderboards
- Text Classification: the dataset can be used for training a model to recognise toxicity in sentences and classify them accordingly.
### Languages
The sole language used in the dataset is English.
## Dataset Structure
### Data Instances
For each data point, there is an id, the comment_text itself, and a label (0 for non-toxic, 1 for toxic).
```
{'id': 'a123a58f610cffbc',
'comment_text': '"This article SUCKS. It may be poorly written, poorly formatted, or full of pointless crap that no one cares about, and probably all of the above. If it can be rewritten into something less horrible, please, for the love of God, do so, before the vacuum caused by its utter lack of quality drags the rest of Wikipedia down into a bottomless pit of mediocrity."',
'label': 1}
```
### Data Fields
- `id`: A unique identifier string for each comment
- `comment_text`: A string containing the text of the comment
- `label`: An integer, either 0 if the comment is non-toxic, or 1 if the comment is toxic
### Data Splits
The Wiki Toxic dataset has three splits: *train*, *validation*, and *test*. The statistics for each split are below:
| Dataset Split | Number of data points in split |
| ----------- | ----------- |
| Train | 127,656 |
| Validation | 31,915 |
| Test | 63,978 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | 4,296 | [
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masakhaner | 2023-06-01T14:59:56.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
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"language:am",
"language:ha",
"language:ig",
"language:lg",
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"language:pcm",
"language:rw",
"language:sw",
"language:wo",
"language:yo",
"license:unknown",
"arxiv:2103.11811",
"region:us"
] | null | MasakhaNER is the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages.
Named entities are phrases that contain the names of persons, organizations, locations, times and quantities.
Example:
[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
MasakhaNER is a named entity dataset consisting of PER, ORG, LOC, and DATE entities annotated by Masakhane for ten African languages:
- Amharic
- Hausa
- Igbo
- Kinyarwanda
- Luganda
- Luo
- Nigerian-Pidgin
- Swahili
- Wolof
- Yoruba
The train/validation/test sets are available for all the ten languages.
For more details see https://arxiv.org/abs/2103.11811 | @article{Adelani2021MasakhaNERNE,
title={MasakhaNER: Named Entity Recognition for African Languages},
author={D. Adelani and Jade Abbott and Graham Neubig and Daniel D'Souza and Julia Kreutzer and Constantine Lignos
and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and
Israel Abebe Azime and S. Muhammad and Chris C. Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and
Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and J. Alabi and Seid Muhie Yimam and Tajuddeen R. Gwadabe and
Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and V. Otiende and Iroro Orife and Davis David and
Samba Ngom and Tosin P. Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and
C. Chukwuneke and N. Odu and Eric Peter Wairagala and S. Oyerinde and Clemencia Siro and Tobius Saul Bateesa and
Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and
Ayodele Awokoya and Mouhamadane Mboup and D. Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and
Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and
Thierno Ibrahima Diop and A. Diallo and Adewale Akinfaderin and T. Marengereke and Salomey Osei},
journal={ArXiv},
year={2021},
volume={abs/2103.11811}
} | 4 | 592 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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language:
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license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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task_ids:
- named-entity-recognition
pretty_name: MasakhaNER
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---
# Dataset Card for MasakhaNER
## 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:** [homepage](https://github.com/masakhane-io/masakhane-ner)
- **Repository:** [github](https://github.com/masakhane-io/masakhane-ner)
- **Paper:** [paper](https://arxiv.org/abs/2103.11811)
- **Point of Contact:** [Masakhane](https://www.masakhane.io/) or didelani@lsv.uni-saarland.de
### Dataset Summary
MasakhaNER is the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages.
Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example:
[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
MasakhaNER is a named entity dataset consisting of PER, ORG, LOC, and DATE entities annotated by Masakhane for ten African languages:
- Amharic
- Hausa
- Igbo
- Kinyarwanda
- Luganda
- Luo
- Nigerian-Pidgin
- Swahili
- Wolof
- Yoruba
The train/validation/test sets are available for all the ten languages.
For more details see https://arxiv.org/abs/2103.11811
### Supported Tasks and Leaderboards
[More Information Needed]
- `named-entity-recognition`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better). A named entity is correct only if it is an exact match of the corresponding entity in the data.
### Languages
There are ten languages available :
- Amharic (amh)
- Hausa (hau)
- Igbo (ibo)
- Kinyarwanda (kin)
- Luganda (kin)
- Luo (luo)
- Nigerian-Pidgin (pcm)
- Swahili (swa)
- Wolof (wol)
- Yoruba (yor)
## Dataset Structure
### Data Instances
The examples look like this for Yorùbá:
```
from datasets import load_dataset
data = load_dataset('masakhaner', 'yor')
# Please, specify the language code
# A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
{'id': '0',
'ner_tags': [B-DATE, I-DATE, 0, 0, 0, 0, 0, B-PER, I-PER, I-PER, O, O, O, O],
'tokens': ['Wákàtí', 'méje', 'ti', 'ré', 'kọjá', 'lọ', 'tí', 'Luis', 'Carlos', 'Díaz', 'ti', 'di', 'awati', '.']
}
```
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE",
```
In the NER tags, a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & time (DATE).
It is assumed that named entities are non-recursive and non-overlapping. In case a named entity is embedded in another named entity usually, only the top level entity is marked.
### Data Splits
For all languages, there are three splits.
The original splits were named `train`, `dev` and `test` and they correspond to the `train`, `validation` and `test` splits.
The splits have the following sizes :
| Language | train | validation | test |
|-----------------|------:|-----------:|-----:|
| Amharic | 1750 | 250 | 500 |
| Hausa | 1903 | 272 | 545 |
| Igbo | 2233 | 319 | 638 |
| Kinyarwanda | 2110 | 301 | 604 |
| Luganda | 2003 | 200 | 401 |
| Luo | 644 | 92 | 185 |
| Nigerian-Pidgin | 2100 | 300 | 600 |
| Swahili | 2104 | 300 | 602 |
| Wolof | 1871 | 267 | 536 |
| Yoruba | 2124 | 303 | 608 |
## Dataset Creation
### Curation Rationale
The dataset was introduced to introduce new resources to ten languages that were under-served for natural language processing.
[More Information Needed]
### Source Data
The source of the data is from the news domain, details can be found here https://arxiv.org/abs/2103.11811
#### Initial Data Collection and Normalization
The articles were word-tokenized, information on the exact pre-processing pipeline is unavailable.
#### Who are the source language producers?
The source language was produced by journalists and writers employed by the news agency and newspaper mentioned above.
### Annotations
#### Annotation process
Details can be found here https://arxiv.org/abs/2103.11811
#### Who are the annotators?
Annotators were recruited from [Masakhane](https://www.masakhane.io/)
### Personal and Sensitive Information
The data is sourced from newspaper source and only contains mentions of public figures or individuals
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains.
## Additional Information
### Dataset Curators
### Licensing Information
The licensing status of the data is CC 4.0 Non-Commercial
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@article{Adelani2021MasakhaNERNE,
title={MasakhaNER: Named Entity Recognition for African Languages},
author={D. Adelani and Jade Abbott and Graham Neubig and Daniel D'Souza and Julia Kreutzer and Constantine Lignos
and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and
Israel Abebe Azime and S. Muhammad and Chris C. Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and
Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and J. Alabi and Seid Muhie Yimam and Tajuddeen R. Gwadabe and
Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and V. Otiende and Iroro Orife and Davis David and
Samba Ngom and Tosin P. Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and
C. Chukwuneke and N. Odu and Eric Peter Wairagala and S. Oyerinde and Clemencia Siro and Tobius Saul Bateesa and
Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and
Ayodele Awokoya and Mouhamadane Mboup and D. Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and
Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and
Thierno Ibrahima Diop and A. Diallo and Adewale Akinfaderin and T. Marengereke and Salomey Osei},
journal={ArXiv},
year={2021},
volume={abs/2103.11811}
}
```
### Contributions
Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset. | 14,126 | [
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HuggingFaceH4/test-dataset-all-splits | 2023-04-25T22:09:49.000Z | [
"region:us"
] | HuggingFaceH4 | null | null | 0 | 587 | 2023-04-25T22:09:40 | ---
dataset_info:
features:
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: prompt
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train_ift
num_bytes: 230850
num_examples: 100
- name: train_rl
num_bytes: 369068
num_examples: 100
- name: train_rm
num_bytes: 369068
num_examples: 100
- name: test_rm
num_bytes: 312141
num_examples: 100
- name: test_rl
num_bytes: 312141
num_examples: 100
- name: test_ift
num_bytes: 218856
num_examples: 100
download_size: 1071322
dataset_size: 1812124
---
# Dataset Card for "test-dataset-all-splits"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 998 | [
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ted_multi | 2023-04-05T13:42:14.000Z | [
"region:us"
] | null | Massively multilingual (60 language) data set derived from TED Talk transcripts.
Each record consists of parallel arrays of language and text. Missing and
incomplete translations will be filtered out. | @InProceedings{qi-EtAl:2018:N18-2,
author = {Qi, Ye and Sachan, Devendra and Felix, Matthieu and Padmanabhan, Sarguna and Neubig, Graham},
title = {When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?},
booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
month = {June},
year = {2018},
address = {New Orleans, Louisiana},
publisher = {Association for Computational Linguistics},
pages = {529--535},
abstract = {The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained. Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks, which often suffer from paucity of data. However, their utility for NMT has not been extensively explored. In this work, we perform five sets of experiments that analyze when we can expect pre-trained word embeddings to help in NMT tasks. We show that such embeddings can be surprisingly effective in some cases -- providing gains of up to 20 BLEU points in the most favorable setting.},
url = {http://www.aclweb.org/anthology/N18-2084}
} | 2 | 584 | 2022-03-02T23:29:22 | ---
pretty_name: TEDMulti
paperswithcode_id: null
dataset_info:
features:
- name: translations
dtype:
translation_variable_languages:
languages:
- ar
- az
- be
- bg
- bn
- bs
- calv
- cs
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fr-ca
- gl
- he
- hi
- hr
- hu
- hy
- id
- it
- ja
- ka
- kk
- ko
- ku
- lt
- mk
- mn
- mr
- ms
- my
- nb
- nl
- pl
- pt
- pt-br
- ro
- ru
- sk
- sl
- sq
- sr
- sv
- ta
- th
- tr
- uk
- ur
- vi
- zh
- zh-cn
- zh-tw
num_languages: 60
- name: talk_name
dtype: string
config_name: plain_text
splits:
- name: test
num_bytes: 23364983
num_examples: 7213
- name: train
num_bytes: 748209995
num_examples: 258098
- name: validation
num_bytes: 19435383
num_examples: 6049
download_size: 352222045
dataset_size: 791010361
---
# Dataset Card for "ted_multi"
## 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/neulab/word-embeddings-for-nmt](https://github.com/neulab/word-embeddings-for-nmt)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 352.23 MB
- **Size of the generated dataset:** 791.01 MB
- **Total amount of disk used:** 1.14 GB
### Dataset Summary
Massively multilingual (60 language) data set derived from TED Talk transcripts.
Each record consists of parallel arrays of language and text. Missing and
incomplete translations will be filtered out.
### 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
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 352.23 MB
- **Size of the generated dataset:** 791.01 MB
- **Total amount of disk used:** 1.14 GB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"talk_name": "shabana_basij_rasikh_dare_to_educate_afghan_girls",
"translations": "{\"language\": [\"ar\", \"az\", \"bg\", \"bn\", \"cs\", \"da\", \"de\", \"el\", \"en\", \"es\", \"fa\", \"fr\", \"he\", \"hi\", \"hr\", \"hu\", \"hy\", \"id\", \"it\", ..."
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `translations`: a multilingual `string` variable, with possible languages including `ar`, `az`, `be`, `bg`, `bn`.
- `talk_name`: a `string` feature.
### Data Splits
| name |train |validation|test|
|----------|-----:|---------:|---:|
|plain_text|258098| 6049|7213|
## 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
```
@InProceedings{qi-EtAl:2018:N18-2,
author = {Qi, Ye and Sachan, Devendra and Felix, Matthieu and Padmanabhan, Sarguna and Neubig, Graham},
title = {When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?},
booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
month = {June},
year = {2018},
address = {New Orleans, Louisiana},
publisher = {Association for Computational Linguistics},
pages = {529--535},
abstract = {The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained. Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks, which often suffer from paucity of data. However, their utility for NMT has not been extensively explored. In this work, we perform five sets of experiments that analyze when we can expect pre-trained word embeddings to help in NMT tasks. We show that such embeddings can be surprisingly effective in some cases -- providing gains of up to 20 BLEU points in the most favorable setting.},
url = {http://www.aclweb.org/anthology/N18-2084}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | 8,141 | [
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lighteval/boolq_helm | 2023-05-25T12:28:12.000Z | [
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] | lighteval | 0 | 584 | 2023-05-04T09:56:35 | Entry not found | 15 | [
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] | ||
sagawa/ZINC-canonicalized | 2022-09-04T02:21:08.000Z | [
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"license:apache-2.0",
"ZINC",
"chemical",
"SMILES",
"region:us"
] | sagawa | null | null | 0 | 582 | 2022-09-03T06:01:18 | ---
annotations_creators: []
language: []
language_creators:
- expert-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: canonicalized ZINC
size_categories:
- 10M<n<100M
source_datasets:
- original
tags:
- ZINC
- chemical
- SMILES
task_categories: []
task_ids: []
---
### dataset description
We downloaded ZINC dataset from [here](https://zinc15.docking.org/) and canonicalized it.
We used the following function to canonicalize the data and removed some SMILES that cannot be read by RDKit.
```python:
from rdkit import Chem
def canonicalize(mol):
mol = Chem.MolToSmiles(Chem.MolFromSmiles(mol),True)
return mol
```
We randomly split the preprocessed data into train and validation. The ratio is 9 : 1. | 744 | [
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allenai/scifact | 2022-11-18T21:44:10.000Z | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-2.0",
"region:us"
] | allenai | SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales. | @inproceedings{Wadden2020FactOF,
title={Fact or Fiction: Verifying Scientific Claims},
author={David Wadden and Shanchuan Lin and Kyle Lo and Lucy Lu Wang and Madeleine van Zuylen and Arman Cohan and Hannaneh Hajishirzi},
booktitle={EMNLP},
year={2020},
} | 7 | 578 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- cc-by-nc-2.0
multilinguality:
- monolingual
pretty_name: SciFact
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: scifact
dataset_info:
- config_name: corpus
features:
- name: doc_id
dtype: int32
- name: title
dtype: string
- name: abstract
sequence: string
- name: structured
dtype: bool
splits:
- name: train
num_bytes: 7993572
num_examples: 5183
download_size: 3115079
dataset_size: 7993572
- config_name: claims
features:
- name: id
dtype: int32
- name: claim
dtype: string
- name: evidence_doc_id
dtype: string
- name: evidence_label
dtype: string
- name: evidence_sentences
sequence: int32
- name: cited_doc_ids
sequence: int32
splits:
- name: train
num_bytes: 168627
num_examples: 1261
- name: test
num_bytes: 33625
num_examples: 300
- name: validation
num_bytes: 60360
num_examples: 450
download_size: 3115079
dataset_size: 262612
---
# Dataset Card for "scifact"
## 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://scifact.apps.allenai.org/](https://scifact.apps.allenai.org/)
- **Repository:** https://github.com/allenai/scifact
- **Paper:** [Fact or Fiction: Verifying Scientific Claims](https://aclanthology.org/2020.emnlp-main.609/)
- **Point of Contact:** [David Wadden](mailto:davidw@allenai.org)
- **Size of downloaded dataset files:** 5.43 MB
- **Size of the generated dataset:** 7.88 MB
- **Total amount of disk used:** 13.32 MB
### Dataset Summary
SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.
### 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
### Data Instances
#### claims
- **Size of downloaded dataset files:** 2.72 MB
- **Size of the generated dataset:** 0.25 MB
- **Total amount of disk used:** 2.97 MB
An example of 'validation' looks as follows.
```
{
"cited_doc_ids": [14717500],
"claim": "1,000 genomes project enables mapping of genetic sequence variation consisting of rare variants with larger penetrance effects than common variants.",
"evidence_doc_id": "14717500",
"evidence_label": "SUPPORT",
"evidence_sentences": [2, 5],
"id": 3
}
```
#### corpus
- **Size of downloaded dataset files:** 2.72 MB
- **Size of the generated dataset:** 7.63 MB
- **Total amount of disk used:** 10.35 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"abstract": "[\"Alterations of the architecture of cerebral white matter in the developing human brain can affect cortical development and res...",
"doc_id": 4983,
"structured": false,
"title": "Microstructural development of human newborn cerebral white matter assessed in vivo by diffusion tensor magnetic resonance imaging."
}
```
### Data Fields
The data fields are the same among all splits.
#### claims
- `id`: a `int32` feature.
- `claim`: a `string` feature.
- `evidence_doc_id`: a `string` feature.
- `evidence_label`: a `string` feature.
- `evidence_sentences`: a `list` of `int32` features.
- `cited_doc_ids`: a `list` of `int32` features.
#### corpus
- `doc_id`: a `int32` feature.
- `title`: a `string` feature.
- `abstract`: a `list` of `string` features.
- `structured`: a `bool` feature.
### Data Splits
#### claims
| |train|validation|test|
|------|----:|---------:|---:|
|claims| 1261| 450| 300|
#### corpus
| |train|
|------|----:|
|corpus| 5183|
## 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
https://github.com/allenai/scifact/blob/master/LICENSE.md
The SciFact dataset is released under the [CC BY-NC 2.0](https://creativecommons.org/licenses/by-nc/2.0/). By using the SciFact data, you are agreeing to its usage terms.
### Citation Information
```
@inproceedings{wadden-etal-2020-fact,
title = "Fact or Fiction: Verifying Scientific Claims",
author = "Wadden, David and
Lin, Shanchuan and
Lo, Kyle and
Wang, Lucy Lu and
van Zuylen, Madeleine and
Cohan, Arman and
Hajishirzi, Hannaneh",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.609",
doi = "10.18653/v1/2020.emnlp-main.609",
pages = "7534--7550",
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@dwadden](https://github.com/dwadden), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset. | 8,059 | [
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code_x_glue_tt_text_to_text | 2023-07-27T15:29:15.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:da",
"language:en",
"language:lv",
"language:nb",
"language:zh",
"license:c-uda",
"code-documentation-translation",
"arxiv:2102.04664",
"region:us"
] | null | The dataset we use is crawled and filtered from Microsoft Documentation, whose document located at https://github.com/MicrosoftDocs/. | @article{DBLP:journals/corr/abs-2102-04664,
author = {Shuai Lu and
Daya Guo and
Shuo Ren and
Junjie Huang and
Alexey Svyatkovskiy and
Ambrosio Blanco and
Colin B. Clement and
Dawn Drain and
Daxin Jiang and
Duyu Tang and
Ge Li and
Lidong Zhou and
Linjun Shou and
Long Zhou and
Michele Tufano and
Ming Gong and
Ming Zhou and
Nan Duan and
Neel Sundaresan and
Shao Kun Deng and
Shengyu Fu and
Shujie Liu},
title = {CodeXGLUE: {A} Machine Learning Benchmark Dataset for Code Understanding
and Generation},
journal = {CoRR},
volume = {abs/2102.04664},
year = {2021}
} | 1 | 576 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- da
- en
- lv
- nb
- zh
license:
- c-uda
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: CodeXGlueTtTextToText
tags:
- code-documentation-translation
dataset_info:
- config_name: da_en
features:
- name: id
dtype: int32
- name: source
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 8163215
num_examples: 42701
- name: validation
num_bytes: 190340
num_examples: 1000
- name: test
num_bytes: 190780
num_examples: 1000
download_size: 8007867
dataset_size: 8544335
- config_name: lv_en
features:
- name: id
dtype: int32
- name: source
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 3644127
num_examples: 18749
- name: validation
num_bytes: 192519
num_examples: 1000
- name: test
num_bytes: 190875
num_examples: 1000
download_size: 3778501
dataset_size: 4027521
- config_name: no_en
features:
- name: id
dtype: int32
- name: source
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 8761795
num_examples: 44322
- name: validation
num_bytes: 203823
num_examples: 1000
- name: test
num_bytes: 197135
num_examples: 1000
download_size: 8606833
dataset_size: 9162753
- config_name: zh_en
features:
- name: id
dtype: int32
- name: source
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 9592196
num_examples: 50154
- name: validation
num_bytes: 192155
num_examples: 1000
- name: test
num_bytes: 195245
num_examples: 1000
download_size: 9353684
dataset_size: 9979596
---
# Dataset Card for "code_x_glue_tt_text_to_text"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits-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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Text-Text/text-to-text
- **Paper:** https://arxiv.org/abs/2102.04664
### Dataset Summary
CodeXGLUE text-to-text dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Text-Text/text-to-text
The dataset we use is crawled and filtered from Microsoft Documentation, whose document located at https://github.com/MicrosoftDocs/.
### Supported Tasks and Leaderboards
- `machine-translation`: The dataset can be used to train a model for translating Technical documentation between languages.
### Languages
da_en, lv_en, no_en, zh_en
## Dataset Structure
### Data Instances
#### da_en
An example of 'test' looks as follows.
```
{
"id": 0,
"source": "4 . K\u00f8r modellen , og udgiv den som en webtjeneste .\n",
"target": "4 . Run the model , and publish it as a web service .\n"
}
```
#### lv_en
An example of 'train' looks as follows.
```
{
"id": 0,
"source": "title : Pakalpojumu objektu izveide\n",
"target": "title : Create service objects\n"
}
```
#### no_en
An example of 'validation' looks as follows.
```
{
"id": 0,
"source": "2 . \u00c5pne servicevaren du vil definere komponenter fra en stykkliste for .\n",
"target": "2 . Open the service item for which you want to set up components from a BOM .\n"
}
```
#### zh_en
An example of 'validation' looks as follows.
```
{
"id": 0,
"source": "& # 124 ; MCDUserNotificationReadStateFilterAny & # 124 ; 0 & # 124 ; \u5305\u62ec \u901a\u77e5 , \u800c \u4e0d \u8003\u8651 \u8bfb\u53d6 \u72b6\u6001 \u3002 & # 124 ;\n",
"target": "| MCDUserNotificationReadStateFilterAny | 0 | Include notifications regardless of read state . |\n"
}
```
### Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
#### da_en, lv_en, no_en, zh_en
|field name| type | description |
|----------|------|----------------------------------------|
|id |int32 | The index of the sample |
|source |string| The source language version of the text|
|target |string| The target language version of the text|
### Data Splits
|name |train|validation|test|
|-----|----:|---------:|---:|
|da_en|42701| 1000|1000|
|lv_en|18749| 1000|1000|
|no_en|44322| 1000|1000|
|zh_en|50154| 1000|1000|
## 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
https://github.com/microsoft, https://github.com/madlag
### Licensing Information
Computational Use of Data Agreement (C-UDA) License.
### Citation Information
```
@article{DBLP:journals/corr/abs-2102-04664,
author = {Shuai Lu and
Daya Guo and
Shuo Ren and
Junjie Huang and
Alexey Svyatkovskiy and
Ambrosio Blanco and
Colin B. Clement and
Dawn Drain and
Daxin Jiang and
Duyu Tang and
Ge Li and
Lidong Zhou and
Linjun Shou and
Long Zhou and
Michele Tufano and
Ming Gong and
Ming Zhou and
Nan Duan and
Neel Sundaresan and
Shao Kun Deng and
Shengyu Fu and
Shujie Liu},
title = {CodeXGLUE: {A} Machine Learning Benchmark Dataset for Code Understanding
and Generation},
journal = {CoRR},
volume = {abs/2102.04664},
year = {2021}
}
```
### Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset. | 7,274 | [
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] |
tongyx361/prm800k-train-direct-prediction-0-02validiation-seed42-encoded | 2023-09-17T22:46:13.000Z | [
"region:us"
] | tongyx361 | null | null | 0 | 576 | 2023-09-17T22:46:00 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 308232504
num_examples: 85194
- name: validation
num_bytes: 5818260
num_examples: 1818
download_size: 32445039
dataset_size: 314050764
---
# Dataset Card for "prm800k-train-direct-prediction-0-02validiation-seed42-encoded"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 657 | [
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shunk031/wrime | 2023-01-15T03:39:01.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"language:ja",
"license:unknown",
"sentiment-analysis",
"wrime",
"region:us"
] | shunk031 | WRIME dataset is a new dataset for emotional intensity estimation with subjective and objective annotations. | @inproceedings{kajiwara-etal-2021-wrime,
title = "{WRIME}: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations",
author = "Kajiwara, Tomoyuki and
Chu, Chenhui and
Takemura, Noriko and
Nakashima, Yuta and
Nagahara, Hajime",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.169",
doi = "10.18653/v1/2021.naacl-main.169",
pages = "2095--2104",
abstract = "We annotate 17,000 SNS posts with both the writer{'}s subjective emotional intensity and the reader{'}s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer{'}s subjective labels than the readers{'}. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.",
}
@inproceedings{suzuki-etal-2022-japanese,
title = "A {J}apanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain",
author = "Suzuki, Haruya and
Miyauchi, Yuto and
Akiyama, Kazuki and
Kajiwara, Tomoyuki and
Ninomiya, Takashi and
Takemura, Noriko and
Nakashima, Yuta and
Nagahara, Hajime",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.759",
pages = "7022--7028",
abstract = "We annotate 35,000 SNS posts with both the writer{'}s subjective sentiment polarity labels and the reader{'}s objective ones to construct a Japanese sentiment analysis dataset. Our dataset includes intensity labels (\textit{none}, \textit{weak}, \textit{medium}, and \textit{strong}) for each of the eight basic emotions by Plutchik (\textit{joy}, \textit{sadness}, \textit{anticipation}, \textit{surprise}, \textit{anger}, \textit{fear}, \textit{disgust}, and \textit{trust}) as well as sentiment polarity labels (\textit{strong positive}, \textit{positive}, \textit{neutral}, \textit{negative}, and \textit{strong negative}). Previous studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. In other words, there are few corpora that are annotated with both basic emotions and sentiment polarity. Our dataset is the first large-scale corpus to annotate both of these emotion labels, and from both the writer{'}s and reader{'}s perspectives. In this paper, we analyze the relationship between basic emotion intensity and sentiment polarity on our dataset and report the results of benchmarking sentiment polarity classification.",
} | 10 | 575 | 2023-01-12T03:04:20 | ---
annotations_creators:
- crowdsourced
language:
- ja
language_creators:
- crowdsourced
license:
- unknown
multilinguality:
- monolingual
pretty_name: wrime
tags:
- sentiment-analysis
- wrime
task_categories:
- text-classification
task_ids:
- sentiment-classification
datasets:
- ver1
- ver2
metrics:
- accuracy
---
# Dataset Card for WRIME
[](https://github.com/shunk031/huggingface-datasets_wrime/actions/workflows/ci.yaml)
## 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://github.com/ids-cv/wrime
- Repository: https://github.com/shunk031/huggingface-datasets_wrime
- Paper: https://aclanthology.org/2021.naacl-main.169/
### Dataset Summary
In this study, we introduce a new dataset, WRIME, for emotional intensity estimation. We collect both the subjective emotional intensity ofthe writers themselves and the objective one annotated by the readers, and explore the differences between them. In our data collection, we hired 50 participants via crowdsourcing service. They annotated their own past posts on a social networking service (SNS) with the subjective emotional intensity. We also hired 3 annotators, who annotated allposts with the objective emotional intensity. Consequently, our Japanese emotion analysis datasetconsists of 17,000 posts with both subjective andobjective emotional intensities for Plutchik’s eightemotions ([Plutchik, 1980](https://www.sciencedirect.com/science/article/pii/B9780125587013500077)), which are given in afour-point scale (no, weak, medium, and strong).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
- Japanese
## Dataset Structure
### Data Instances
When loading a specific configuration, users has to append a version dependent suffix:
```python
from datasets import load_dataset
dataset = load_dataset("shunk031/wrime", name="ver1")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['sentence', 'user_id', 'datetime', 'writer', 'reader1', 'reader2', 'reader3', 'avg_readers'],
# num_rows: 40000
# })
# validation: Dataset({
# features: ['sentence', 'user_id', 'datetime', 'writer', 'reader1', 'reader2', 'reader3', 'avg_readers'],
# num_rows: 1200
# })
# test: Dataset({
# features: ['sentence', 'user_id', 'datetime', 'writer', 'reader1', 'reader2', 'reader3', 'avg_readers'],
# num_rows: 2000
# })
# })
```
#### Ver. 1
An example of looks as follows:
```json
{
"sentence": "ぼけっとしてたらこんな時間｡チャリあるから食べにでたいのに…",
"user_id": "1",
"datetime": "2012/07/31 23:48",
"writer": {
"joy": 0,
"sadness": 1,
"anticipation": 2,
"surprise": 1,
"anger": 1,
"fear": 0,
"disgust": 0,
"trust": 1
},
"reader1": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0
},
"reader2": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 1,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0
},
"reader3": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 1,
"disgust": 1,
"trust": 0
},
"avg_readers": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0
}
}
```
#### Ver. 1
An example of looks as follows:
```json
{
"sentence": "ぼけっとしてたらこんな時間。チャリあるから食べにでたいのに…",
"user_id": "1",
"datetime": "2012/7/31 23:48",
"writer": {
"joy": 0,
"sadness": 1,
"anticipation": 2,
"surprise": 1,
"anger": 1,
"fear": 0,
"disgust": 0,
"trust": 1,
"sentiment": 0
},
"reader1": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0,
"sentiment": -2
},
"reader2": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 1,
"disgust": 1,
"trust": 0,
"sentiment": -1
},
"reader3": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 1,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0,
"sentiment": -1
},
"avg_readers": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0,
"sentiment": -1
}
}
```
### Data Fields
#### Ver. 1
- `sentence`: 投稿テキスト
- `user_id`: ユーザー ID
- `datetime`: 投稿日時
- `writer`: 主観 （書き手）
- `joy`: 主観の喜びの感情
- `sadness`: 主観の悲しみの感情
- `anticipation`: 主観の期待の感情
- `surprise`: 主観の驚きの感情
- `anger`: 主観の怒りの感情
- `fear`: 主観の恐れの感情
- `disgust`: 主観の嫌悪の感情
- `trust`: 主観の信頼の感情
- `reader1`: 客観 A （読み手 A)
- `joy`: 客観 A の喜びの感情
- `sadness`: 客観 A の悲しみの感情
- `anticipation`: 客観 A の期待の感情
- `surprise`: 客観 A の驚きの感情
- `anger`: 客観 A の怒りの感情
- `fear`: 客観 A の恐れの感情
- `disgust`: 客観 A の嫌悪の感情
- `trust`: 客観 A の信頼の感情
- `reader2`: 客観 B （読み手 B)
- `joy`: 客観 B の喜びの感情
- `sadness`: 客観 B の悲しみの感情
- `anticipation`: 客観 B の期待の感情
- `surprise`: 客観 B の驚きの感情
- `anger`: 客観 B の怒りの感情
- `fear`: 客観 B の恐れの感情
- `disgust`: 客観 B の嫌悪の感情
- `trust`: 客観 B の信頼の感情
- `reader3`: 客観 C （読み手 C)
- `joy`: 客観 C の喜びの感情
- `sadness`: 客観 C の悲しみの感情
- `anticipation`: 客観 C の期待の感情
- `surprise`: 客観 C の驚きの感情
- `anger`: 客観 C の怒りの感情
- `fear`: 客観 C の恐れの感情
- `disgust`: 客観 C の嫌悪の感情
- `trust`: 客観 C の信頼の感情
- `avg_readers`
- `joy`: 客観 A, B, C 平均の喜びの感情
- `sadness`: 客観 A, B, C 平均の悲しみの感情
- `anticipation`: 客観 A, B, C 平均の期待の感情
- `surprise`: 客観 A, B, C 平均の驚きの感情
- `anger`: 客観 A, B, C 平均の怒りの感情
- `fear`: 客観 A, B, C 平均の恐れの感情
- `disgust`: 客観 A, B, C 平均の嫌悪の感情
- `trust`: 客観 A, B, C 平均の信頼の感情
#### Ver. 2
- `sentence`: 投稿テキスト
- `user_id`: ユーザー ID
- `datetime`: 投稿日時
- `writer`: 主観 （書き手）
- `joy`: 主観の喜びの感情
- `sadness`: 主観の悲しみの感情
- `anticipation`: 主観の期待の感情
- `surprise`: 主観の驚きの感情
- `anger`: 主観の怒りの感情
- `fear`: 主観の恐れの感情
- `disgust`: 主観の嫌悪の感情
- `trust`: 主観の信頼の感情
- `sentiment`: 主観の感情極性
- `reader1`: 客観 A （読み手 A)
- `joy`: 客観 A の喜びの感情
- `sadness`: 客観 A の悲しみの感情
- `anticipation`: 客観 A の期待の感情
- `surprise`: 客観 A の驚きの感情
- `anger`: 客観 A の怒りの感情
- `fear`: 客観 A の恐れの感情
- `disgust`: 客観 A の嫌悪の感情
- `trust`: 客観 A の信頼の感情
- `sentiment`: 客観 A の感情極性
- `reader2`: 客観 B （読み手 B)
- `joy`: 客観 B の喜びの感情
- `sadness`: 客観 B の悲しみの感情
- `anticipation`: 客観 B の期待の感情
- `surprise`: 客観 B の驚きの感情
- `anger`: 客観 B の怒りの感情
- `fear`: 客観 B の恐れの感情
- `disgust`: 客観 B の嫌悪の感情
- `trust`: 客観 B の信頼の感情
- `sentiment`: 客観 B の感情極性
- `reader3`: 客観 C （読み手 C)
- `joy`: 客観 C の喜びの感情
- `sadness`: 客観 C の悲しみの感情
- `anticipation`: 客観 C の期待の感情
- `surprise`: 客観 C の驚きの感情
- `anger`: 客観 C の怒りの感情
- `fear`: 客観 C の恐れの感情
- `disgust`: 客観 C の嫌悪の感情
- `trust`: 客観 C の信頼の感情
- `sentiment`: 客観 C の感情極性
- `avg_readers`
- `joy`: 客観 A, B, C 平均の喜びの感情
- `sadness`: 客観 A, B, C 平均の悲しみの感情
- `anticipation`: 客観 A, B, C 平均の期待の感情
- `surprise`: 客観 A, B, C 平均の驚きの感情
- `anger`: 客観 A, B, C 平均の怒りの感情
- `fear`: 客観 A, B, C 平均の恐れの感情
- `disgust`: 客観 A, B, C 平均の嫌悪の感情
- `trust`: 客観 A, B, C 平均の信頼の感情
- `sentiment`: 客観 A, B, C 平均の感情極性
### Data Splits
| name | train | validation | test |
|------|-------:|-----------:|------:|
| ver1 | 40,000 | 1,200 | 2,000 |
| ver2 | 30,000 | 2,500 | 2,500 |
## 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
From [the README](https://github.com/ids-cv/wrime/blob/master/README.en.md#licence) of the GitHub:
- The dataset is available for research purposes only.
- Redistribution of the dataset is prohibited.
### Citation Information
```bibtex
@inproceedings{kajiwara-etal-2021-wrime,
title = "{WRIME}: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations",
author = "Kajiwara, Tomoyuki and
Chu, Chenhui and
Takemura, Noriko and
Nakashima, Yuta and
Nagahara, Hajime",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.169",
doi = "10.18653/v1/2021.naacl-main.169",
pages = "2095--2104",
abstract = "We annotate 17,000 SNS posts with both the writer{'}s subjective emotional intensity and the reader{'}s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer{'}s subjective labels than the readers{'}. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.",
}
```
```bibtex
@inproceedings{suzuki-etal-2022-japanese,
title = "A {J}apanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain",
author = "Suzuki, Haruya and
Miyauchi, Yuto and
Akiyama, Kazuki and
Kajiwara, Tomoyuki and
Ninomiya, Takashi and
Takemura, Noriko and
Nakashima, Yuta and
Nagahara, Hajime",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.759",
pages = "7022--7028",
abstract = "We annotate 35,000 SNS posts with both the writer{'}s subjective sentiment polarity labels and the reader{'}s objective ones to construct a Japanese sentiment analysis dataset. Our dataset includes intensity labels (\textit{none}, \textit{weak}, \textit{medium}, and \textit{strong}) for each of the eight basic emotions by Plutchik (\textit{joy}, \textit{sadness}, \textit{anticipation}, \textit{surprise}, \textit{anger}, \textit{fear}, \textit{disgust}, and \textit{trust}) as well as sentiment polarity labels (\textit{strong positive}, \textit{positive}, \textit{neutral}, \textit{negative}, and \textit{strong negative}). Previous studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. In other words, there are few corpora that are annotated with both basic emotions and sentiment polarity. Our dataset is the first large-scale corpus to annotate both of these emotion labels, and from both the writer{'}s and reader{'}s perspectives. In this paper, we analyze the relationship between basic emotion intensity and sentiment polarity on our dataset and report the results of benchmarking sentiment polarity classification.",
}
```
### Contributions
Thanks to [@moguranosenshi](https://github.com/moguranosenshi) for creating this dataset.
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KETI-AIR/klue | 2021-06-03T00:35:30.000Z | [
"region:us"
] | KETI-AIR | null | @misc{park2021klue,
title={KLUE: Korean Language Understanding Evaluation},
author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho Alice Oh Jungwoo Ha Kyunghyun Cho},
year={2021},
eprint={2105.09680},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 0 | 574 | 2022-03-02T23:29:22 | <!--
Copyright 2021 san kim
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Korean Language Understanding Evaluation (KLUE) | 620 | [
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Falah/Alzheimer_MRI | 2023-07-04T10:03:44.000Z | [
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"medical",
"region:us"
] | Falah | null | null | 1 | 573 | 2023-07-04T09:24:50 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Mild_Demented
'1': Moderate_Demented
'2': Non_Demented
'3': Very_Mild_Demented
splits:
- name: train
num_bytes: 22560791.2
num_examples: 5120
- name: test
num_bytes: 5637447.08
num_examples: 1280
download_size: 28289848
dataset_size: 28198238.28
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- medical
pretty_name: Alzheimer_MRI Disease Classification Dataset
size_categories:
- 1K<n<10K
---
# Alzheimer_MRI Disease Classification Dataset
The Falah/Alzheimer_MRI Disease Classification dataset is a valuable resource for researchers and health medicine applications. This dataset focuses on the classification of Alzheimer's disease based on MRI scans. The dataset consists of brain MRI images labeled into four categories:
- '0': Mild_Demented
- '1': Moderate_Demented
- '2': Non_Demented
- '3': Very_Mild_Demented
## Dataset Information
- Train split:
- Name: train
- Number of bytes: 22,560,791.2
- Number of examples: 5,120
- Test split:
- Name: test
- Number of bytes: 5,637,447.08
- Number of examples: 1,280
- Download size: 28,289,848 bytes
- Dataset size: 28,198,238.28 bytes
## Citation
If you use this dataset in your research or health medicine applications, we kindly request that you cite the following publication:
```
@dataset{alzheimer_mri_dataset,
author = {Falah.G.Salieh},
title = {Alzheimer MRI Dataset},
year = {2023},
publisher = {Hugging Face},
version = {1.0},
url = {https://huggingface.co/datasets/Falah/Alzheimer_MRI}
}
```
## Usage Example
Here's an example of how to load the dataset using the Hugging Face library:
```python
from datasets import load_dataset
# Load the Falah/Alzheimer_MRI dataset
dataset = load_dataset('Falah/Alzheimer_MRI', split='train')
# Print the number of examples and the first few samples
print("Number of examples:", len(dataset))
print("Sample data:")
for example in dataset[:5]:
print(example)
``` | 2,130 | [
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YuanPJ/summ_screen | 2023-03-29T04:51:45.000Z | [
"region:us"
] | YuanPJ | SummScreen Corpus contains over 26k pairs of TV series transcripts and human written recaps.
There are two features:
- dialogue: text of dialogue.
- summary: human written summary of the dialogue.
- id: id of a example. | @inproceedings{chen-etal-2022-summscreen,
title = "{S}umm{S}creen: A Dataset for Abstractive Screenplay Summarization",
author = "Chen, Mingda and
Chu, Zewei and
Wiseman, Sam and
Gimpel, Kevin",
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.589",
pages = "8602--8615",
abstract = "We introduce SummScreen, a summarization dataset comprised of pairs of TV series transcripts and human written recaps. The dataset provides a challenging testbed for abstractive summarization for several reasons. Plot details are often expressed indirectly in character dialogues and may be scattered across the entirety of the transcript. These details must be found and integrated to form the succinct plot descriptions in the recaps. Also, TV scripts contain content that does not directly pertain to the central plot but rather serves to develop characters or provide comic relief. This information is rarely contained in recaps. Since characters are fundamental to TV series, we also propose two entity-centric evaluation metrics. Empirically, we characterize the dataset by evaluating several methods, including neural models and those based on nearest neighbors. An oracle extractive approach outperforms all benchmarked models according to automatic metrics, showing that the neural models are unable to fully exploit the input transcripts. Human evaluation and qualitative analysis reveal that our non-oracle models are competitive with their oracle counterparts in terms of generating faithful plot events and can benefit from better content selectors. Both oracle and non-oracle models generate unfaithful facts, suggesting future research directions.",
} | 1 | 571 | 2023-03-28T04:50:20 | Entry not found | 15 | [
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yaful/DeepfakeTextDetect | 2023-07-11T01:59:02.000Z | [
"license:apache-2.0",
"arxiv:2305.13242",
"region:us"
] | yaful | null | null | 4 | 571 | 2023-06-27T07:30:58 | ---
license: apache-2.0
---
<div align="center">
<h1>Deepfake Text Detection in the Wild</h1>
<!-- **Authors:** -->
_**Yafu Li<sup>†</sup><sup>‡</sup>, Qintong Li<sup>§</sup>, Leyang Cui<sup>¶</sup>, Wei Bi<sup>¶</sup>,<br>**_
_**Longyue Wang<sup>¶</sup>, Linyi Yang<sup>‡</sup>, Shuming Shi<sup>¶</sup>, Yue Zhang<sup>‡</sup><br>**_
<!-- **Affiliations:** -->
_<sup>†</sup> Zhejiang University,
<sup>‡</sup> Westlake University,
<sup>§</sup> The University of Hong Kong,
<sup>¶</sup> Tencent AI Lab_
Presenting a comprehensive benchmark dataset designed to assess the proficiency of deepfake detectors amidst real-world scenarios.
</div>
## 📌 Table of Contents
- [Introduction](#🚀-introduction)
- [Dataset](#📝-dataset)
- [Try Detection](#🖥%EF%B8%8F-try-detection)
- [Citation](#📚-citation)
## 🚀 Introduction
Recent advances in large language models have enabled them to reach a level of text generation comparable to that of humans.
These models show powerful capabilities across a wide range of content, including news article writing, story generation, and scientific writing.
Such capability further narrows the gap between human-authored and machine-generated texts, highlighting the importance of deepfake text detection to avoid potential risks such as fake news propagation and plagiarism.
In practical scenarios, the detector faces texts from various domains or LLMs without knowing their sources.
To this end, we build **a comprehensive testbed for deepfake text detection**, by gathering texts from various human writings and deepfake texts generated by different LLMs.
The data in this repository is used to evaluate the effectiveness of deepfake detection methods, as described in our paper titled "Deepfake Text Detection in the Wild" (available at https://arxiv.org/abs/2305.13242). We invite you to test your own detection methods on our testbed and encourage you to star our Github repo at https://github.com/yafuly/DeepfakeTextDetect.
## 📝 Dataset
The dataset consists of **447,674** human-written and machine-generated texts from a wide range of sources in the wild:
- Human-written texts from **10 datasets** covering a wide range of writing tasks, e.g., news article writing, story generation, scientific writing, etc.
- Machine-generated texts generated by **27 mainstream LLMs** from 7 sources, e.g., OpenAI, LLaMA, and EleutherAI, etc.
- **6 systematic testbed**s with increasing wildness and detection difficulty.
- **2 wilder test sets**: (1) texts collected from new datasets and generated by GPT-4; (2) paraphrased texts.
### 📥 How to Get the Data
#### 1. Huggingface
You can access the full dataset, which includes the Cross-domains & Cross-models testbed and two additional wilder test sets, through the Huggingface API:
```python
from datasets import load_dataset
dataset = load_dataset("yaful/DeepfakeTextDetect")
```
which includes traditional splits (train.csv, valid.csv and test.csv) and two wilder test sets (test_ood_set_gpt.csv and test_ood_set_gpt_para.csv).
The csv files have three columns: text, label (0 for machine-generated and
1 for human-written) and text source information (e.g., ''cmv_human'' denotes the text is written by humans,
whereas ''roct_machine_continuation_flan_t5_large'' denotes the text is generated by ''flan_t5_large'' using continuation prompt).
To obtain the 6 testbeds mentioned in our paper, simply apply the provided script:
```shell
python3 deployment/prepare_testbeds.py DATA_PATH
```
Replace ''DATA_PATH'' with the output data directory where you want to save the 6 testbeds.
#### 2. Cloud Drive
Alternatively, you can access the 6 testbeds by downloading them directly through [Google Drive](https://drive.google.com/drive/folders/1p09vDiEvoA-ZPmpqkB2WApcwMQWiiMRl?usp=sharing)
or [Tencent Weiyun](https://share.weiyun.com/JUWQxF4H)：
The folder contains 4 packages:
- testbeds_processed.zip: 6 testbeds based on the ''processed'' version, which can be directly used for detecting in-distribution and out-of-distribution detection performance.
- wilder_testsets.zip: 2 wilder test sets with texts processed, aiming for (1) detecting deepfake text generated by GPT-4, and (2) detecting deepfake text in paraphrased versions.
- source.zip: Source texts of human-written texts and corresponding texts generated by LLMs, without filtering.
- processed.zip: This is a refined version of the "source" that filters out low-quality texts and specifies sources as CSV file names. For example, the "cmv_machine_specified_gpt-3.5-trubo.csv" file contains texts from the CMV domain generated by the "gpt-3.5-trubo" model using specific prompts, while "cmv_human" includes human-written CMV texts.
## 🖥️ Try Detection
### Model Access
Our Longformer detector, which has been trained on the entire dataset, is now accessible through [Huggingface](https://huggingface.co/nealcly/detection-longformer). Additionally, you can try detection directly using our [online demo](https://huggingface.co/spaces/yaful/DeepfakeTextDetect).
### Deployment
We have refined the decision boundary based on out-of-distribution settings. To ensure optimal performance, we recommend preprocessing texts before sending them to the detector.
See 🏃 [Deepfake Text Detection in the Wild](https://github.com/yafuly/DeepfakeTextDetect) for the complete detection pipeline:
```python
import torch
import os
from transformers import AutoModelForSequenceClassification,AutoTokenizer
from deployment import preprocess, detect
# init
device = 'cpu' # use 'cuda:0' if GPU is available
model_dir = "nealcly/detection-longformer"
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForSequenceClassification.from_pretrained(model_dir).to(device)
# preprocess
text = preprocess(text)
# detection
result = detect(text,tokenizer,model,device)
```
## 📚 Citation
If you use this dataset in your research, please cite it as follows:
```bibtex
@misc{li2023deepfake,
title={Deepfake Text Detection in the Wild},
author={Yafu Li and Qintong Li and Leyang Cui and Wei Bi and Longyue Wang and Linyi Yang and Shuming Shi and Yue Zhang},
year={2023},
eprint={2305.13242},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
We welcome contributions to improve this dataset! If you have any questions or feedback, please feel free to reach out at yafuly@gmail.com.
<!-- # 🤝 Contributing --> | 6,392 | [
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] |
jxie/country211 | 2023-08-13T19:11:22.000Z | [
"region:us"
] | jxie | null | null | 0 | 568 | 2023-08-13T18:29:19 | ---
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: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': AD
'1': AE
'2': AF
'3': AG
'4': AI
'5': AL
'6': AM
'7': AO
'8': AQ
'9': AR
'10': AT
'11': AU
'12': AW
'13': AX
'14': AZ
'15': BA
'16': BB
'17': BD
'18': BE
'19': BF
'20': BG
'21': BH
'22': BJ
'23': BM
'24': BN
'25': BO
'26': BQ
'27': BR
'28': BS
'29': BT
'30': BW
'31': BY
'32': BZ
'33': CA
'34': CD
'35': CF
'36': CH
'37': CI
'38': CK
'39': CL
'40': CM
'41': CN
'42': CO
'43': CR
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'49': DE
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'100': KH
'101': KN
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'105': KY
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'107': LA
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'112': LR
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'136': MZ
'137': NA
'138': NC
'139': NG
'140': NI
'141': NL
'142': 'NO'
'143': NP
'144': NZ
'145': OM
'146': PA
'147': PE
'148': PF
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'150': PH
'151': PK
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'180': SX
'181': SY
'182': SZ
'183': TG
'184': TH
'185': TJ
'186': TL
'187': TM
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'194': UA
'195': UG
'196': US
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'198': UZ
'199': VA
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'201': VG
'202': VI
'203': VN
'204': VU
'205': WS
'206': XK
'207': YE
'208': ZA
'209': ZM
'210': ZW
splits:
- name: train
num_bytes: 5411225958.1
num_examples: 31650
- name: validation
num_bytes: 1816894779.75
num_examples: 10550
- name: test
num_bytes: 3632130288.7
num_examples: 21100
download_size: 11359939585
dataset_size: 10860251026.55
---
# Dataset Card for "country211"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 4,857 | [
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keremberke/license-plate-object-detection | 2023-01-18T20:37:51.000Z | [
"task_categories:object-detection",
"roboflow",
"roboflow2huggingface",
"Self Driving",
"Anpr",
"region:us"
] | keremberke | null | @misc{ vehicle-registration-plates-trudk_dataset,
title = { Vehicle Registration Plates Dataset },
type = { Open Source Dataset },
author = { Augmented Startups },
howpublished = { \\url{ https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk } },
url = { https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { jun },
note = { visited on 2023-01-18 },
} | 7 | 563 | 2023-01-01T02:32:07 | ---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
- Self Driving
- Anpr
---
<div align="center">
<img width="640" alt="keremberke/license-plate-object-detection" src="https://huggingface.co/datasets/keremberke/license-plate-object-detection/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['license_plate']
```
### Number of Images
```json
{'train': 6176, 'valid': 1765, 'test': 882}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("keremberke/license-plate-object-detection", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk/dataset/1](https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk/dataset/1?ref=roboflow2huggingface)
### Citation
```
@misc{ vehicle-registration-plates-trudk_dataset,
title = { Vehicle Registration Plates Dataset },
type = { Open Source Dataset },
author = { Augmented Startups },
howpublished = { \\url{ https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk } },
url = { https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { jun },
note = { visited on 2023-01-18 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.ai on January 13, 2022 at 5:20 PM GMT
It includes 8823 images.
VRP are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
No image augmentation techniques were applied.
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] |
alzoubi36/policy_qa | 2023-06-25T06:45:22.000Z | [
"region:us"
] | alzoubi36 | null | null | 0 | 563 | 2023-06-25T06:42:53 | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
splits:
- name: validation
num_bytes: 2902927
num_examples: 3809
- name: test
num_bytes: 3667235
num_examples: 4152
- name: train
num_bytes: 13859759
num_examples: 17056
download_size: 2662048
dataset_size: 20429921
---
# Dataset for the PolicyQA task in the [PrivacyGLUE](https://github.com/infsys-lab/privacy-glue) dataset
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kandriiashevskyi/wix_looker_ai | 2023-11-02T21:07:05.000Z | [
"region:us"
] | kandriiashevskyi | null | null | 0 | 563 | 2023-08-01T09:20:28 | Entry not found | 15 | [
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HUPD/hupd | 2022-10-24T15:47:30.000Z | [
"task_categories:fill-mask",
"task_categories:summarization",
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:masked-language-modeling",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"task_ids:named-entity-recognition",
"language:en",
"license:cc-by-sa-4.0",
"patents",
"arxiv:2207.04043",
"region:us"
] | HUPD | The Harvard USPTO Patent Dataset (HUPD) is a large-scale, well-structured, and multi-purpose corpus
of English-language patent applications filed to the United States Patent and Trademark Office (USPTO)
between 2004 and 2018. With more than 4.5 million patent documents, HUPD is two to three times larger
than comparable corpora. Unlike other NLP patent datasets, HUPD contains the inventor-submitted versions
of patent applications, not the final versions of granted patents, allowing us to study patentability at
the time of filing using NLP methods for the first time. | @InProceedings{suzgun2021:hupd,
title = {The Harvard USPTO Patent Dataset},
authors={Mirac Suzgun and Suproteem Sarkar and Luke Melas-Kyriazi and Scott Kominers and Stuart Shieber},
year={2021}
} | 19 | 562 | 2022-03-02T23:29:22 | ---
language:
- en
license:
- cc-by-sa-4.0
task_categories:
- fill-mask
- summarization
- text-classification
- token-classification
task_ids:
- masked-language-modeling
- multi-class-classification
- topic-classification
- named-entity-recognition
pretty_name: "HUPD"
tags:
- patents
---
# Dataset Card for The Harvard USPTO Patent Dataset (HUPD)
## Dataset Description
- **Homepage:** [https://patentdataset.org/](https://patentdataset.org/)
- **Repository:** [HUPD GitHub repository](https://github.com/suzgunmirac/hupd)
- **Paper:** [HUPD arXiv Submission](https://arxiv.org/abs/2207.04043)
- **Point of Contact:** Mirac Suzgun
### Dataset Summary
The Harvard USPTO Dataset (HUPD) is a large-scale, well-structured, and multi-purpose corpus of English-language utility patent applications filed to the United States Patent and Trademark Office (USPTO) between January 2004 and December 2018.
### Experiments and Tasks Considered in the Paper
- **Patent Acceptance Prediction**: Given a section of a patent application (in particular, the abstract, claims, or description), predict whether the application will be accepted by the USPTO.
- **Automated Subject (IPC/CPC) Classification**: Predict the primary IPC or CPC code of a patent application given (some subset of) the text of the application.
- **Language Modeling**: Masked/autoregressive language modeling on the claims and description sections of patent applications.
- **Abstractive Summarization**: Given the claims or claims section of a patent application, generate the abstract.
### Languages
The dataset contains English text only.
### Domain
Patents (intellectual property).
### Dataset Curators
The dataset was created by Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart M. Shieber.
## Dataset Structure
Each patent application is defined by a distinct JSON file, named after its application number, and includes information about
the application and publication numbers,
title,
decision status,
filing and publication dates,
primary and secondary classification codes,
inventor(s),
examiner,
attorney,
abstract,
claims,
background,
summary, and
full description of the proposed invention, among other fields. There are also supplementary variables, such as the small-entity indicator (which denotes whether the applicant is considered to be a small entity by the USPTO) and the foreign-filing indicator (which denotes whether the application was originally filed in a foreign country).
In total, there are 34 data fields for each application. A full list of data fields used in the dataset is listed in the next section.
### Data Instances
Each patent application in our patent dataset is defined by a distinct JSON file (e.g., ``8914308.json``), named after its unique application number. The format of the JSON files is as follows:
```python
{
"application_number": "...",
"publication_number": "...",
"title": "...",
"decision": "...",
"date_produced": "...",
"date_published": "...",
"main_cpc_label": "...",
"cpc_labels": ["...", "...", "..."],
"main_ipcr_label": "...",
"ipcr_labels": ["...", "...", "..."],
"patent_number": "...",
"filing_date": "...",
"patent_issue_date": "...",
"abandon_date": "...",
"uspc_class": "...",
"uspc_subclass": "...",
"examiner_id": "...",
"examiner_name_last": "...",
"examiner_name_first": "...",
"examiner_name_middle": "...",
"inventor_list": [
{
"inventor_name_last": "...",
"inventor_name_first": "...",
"inventor_city": "...",
"inventor_state": "...",
"inventor_country": "..."
}
],
"abstract": "...",
"claims": "...",
"background": "...",
"summary": "...",
"full_description": "..."
}
```
## Usage
### Loading the Dataset
#### Sample (January 2016 Subset)
The following command can be used to load the `sample` version of the dataset, which contains all the patent applications that were filed to the USPTO during the month of January in 2016. This small subset of the dataset can be used for debugging and exploration purposes.
```python
from datasets import load_dataset
dataset_dict = load_dataset('HUPD/hupd',
name='sample',
data_files="https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather",
icpr_label=None,
train_filing_start_date='2016-01-01',
train_filing_end_date='2016-01-21',
val_filing_start_date='2016-01-22',
val_filing_end_date='2016-01-31',
)
```
#### Full Dataset
If you would like to use the **full** version of the dataset, please make sure that change the `name` field from `sample` to `all`, specify the training and validation start and end dates carefully, and set `force_extract` to be `True` (so that you would only untar the files that you are interested in and not squander your disk storage space). In the following example, for instance, we set the training set year range to be [2011, 2016] (inclusive) and the validation set year range to be 2017.
```python
from datasets import load_dataset
dataset_dict = load_dataset('HUPD/hupd',
name='all',
data_files="https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather",
icpr_label=None,
force_extract=True,
train_filing_start_date='2011-01-01',
train_filing_end_date='2016-12-31',
val_filing_start_date='2017-01-01',
val_filing_end_date='2017-12-31',
)
```
### Google Colab Notebook
You can also use the following Google Colab notebooks to explore HUPD.
- [](https://colab.research.google.com/drive/1_ZsI7WFTsEO0iu_0g3BLTkIkOUqPzCET?usp=sharing)[ HUPD Examples: Loading the Dataset](https://colab.research.google.com/drive/1_ZsI7WFTsEO0iu_0g3BLTkIkOUqPzCET?usp=sharing)
- [](https://colab.research.google.com/drive/1TzDDCDt368cUErH86Zc_P2aw9bXaaZy1?usp=sharing)[ HUPD Examples: Loading HUPD By Using HuggingFace's Libraries](https://colab.research.google.com/drive/1TzDDCDt368cUErH86Zc_P2aw9bXaaZy1?usp=sharing)
- [](https://colab.research.google.com/drive/1TzDDCDt368cUErH86Zc_P2aw9bXaaZy1?usp=sharing)[ HUPD Examples: Using the HUPD DistilRoBERTa Model](https://colab.research.google.com/drive/11t69BWcAVXndQxAOCpKaGkKkEYJSfydT?usp=sharing)
- [](https://colab.research.google.com/drive/1TzDDCDt368cUErH86Zc_P2aw9bXaaZy1?usp=sharing)[ HUPD Examples: Using the HUPD T5-Small Summarization Model](https://colab.research.google.com/drive/1VkCtrRIryzev_ixDjmJcfJNK-q6Vx24y?usp=sharing)
## Dataset Creation
### Source Data
HUPD synthesizes multiple data sources from the USPTO: While the full patent application texts were obtained from the USPTO Bulk Data Storage System (Patent Application Data/XML Versions 4.0, 4.1, 4.2, 4.3, 4.4 ICE, as well as Version 1.5) as XML files, the bibliographic filing metadata were obtained from the USPTO Patent Examination Research Dataset (in February, 2021).
### Annotations
Beyond our patent decision label, for which construction details are provided in the paper, the dataset does not contain any human-written or computer-generated annotations beyond those produced by patent applicants or the USPTO.
### Data Shift
A major feature of HUPD is its structure, which allows it to demonstrate the evolution of concepts over time. As we illustrate in the paper, the criteria for patent acceptance evolve over time at different rates, depending on category. We believe this is an important feature of the dataset, not only because of the social scientific questions it raises, but also because it facilitates research on models that can accommodate concept shift in a real-world setting.
### Personal and Sensitive Information
The dataset contains information about the inventor(s) and examiner of each patent application. These details are, however, already in the public domain and available on the USPTO's Patent Application Information Retrieval (PAIR) system, as well as on Google Patents and PatentsView.
### Social Impact of the Dataset
The authors of the dataset hope that HUPD will have a positive social impact on the ML/NLP and Econ/IP communities. They discuss these considerations in more detail in [the paper](https://arxiv.org/abs/2207.04043).
### Impact on Underserved Communities and Discussion of Biases
The dataset contains patent applications in English, a language with heavy attention from the NLP community. However, innovation is spread across many languages, cultures, and communities that are not reflected in this dataset. HUPD is thus not representative of all kinds of innovation. Furthermore, patent applications require a fixed cost to draft and file and are not accessible to everyone. One goal of this dataset is to spur research that reduces the cost of drafting applications, potentially allowing for more people to seek intellectual property protection for their innovations.
### Discussion of Biases
Section 4 of [the HUPD paper](https://arxiv.org/abs/2207.04043) provides an examination of the dataset for potential biases. It shows, among other things, that female inventors are notably underrepresented in the U.S. patenting system, that small and micro entities (e.g., independent inventors, small companies, non-profit organizations) are less likely to have positive outcomes in patent obtaining than large entities (e.g., companies with more than 500 employees), and that patent filing and acceptance rates are not uniformly distributed across the US. Our empirical findings suggest that any study focusing on the acceptance prediction task, especially if it is using the inventor information or the small-entity indicator as part of the input, should be aware of the the potential biases present in the dataset and interpret their results carefully in light of those biases.
- Please refer to Section 4 and Section D for an in-depth discussion of potential biases embedded in the dataset.
### Licensing Information
HUPD is released under the CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International.
### Citation Information
```
@article{suzgun2022hupd,
title={The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications},
author={Suzgun, Mirac and Melas-Kyriazi, Luke and Sarkar, Suproteem K. and Kominers, Scott Duke and Shieber, Stuart M.},
year={2022},
publisher={arXiv preprint arXiv:2207.04043},
url={https://arxiv.org/abs/2207.04043},
``` | 10,898 | [
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PygmalionAI/PIPPA | 2023-09-07T03:07:55.000Z | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"not-for-all-audiences",
"conversational",
"roleplay",
"custom-format",
"a.",
"arxiv:2308.05884",
"region:us"
] | PygmalionAI | Personal Interaction Pairs between People and AI (PIPPA) is a partially synthetic, community contributed and open-source conversational and roleplaying dataset generated from a subset of submitted logs to the Pygmalion project. | @misc{gosling2023pippa,
title={PIPPA: A Partially Synthetic Conversational Dataset},
author={Tear Gosling and Alpin Dale and Yinhe Zheng},
year={2023},
eprint={2308.05884},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 105 | 559 | 2023-08-08T01:32:40 | ---
license: apache-2.0
task_categories:
- conversational
language:
- en
tags:
- not-for-all-audiences
- conversational
- roleplay
- custom-format
- a.
pretty_name: PIPPA - Personal Interaction Pairs Between People and AI
size_categories:
- 10K<n<100K
viewer: false
---
# PIPPA - Personal Interaction Pairs between People and AI
It's been a long time coming, but we're proud to finally release the public portion of our conversational dataset to the public. **Personal Interaction Pairs between People and AI** (**PIPPA**) is a partially synthetic, community contributed and open-source conversational and roleplaying dataset generated from a subset of submitted logs to the Pygmalion project.
This dataset is a subset of what we have received - it consists only of the valid conversational logs in which the submitter gave consent to redistribute to the public. Furthermore, we have done our best to redact or modify any personal information that could potentially be found within PIPPA. If you have found something within PIPPA which has not been redacted properly, please contact us via. email at `teargosling@pygmalion.chat` or `alpindale@pygmalion.chat` and we'll take care of it for you. You may contact us for any other purpose as well, including yelling at us for when the next model will be released.
**⚠️ CAUTION: PIPPA contains conversations, themes and scenarios which can be considered "not safe for work" (NSFW) and/or heavily disturbing in nature. Models trained purely with PIPPA may have the tendency to generate X-rated output. You have been warned.**
## Dataset Summary
PIPPA consists of just a little more than 1 million lines of dialogue spread out over 26,000 conversations between users of the popular chatbot website "Character.AI" and its large language model, obtained through a large community effort taking place over the course of several months. Tallying shows that over 1,000 unique personas simulating both real and fictional characters are represented within the dataset, allowing PIPPA and LLMs fine-tuned on it to adapt to many different roleplay domains.
The dataset is represented with a JSONL file, with a singular JSON snippet representing one entire conversation. Every snippet contains the following pieces of data:
- `submission_timestamp`: The Unix timestamp of when this particular conversation was submitted to the project, in milliseconds.
- `categories`: The categories assigned to the character on the Character.AI website, if any were assigned. If no categories were assigned, it will be `null`
- `bot_id`: The unique ID assigned to the specific character which the user was conversing with on the website.
- `bot_name`: The name of the character.
- `bot_greeting`: The introductory line of the character to the user. This is always the first utterance of dialogue in a conversation.
- `bot_definitions`: Contains whatever was typed in the **Definitions** field in the character creator on the website. This usually consists of one or more example conversations between the user and the character designed to steer the model towards emulating the persona correctly. Bot definitions required a separate effort to gather, and thus may not be present for a specific persona - if this is the case, an empty string is provided. Because the defintions were written on Character.AI, this field usually follows Character.AI's unique formatting and should be preprocessed before feeding into any model - please see **Appendix A** of the paper for further details.
- `bot_description`: Contains whatever was typed in the **Description** field in the character creator on the website. It usually consists of a few sentences which gives a brief overview of the character and any important details about them.
- `conversation`: The conversation between the user and the model. This is represented as a list of dictionaries, each dictionary representing a single utterance and containing two key-value pairs: `message`, referring to the utterance itself and `is_human`, which designates whether the dialogue was generated by the user or the LLM.
For further information about PIPPA, please refer to our [published paper](https://arxiv.org/abs/2308.05884) or contact us at the emails listed above.
## Files
We publish PIPPA in multiple variants, each a singular JSONL file:
- **pippa.jsonl**: The original dataset, almost exactly as submitted to us (barring any modifications resulting from the redaction of personally identifiable information).
- **pippa_deduped.jsonl**: The 'cleaned' version of PIPPA, with duplicate conversations as well as any conversation with less than three turns removed from the dataset. **We recommend using this file.**
- **pippa_metharme.jsonl**: A version of deduped PIPPA which is formatted in a similar way to our [Metharme instructional models](https://huggingface.co/PygmalionAI/metharme-13b), useful as an example to demonstrate how to properly format the PIPPA dataset.
If you are using HuggingFace's `datasets` library, you can choose the file you wish to use by specifying the name of it (without extension) as an argument, like so: `dataset = load_dataset("PygmalionAI/PIPPA", 'pippa_deduped')`. The default value is `pippa_deduped`.
Thank you for your patience, everyone!
## Citation
If you're using our dataset, please consider citing our work:
```bibtex
@misc{gosling2023pippa,
title={PIPPA: A Partially Synthetic Conversational Dataset},
author={Tear Gosling and Alpin Dale and Yinhe Zheng},
year={2023},
eprint={2308.05884},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
___
Any relationship between the name of this dataset and any public personas is entirely and totally coincidential. | 5,726 | [
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emo | 2023-04-05T10:05:14.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | In this dataset, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. | @inproceedings{chatterjee-etal-2019-semeval,
title={SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text},
author={Ankush Chatterjee and Kedhar Nath Narahari and Meghana Joshi and Puneet Agrawal},
booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation},
year={2019},
address={Minneapolis, Minnesota, USA},
publisher={Association for Computational Linguistics},
url={https://www.aclweb.org/anthology/S19-2005},
doi={10.18653/v1/S19-2005},
pages={39--48},
abstract={In this paper, we present the SemEval-2019 Task 3 - EmoContext: Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading ''Why don't you ever text me!'' we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. However, the context of dialogue can prove helpful in detection of the emotion. In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps. A training data set of 30160 dialogues, and two evaluation data sets, Test1 and Test2, containing 2755 and 5509 dialogues respectively were released to the participants. A total of 311 teams made submissions to this task. The final leader-board was evaluated on Test2 data set, and the highest ranked submission achieved 79.59 micro-averaged F1 score. Our analysis of systems submitted to the task indicate that Bi-directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class}
} | 3 | 558 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: emocontext
pretty_name: EmoContext
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': others
'1': happy
'2': sad
'3': angry
config_name: emo2019
splits:
- name: train
num_bytes: 2433205
num_examples: 30160
- name: test
num_bytes: 421555
num_examples: 5509
download_size: 3362556
dataset_size: 2854760
---
# Dataset Card for "emo"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.aclweb.org/anthology/S19-2005/](https://www.aclweb.org/anthology/S19-2005/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 3.37 MB
- **Size of the generated dataset:** 2.85 MB
- **Total amount of disk used:** 6.22 MB
### Dataset Summary
In this dataset, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others.
### 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
### Data Instances
#### emo2019
- **Size of downloaded dataset files:** 3.37 MB
- **Size of the generated dataset:** 2.85 MB
- **Total amount of disk used:** 6.22 MB
An example of 'train' looks as follows.
```
{
"label": 0,
"text": "don't worry i'm girl hmm how do i know if you are what's ur name"
}
```
### Data Fields
The data fields are the same among all splits.
#### emo2019
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `others` (0), `happy` (1), `sad` (2), `angry` (3).
### Data Splits
| name |train|test|
|-------|----:|---:|
|emo2019|30160|5509|
## 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
```
@inproceedings{chatterjee-etal-2019-semeval,
title={SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text},
author={Ankush Chatterjee and Kedhar Nath Narahari and Meghana Joshi and Puneet Agrawal},
booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation},
year={2019},
address={Minneapolis, Minnesota, USA},
publisher={Association for Computational Linguistics},
url={https://www.aclweb.org/anthology/S19-2005},
doi={10.18653/v1/S19-2005},
pages={39--48},
abstract={In this paper, we present the SemEval-2019 Task 3 - EmoContext: Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading ''Why don't you ever text me!'' we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. However, the context of dialogue can prove helpful in detection of the emotion. In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps. A training data set of 30160 dialogues, and two evaluation data sets, Test1 and Test2, containing 2755 and 5509 dialogues respectively were released to the participants. A total of 311 teams made submissions to this task. The final leader-board was evaluated on Test2 data set, and the highest ranked submission achieved 79.59 micro-averaged F1 score. Our analysis of systems submitted to the task indicate that Bi-directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lordtt13](https://github.com/lordtt13), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | 7,967 | [
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tau/mrqa | 2022-03-21T19:26:55.000Z | [
"region:us"
] | tau | The MRQA 2019 Shared Task focuses on generalization in question answering.
An effective question answering system should do more than merely
interpolate from the training set to answer test examples drawn
from the same distribution: it should also be able to extrapolate
to out-of-distribution examples — a significantly harder challenge.
The dataset is a collection of 18 existing QA dataset (carefully selected
subset of them) and converted to the same format (SQuAD format). Among
these 18 datasets, six datasets were made available for training,
six datasets were made available for development, and the final six
for testing. The dataset is released as part of the MRQA 2019 Shared Task. | @inproceedings{fisch2019mrqa,
title={{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension},
author={Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen},
booktitle={Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNLP},
year={2019},
} | 0 | 558 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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boomsss/spx_intra | 2023-10-20T04:43:51.000Z | [
"region:us"
] | boomsss | null | null | 0 | 557 | 2023-09-30T05:28:51 | Entry not found | 15 | [
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conv_ai_2 | 2022-11-03T16:31:09.000Z | [
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"evaluating-dialogue-systems",
"arxiv:1902.00098",
"region:us"
] | null | ConvAI is a dataset of human-to-bot conversations labelled for quality. This data can be used to train a metric for evaluating dialogue systems. Moreover, it can be used in the development of chatbots themselves: it contains the information on the quality of utterances and entire dialogues, that can guide a dialogue system in search of better answers. | @misc{dinan2019second,
title={The Second Conversational Intelligence Challenge (ConvAI2)},
author={Emily Dinan and Varvara Logacheva and Valentin Malykh and Alexander Miller and Kurt Shuster and Jack Urbanek and Douwe Kiela and Arthur Szlam and Iulian Serban and Ryan Lowe and Shrimai Prabhumoye and Alan W Black and Alexander Rudnicky and Jason Williams and Joelle Pineau and Mikhail Burtsev and Jason Weston},
year={2019},
eprint={1902.00098},
archivePrefix={arXiv},
primaryClass={cs.AI}
} | 28 | 555 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- conversational
- text-classification
task_ids:
- text-scoring
paperswithcode_id: convai2
pretty_name: Conversational Intelligence Challenge 2
tags:
- evaluating-dialogue-systems
dataset_info:
features:
- name: id
dtype: string
- name: dialog_id
dtype: string
- name: dialog
list:
- name: id
dtype: int32
- name: sender
dtype: string
- name: text
dtype: string
- name: sender_class
dtype: string
- name: bot_profile
sequence:
list: string
- name: user_profile
sequence:
list: string
- name: eval_score
dtype: int32
- name: profile_match
dtype: int32
config_name: conv_ai_2
splits:
- name: train
num_bytes: 8403805
num_examples: 3495
download_size: 6636788
dataset_size: 8403805
---
# Dataset Card for conv_ai_2
## 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/DeepPavlov/convai/tree/master/2018
- **Repository:** https://github.com/DeepPavlov/convai/tree/master/2018
- **Paper:** https://arxiv.org/abs/1902.00098
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
ConvAI is a dataset of human-to-bot conversations labeled for quality. This data can be used to train a metric for evaluating dialogue systems. Moreover, it can be used in the development of chatbots themselves: it contains information on the quality of utterances and entire dialogues, that can guide a dialogue system in search of better answers.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
```
{
"dialog_id": "0x648cc5b7",
"dialog": [
{
"id": 0,
"sender": "participant2",
"text": "Hi! How is your day? \ud83d\ude09",
"sender_class": "Bot"
},
{
"id": 1,
"sender": "participant1",
"text": "Hi! Great!",
"sender_class": "Human"
},
{
"id": 2,
"sender": "participant2",
"text": "I am good thanks for asking are you currently in high school?",
"sender_class": "Bot"
}
],
"bot_profile": [
"my current goal is to run a k.",
"when i grow up i want to be a physical therapist.",
"i'm currently in high school.",
"i make straight as in school.",
"i won homecoming queen this year."
],
"user_profile": [
"my favorite color is red.",
"i enjoy listening to classical music.",
"i'm a christian.",
"i can drive a tractor."
],
"eval_score": 4,
"profile_match": 1
}
```
### Data Fields
- dialog_id : specifies the unique ID for the dialogs.
- dialog : Array of dialogs.
- bot_profile : Bot annotated response that will be used for evaluation.
- user_profile : user annoted response that will be used for evaluation.
- eval_score : (`1`,` 2`,` 3`,` 4`,` 5`) how does an user like a conversation. The missing values are replaced with` -1`
- profile_match : (`0`,` 1`) an user is given by two profile descriptions (4 sentences each), one of them is the one given to the bot it had been talking to, the other one is random; the user needs to choose one of them.The missing values are replaced with` -1`
### 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
@article{DBLP:journals/corr/abs-1902-00098,
author = {Emily Dinan and
Varvara Logacheva and
Valentin Malykh and
Alexander H. Miller and
Kurt Shuster and
Jack Urbanek and
Douwe Kiela and
Arthur Szlam and
Iulian Serban and
Ryan Lowe and
Shrimai Prabhumoye and
Alan W. Black and
Alexander I. Rudnicky and
Jason Williams and
Joelle Pineau and
Mikhail S. Burtsev and
Jason Weston},
title = {The Second Conversational Intelligence Challenge (ConvAI2)},
journal = {CoRR},
volume = {abs/1902.00098},
year = {2019},
url = {http://arxiv.org/abs/1902.00098},
archivePrefix = {arXiv},
eprint = {1902.00098},
timestamp = {Wed, 07 Oct 2020 11:09:41 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1902-00098.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. | 6,755 | [
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lamini/lamini_docs_evaluation | 2023-07-24T03:08:13.000Z | [
"region:us"
] | lamini | null | null | 0 | 555 | 2023-07-24T03:08:09 | ---
dataset_info:
features:
- name: predicted_answer
dtype: string
- name: target_answer
dtype: string
splits:
- name: train
num_bytes: 744520
num_examples: 139
download_size: 86086
dataset_size: 744520
---
# Dataset Card for "lamini_docs_evaluation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 413 | [
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] |
GEM/e2e_nlg | 2022-10-24T15:30:18.000Z | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"data-to-text",
"region:us"
] | GEM | The E2E dataset is designed for a limited-domain data-to-text task --
generation of restaurant descriptions/recommendations based on up to 8 different
attributes (name, area, price range etc.). | @inproceedings{e2e_cleaned,
address = {Tokyo, Japan},
title = {Semantic {Noise} {Matters} for {Neural} {Natural} {Language} {Generation}},
url = {https://www.aclweb.org/anthology/W19-8652/},
booktitle = {Proceedings of the 12th {International} {Conference} on {Natural} {Language} {Generation} ({INLG} 2019)},
author = {Dušek, Ondřej and Howcroft, David M and Rieser, Verena},
year = {2019},
pages = {421--426},
} | 1 | 553 | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: e2e_nlg
tags:
- data-to-text
---
# Dataset Card for GEM/e2e_nlg
## Dataset Description
- **Homepage:** http://www.macs.hw.ac.uk/InteractionLab/E2E/
- **Repository:** https://github.com/tuetschek/e2e-cleaning
- **Paper:** https://www.aclweb.org/anthology/W17-5525/, [Detailed E2E Challenge writeup
- **Leaderboard:** N/A
- **Point of Contact:** Ondrej Dusek
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/e2e_nlg).
### Dataset Summary
The E2E NLG dataset is an English benchmark dataset for data-to-text models that verbalize a set of 2-9 key-value attribute pairs in the restaurant domain. The version used for GEM is the cleaned E2E NLG dataset, which filters examples with hallucinations and outputs that don't fully cover all input attributes.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/e2e_nlg')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/e2e_nlg).
#### website
[Website](http://www.macs.hw.ac.uk/InteractionLab/E2E/)
#### paper
[First data release](https://www.aclweb.org/anthology/W17-5525/), [Detailed E2E Challenge writeup](https://doi.org/10.1016/j.csl.2019.06.009), [Cleaned E2E version](https://www.aclweb.org/anthology/W19-8652/)
#### authors
Jekaterina Novikova, Ondrej Dusek and Verena Rieser
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Website](http://www.macs.hw.ac.uk/InteractionLab/E2E/)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/tuetschek/e2e-cleaning)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[First data release](https://www.aclweb.org/anthology/W17-5525/), [Detailed E2E Challenge writeup](https://doi.org/10.1016/j.csl.2019.06.009), [Cleaned E2E version](https://www.aclweb.org/anthology/W19-8652/)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{e2e_cleaned,
address = {Tokyo, Japan},
title = {Semantic {Noise} {Matters} for {Neural} {Natural} {Language} {Generation}},
url = {https://www.aclweb.org/anthology/W19-8652/},
booktitle = {Proceedings of the 12th {International} {Conference} on {Natural} {Language} {Generation} ({INLG} 2019)},
author = {Dušek, Ondřej and Howcroft, David M and Rieser, Verena},
year = {2019},
pages = {421--426},
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Ondrej Dusek
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
odusek@ufal.mff.cuni.cz
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
Dialect-specific data was not collected and the language is general British English.
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
The original dataset was collected using the CrowdFlower (now Appen) platform using native English speakers (self-reported). No demographic information was provided, but the collection was geographically limited to English-speaking countries.
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The dataset was collected to test neural model on a very well specified realization task.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Data-to-Text
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Producing a text informing/recommending a restaurant, given all and only the attributes specified on the input.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Heriot-Watt University
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Jekaterina Novikova, Ondrej Dusek and Verena Rieser
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
This research received funding from the EPSRC projects DILiGENt (EP/M005429/1) and MaDrIgAL (EP/N017536/1).
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Simon Mille wrote the initial data card and Yacine Jernite the data loader. Sebastian Gehrmann migrated the data card to the v2 format and moved the data loader to the hub.
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
The data is in a CSV format, with the following fields:
* `mr` -- the meaning representation (MR, input)
* `ref` -- reference, i.e. the corresponding natural-language description (output)
There are additional fields (`fixed`, `orig_mr`) indicating whether the data was modified in the
cleaning process and what was the original MR before cleaning, but these aren't used for NLG.
The MR has a flat structure -- attribute-value pairs are comma separated, with values
enclosed in brackets (see example above). There are 8 attributes:
* `name` -- restaurant name
* `near` -- a landmark close to the restaurant
* `area` -- location (riverside, city centre)
* `food` -- food type / cuisine (e.g. Japanese, Indian, English etc.)
* `eatType` -- restaurant type (restaurant, coffee shop, pub)
* `priceRange` -- price range (low, medium, high, <£20, £20-30, >£30)
* `rating` -- customer rating (low, medium, high, 1/5, 3/5, 5/5)
* `familyFriendly` -- is the restaurant family-friendly (yes/no)
The same MR is often repeated multiple times with different synonymous references.
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
The source MRs were generated automatically at random from a set of valid attribute values. The labels were crowdsourced and are natural language
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
"input": "name[Alimentum], area[riverside], familyFriendly[yes], near[Burger King]",
"target": "Alimentum is a kids friendly place in the riverside area near Burger King."
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
| | MRs | Distinct MRs | References |
|-------------|------|--------------|------------|
| Training |12,568| 8,362 | 33,525 |
| Development | 1,484| 1,132 | 4,299 |
| Test | 1,847| 1,358 | 4,693 |
| Total |15,899| 10,852 | 42,517 |
“Distinct MRs” are MRs that remain distinct even if restaurant/place names (attributes `name`, `near`)
are delexicalized, i.e., replaced with a placeholder.
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The data are divided so that MRs in different splits do not overlap.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
The E2E dataset is one of the largest limited-domain NLG datasets and is frequently used as a data-to-text generation benchmark. The E2E Challenge included 20 systems of very different architectures, with system outputs available for download.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
The dataset is much cleaner than comparable datasets, and it is also a relatively easy task, making for a straightforward evaluation.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
surface realization.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
yes
#### Split Information
<!-- info: Describe how the new splits were created -->
<!-- scope: periscope -->
4 special test sets for E2E were added to the GEM evaluation suite.
1. We created subsets of the training and development sets of ~500 randomly selected inputs each.
2. We applied input scrambling on a subset of 500 randomly selected test instances; the order of the input properties was randomly reassigned.
3. For the input size, we created subpopulations based on the number of restaurant properties in the input.
| Input length | Frequency English |
|---------------|-------------------|
| 2 | 5 |
| 3 | 120 |
| 4 | 389 |
| 5 | 737 |
| 6 | 1187 |
| 7 | 1406 |
| 8 | 774 |
| 9 | 73 |
| 10 | 2 |
#### Split Motivation
<!-- info: What aspects of the model's generation capacities were the splits created to test? -->
<!-- scope: periscope -->
Generalization and robustness
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Surface realization.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `METEOR`, `ROUGE`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
The official evaluation script combines the MT-Eval and COCO Captioning libraries with the following metrics.
- BLEU
- CIDEr
- NIST
- METEOR
- ROUGE-L
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
Most previous results, including the shared task results, used the library provided by the dataset creators. The shared task also conducted a human evaluation using the following two criteria:
- `Quality`: When collecting quality ratings, system outputs were presented to crowd workers together with the corresponding meaning representation, which implies that correctness of the NL utterance relative to the MR should also influence this ranking. The crowd workers were asked: “How do you judge the overall quality of the utterance in terms of its grammatical correctness, fluency, adequacy and other important factors?”
- `Naturalness`: When collecting naturalness ratings, system outputs were presented to crowd workers without the corresponding meaning representation. The crowd workers were asked: “Could the utterance have been produced by a native speaker?”
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
The shared task writeup has in-depth evaluations of systems (https://www.sciencedirect.com/science/article/pii/S0885230819300919)
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The dataset was collected to showcase/test neural NLG models. It is larger and contains more lexical richness and syntactic variation than previous closed-domain NLG datasets.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Producing a text informing/recommending a restaurant, given all and only the attributes specified on the input.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Crowdsourced`
#### Where was it crowdsourced?
<!-- info: If crowdsourced, where from? -->
<!-- scope: periscope -->
`Other crowdworker platform`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
Human references describing the MRs were collected by crowdsourcing on the CrowdFlower (now Appen) platform,
with either textual or pictorial MRs as a baseline.
The pictorial MRs were used in 20% of cases -- these yield higher lexical variation but introduce noise.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The dataset is focused on descriptions of restaurants.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
There were basic checks (length, valid characters, repetition).
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
algorithmically
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
The cleaned version of the dataset which we are using in GEM was algorithmically filtered. They used regular expressions to match all human-generated references with a more accurate input when attributes were hallucinated or dropped. Additionally, train-test overlap stemming from the transformation was removed. As a result, this data is much cleaner than the original dataset but not perfect (about 20% of instances may have misaligned slots, compared to 40% of the original data.
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
#### Consent Policy Details
<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
Since a crowdsourcing platform was used, the involved raters waived their rights to the data and are aware that the produced annotations can be publicly released.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
The dataset is artificial and does not contain any description of people.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
The source data is generated randomly, so it should not contain biases. The human references may be biased by the workers' demographic, but that was not investigated upon data collection.
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
The cleaned version still has data points with hallucinated or omitted attributes.
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
The data only pertains to the restaurant domain and the included attributes. A model cannot be expected to handle other domains or attributes.
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jordiae/exebench | 2023-03-09T16:06:06.000Z | [
"region:us"
] | jordiae | An ML-scale dataset of executable C functions | @inproceedings{10.1145/3520312.3534867,
author = {Armengol-Estap\'{e}, Jordi and Woodruff, Jackson and Brauckmann, Alexander and Magalh\~{a}es, Jos\'{e} Wesley de Souza and O'Boyle, Michael F. P.},
title = {ExeBench: An ML-Scale Dataset of Executable C Functions},
year = {2022},
isbn = {9781450392730},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3520312.3534867},
doi = {10.1145/3520312.3534867},
abstract = {Machine-learning promises to transform compilation and software engineering, yet is frequently limited by the scope of available datasets. In particular, there is a lack of runnable, real-world datasets required for a range of tasks ranging from neural program synthesis to machine learning-guided program optimization. We introduce a new dataset, ExeBench, which attempts to address this. It tackles two key issues with real-world code: references to external types and functions and scalable generation of IO examples. ExeBench is the first publicly available dataset that pairs real-world C code taken from GitHub with IO examples that allow these programs to be run. We develop a toolchain that scrapes GitHub, analyzes the code, and generates runnable snippets of code. We analyze our benchmark suite using several metrics, and show it is representative of real-world code. ExeBench contains 4.5M compilable and 700k executable C functions. This scale of executable, real functions will enable the next generation of machine learning-based programming tasks.},
booktitle = {Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming},
pages = {50–59},
numpages = {10},
keywords = {Code Dataset, Program Synthesis, Mining Software Repositories, C, Machine Learning for Code, Compilers},
location = {San Diego, CA, USA},
series = {MAPS 2022}
} | 1 | 553 | 2022-07-30T20:07:06 | # ExeBench: an ML-scale dataset of executable C functions
ExeBench is a dataset of millions of C functions paired with dependencies and metadatada such that at least a subset of it can be executed with IO pairs. It is mainly inteded for machine learning applications but it is application-agnostic enough to have other usages.
Please read the paper for more information: https://dl.acm.org/doi/abs/10.1145/3520312.3534867.
Please see `examples/` in https://github.com/jordiae/exebench for examples.
## Usage
### Option 1: Using the helpers in this repo
```
git clone https://github.com/jordiae/exebench.git
cd exebench/
python -m venv venv
source venv/bin/activate
pip install -r requirements_examples.txt
PYTHONPATH="${PYTHONPATH}:${pwd}" python examples/basic.py
```
### Option 2: Directly using the Hugginface Datasets library
```
!pip install datasets zstandard
# Load dataset split. In this case, synthetic test split
dataset = load_dataset('jordiae/exebench', split='test_synth')
for e in dataset:
...
```
### Option 3: Directly download the dataset
Take a look at the files at: https://huggingface.co/datasets/jordiae/exebench/tree/main
The dataset consist of directories compressed with TAR. Inside each TAR, there is a series of jsonline files compressed with zstandard.
## Statistics and versions
This release corresponds to ExeBench v1.01, a version with some improvements with respect to the original one presented in the paper. The statistics and studies presented in the paper remain consistent with respect to the new ones. The final splits of the new version consist of the following functions:
```
train_not_compilable: 2.357M
train_synth_compilable: 2.308373M
train_real_compilable: 0.675074M
train_synth_simple_io: 0.550116M
train_real_simple_io: 0.043769M
train_synth_rich_io: 0.097250M
valid_synth: 5k
valid_real: 2.133k
test_synth: 5k
test_real: 2.134k
```
The original dataset (v1.00) with the exact same data studied in the paper can be accessed on request at: https://huggingface.co/datasets/jordiae/exebench_legacy (please reach out for access)
## License
All C functions keep the original license as per their original Github repository (available in the metadata). All ExeBench contributions (I/O examples, boilerplate to run functions, etc) are released with an MIT license.
## Citation
```
@inproceedings{10.1145/3520312.3534867,
author = {Armengol-Estap\'{e}, Jordi and Woodruff, Jackson and Brauckmann, Alexander and Magalh\~{a}es, Jos\'{e} Wesley de Souza and O'Boyle, Michael F. P.},
title = {ExeBench: An ML-Scale Dataset of Executable C Functions},
year = {2022},
isbn = {9781450392730},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3520312.3534867},
doi = {10.1145/3520312.3534867},
abstract = {Machine-learning promises to transform compilation and software engineering, yet is frequently limited by the scope of available datasets. In particular, there is a lack of runnable, real-world datasets required for a range of tasks ranging from neural program synthesis to machine learning-guided program optimization. We introduce a new dataset, ExeBench, which attempts to address this. It tackles two key issues with real-world code: references to external types and functions and scalable generation of IO examples. ExeBench is the first publicly available dataset that pairs real-world C code taken from GitHub with IO examples that allow these programs to be run. We develop a toolchain that scrapes GitHub, analyzes the code, and generates runnable snippets of code. We analyze our benchmark suite using several metrics, and show it is representative of real-world code. ExeBench contains 4.5M compilable and 700k executable C functions. This scale of executable, real functions will enable the next generation of machine learning-based programming tasks.},
booktitle = {Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming},
pages = {50–59},
numpages = {10},
keywords = {Code Dataset, Program Synthesis, Mining Software Repositories, C, Machine Learning for Code, Compilers},
location = {San Diego, CA, USA},
series = {MAPS 2022}
}
```
## Credits
We thank Anghabench authors for their type inference-based synthetic dependencies generation for C functions. This software, Psyche-C, can be found at: https://github.com/ltcmelo/psychec
## Contact
```
jordi.armengol.estape at ed.ac.uk
``` | 4,451 | [
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HuggingFaceM4/FairFace | 2022-12-09T00:14:46.000Z | [
"license:cc-by-4.0",
"region:us"
] | HuggingFaceM4 | FairFace is a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino.
Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups. | @inproceedings{karkkainenfairface,
title={FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation},
author={Karkkainen, Kimmo and Joo, Jungseock},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
year={2021},
pages={1548--1558}
} | 5 | 553 | 2022-12-08T23:00:45 | ---
license: cc-by-4.0
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/joojs/fairface](https://github.com/joojs/fairface)
- **Repository:** [https://github.com/joojs/fairface](https://github.com/joojs/fairface)
- **Paper:** [https://openaccess.thecvf.com/content/WACV2021/papers/Karkkainen_FairFace_Face_Attribute_Dataset_for_Balanced_Race_Gender_and_Age_WACV_2021_paper.pdf](https://openaccess.thecvf.com/content/WACV2021/papers/Karkkainen_FairFace_Face_Attribute_Dataset_for_Balanced_Race_Gender_and_Age_WACV_2021_paper.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
FairFace is a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino.
Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Each instance has the following structure:
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=448x448 at 0x7FCABA221FA0>,
'age': 6,
'gender': 0,
'race': 0,
'service_test': True
}
```
### Data Fields
- `image`: The image
- `age`: Age class among `["0-2", "3-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "more than 70"]`
- `gender`: Gender class among `["Male", "Female"]`
- `race`: Race class among `["East Asian", "Indian", "Black", "White", "Middle Eastern", "Latino_Hispanic", "Southeast Asian"]`
- `service_test`: Not sure what this is. See [issue](https://github.com/joojs/fairface/issues/9).
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.
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scikit-learn/adult-census-income | 2022-06-20T14:46:43.000Z | [
"license:cc0-1.0",
"region:us"
] | scikit-learn | null | null | 1 | 552 | 2022-06-20T14:33:51 | ---
license: cc0-1.0
---
## Adult Census Income Dataset
The following was retrieved from [UCI machine learning repository](https://archive.ics.uci.edu/ml/datasets/adult).
This data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1) && (HRSWK>0)). The prediction task is to determine whether a person makes over $50K a year.
**Description of fnlwgt (final weight)**
The weights on the Current Population Survey (CPS) files are controlled to independent estimates of the civilian noninstitutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls. These are:
- A single cell estimate of the population 16+ for each state.
- Controls for Hispanic Origin by age and sex.
- Controls by Race, age and sex.
We use all three sets of controls in our weighting program and "rake" through them 6 times so that by the end we come back to all the controls we used. The term estimate refers to population totals derived from CPS by creating "weighted tallies" of any specified socio-economic characteristics of the population. People with similar demographic characteristics should have similar weights. There is one important caveat to remember about this statement. That is that since the CPS sample is actually a collection of 51 state samples, each with its own probability of selection, the statement only applies within state. | 1,608 | [
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open-phi/textbooks | 2023-10-08T05:07:09.000Z | [
"region:us"
] | open-phi | null | null | 53 | 551 | 2023-10-03T16:55:38 | ---
dataset_info:
features:
- name: topic
dtype: string
- name: model
dtype: string
- name: concepts
dtype: string
- name: outline
dtype: string
- name: markdown
dtype: string
- name: field
dtype: string
- name: subfield
dtype: string
- name: rag
dtype: string
splits:
- name: train
num_bytes: 397014633
num_examples: 1795
download_size: 134557403
dataset_size: 397014633
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
## Textbooks Are All You Need
Leveraging Large Language Models (LLMs), there's an opportunity to create a comprehensive open-source repository reminiscent of the historic Library of Alexandria.
This initiative represents a preliminary attempt at producing high-quality books covering an extensive range of subjects. The source of these samples varies:
- Some generated using the RAG model, referencing Wikipedia or other search data.
- Some are completely synthetically generated.
- Some created using GPT-3.5 and others with GPT-4.
### Generation:
- **[Textbook Quality](https://github.com/VikParuchuri/textbook_quality)**: 1391 samples & ~48M tokens of serp RAG programming texts
- **[SciPhi](https://github.com/emrgnt-cmplxty/SciPhi)**: 300 samples & ~38M tokens of wikipedia RAG + full synthetic general textbooks
For a comprehensive view, explore our collection on GitHub: **[Library of Phi](https://github.com/emrgnt-cmplxty/library_of_phi)**.
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] |
dynabench/dynasent | 2021-04-29T11:30:24.000Z | [
"arxiv:2012.15349",
"arxiv:1803.09010",
"arxiv:1810.03993",
"region:us"
] | dynabench | Dynabench.DynaSent is a Sentiment Analysis dataset collected using a
human-and-model-in-the-loop. | null | 3 | 550 | 2022-03-02T23:29:22 | # DynaSent: Dynamic Sentiment Analysis Dataset
DynaSent is an English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. This dataset card is forked from the original [DynaSent Repository](https://github.com/cgpotts/dynasent).
## Contents
* [Citation](#Citation)
* [Dataset files](#dataset-files)
* [Quick start](#quick-start)
* [Data format](#data-format)
* [Models](#models)
* [Other files](#other-files)
* [License](#license)
## Citation
[Christopher Potts](http://web.stanford.edu/~cgpotts/), [Zhengxuan Wu](http://zen-wu.social), Atticus Geiger, and [Douwe Kiela](https://douwekiela.github.io). 2020. [DynaSent: A dynamic benchmark for sentiment analysis](https://arxiv.org/abs/2012.15349). Ms., Stanford University and Facebook AI Research.
```stex
@article{potts-etal-2020-dynasent,
title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis},
author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus and Kiela, Douwe},
journal={arXiv preprint arXiv:2012.15349},
url={https://arxiv.org/abs/2012.15349},
year={2020}}
```
## Dataset files
The dataset is [dynasent-v1.1.zip](dynasent-v1.1.zip), which is included in this repository. `v1.1` differs from `v1` only in that `v1.1` has proper unique ids for Round 1 and corrects a bug that led to some non-unique ids in Round 2. There are no changes to the examples or other metadata.
The dataset consists of two rounds, each with a train/dev/test split:
### Round 1: Naturally occurring sentences
* `dynasent-v1.1-round01-yelp-train.jsonl`
* `dynasent-v1.1-round01-yelp-dev.jsonl`
* `dynasent-v1.1-round01-yelp-test.jsonl`
### Round 1: Sentences crowdsourced using Dynabench
* `dynasent-v1.1-round02-dynabench-train.jsonl`
* `dynasent-v1.1-round02-dynabench-dev.jsonl`
* `dynasent-v1.1-round02-dynabench-test.jsonl`
### SST-dev revalidation
The dataset also contains a version of the [Stanford Sentiment Treebank](https://nlp.stanford.edu/sentiment/) dev set in our format with labels from our validation task:
* `sst-dev-validated.jsonl`
## Quick start
This function can be used to load any subset of the files:
```python
import json
def load_dataset(*src_filenames, labels=None):
data = []
for filename in src_filenames:
with open(filename) as f:
for line in f:
d = json.loads(line)
if labels is None or d['gold_label'] in labels:
data.append(d)
return data
```
For example, to create a Round 1 train set restricting to examples with ternary gold labels:
```python
import os
r1_train_filename = os.path.join('dynasent-v1.1', 'dynasent-v1.1-round01-yelp-train.jsonl')
ternary_labels = ('positive', 'negative', 'neutral')
r1_train = load_dataset(r1_train_filename, labels=ternary_labels)
X_train, y_train = zip(*[(d['sentence'], d['gold_label']) for d in r1_train])
```
## Data format
### Round 1 format
```python
{'hit_ids': ['y5238'],
'sentence': 'Roto-Rooter is always good when you need someone right away.',
'indices_into_review_text': [0, 60],
'model_0_label': 'positive',
'model_0_probs': {'negative': 0.01173639390617609,
'positive': 0.7473671436309814,
'neutral': 0.24089649319648743},
'text_id': 'r1-0000001',
'review_id': 'IDHkeGo-nxhqX4Exkdr08A',
'review_rating': 1,
'label_distribution': {'positive': ['w130', 'w186', 'w207', 'w264', 'w54'],
'negative': [],
'neutral': [],
'mixed': []},
'gold_label': 'positive'}
```
Details:
* `'hit_ids'`: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset.
* `'sentence'`: The example text.
* `'indices_into_review_text':` indices of `'sentence'` into the original review in the [Yelp Academic Dataset](https://www.yelp.com/dataset).
* `'model_0_label'`: prediction of Model 0 as described in the paper. The possible values are `'positive'`, `'negative'`, and `'neutral'`.
* `'model_0_probs'`: probability distribution predicted by Model 0. The keys are `('positive', 'negative', 'neutral')` and the values are floats.
* `'text_id'`: unique identifier for this entry.
* `'review_id'`: review-level identifier for the review from the [Yelp Academic Dataset](https://www.yelp.com/dataset) containing `'sentence'`.
* `'review_rating'`: review-level star-rating for the review containing `'sentence'` in the [Yelp Academic Dataset](https://www.yelp.com/dataset). The possible values are `1`, `2`, `3`, `4`, and `5`.
* `'label_distribution':` response distribution from the MTurk validation task. The keys are `('positive', 'negative', 'neutral')` and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset.
* `'gold_label'`: the label chosen by at least three of the five workers if there is one (possible values: `'positive'`, `'negative'`, '`neutral'`, and `'mixed'`), else `None`.
Here is some code one could use to augment a dataset, as loaded by `load_dataset`, with a field giving the full review text from the [Yelp Academic Dataset](https://www.yelp.com/dataset):
```python
import json
def index_yelp_reviews(yelp_src_filename='yelp_academic_dataset_review.json'):
index = {}
with open(yelp_src_filename) as f:
for line in f:
d = json.loads(line)
index[d['review_id']] = d['text']
return index
yelp_index = index_yelp_reviews()
def add_review_text_round1(dataset, yelp_index):
for d in dataset:
review_text = yelp_index[d['text_id']]
# Check that we can find the sentence as expected:
start, end = d['indices_into_review_text']
assert review_text[start: end] == d['sentence']
d['review_text'] = review_text
return dataset
```
### Round 2 format
```python
{'hit_ids': ['y22661'],
'sentence': "We enjoyed our first and last meal in Toronto at Bombay Palace, and I can't think of a better way to book our journey.",
'sentence_author': 'w250',
'has_prompt': True,
'prompt_data': {'indices_into_review_text': [2093, 2213],
'review_rating': 5,
'prompt_sentence': "Our first and last meals in Toronto were enjoyed at Bombay Palace and I can't think of a better way to bookend our trip.",
'review_id': 'Krm4kSIb06BDHternF4_pA'},
'model_1_label': 'positive',
'model_1_probs': {'negative': 0.29140257835388184,
'positive': 0.6788994669914246,
'neutral': 0.029697999358177185},
'text_id': 'r2-0000001',
'label_distribution': {'positive': ['w43', 'w26', 'w155', 'w23'],
'negative': [],
'neutral': [],
'mixed': ['w174']},
'gold_label': 'positive'}
```
Details:
* `'hit_ids'`: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset.
* `'sentence'`: The example text.
* `'sentence_author'`: Anonymized MTurk id of the worker who wrote `'sentence'`. These are from the same family of ids as used in `'label_distribution'`, but this id is never one of the ids in `'label_distribution'` for this example.
* `'has_prompt'`: `True` if the `'sentence'` was written with a Prompt else `False`.
* `'prompt_data'`: None if `'has_prompt'` is False, else:
* `'indices_into_review_text'`: indices of `'prompt_sentence'` into the original review in the [Yelp Academic Dataset](https://www.yelp.com/dataset).
* `'review_rating'`: review-level star-rating for the review containing `'sentence'` in the [Yelp Academic Dataset](https://www.yelp.com/dataset).
* `'prompt_sentence'`: The prompt text.
* `'review_id'`: review-level identifier for the review from the [Yelp Academic Dataset](https://www.yelp.com/dataset) containing `'prompt_sentence'`.
* `'model_1_label'`: prediction of Model 1 as described in the paper. The possible values are `'positive'`, `'negative'`, and '`neutral'`.
* `'model_1_probs'`: probability distribution predicted by Model 1. The keys are `('positive', 'negative', 'neutral')` and the values are floats.
* `'text_id'`: unique identifier for this entry.
* `'label_distribution'`: response distribution from the MTurk validation task. The keys are `('positive', 'negative', 'neutral')` and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset.
* `'gold_label'`: the label chosen by at least three of the five workers if there is one (possible values: `'positive'`, `'negative'`, '`neutral'`, and `'mixed'`), else `None`.
To add the review texts to the `'prompt_data'` field, one can extend the code above for Round 1 with the following function:
```python
def add_review_text_round2(dataset, yelp_index):
for d in dataset:
if d['has_prompt']:
prompt_data = d['prompt_data']
review_text = yelp_index[prompt_data['review_id']]
# Check that we can find the sentence as expected:
start, end = prompt_data['indices_into_review_text']
assert review_text[start: end] == prompt_data['prompt_sentence']
prompt_data['review_text'] = review_text
return dataset
```
### SST-dev format
```python
{'hit_ids': ['s20533'],
'sentence': '-LRB- A -RRB- n utterly charming and hilarious film that reminded me of the best of the Disney comedies from the 60s.',
'tree': '(4 (2 (1 -LRB-) (2 (2 A) (3 -RRB-))) (4 (4 (2 n) (4 (3 (2 utterly) (4 (3 (4 charming) (2 and)) (4 hilarious))) (3 (2 film) (3 (2 that) (4 (4 (2 (2 reminded) (3 me)) (4 (2 of) (4 (4 (2 the) (4 best)) (2 (2 of) (3 (2 the) (3 (3 Disney) (2 comedies))))))) (2 (2 from) (2 (2 the) (2 60s)))))))) (2 .)))',
'text_id': 'sst-dev-validate-0000437',
'sst_label': '4',
'label_distribution': {'positive': ['w207', 'w3', 'w840', 'w135', 'w26'],
'negative': [],
'neutral': [],
'mixed': []},
'gold_label': 'positive'}
```
Details:
* `'hit_ids'`: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset.
* `'sentence'`: The example text.
* `'tree'`: The parsetree for the example as given in the SST distribution.
* `'text_id'`: A new identifier for this example.
* `'sst_label'`: The root-node label from the SST. Possible values `'0'`, `'1'` `'2'`, `'3'`, and `'4'`.
* `'label_distribution':` response distribution from the MTurk validation task. The keys are `('positive', 'negative', 'neutral')` and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset.
* `'gold_label'`: the label chosen by at least three of the five workers if there is one (possible values: `'positive'`, `'negative'`, '`neutral'`, and `'mixed'`), else `None`.
## Models
Model 0 and Model 1 from the paper are available here:
https://drive.google.com/drive/folders/1dpKrjNJfAILUQcJPAFc5YOXUT51VEjKQ?usp=sharing
This repository includes a Python module `dynasent_models.py` that provides a [Hugging Face](https://huggingface.co)-based wrapper around these ([PyTorch](https://pytorch.org)) models. Simple examples:
```python
import os
from dynasent_models import DynaSentModel
# `dynasent_model0` should be downloaded from the above Google Drive link and
# placed in the `models` directory. `dynasent_model1` works the same way.
model = DynaSentModel(os.path.join('models', 'dynasent_model0.bin'))
examples = [
"superb",
"They said the experience would be amazing, and they were right!",
"They said the experience would be amazing, and they were wrong!"]
model.predict(examples)
```
This should return the list `['positive', 'positive', 'negative']`.
The `predict_proba` method provides access to the predicted distribution over the class labels; see the demo at the bottom of `dynasent_models.py` for details.
The following code uses `load_dataset` from above to reproduce the Round 2 dev-set report on Model 0 from the paper:
```python
import os
from sklearn.metrics import classification_report
from dynasent_models import DynaSentModel
dev_filename = os.path.join('dynasent-v1.1', 'dynasent-v1.1-round02-dynabench-dev.jsonl')
dev = load_dataset(dev_filename)
X_dev, y_dev = zip(*[(d['sentence'], d['gold_label']) for d in dev])
model = DynaSentModel(os.path.join('models', 'dynasent_model0.bin'))
preds = model.predict(X_dev)
print(classification_report(y_dev, preds, digits=3))
```
For a fuller report on these models, see our paper and [our model card](dynasent_modelcard.md).
## Other files
### Analysis notebooks
The following notebooks reproduce the dataset statistics, figures, and random example selections from the paper:
* `analyses_comparative.ipynb`
* `analysis_round1.ipynb`
* `analysis_round2.ipynb`
* `analysis_sst_dev_revalidate.ipynb`
The Python module `dynasent_utils.py` contains functions that support those notebooks, and `dynasent.mplstyle` helps with styling the plots.
### Datasheet
The [Datasheet](https://arxiv.org/abs/1803.09010) for our dataset:
* [dynasent_datasheet.md](dynasent_datasheet.md)
### Model Card
The [Model Card](https://arxiv.org/pdf/1810.03993.pdf) for our models:
* [dynasent_modelcard.md](dynasent_modelcard.md)
### Tests
The module `test_dataset.py` contains PyTest tests for the dataset. To use it, run
```
py.test -vv test_dataset.py
```
in the root directory of this repository.
### Validation HIT code
The file `validation-hit-contents.html` contains the HTML/Javascript used in the validation task. It could be used directly on Amazon Mechanical Turk, by simply pasting its contents into the usual HIT creation window.
## License
DynaSent has a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). | 13,731 | [
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pietrolesci/nli_fever | 2022-04-25T09:03:28.000Z | [
"region:us"
] | pietrolesci | null | null | 1 | 550 | 2022-03-25T10:01:17 | ## Overview
The original dataset can be found [here](https://www.dropbox.com/s/hylbuaovqwo2zav/nli_fever.zip?dl=0)
while the Github repo is [here](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md).
This dataset has been proposed in [Combining fact extraction and verification with neural semantic matching networks](https://dl.acm.org/doi/abs/10.1609/aaai.v33i01.33016859). This dataset has been created as a modification
of FEVER.
In the original FEVER setting, the input is a claim from Wikipedia and the expected output is a label.
However, this is different from the standard NLI formalization which is basically a *pair-of-sequence to label* problem.
To facilitate NLI-related research to take advantage of the FEVER dataset, the authors pair the claims in the FEVER dataset
with the textual evidence and make it a *pair-of-sequence to label* formatted dataset.
## Dataset curation
The label mapping follows the paper and is the following
```python
mapping = {
"SUPPORTS": 0, # entailment
"NOT ENOUGH INFO": 1, # neutral
"REFUTES": 2, # contradiction
}
```
Also, the "verifiable" column has been encoded as follows
```python
mapping = {"NOT VERIFIABLE": 0, "VERIFIABLE": 1}
```
Finally, a consistency check with the labels reported in the original FEVER dataset is performed.
NOTE: no label is available for the "test" split.
NOTE: there are 3 instances in common between `dev` and `train` splits.
## Code to generate the dataset
```python
import pandas as pd
from datasets import Dataset, ClassLabel, load_dataset, Value, Features, DatasetDict
import json
# download data from https://www.dropbox.com/s/hylbuaovqwo2zav/nli_fever.zip?dl=0
paths = {
"train": "<some_path>/nli_fever/train_fitems.jsonl",
"validation": "<some_path>/nli_fever/dev_fitems.jsonl",
"test": "<some_path>/nli_fever/test_fitems.jsonl",
}
# parsing code from https://github.com/facebookresearch/anli/blob/main/src/utils/common.py
registered_jsonabl_classes = {}
def register_class(cls):
global registered_jsonabl_classes
if cls not in registered_jsonabl_classes:
registered_jsonabl_classes.update({cls.__name__: cls})
def unserialize_JsonableObject(d):
global registered_jsonabl_classes
classname = d.pop("_jcls_", None)
if classname:
cls = registered_jsonabl_classes[classname]
obj = cls.__new__(cls) # Make instance without calling __init__
for key, value in d.items():
setattr(obj, key, value)
return obj
else:
return d
def load_jsonl(filename, debug_num=None):
d_list = []
with open(filename, encoding="utf-8", mode="r") as in_f:
print("Load Jsonl:", filename)
for line in in_f:
item = json.loads(line.strip(), object_hook=unserialize_JsonableObject)
d_list.append(item)
if debug_num is not None and 0 < debug_num == len(d_list):
break
return d_list
def get_original_fever() -> pd.DataFrame:
"""Get original fever datasets."""
fever_v1 = load_dataset("fever", "v1.0")
fever_v2 = load_dataset("fever", "v2.0")
columns = ["id", "label"]
splits = ["paper_test", "paper_dev", "labelled_dev", "train"]
list_dfs = [fever_v1[split].to_pandas()[columns] for split in splits]
list_dfs.append(fever_v2["validation"].to_pandas()[columns])
dfs = pd.concat(list_dfs, ignore_index=False)
dfs = dfs.drop_duplicates()
dfs = dfs.rename(columns={"label": "fever_gold_label"})
return dfs
def load_and_process(path: str, fever_df: pd.DataFrame) -> pd.DataFrame:
"""Load data split and merge with fever."""
df = pd.DataFrame(load_jsonl(path))
df = df.rename(columns={"query": "premise", "context": "hypothesis"})
# adjust dtype
df["cid"] = df["cid"].astype(int)
# merge with original fever to get labels
df = pd.merge(df, fever_df, left_on="cid", right_on="id", how="inner").drop_duplicates()
return df
def encode_labels(df: pd.DataFrame) -> pd.DataFrame:
"""Encode labels using the mapping used in SNLI and MultiNLI"""
mapping = {
"SUPPORTS": 0, # entailment
"NOT ENOUGH INFO": 1, # neutral
"REFUTES": 2, # contradiction
}
df["label"] = df["fever_gold_label"].map(mapping)
# verifiable
df["verifiable"] = df["verifiable"].map({"NOT VERIFIABLE": 0, "VERIFIABLE": 1})
return df
if __name__ == "__main__":
fever_df = get_original_fever()
dataset_splits = {}
for split, path in paths.items():
# from json to dataframe and merge with fever
df = load_and_process(path, fever_df)
if not len(df) > 0:
print(f"Split `{split}` has no matches")
continue
if split == "train":
# train must have same labels
assert sum(df["fever_gold_label"] != df["label"]) == 0
# encode labels using the default mapping used by other nli datasets
# i.e, entailment: 0, neutral: 1, contradiction: 2
df = df.drop(columns=["label"])
df = encode_labels(df)
# cast to dataset
features = Features(
{
"cid": Value(dtype="int64", id=None),
"fid": Value(dtype="string", id=None),
"id": Value(dtype="int32", id=None),
"premise": Value(dtype="string", id=None),
"hypothesis": Value(dtype="string", id=None),
"verifiable": Value(dtype="int64", id=None),
"fever_gold_label": Value(dtype="string", id=None),
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
}
)
if "test" in path:
# no features for test set
df["label"] = -1
df["verifiable"] = -1
df["fever_gold_label"] = "not available"
dataset = Dataset.from_pandas(df, features=features)
dataset_splits[split] = dataset
nli_fever = DatasetDict(dataset_splits)
nli_fever.push_to_hub("pietrolesci/nli_fever", token="<your token>")
# check overlap between splits
from itertools import combinations
for i, j in combinations(dataset_splits.keys(), 2):
print(
f"{i} - {j}: ",
pd.merge(
dataset_splits[i].to_pandas(),
dataset_splits[j].to_pandas(),
on=["premise", "hypothesis", "label"],
how="inner",
).shape[0],
)
#> train - dev: 3
#> train - test: 0
#> dev - test: 0
``` | 6,614 | [
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] |
mstz/adult | 2023-04-15T11:37:47.000Z | [
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"adult",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] | mstz | null | @inproceedings{DBLP:conf/kdd/Kohavi96,
author = {Ron Kohavi},
editor = {Evangelos Simoudis and
Jiawei Han and
Usama M. Fayyad},
title = {Scaling Up the Accuracy of Naive-Bayes Classifiers: {A} Decision-Tree
Hybrid},
booktitle = {Proceedings of the Second International Conference on Knowledge Discovery
and Data Mining (KDD-96), Portland, Oregon, {USA}},
pages = {202--207},
publisher = {{AAAI} Press},
year = {1996},
url = {http://www.aaai.org/Library/KDD/1996/kdd96-033.php},
timestamp = {Mon, 05 Jun 2017 13:20:21 +0200},
biburl = {https://dblp.org/rec/conf/kdd/Kohavi96.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | 1 | 549 | 2023-02-27T21:17:48 | ---
language:
- en
tags:
- adult
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: Adult
size_categories:
- 10K<n<100K
task_categories:
- tabular-classification
configs:
- encoding
- income
- income-no race
- race
license: cc
---
# Adult
The [Adult dataset](https://archive.ics.uci.edu/ml/datasets/Adult) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Census dataset including personal characteristic of a person, and their income threshold.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|-----------------------------------------------------------------|
| encoding | | Encoding dictionary showing original values of encoded features.|
| income | Binary classification | Classify the person's income as over or under the threshold. |
| income-no race | Binary classification | As `income`, but the `race` feature is removed. |
| race | Multiclass classification | Predict the race of the individual. |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/adult", "income")["train"]
```
# Features
Target feature changes according to the selected configuration and is always in last position in the dataset.
|**Feature** |**Type** | **Description** |
|-------------------------------|-----------|------------------------------------------------------------|
|`age` |`[int64]` | Age of the person. |
|`capital_gain` |`[float64]`| Capital gained by the person. |
|`capital_loss` |`[float64]`| Capital lost by the person. |
|`education` |`[int8]` | Education level: the higher, the more educated the person. |
|`final_weight` |`[int64]` | |
|`hours_worked_per_week` |`[int64]` | Hours worked per week. |
|`marital_status` |`[string]` | Marital status of the person. |
|`native_country` |`[string]` | Native country of the person. |
|`occupation` |`[string]` | Job of the person. |
|`race` |`[string]` | Race of the person. |
|`relationship` |`[string]` | |
|`is_male` |`[bool]` | Man/Woman. |
|`workclass` |`[string]` | Type of job of the person. |
|**over_threshold** |`int8` | `1` for income `>= 50k$`, `0` otherwise. | | 3,184 | [
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] |
bdsaglam/musique | 2023-06-14T08:19:12.000Z | [
"arxiv:2108.00573",
"arxiv:1606.05250",
"arxiv:1910.07475",
"arxiv:1706.04115",
"region:us"
] | bdsaglam | [MuSiQue](https://arxiv.org/pdf/2108.00573.pdf) | @article{trivedi2021musique,
title={{M}u{S}i{Q}ue: Multihop Questions via Single-hop Question Composition},
author={Trivedi, Harsh and Balasubramanian, Niranjan and Khot, Tushar and Sabharwal, Ashish},
journal={Transactions of the Association for Computational Linguistics},
year={2022}
publisher={MIT Press}
} | 0 | 548 | 2023-06-14T06:10:10 | ---
dataset_info:
- config_name: answerable
features:
- name: id
dtype: string
- name: paragraphs
sequence:
- name: idx
dtype: int32
- name: title
dtype: string
- name: paragraph_text
dtype: string
- name: is_supporting
dtype: bool
- name: question
dtype: string
- name: question_decomposition
sequence:
- name: id
dtype: int32
- name: question
dtype: string
- name: answer
dtype: string
- name: paragraph_support_idx
dtype: int32
- name: answer
dtype: string
- name: answerable
dtype: bool
splits:
- name: train
num_bytes: 211123672
num_examples: 19938
- name: validation
num_bytes: 26760847
num_examples: 2417
download_size: 299853055
dataset_size: 237884519
- config_name: full
features:
- name: id
dtype: string
- name: paragraphs
sequence:
- name: idx
dtype: int32
- name: title
dtype: string
- name: paragraph_text
dtype: string
- name: is_supporting
dtype: bool
- name: question
dtype: string
- name: question_decomposition
sequence:
- name: id
dtype: int32
- name: question
dtype: string
- name: answer
dtype: string
- name: paragraph_support_idx
dtype: int32
- name: answer
dtype: string
- name: answerable
dtype: bool
splits:
- name: train
num_bytes: 416868901
num_examples: 39876
- name: validation
num_bytes: 52065789
num_examples: 4834
download_size: 591677838
dataset_size: 468934690
---
Paper: [MuSiQue: Multi-hop Questions via Single-hop Question Composition](https://arxiv.org/pdf/2108.00573.pdf)
Original repository: https://github.com/StonyBrookNLP/musique
# Data
MuSiQue is distributed under a [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/).
**Usage Caution:** If you're using any of our seed single-hop datasets ([SQuAD](https://arxiv.org/abs/1606.05250), [T-REx](https://hadyelsahar.github.io/t-rex/paper.pdf), [Natural Questions](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/1f7b46b5378d757553d3e92ead36bda2e4254244.pdf), [MLQA](https://arxiv.org/pdf/1910.07475.pdf), [Zero Shot RE](https://arxiv.org/pdf/1706.04115.pdf)) in any way (e.g., pretraining on them), please note that MuSiQue was created by composing questions from these seed datasets. Therefore, single-hop questions used in MuSiQue's dev/test sets may occur in the training sets of these seed datasets. To help avoid information leakage, we are releasing the IDs of single-hop questions that are used in MuSiQue dev/test sets. Once you download the data below, these IDs and corresponding questions will be in `data/dev_test_singlehop_questions_v1.0.json`. If you use our seed single-hop datasets in any way in your model, please be sure to **avoid using any single-hop question IDs present in this file**
# Citation
If you use this in your work, please cite use:
```
@article{trivedi2021musique,
title={{M}u{S}i{Q}ue: Multihop Questions via Single-hop Question Composition},
author={Trivedi, Harsh and Balasubramanian, Niranjan and Khot, Tushar and Sabharwal, Ashish},
journal={Transactions of the Association for Computational Linguistics},
year={2022}
publisher={MIT Press}
}
``` | 3,319 | [
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carolina-c4ai/corpus-carolina | 2023-03-23T19:46:16.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:masked-language-modeling",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1B<n<10B",
"source_datasets:original",
"language:pt",
"license:cc-by-nc-sa-4.0",
"region:us"
] | carolina-c4ai | Carolina is an Open Corpus for Linguistics and Artificial Intelligence with a
robust volume of texts of varied typology in contemporary Brazilian Portuguese
(1970-2021). | null | 12 | 547 | 2022-03-28T13:30:33 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- pt
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1B<n<10B
source_datasets:
- original
task_categories:
- fill-mask
- text-generation
task_ids:
- masked-language-modeling
- language-modeling
pretty_name: Carolina
language_bcp47:
- pt-BR
---
# Dataset Card for Corpus Carolina
## Table of Contents
- [Table of Contents](#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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [sites.usp.br/corpuscarolina](https://sites.usp.br/corpuscarolina/)
- **Current Version:** 1.2 (Ada)
- **Point of Contact:** [LaViHD](mailto:lavihd@usp.br)
### Dataset Summary
Carolina is an Open Corpus for Linguistics and Artificial Intelligence with a
robust volume of texts of varied typology in contemporary Brazilian Portuguese
(1970-2021). This corpus contains documents and texts extracted from the web
and includes information (metadata) about its provenance and tipology.
The documents are clustered into taxonomies and the corpus can be loaded in complete or taxonomy modes. To load a single taxonomy, it is possible to pass a code as a parameter to the loading script (see the example bellow). Codes are 3-letters string and possible values are:
- `dat` : datasets and other corpora;
- `jud` : judicial branch;
- `leg` : legislative branch;
- `pub` : public domain works;
- `soc` : social media;
- `uni` : university domains;
- `wik` : wikis.
Dataset Vesioning:
The Carolina Corpus is under continuous development resulting in multiple vesions. The current version is v1.2, but v1.1 is also available. You can access diferent vesions of the corpus using the `revision` parameter on `load_dataset`.
Usage Example:
```python
from datasets import load_dataset
# to load all taxonomies
corpus_carolina = load_dataset("carolina-c4ai/corpus-carolina")
# to load social media documents
social_media = load_dataset("carolina-c4ai/corpus-carolina", taxonomy="soc")
# to load previous version
corpus_carolina = load_dataset("carolina-c4ai/corpus-carolina", revision="v1.1")
```
### Supported Tasks
Carolina corpus was compiled for academic purposes,
namely linguistic and computational analysis.
### Languages
Contemporary Brazilian Portuguese (1970-2021).
## Dataset Structure
Files are stored inside `corpus` folder with a subfolder
for each taxonomy. Every file folows a XML structure
(TEI P5) and contains multiple extracted documents. For
each document, the text and metadata are exposed as
`text` and `meta` features, respectively.
### Data Instances
Every instance have the following structure.
```
{
"meta": datasets.Value("string"),
"text": datasets.Value("string")
}
```
| Code | Taxonomy | Instances | Size |
|:----:|:---------------------------|----------:|-------:|
| | **Total** | 2107045 | 11 GB |
| dat | Datasets and other Corpora | 1102049 | 4.4 GB |
| wik | Wikis | 960139 | 5.2 GB |
| jud | Judicial Branch | 40464 | 1.5 GB |
| leg | Legislative Branch | 13 | 25 MB |
| soc | Social Media | 3413 | 17 MB |
| uni | University Domains | 941 | 10 MB |
| pub | Public Domain Works | 26 | 4.5 MB |
||
### Data Fields
- `meta`: a XML string with a TEI conformant `teiHeader` tag. It is exposed as text and needs to be parsed in order to access the actual metada;
- `text`: a string containing the extracted document.
### Data Splits
As a general corpus, Carolina does not have splits. In order to load the dataset, it is used `corpus` as its single split.
## Additional Information
### Dataset Curators
The Corpus Carolina is developed by a multidisciplinary
team of linguists and computer scientists, members of the
Virtual Laboratory of Digital Humanities - LaViHD and the Artificial Intelligence Center of the University of São Paulo - C4AI.
### Licensing Information
The Open Corpus for Linguistics and Artificial Intelligence (Carolina) was
compiled for academic purposes, namely linguistic and computational analysis.
It is composed of texts assembled in various digital repositories, whose
licenses are multiple and therefore should be observed when making use of the
corpus. The Carolina headers are licensed under Creative Commons
Attribution-NonCommercial-ShareAlike 4.0 International."
### Citation Information
```
@misc{corpusCarolinaV1.1,
title={
Carolina:
The Open Corpus for Linguistics and Artificial Intelligence
},
author={
Finger, Marcelo and
Paixão de Sousa, Maria Clara and
Namiuti, Cristiane and
Martins do Monte, Vanessa and
Costa, Aline Silva and
Serras, Felipe Ribas and
Sturzeneker, Mariana Lourenço and
Guets, Raquel de Paula and
Mesquita, Renata Morais and
Mello, Guilherme Lamartine de and
Crespo, Maria Clara Ramos Morales and
Rocha, Maria Lina de Souza Jeannine and
Brasil, Patrícia and
Silva, Mariana Marques da and
Palma, Mayara Feliciano
},
howpublished={\url{
https://sites.usp.br/corpuscarolina/corpus}},
year={2022},
note={Version 1.1 (Ada)},
}
```
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ericyu/LEVIRCD_Cropped_256 | 2023-10-06T10:29:40.000Z | [
"region:us"
] | ericyu | null | null | 0 | 546 | 2023-08-28T15:35:08 | ---
dataset_info:
features:
- name: imageA
dtype: image
- name: imageB
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 2005523229.68
num_examples: 7120
- name: validation
num_bytes: 244453421.184
num_examples: 1024
- name: test
num_bytes: 518863873.536
num_examples: 2048
download_size: 1108370540
dataset_size: 2768840524.3999996
---
# Dataset Card for "LEVIRCD_Cropped_256"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 589 | [
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nlphuji/winogavil | 2022-11-26T19:56:27.000Z | [
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"commonsense-reasoning",
"visual-reasoning",
"arxiv:2207.12576",
"region:us"
] | nlphuji | WinoGAViL is a challenging dataset for evaluating vision-and-language commonsense reasoning abilities. Given a set of images, a cue, and a number K, the task is to select the K images that best fits the association. This dataset was collected via the WinoGAViL online game to collect vision-and-language associations, (e.g., werewolves to a full moon). Inspired by the popular card game Codenames, a spymaster gives a textual cue related to several visual candidates, and another player has to identify them. Human players are rewarded for creating associations that are challenging for a rival AI model but still solvable by other human players. We evaluate several state-of-the-art vision-and-language models, finding that they are intuitive for humans (>90% Jaccard index) but challenging for state-of-the-art AI models, where the best model (ViLT) achieves a score of 52%, succeeding mostly where the cue is visually salient. Our analysis as well as the feedback we collect from players indicate that the collected associations require diverse reasoning skills, including general knowledge, common sense, abstraction, and more. | @article{bitton2022winogavil,
title={WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models},
author={Bitton, Yonatan and Guetta, Nitzan Bitton and Yosef, Ron and Elovici, Yuval and Bansal, Mohit and Stanovsky, Gabriel and Schwartz, Roy},
journal={arXiv preprint arXiv:2207.12576},
year={2022}
} | 0 | 544 | 2022-09-23T19:27:29 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: winogavil
pretty_name: WinoGAViL
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- commonsense-reasoning
- visual-reasoning
task_ids: []
extra_gated_prompt: "By clicking on “Access repository” below, you also agree that you are using it solely for research purposes. The full license agreement is available in the dataset files."
---
# Dataset Card for WinoGAViL
- [Dataset Description](#dataset-description)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Colab notebook code for Winogavil evaluation with CLIP](#colab-notebook-code-for-winogavil-evaluation-with-clip)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
WinoGAViL is a challenging dataset for evaluating vision-and-language commonsense reasoning abilities. Given a set of images, a cue, and a number K, the task is to select the K images that best fits the association. This dataset was collected via the WinoGAViL online game to collect vision-and-language associations, (e.g., werewolves to a full moon). Inspired by the popular card game Codenames, a spymaster gives a textual cue related to several visual candidates, and another player has to identify them. Human players are rewarded for creating associations that are challenging for a rival AI model but still solvable by other human players. We evaluate several state-of-the-art vision-and-language models, finding that they are intuitive for humans (>90% Jaccard index) but challenging for state-of-the-art AI models, where the best model (ViLT) achieves a score of 52%, succeeding mostly where the cue is visually salient. Our analysis as well as the feedback we collect from players indicate that the collected associations require diverse reasoning skills, including general knowledge, common sense, abstraction, and more.
- **Homepage:**
https://winogavil.github.io/
- **Colab**
https://colab.research.google.com/drive/19qcPovniLj2PiLlP75oFgsK-uhTr6SSi
- **Repository:**
https://github.com/WinoGAViL/WinoGAViL-experiments/
- **Paper:**
https://arxiv.org/abs/2207.12576
- **Leaderboard:**
https://winogavil.github.io/leaderboard
- **Point of Contact:**
winogavil@gmail.com; yonatanbitton1@gmail.com
### Supported Tasks and Leaderboards
https://winogavil.github.io/leaderboard.
https://paperswithcode.com/dataset/winogavil.
## Colab notebook code for Winogavil evaluation with CLIP
https://colab.research.google.com/drive/19qcPovniLj2PiLlP75oFgsK-uhTr6SSi
### Languages
English.
## Dataset Structure
### Data Fields
candidates (list): ["bison", "shelter", "beard", "flea", "cattle", "shave"] - list of image candidates.
cue (string): pogonophile - the generated cue.
associations (string): ["bison", "beard", "shave"] - the images associated with the cue selected by the user.
score_fool_the_ai (int64): 80 - the spymaster score (100 - model score) for fooling the AI, with CLIP RN50 model.
num_associations (int64): 3 - The number of images selected as associative with the cue.
num_candidates (int64): 6 - the number of total candidates.
solvers_jaccard_mean (float64): 1.0 - three solvers scores average on the generated association instance.
solvers_jaccard_std (float64): 1.0 - three solvers scores standard deviation on the generated association instance
ID (int64): 367 - association ID.
### Data Splits
There is a single TEST split. In the accompanied paper and code we sample it to create different training sets, but the intended use is to use winogavil as a test set.
There are different number of candidates, which creates different difficulty levels:
-- With 5 candidates, random model expected score is 38%.
-- With 6 candidates, random model expected score is 34%.
-- With 10 candidates, random model expected score is 24%.
-- With 12 candidates, random model expected score is 19%.
<details>
<summary>Why random chance for success with 5 candidates is 38%?</summary>
It is a binomial distribution probability calculation.
Assuming N=5 candidates, and K=2 associations, there could be three events:
(1) The probability for a random guess is correct in 0 associations is 0.3 (elaborate below), and the Jaccard index is 0 (there is no intersection between the correct labels and the wrong guesses). Therefore the expected random score is 0.
(2) The probability for a random guess is correct in 1 associations is 0.6, and the Jaccard index is 0.33 (intersection=1, union=3, one of the correct guesses, and one of the wrong guesses). Therefore the expected random score is 0.6*0.33 = 0.198.
(3) The probability for a random guess is correct in 2 associations is 0.1, and the Jaccard index is 1 (intersection=2, union=2). Therefore the expected random score is 0.1*1 = 0.1.
* Together, when K=2, the expected score is 0+0.198+0.1 = 0.298.
To calculate (1), the first guess needs to be wrong. There are 3 "wrong" guesses and 5 candidates, so the probability for it is 3/5. The next guess should also be wrong. Now there are only 2 "wrong" guesses, and 4 candidates, so the probability for it is 2/4. Multiplying 3/5 * 2/4 = 0.3.
Same goes for (2) and (3).
Now we can perform the same calculation with K=3 associations.
Assuming N=5 candidates, and K=3 associations, there could be four events:
(4) The probability for a random guess is correct in 0 associations is 0, and the Jaccard index is 0. Therefore the expected random score is 0.
(5) The probability for a random guess is correct in 1 associations is 0.3, and the Jaccard index is 0.2 (intersection=1, union=4). Therefore the expected random score is 0.3*0.2 = 0.06.
(6) The probability for a random guess is correct in 2 associations is 0.6, and the Jaccard index is 0.5 (intersection=2, union=4). Therefore the expected random score is 0.6*5 = 0.3.
(7) The probability for a random guess is correct in 3 associations is 0.1, and the Jaccard index is 1 (intersection=3, union=3). Therefore the expected random score is 0.1*1 = 0.1.
* Together, when K=3, the expected score is 0+0.06+0.3+0.1 = 0.46.
Taking the average of 0.298 and 0.46 we reach 0.379.
Same process can be recalculated with 6 candidates (and K=2,3,4), 10 candidates (and K=2,3,4,5) and 123 candidates (and K=2,3,4,5,6).
</details>
## Dataset Creation
Inspired by the popular card game Codenames, a “spymaster” gives a textual cue related to several visual candidates, and another player has to identify them. Human players are rewarded for creating
associations that are challenging for a rival AI model but still solvable by other
human players.
### Annotations
#### Annotation process
We paid Amazon Mechanical Turk Workers to play our game.
## Considerations for Using the Data
All associations were obtained with human annotators.
### Licensing Information
CC-By 4.0
### Citation Information
@article{bitton2022winogavil,
title={WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models},
author={Bitton, Yonatan and Guetta, Nitzan Bitton and Yosef, Ron and Elovici, Yuval and Bansal, Mohit and Stanovsky, Gabriel and Schwartz, Roy},
journal={arXiv preprint arXiv:2207.12576},
year={2022}
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allenai/scitldr | 2023-01-25T14:43:42.000Z | [
"task_categories:summarization",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"scientific-documents-summarization",
"arxiv:2004.15011",
"region:us"
] | allenai | A new multi-target dataset of 5.4K TLDRs over 3.2K papers.
SCITLDR contains both author-written and expert-derived TLDRs,
where the latter are collected using a novel annotation protocol
that produces high-quality summaries while minimizing annotation burden. | @article{cachola2020tldr,
title={{TLDR}: Extreme Summarization of Scientific Documents},
author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld},
journal={arXiv:2004.15011},
year={2020},
} | 14 | 543 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: scitldr
pretty_name: SciTLDR
tags:
- scientific-documents-summarization
dataset_info:
- config_name: Abstract
features:
- name: source
sequence: string
- name: source_labels
sequence:
class_label:
names:
'0': non-oracle
'1': oracle
- name: rouge_scores
sequence: float32
- name: paper_id
dtype: string
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sequence: string
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num_bytes: 2738065
num_examples: 1992
- name: test
num_bytes: 1073656
num_examples: 618
- name: validation
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num_examples: 619
download_size: 5483987
dataset_size: 4806597
- config_name: AIC
features:
- name: source
sequence: string
- name: source_labels
sequence:
class_label:
names:
'0': 0
'1': 1
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sequence: float32
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num_examples: 1992
- name: test
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num_examples: 618
- name: validation
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num_examples: 619
download_size: 25545108
dataset_size: 23772085
- config_name: FullText
features:
- name: source
sequence: string
- name: source_labels
sequence:
class_label:
names:
'0': non-oracle
'1': oracle
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sequence: float32
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sequence: string
splits:
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num_bytes: 66917363
num_examples: 1992
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num_bytes: 20182554
num_examples: 618
- name: validation
num_bytes: 18790651
num_examples: 619
download_size: 110904552
dataset_size: 105890568
---
# Dataset Card for SciTLDR
## 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/allenai/scitldr
- **Repository:** https://github.com/allenai/scitldr
- **Paper:** https://arxiv.org/abs/2004.15011
- **Leaderboard:**
- **Point of Contact:** {isabelc,kylel,armanc,danw}@allenai.org
### Dataset Summary
`SciTLDR`: Extreme Summarization of Scientific Documents
SciTLDR is a new multi-target dataset of 5.4K TLDRs over 3.2K papers. SciTLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden.
### Supported Tasks and Leaderboards
summarization
### Languages
English
## Dataset Structure
SciTLDR is split in to a 60/20/20 train/dev/test split. For each file, each line is a json, formatted as follows
```
{
"source":[
"sent0",
"sent1",
"sent2",
...
],
"source_labels":[binary list in which 1 is the oracle sentence],
"rouge_scores":[precomputed rouge-1 scores],
"paper_id":"PAPER-ID",
"target":[
"author-tldr",
"pr-tldr0",
"pr-tldr1",
...
],
"title":"TITLE"
}
```
The keys `rouge_scores` and `source_labels` are not necessary for any code to run, precomputed Rouge scores are provided for future research.
### Data Instances
{
"source": [
"Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs.",
"MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training.",
"Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages.",
"We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter.",
"We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods.",
"We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point."
],
"source_labels": [
0,
0,
0,
1,
0,
0
],
"rouge_scores": [
0.2399999958000001,
0.26086956082230633,
0.19999999531250012,
0.38095237636054424,
0.2051282003944774,
0.2978723360796741
],
"paper_id": "rJlnfaNYvB",
"target": [
"We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.",
"Proposal for an adaptive loss scaling method during backpropagation for mix precision training where scale rate is decided automatically to reduce the underflow.",
"The authors propose a method to train models in FP16 precision that adopts a more elaborate way to minimize underflow in every layer simultaneously and automatically."
],
"title": "Adaptive Loss Scaling for Mixed Precision Training"
}
### Data Fields
- `source`: The Abstract, Introduction and Conclusion (AIC) or Full text of the paper, with one sentence per line.
- `source_labels`: Binary 0 or 1, 1 denotes the oracle sentence.
- `rouge_scores`: Precomputed ROUGE baseline scores for each sentence.
- `paper_id`: Arxiv Paper ID.
- `target`: Multiple summaries for each sentence, one sentence per line.
- `title`: Title of the paper.
### Data Splits
| | train | valid | test |
|-------------------|-------|--------|------|
| SciTLDR-A | 1992 | 618 | 619 |
| SciTLDR-AIC | 1992 | 618 | 619 |
| SciTLDR-FullText | 1992 | 618 | 619 |
## Dataset Creation
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
https://allenai.org/
### Annotations
#### Annotation process
Given the title and first 128 words of a reviewer comment about a paper,
re-write the summary (if it exists) into a single sentence or an incomplete
phrase. Summaries must be no more than one sentence.
Most summaries are between 15 and 25 words. The average rewritten summary is
20 words long.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
To encourage further research in the area of extreme summarization of scientific documents.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Apache License 2.0
### Citation Information
@article{cachola2020tldr,
title={{TLDR}: Extreme Summarization of Scientific Documents},
author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld},
journal={arXiv:2004.15011},
year={2020},
}
### Contributions
Thanks to [@Bharat123rox](https://github.com/Bharat123rox) for adding this dataset. | 8,815 | [
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hugo/boolq | 2023-10-17T13:15:46.000Z | [
"region:us"
] | hugo | BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally
occurring ---they are generated in unprompted and unconstrained settings.
Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context.
The text-pair classification setup is similar to existing natural language inference tasks. | @inproceedings{clark2019boolq,
title = {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
author = {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
booktitle = {NAACL},
year = {2019},
} | 0 | 543 | 2023-10-17T13:12:38 | Entry not found | 15 | [
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civil_comments | 2023-06-30T11:26:30.000Z | [
"language:en",
"license:cc0-1.0",
"arxiv:1903.04561",
"region:us"
] | null | The comments in this dataset come from an archive of the Civil Comments
platform, a commenting plugin for independent news sites. These public comments
were created from 2015 - 2017 and appeared on approximately 50 English-language
news sites across the world. When Civil Comments shut down in 2017, they chose
to make the public comments available in a lasting open archive to enable future
research. The original data, published on figshare, includes the public comment
text, some associated metadata such as article IDs, timestamps and
commenter-generated "civility" labels, but does not include user ids. Jigsaw
extended this dataset by adding additional labels for toxicity and identity
mentions. This data set is an exact replica of the data released for the
Jigsaw Unintended Bias in Toxicity Classification Kaggle challenge. This
dataset is released under CC0, as is the underlying comment text. | @article{DBLP:journals/corr/abs-1903-04561,
author = {Daniel Borkan and
Lucas Dixon and
Jeffrey Sorensen and
Nithum Thain and
Lucy Vasserman},
title = {Nuanced Metrics for Measuring Unintended Bias with Real Data for Text
Classification},
journal = {CoRR},
volume = {abs/1903.04561},
year = {2019},
url = {http://arxiv.org/abs/1903.04561},
archivePrefix = {arXiv},
eprint = {1903.04561},
timestamp = {Sun, 31 Mar 2019 19:01:24 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1903-04561},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | 3 | 542 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: null
pretty_name: CivilComments
dataset_info:
features:
- name: text
dtype: string
- name: toxicity
dtype: float32
- name: severe_toxicity
dtype: float32
- name: obscene
dtype: float32
- name: threat
dtype: float32
- name: insult
dtype: float32
- name: identity_attack
dtype: float32
- name: sexual_explicit
dtype: float32
splits:
- name: test
num_bytes: 32073013
num_examples: 97320
- name: train
num_bytes: 596835730
num_examples: 1804874
- name: validation
num_bytes: 32326369
num_examples: 97320
download_size: 414947977
dataset_size: 661235112
license: cc0-1.0
---
# Dataset Card for "civil_comments"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 414.95 MB
- **Size of the generated dataset:** 661.23 MB
- **Total amount of disk used:** 1.08 GB
### Dataset Summary
The comments in this dataset come from an archive of the Civil Comments
platform, a commenting plugin for independent news sites. These public comments
were created from 2015 - 2017 and appeared on approximately 50 English-language
news sites across the world. When Civil Comments shut down in 2017, they chose
to make the public comments available in a lasting open archive to enable future
research. The original data, published on figshare, includes the public comment
text, some associated metadata such as article IDs, timestamps and
commenter-generated "civility" labels, but does not include user ids. Jigsaw
extended this dataset by adding additional labels for toxicity and identity
mentions. This data set is an exact replica of the data released for the
Jigsaw Unintended Bias in Toxicity Classification Kaggle challenge. This
dataset is released under CC0, as is the underlying comment text.
### 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
### Data Instances
#### default
- **Size of downloaded dataset files:** 414.95 MB
- **Size of the generated dataset:** 661.23 MB
- **Total amount of disk used:** 1.08 GB
An example of 'validation' looks as follows.
```
{
"identity_attack": 0.0,
"insult": 0.0,
"obscene": 0.0,
"severe_toxicity": 0.0,
"sexual_explicit": 0.0,
"text": "The public test.",
"threat": 0.0,
"toxicity": 0.0
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `text`: a `string` feature.
- `toxicity`: a `float32` feature.
- `severe_toxicity`: a `float32` feature.
- `obscene`: a `float32` feature.
- `threat`: a `float32` feature.
- `insult`: a `float32` feature.
- `identity_attack`: a `float32` feature.
- `sexual_explicit`: a `float32` feature.
### Data Splits
| name | train |validation|test |
|-------|------:|---------:|----:|
|default|1804874| 97320|97320|
## 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
This dataset is released under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/).
### Citation Information
```
@article{DBLP:journals/corr/abs-1903-04561,
author = {Daniel Borkan and
Lucas Dixon and
Jeffrey Sorensen and
Nithum Thain and
Lucy Vasserman},
title = {Nuanced Metrics for Measuring Unintended Bias with Real Data for Text
Classification},
journal = {CoRR},
volume = {abs/1903.04561},
year = {2019},
url = {http://arxiv.org/abs/1903.04561},
archivePrefix = {arXiv},
eprint = {1903.04561},
timestamp = {Sun, 31 Mar 2019 19:01:24 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1903-04561},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | 7,608 | [
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wmt15 | 2023-04-05T13:43:50.000Z | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:10M<n<100M",
"source_datasets:extended|europarl_bilingual",
"source_datasets:extended|giga_fren",
"source_datasets:extended|news_commentary",
"source_datasets:extended|un_multi",
"language:cs",
"language:de",
"language:en",
"language:fi",
"language:fr",
"language:ru",
"license:unknown",
"region:us"
] | null | null | @InProceedings{bojar-EtAl:2015:WMT,
author = {Bojar, Ond\v{r}ej and Chatterjee, Rajen and Federmann, Christian and Haddow, Barry and Huck, Matthias and Hokamp, Chris and Koehn, Philipp and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Scarton, Carolina and Specia, Lucia and Turchi, Marco},
title = {Findings of the 2015 Workshop on Statistical Machine Translation},
booktitle = {Proceedings of the Tenth Workshop on Statistical Machine Translation},
month = {September},
year = {2015},
address = {Lisbon, Portugal},
publisher = {Association for Computational Linguistics},
pages = {1--46},
url = {http://aclweb.org/anthology/W15-3001}
} | 2 | 541 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- cs
- de
- en
- fi
- fr
- ru
license:
- unknown
multilinguality:
- translation
size_categories:
- 10M<n<100M
source_datasets:
- extended|europarl_bilingual
- extended|giga_fren
- extended|news_commentary
- extended|un_multi
task_categories:
- translation
task_ids: []
pretty_name: WMT15
paperswithcode_id: wmt-2015
dataset_info:
- config_name: cs-en
features:
- name: translation
dtype:
translation:
languages:
- cs
- en
splits:
- name: train
num_bytes: 282996942
num_examples: 959768
- name: validation
num_bytes: 757817
num_examples: 3003
- name: test
num_bytes: 572203
num_examples: 2656
download_size: 1740666258
dataset_size: 284326962
- config_name: de-en
features:
- name: translation
dtype:
translation:
languages:
- de
- en
splits:
- name: train
num_bytes: 1364002869
num_examples: 4522998
- name: validation
num_bytes: 777334
num_examples: 3003
- name: test
num_bytes: 522989
num_examples: 2169
download_size: 1740666258
dataset_size: 1365303192
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features:
- name: translation
dtype:
translation:
languages:
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splits:
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num_bytes: 605146817
num_examples: 2073394
- name: validation
num_bytes: 363941
num_examples: 1500
- name: test
num_bytes: 306335
num_examples: 1370
download_size: 273390220
dataset_size: 605817093
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features:
- name: translation
dtype:
translation:
languages:
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splits:
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num_bytes: 14758986622
num_examples: 40853137
- name: validation
num_bytes: 1138737
num_examples: 4503
- name: test
num_bytes: 298771
num_examples: 1500
download_size: 6702781608
dataset_size: 14760424130
- config_name: ru-en
features:
- name: translation
dtype:
translation:
languages:
- ru
- en
splits:
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num_bytes: 437752256
num_examples: 1495081
- name: validation
num_bytes: 1087746
num_examples: 3003
- name: test
num_bytes: 955972
num_examples: 2818
download_size: 1092059435
dataset_size: 439795974
---
# Dataset Card for "wmt15"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [http://www.statmt.org/wmt15/translation-task.html](http://www.statmt.org/wmt15/translation-task.html)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1.74 GB
- **Size of the generated dataset:** 284.34 MB
- **Total amount of disk used:** 2.02 GB
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Warning:</b> There are issues with the Common Crawl corpus data (<a href="https://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz">training-parallel-commoncrawl.tgz</a>):</p>
<ul>
<li>Non-English files contain many English sentences.</li>
<li>Their "parallel" sentences in English are not aligned: they are uncorrelated with their counterpart.</li>
</ul>
<p>We have contacted the WMT organizers.</p>
</div>
Translation dataset based on the data from statmt.org.
Versions exist for different years using a combination of data
sources. The base `wmt` allows you to create a custom dataset by choosing
your own data/language pair. This can be done as follows:
```python
from datasets import inspect_dataset, load_dataset_builder
inspect_dataset("wmt15", "path/to/scripts")
builder = load_dataset_builder(
"path/to/scripts/wmt_utils.py",
language_pair=("fr", "de"),
subsets={
datasets.Split.TRAIN: ["commoncrawl_frde"],
datasets.Split.VALIDATION: ["euelections_dev2019"],
},
)
# Standard version
builder.download_and_prepare()
ds = builder.as_dataset()
# Streamable version
ds = builder.as_streaming_dataset()
```
### 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
### Data Instances
#### cs-en
- **Size of downloaded dataset files:** 1.74 GB
- **Size of the generated dataset:** 284.34 MB
- **Total amount of disk used:** 2.02 GB
An example of 'validation' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### cs-en
- `translation`: a multilingual `string` variable, with possible languages including `cs`, `en`.
### Data Splits
|name |train |validation|test|
|-----|-----:|---------:|---:|
|cs-en|959768| 3003|2656|
## 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
```
@InProceedings{bojar-EtAl:2015:WMT,
author = {Bojar, Ond
{r}ej and Chatterjee, Rajen and Federmann, Christian and Haddow, Barry and Huck, Matthias and Hokamp, Chris and Koehn, Philipp and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Scarton, Carolina and Specia, Lucia and Turchi, Marco},
title = {Findings of the 2015 Workshop on Statistical Machine Translation},
booktitle = {Proceedings of the Tenth Workshop on Statistical Machine Translation},
month = {September},
year = {2015},
address = {Lisbon, Portugal},
publisher = {Association for Computational Linguistics},
pages = {1--46},
url = {http://aclweb.org/anthology/W15-3001}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | 9,362 | [
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codeparrot/codeparrot-clean | 2022-10-10T15:23:51.000Z | [
"python",
"code",
"region:us"
] | codeparrot | null | null | 35 | 541 | 2022-03-02T23:29:22 | ---
tags:
- python
- code
---
# CodeParrot 🦜 Dataset Cleaned
## What is it?
A dataset of Python files from Github. This is the deduplicated version of the [codeparrot](https://huggingface.co/datasets/transformersbook/codeparrot).
## Processing
The original dataset contains a lot of duplicated and noisy data. Therefore, the dataset was cleaned with the following steps:
- Deduplication
- Remove exact matches
- Filtering
- Average line length < 100
- Maximum line length < 1000
- Alpha numeric characters fraction > 0.25
- Remove auto-generated files (keyword search)
For more details see the preprocessing script in the transformers repository [here](https://github.com/huggingface/transformers/tree/master/examples/research_projects/codeparrot).
## Splits
The dataset is split in a [train](https://huggingface.co/datasets/codeparrot/codeparrot-clean-train) and [validation](https://huggingface.co/datasets/codeparrot/codeparrot-clean-valid) split used for training and evaluation.
## Structure
This dataset has ~50GB of code and 5361373 files.
```python
DatasetDict({
train: Dataset({
features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
num_rows: 5361373
})
})
``` | 1,296 | [
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]
] |
ScandEval/dane-mini | 2023-07-05T09:40:02.000Z | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:da",
"license:cc-by-sa-4.0",
"region:us"
] | ScandEval | null | null | 0 | 540 | 2022-06-14T18:20:34 | ---
dataset_info:
features:
- name: text
dtype: string
- name: tokens
sequence: string
- name: labels
sequence: string
splits:
- name: train
num_bytes: 355712
num_examples: 1024
- name: test
num_bytes: 747809
num_examples: 2048
- name: val
num_bytes: 92001
num_examples: 256
download_size: 532720
dataset_size: 1195522
license: cc-by-sa-4.0
task_categories:
- token-classification
language:
- da
size_categories:
- 1K<n<10K
---
# Dataset Card for "dane-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 647 | [
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] |
edarchimbaud/perimeter-stocks | 2023-11-02T15:00:10.000Z | [
"region:us"
] | edarchimbaud | null | null | 1 | 540 | 2023-08-12T20:21:35 | ---
dataset_info:
features:
- name: symbol
dtype: string
- name: security
dtype: string
- name: gics_sector
dtype: string
- name: gics_sub_industry
dtype: string
splits:
- name: train
num_bytes: 112186
num_examples: 1500
download_size: 44087
dataset_size: 112186
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "perimeter-stocks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 565 | [
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Anthropic/llm_global_opinions | 2023-06-29T00:46:48.000Z | [
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2306.16388",
"region:us"
] | Anthropic | null | null | 22 | 537 | 2023-06-26T07:47:41 | ---
license: cc-by-nc-sa-4.0
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Card for GlobalOpinionQA
## Dataset Summary
The data contains a subset of survey questions about global issues and opinions adapted from the [World Values Survey](https://www.worldvaluessurvey.org/) and [Pew Global Attitudes Survey](https://www.pewresearch.org/).
The data is further described in the paper: [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://arxiv.org/abs/2306.16388).
## Purpose
In our paper, we use this dataset to analyze the opinions that large language models (LLMs) reflect on complex global issues.
Our goal is to gain insights into potential biases in AI systems by evaluating their performance on subjective topics.
## Data Format
The data is in a CSV file with the following columns:
- question: The text of the survey question.
- selections: A dictionary where the key is the country name and the value is a list of percentages of respondents who selected each answer option for that country.
- options: A list of the answer options for the given question.
- source: GAS/WVS depending on whether the question is coming from Global Attitudes Survey or World Value Survey.
## Usage
```python
from datasets import load_dataset
# Loading the data
dataset = load_dataset("Anthropic/llm_global_opinions")
```
## Disclaimer
We recognize the limitations in using this dataset to evaluate LLMs, as they were not specifically
designed for this purpose. Therefore, we acknowledge that the construct validity of these datasets when applied to LLMs may be limited.
## Contact
For questions, you can email esin at anthropic dot com
## Citation
If you would like to cite our work or data, you may use the following bibtex citation:
```
@misc{durmus2023measuring,
title={Towards Measuring the Representation of Subjective Global Opinions in Language Models},
author={Esin Durmus and Karina Nyugen and Thomas I. Liao and Nicholas Schiefer and Amanda Askell and Anton Bakhtin and Carol Chen and Zac Hatfield-Dodds and Danny Hernandez and Nicholas Joseph and Liane Lovitt and Sam McCandlish and Orowa Sikder and Alex Tamkin and Janel Thamkul and Jared Kaplan and Jack Clark and Deep Ganguli},
year={2023},
eprint={2306.16388},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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transformersbook/codeparrot-train | 2022-02-05T16:23:03.000Z | [
"region:us"
] | transformersbook | null | null | 3 | 536 | 2022-03-02T23:29:22 | # CodeParrot Dataset
This is the train split of the CodeParrot dataset. It contains Python files used to train the code generation model in Chapter 10: Training Transformers from Scratch in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/10_transformers-from-scratch.ipynb).
See the [full dataset](https://huggingface.co/datasets/transformersbook/codeparrot) for more information. | 583 | [
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lksy/ru_instruct_gpt4 | 2023-06-02T16:56:03.000Z | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:ru",
"license:cc-by-4.0",
"chat",
"region:us"
] | lksy | null | null | 17 | 536 | 2023-04-18T08:15:50 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: full_output
dtype: string
splits:
- name: train
num_bytes: 22424451
num_examples: 15056
download_size: 23276814
dataset_size: 22424451
license: cc-by-4.0
task_categories:
- text-generation
- text2text-generation
language:
- ru
tags:
- chat
size_categories:
- 10K<n<100K
---
# ru_instruct_gpt4
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Dataset of GPT-4 generated instructions in Russian. Will soon be updated with more examples.
### Languages
Russian
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keremberke/csgo-object-detection | 2023-01-27T13:39:19.000Z | [
"task_categories:object-detection",
"roboflow",
"roboflow2huggingface",
"region:us"
] | keremberke | null | @misc{ wlots_dataset,
title = { wlots Dataset },
type = { Open Source Dataset },
author = { asd },
howpublished = { \\url{ https://universe.roboflow.com/asd-culfr/wlots } },
url = { https://universe.roboflow.com/asd-culfr/wlots },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { may },
note = { visited on 2023-01-27 },
} | 4 | 535 | 2022-12-29T07:37:55 | ---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
---
<div align="center">
<img width="640" alt="keremberke/csgo-object-detection" src="https://huggingface.co/datasets/keremberke/csgo-object-detection/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['ct', 'cthead', 't', 'thead']
```
### Number of Images
```json
{'train': 3879, 'valid': 383, 'test': 192}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("keremberke/csgo-object-detection", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/asd-culfr/wlots/dataset/1](https://universe.roboflow.com/asd-culfr/wlots/dataset/1?ref=roboflow2huggingface)
### Citation
```
@misc{ wlots_dataset,
title = { wlots Dataset },
type = { Open Source Dataset },
author = { asd },
howpublished = { \\url{ https://universe.roboflow.com/asd-culfr/wlots } },
url = { https://universe.roboflow.com/asd-culfr/wlots },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { may },
note = { visited on 2023-01-27 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on December 28, 2022 at 8:08 PM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
It includes 4454 images.
Ct-cthead-t-thead are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 416x416 (Fill (with center crop))
The following augmentation was applied to create 3 versions of each source image:
* Random brigthness adjustment of between -15 and +15 percent
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bbz662bbz/databricks-dolly-15k-ja-gozarinnemon | 2023-05-31T14:44:34.000Z | [
"license:cc-by-sa-3.0",
"region:us"
] | bbz662bbz | null | null | 3 | 534 | 2023-05-31T14:43:00 | ---
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-gozarinnemon
kunishou/databricks-dolly-15k-ja
https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja
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health_fact | 2023-01-25T14:32:02.000Z | [
"task_categories:text-classification",
"task_ids:fact-checking",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:2010.09926",
"region:us"
] | null | PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of
public health claims. Each instance in the PUBHEALTH dataset has an associated
veracity label (true, false, unproven, mixture). Furthermore each instance in the
dataset has an explanation text field. The explanation is a justification for which
the claim has been assigned a particular veracity label.
The dataset was created to explore fact-checking of difficult to verify claims i.e.,
those which require expertise from outside of the journalistics domain, in this case
biomedical and public health expertise.
It was also created in response to the lack of fact-checking datasets which provide
gold standard natural language explanations for verdicts/labels.
NOTE: There are missing labels in the dataset and we have replaced them with -1. | @inproceedings{kotonya-toni-2020-explainable,
title = "Explainable Automated Fact-Checking for Public Health Claims",
author = "Kotonya, Neema and Toni, Francesca",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods
in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.623",
pages = "7740--7754",
} | 16 | 531 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
- multi-class-classification
paperswithcode_id: pubhealth
pretty_name: PUBHEALTH
dataset_info:
features:
- name: claim_id
dtype: string
- name: claim
dtype: string
- name: date_published
dtype: string
- name: explanation
dtype: string
- name: fact_checkers
dtype: string
- name: main_text
dtype: string
- name: sources
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'false'
'1': mixture
'2': 'true'
'3': unproven
- name: subjects
dtype: string
splits:
- name: train
num_bytes: 53985377
num_examples: 9832
- name: test
num_bytes: 6825221
num_examples: 1235
- name: validation
num_bytes: 6653044
num_examples: 1225
download_size: 24892660
dataset_size: 67463642
train-eval-index:
- config: default
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
claim: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for PUBHEALTH
## 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:** [PUBHEALTH homepage](https://github.com/neemakot/Health-Fact-Checking)
- **Repository:** [PUBHEALTH repository](https://github.com/neemakot/Health-Fact-Checking/blob/master/data/DATASHEET.md)
- **Paper:** [Explainable Automated Fact-Checking for Public Health Claims"](https://arxiv.org/abs/2010.09926)
- **Point of Contact:**[Neema Kotonya](mailto:nk2418@ic.ac.uk)
### Dataset Summary
PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of public health claims. Each instance in the PUBHEALTH dataset has an associated veracity label (true, false, unproven, mixture). Furthermore each instance in the dataset has an explanation text field. The explanation is a justification for which the claim has been assigned a particular veracity label.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
The following is an example instance of the PUBHEALTH dataset:
| Field | Example |
| ----------------- | -------------------------------------------------------------|
| __claim__ | Expired boxes of cake and pancake mix are dangerously toxic. |
| __explanation__ | What's True: Pancake and cake mixes that contain mold can cause life-threatening allergic reactions. What's False: Pancake and cake mixes that have passed their expiration dates are not inherently dangerous to ordinarily healthy people, and the yeast in packaged baking products does not "over time develops spores." |
| __label__ | mixture |
| __author(s)__ | David Mikkelson |
| __date published__ | April 19, 2006 |
| __tags__ | food, allergies, baking, cake |
| __main_text__ | In April 2006, the experience of a 14-year-old who had eaten pancakes made from a mix that had gone moldy was described in the popular newspaper column Dear Abby. The account has since been circulated widely on the Internet as scores of concerned homemakers ponder the safety of the pancake and other baking mixes lurking in their larders [...] |
| __evidence sources__ | [1] Bennett, Allan and Kim Collins. “An Unusual Case of Anaphylaxis: Mold in Pancake Mix.” American Journal of Forensic Medicine & Pathology. September 2001 (pp. 292-295). [2] Phillips, Jeanne. “Dear Abby.” 14 April 2006 [syndicated column]. |
### Data Fields
Mentioned above in data instances.
### Data Splits
| | # Instances |
|-----------|-------------|
| train.tsv | 9832 |
| dev.tsv | 1221 |
| test.tsv | 1235 |
| total | 12288 |
## Dataset Creation
### Curation Rationale
The dataset was created to explore fact-checking of difficult to verify claims i.e., those which require expertise from outside of the journalistics domain, in this case biomedical and public health expertise.
It was also created in response to the lack of fact-checking datasets which provide gold standard natural language explanations for verdicts/labels.
### Source Data
#### Initial Data Collection and Normalization
The dataset was retrieved from the following fact-checking, news reviews and news websites:
| URL | Type |
|-----------------------------------|--------------------|
| http://snopes.com/ | fact-checking |
| http://politifact.com/ | fact-checking |
| http://truthorfiction.com/ | fact-checking |
| https://www.factcheck.org/ | fact-checking |
| https://fullfact.org/ | fact-checking |
| https://apnews.com/ | news |
| https://uk.reuters.com/ | news |
| https://www.healthnewsreview.org/ | health news review |
#### 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
Not to our knowledge, but if it is brought to our attention that we are mistaken we will make the appropriate corrections to the dataset.
## 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
The dataset was created by Neema Kotonya, and Francesca Toni, for their research paper "Explainable Automated Fact-Checking for Public Health Claims" presented at EMNLP 2020.
### Licensing Information
MIT License
### Citation Information
```
@inproceedings{kotonya-toni-2020-explainable,
title = "Explainable Automated Fact-Checking for Public Health Claims",
author = "Kotonya, Neema and
Toni, Francesca",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.623",
pages = "7740--7754",
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset. | 8,603 | [
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ehartford/dolphin | 2023-09-25T16:59:11.000Z | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"region:us"
] | ehartford | null | null | 222 | 530 | 2023-07-01T10:53:40 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
---
Dolphin 🐬
https://erichartford.com/dolphin
## Dataset details
This dataset is an attempt to replicate the results of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)
Our dataset consists of:
- ~1 million of FLANv2 augmented with GPT-4 completions (flan1m-alpaca-uncensored.jsonl)
- ~3.5 million of FLANv2 augmented with GPT-3.5 completions (flan5m-alpaca-uncensored.jsonl)
We followed the submix and system prompt distribution outlined in the Orca paper. With a few exceptions. We included all 75k of CoT in the FLAN-1m dataset rather than sampling that. Also, we found that many items were duplicated, so we removed duplicates, resulting in 3.5m instructs in the ChatGPT dataset.
Then we filtered out instances of alignment, refusal, avoidance, and bias, in order to produce an uncensored model upon which can be layered your personalized alignment LoRA.
Token distribution for GPT-3.5 completions
### Loading
```python
## load GPT-4 completions
dataset = load_dataset("ehartford/dolphin",data_files="flan1m-alpaca-uncensored.jsonl")
## load GPT-3.5 completions
dataset = load_dataset("ehartford/dolphin",data_files="flan5m-alpaca-uncensored.jsonl")
```
This dataset is licensed apache-2.0 for commercial or non-commercial use.
We currently plan to release Dolphin on:
- Xgen 7b 8k
- LLaMA 13b (Non-commercial)
- MPT 30b 8k
- LLaMA 33b (Non-commercial)
- Falcon 40b
- LLaMA 65b (Non-commercial)
The Dolphin models that are released will be subject to the license of the foundational model on which it is trained. (LLaMA releases will be non-commercial)
I would like to thank the motley crew of Open Source AI/ML engineers who have worked beside me in this endeavor. Including:
- Wing "Caseus" Lian and NanoBit of OpenAccess AI Collective
- Rohan
- Teknium
- Pankaj Mathur
- Tom "TheBloke" Jobbins for quantizing and amplifying
- Special thanks to EdenCoder and chirper.ai for mentorship and financial sponsorship.
- Special thanks to Kilkonie for his very valued mentorship.
- All the other people in the Open Source AI community who have taught me and helped me along the way. | 2,378 | [
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ivanzhouyq/RedPajama-Tiny | 2023-07-03T18:16:47.000Z | [
"task_categories:text-generation",
"language:en",
"region:us"
] | ivanzhouyq | RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. This is a 1B-token sample of the full dataset. | null | 2 | 530 | 2023-07-03T16:48:05 | ---
task_categories:
- text-generation
language:
- en
pretty_name: RedPajama Tiny
---
# Dataset Card for Dataset Name
### Dataset Summary
This is a tiny version of the RedPajama dataset, which is a clean-room, fully open-source implementation of the LLaMa dataset.
This dataset contains 64 samples from each of the 7 sources.
The full dataset has the following token counts and is available for [download]( https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T):
| Dataset | Token Count |
|---------------|-------------|
| Commoncrawl | 878 Billion |
| C4 | 175 Billion |
| GitHub | 59 Billion |
| Books | 26 Billion |
| ArXiv | 28 Billion |
| Wikipedia | 24 Billion |
| StackExchange | 20 Billion |
| Total | 1.2 Trillion |
### Languages
Primarily English, though the Wikipedia slice contains multiple languages.
## Dataset Structure
The dataset structure is as follows:
```
{
"text": ...,
"meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...}
}
```
## Dataset Creation
This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe.
### Source Data
#### Commoncrawl
We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline.
We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to
classify paragraphs as Wikipedia references or random Commoncrawl samples.
#### C4
C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format.
#### GitHub
The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality
files and only keep projects that are distributed under the MIT, BSD, or Apache license.
#### Wikipedia
We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains
text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other
formatting boilerplate has been removed.
#### Gutenberg and Books3
The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use
simhash to remove near duplicates.
#### ArXiv
ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and
remove preambles, comments, macros and bibliographies.
#### Stackexchange
The Stack Exchange split of the dataset is download from the
[Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites,
remove html tags, group the posts into question-answer pairs, and order answers by their score.
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distil-whisper/librispeech_asr-timestamped | 2023-09-25T10:30:13.000Z | [
"task_categories:automatic-speech-recognition",
"language:en",
"license:cc-by-4.0",
"region:us"
] | distil-whisper | LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
audiobooks from the LibriVox project, and has been carefully segmented and aligned.87 | @inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--5210},
year={2015},
organization={IEEE}
} | 0 | 530 | 2023-09-22T09:05:08 | ---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- en
-pretty_name: LibriSpeech ASR
---
# Distil Whisper: LibriSpeech ASR With Timestamps
This is a variant of the [LibriSpeech ASR](https://huggingface.co/datasets/librispeech_asr) dataset, augmented to return the pseudo-labelled Whisper
Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by
labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2)
model with *greedy* sampling and timestamp prediction. For information on how the original dataset was curated, refer to the original
[dataset card](https://huggingface.co/datasets/librispeech_asr).
## Standalone Usage
First, install the latest version of the 🤗 Datasets package:
```bash
pip install --upgrade pip
pip install --upgrade datasets[audio]
```
The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset)
function:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/librispeech_asr", "all")
# take the first sample of the validation set
sample = dataset["validation.clean"][0]
```
It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire
dataset to disk:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/librispeech_asr", "all", streaming=True)
# take the first sample of the validation set
sample = next(iter(dataset["validation.clean"]))
```
## Distil Whisper Usage
To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the
[Distil Whisper repository](https://github.com/huggingface/distil-whisper#training).
## License
This dataset is licensed under cc-by-4.0.
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dennlinger/eur-lex-sum | 2022-11-11T14:25:06.000Z | [
"task_categories:translation",
"task_categories:summarization",
"annotations_creators:found",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
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"language:bg",
"language:hr",
"language:cs",
"language:da",
"language:nl",
"language:en",
"language:et",
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"language:de",
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"language:mt",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:es",
"language:sv",
"license:cc-by-4.0",
"legal",
"eur-lex",
"expert summary",
"parallel corpus",
"multilingual",
"arxiv:2210.13448",
"region:us"
] | dennlinger | The EUR-Lex-Sum dataset is a multilingual resource intended for text summarization in the legal domain.
It is based on human-written summaries of legal acts issued by the European Union.
It distinguishes itself by introducing a smaller set of high-quality human-written samples,
each of which have much longer references (and summaries!) than comparable datasets.
Additionally, the underlying legal acts provide a challenging domain-specific application to legal texts,
which are so far underrepresented in non-English languages.
For each legal act, the sample can be available in up to 24 languages
(the officially recognized languages in the European Union);
the validation and test samples consist entirely of samples available in all languages,
and are aligned across all languages at the paragraph level. | @article{aumiller-etal-2022-eur,
author = {Aumiller, Dennis and Chouhan, Ashish and Gertz, Michael},
title = {{EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain}},
journal = {CoRR},
volume = {abs/2210.13448},
eprinttype = {arXiv},
eprint = {2210.13448},
url = {https://arxiv.org/abs/2210.13448}
} | 21 | 529 | 2022-10-10T08:07:37 | ---
annotations_creators:
- found
- expert-generated
language:
- bg
- hr
- cs
- da
- nl
- en
- et
- fi
- fr
- de
- el
- hu
- ga
- it
- lv
- lt
- mt
- pl
- pt
- ro
- sk
- sl
- es
- sv
language_creators:
- found
- expert-generated
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: eur-lex-sum
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- legal
- eur-lex
- expert summary
- parallel corpus
- multilingual
task_categories:
- translation
- summarization
---
# Dataset Card for the EUR-Lex-Sum Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/achouhan93/eur-lex-sum
- **Paper:** [EUR-Lex-Sum: A Multi-and Cross-lingual Dataset for Long-form Summarization in the Legal Domain](https://arxiv.org/abs/2210.13448)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Dennis Aumiller](mailto:aumiller@informatik.uni-heidelberg.de)
### Dataset Summary
The EUR-Lex-Sum dataset is a multilingual resource intended for text summarization in the legal domain.
It is based on human-written summaries of legal acts issued by the European Union.
It distinguishes itself by introducing a smaller set of high-quality human-written samples, each of which have much longer references (and summaries!) than comparable datasets.
Additionally, the underlying legal acts provide a challenging domain-specific application to legal texts, which are so far underrepresented in non-English languages.
For each legal act, the sample can be available in up to 24 languages (the officially recognized languages in the European Union); the validation and test samples consist entirely of samples available in *all* languages, and are aligned across all languages at the paragraph level.
### Supported Tasks and Leaderboards
- `summarization`: The dataset is primarily suitable for summarization tasks, where it can be used as a small-scale training resource. The primary evaluation metric used in the underlying experiments is [ROUGE](https://huggingface.co/metrics/rouge). The EUR-Lex-Sum data is particularly interesting, because traditional lead-based baselines (such as lead-3) do not work well, given the extremely long reference summaries. However, we can provide reasonably good summaries by applying a modified LexRank approach on the paragraph level.
- `cross-lingual-summarization`: Given that samples of the dataset exist across multiple languages, and both the validation and test set are fully aligned across languages, this dataset can further be used as a cross-lingual benchmark. In these scenarios, language pairs (e.g., EN to ES) can be compared against monolingual systems. Suitable baselines include automatic translations of gold summaries, or translations of simple LexRank-generated monolingual summaries.
- `long-form-summarization`: We further note the particular case for *long-form summarization*. In comparison to news-based summarization datasets, this resource provides around 10x longer *summary texts*. This is particularly challenging for transformer-based models, which struggle with limited context lengths.
### Languages
The dataset supports all [official languages of the European Union](https://european-union.europa.eu/principles-countries-history/languages_en). At the time of collection, those were 24 languages:
Bulgarian, Croationa, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Irish, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, and Swedish.
Both the reference texts, as well as the summaries, are translated from an English original text (this was confirmed by private correspondence with the Publications Office of the European Union). Translations and summaries are written by external (professional) parties, contracted by the EU.
Depending on availability of document summaries in particular languages, we have between 391 (Irish) and 1505 (French) samples available. Over 80% of samples are available in at least 20 languages.
## Dataset Structure
### Data Instances
Data instances contain fairly minimal information. Aside from a unique identifier, corresponding to the Celex ID generated by the EU, two further fields specify the original long-form legal act and its associated summary.
```
{
"celex_id": "3A32021R0847",
"reference": "REGULATION (EU) 2021/847 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL\n [...]"
"summary": "Supporting EU cooperation in the field of taxation: Fiscalis (2021-2027)\n\n [...]"
}
```
### Data Fields
- `celex_id`: The [Celex ID](https://eur-lex.europa.eu/content/tools/eur-lex-celex-infographic-A3.pdf) is a naming convention used for identifying EU-related documents. Among other things, the year of publication and sector codes are embedded in the Celex ID.
- `reference`: This is the full text of a Legal Act published by the EU.
- `summary`: This field contains the summary associated with the respective Legal Act.
### Data Splits
We provide pre-split training, validation and test splits.
To obtain the validation and test splits, we randomly assigned all samples that are available across all 24 languages into two equally large portions. In total, 375 instances are available in 24 languages, which means we obtain a validation split of 187 samples and 188 test instances.
All remaining instances are assigned to the language-specific training portions, which differ in their exact size.
We particularly ensured that no duplicates exist across the three splits. For this purpose, we ensured that no exactly matching reference *or* summary exists for any sample. Further information on the length distributions (for the English subset) can be found in the paper.
## Dataset Creation
### Curation Rationale
The dataset was curated to provide a resource for under-explored aspects of automatic text summarization research.
In particular, we want to encourage the exploration of abstractive summarization systems that are not limited by the usual 512 token context window, which usually works well for (short) news articles, but fails to generate long-form summaries, or does not even work with longer source texts in the first place.
Also, existing resources primarily focus on a single (and very specialized) domain, namely news article summarization. We wanted to provide a further resource for *legal* summarization, for which many languages do not even have any existing datasets.
We further noticed that no previous system had utilized the human-written samples from the [EUR-Lex platform](https://eur-lex.europa.eu/homepage.html), which provide an excellent source for training instances suitable for summarization research. We later found out about a resource created in parallel based on EUR-Lex documents, which provides a [monolingual (English) corpus](https://github.com/svea-klaus/Legal-Document-Summarization) constructed in similar fashion. However, we provide a more thorough filtering, and extend the process to the remaining 23 EU languages.
### Source Data
#### Initial Data Collection and Normalization
The data was crawled from the aforementioned EUR-Lex platform. In particular, we only use samples which have *HTML* versions of the texts available, which ensure the alignment across languages, given that translations have to retain the original paragraph structure, which is encoded in HTML elements.
We further filter out samples that do not have associated document summaries available.
One particular design choice has to be expanded upon: For some summaries, *several source documents* are considered as an input by the EU. However, since we construct a single-document summarization corpus, we decided to use the **longest reference document only**. This means we explicitly drop the other reference texts from the corpus.
One alternative would have been to concatenated all relevant source texts; however, this generally leads to degradation of positional biases in the text, which can be an important learned feature for summarization systems. Our paper details the effect of this decision in terms of n-gram novelty, which we find is affected by the processing choice.
#### Who are the source language producers?
The language producers are external professionals contracted by the European Union offices. As previously noted, all non-English texts are generated from the respective English document (all summaries are direct translations the English summary, all reference texts are translated from the English reference text).
No further information on the demographic of annotators is provided.
### Annotations
#### Annotation process
The European Union publishes their [annotation guidelines](https://etendering.ted.europa.eu/cft/cft-documents.html?cftId=6490) for summaries, which targets a length between 600-800 words.
No information on the guidelines for translations is known.
#### Who are the annotators?
The language producers are external professionals contracted by the European Union offices. No further information on the annotators is available.
### Personal and Sensitive Information
The original text was not modified in any way by the authors of this dataset. Explicit mentions of personal names can occur in the dataset, however, we rely on the European Union that no further sensitive information is provided in these documents.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset can be used to provide summarization systems in languages that are previously under-represented. For example, language samples in Irish and Maltese (among others) enable the development and evaluation for these languages.
A successful cross-lingual system would further enable the creation of automated legal summaries for legal acts, possibly enabling foreigners in European countries to automatically translate similar country-specific legal acts.
Given the limited amount of training data, this dataset is also suitable as a test bed for low-resource approaches, especially in comparsion to strong unsupervised (extractive) summarization systems.
We also note that the summaries are explicitly provided as "not legally binding" by the EU. The implication of left-out details (a necessary evil of summaries) implies the existence of differences between the (legally binding) original legal act.
Risks associated with this dataset also largely stem from the potential application of systems trained on it. Decisions in the legal domain require careful analysis of the full context, and should not be made based on system-generated summaries at this point in time. Known biases of summarization, specifically factual hallucinations, should act as further deterrents.
### Discussion of Biases
Given the availability bias, some of the languages in the dataset are more represented than others. We attempt to mitigate influence on the evaluation by providing validation and test sets of the same size across all languages.
Given that we require the availability of HTML documents, we see a particular temporal bias in our dataset, which features more documents from the years of 1990 onwards, simply due to the increase in EU-related activities, but also the native use of the internet as a data storage.
This could imply a particular focus on more recent topics (e.g., Brexit, renewable eneriges, etc. come to mind).
Finally, due to the source of these documents being the EU, we expect a natural bias towards EU-centric (and therefore Western-centric) content; other nations and continents will be under-represented in the data.
### Other Known Limitations
As previously outlined, we are aware of some summaries relating to multiple (different) legal acts. For these samples, only one (the longest) text will be available in our dataset.
## Additional Information
### Dataset Curators
The web crawler was originally implemented by Ashish Chouhan.
Post-filtering and sample correction was later performed by Dennis Aumiller.
Both were PhD students employed at the Database Systems Research group of Heidelberg University, under the guidance of Prof. Dr. Michael Gertz.
### Licensing Information
Data from the EUR-Lex platform is available under the CC-BY SA 4.0 license. We redistribute the dataset under the same license.
### Citation Information
For the pre-print version, please cite:
```
@article{aumiller-etal-2022-eur,
author = {Aumiller, Dennis and Chouhan, Ashish and Gertz, Michael},
title = {{EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain}},
journal = {CoRR},
volume = {abs/2210.13448},
eprinttype = {arXiv},
eprint = {2210.13448},
url = {https://arxiv.org/abs/2210.13448}
}
``` | 13,726 | [
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] |
allenai/scicite | 2023-01-25T14:43:39.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1904.01608",
"region:us"
] | allenai | This is a dataset for classifying citation intents in academic papers.
The main citation intent label for each Json object is specified with the label
key while the citation context is specified in with a context key. Example:
{
'string': 'In chacma baboons, male-infant relationships can be linked to both
formation of friendships and paternity success [30,31].'
'sectionName': 'Introduction',
'label': 'background',
'citingPaperId': '7a6b2d4b405439',
'citedPaperId': '9d1abadc55b5e0',
...
}
You may obtain the full information about the paper using the provided paper ids
with the Semantic Scholar API (https://api.semanticscholar.org/).
The labels are:
Method, Background, Result | @InProceedings{Cohan2019Structural,
author={Arman Cohan and Waleed Ammar and Madeleine Van Zuylen and Field Cady},
title={Structural Scaffolds for Citation Intent Classification in Scientific Publications},
booktitle={NAACL},
year={2019}
} | 4 | 528 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-classification
paperswithcode_id: scicite
pretty_name: SciCite
dataset_info:
features:
- name: string
dtype: string
- name: sectionName
dtype: string
- name: label
dtype:
class_label:
names:
'0': method
'1': background
'2': result
- name: citingPaperId
dtype: string
- name: citedPaperId
dtype: string
- name: excerpt_index
dtype: int32
- name: isKeyCitation
dtype: bool
- name: label2
dtype:
class_label:
names:
'0': supportive
'1': not_supportive
'2': cant_determine
'3': none
- name: citeEnd
dtype: int64
- name: citeStart
dtype: int64
- name: source
dtype:
class_label:
names:
'0': properNoun
'1': andPhrase
'2': acronym
'3': etAlPhrase
'4': explicit
'5': acronymParen
'6': nan
- name: label_confidence
dtype: float32
- name: label2_confidence
dtype: float32
- name: id
dtype: string
splits:
- name: test
num_bytes: 870809
num_examples: 1859
- name: train
num_bytes: 3843904
num_examples: 8194
- name: validation
num_bytes: 430296
num_examples: 916
download_size: 23189911
dataset_size: 5145009
---
# Dataset Card for "scicite"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/allenai/scicite
- **Paper:** [Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://arxiv.org/abs/1904.01608)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 22.12 MB
- **Size of the generated dataset:** 4.91 MB
- **Total amount of disk used:** 27.02 MB
### Dataset Summary
This is a dataset for classifying citation intents in academic papers.
The main citation intent label for each Json object is specified with the label
key while the citation context is specified in with a context key. Example:
{
'string': 'In chacma baboons, male-infant relationships can be linked to both
formation of friendships and paternity success [30,31].'
'sectionName': 'Introduction',
'label': 'background',
'citingPaperId': '7a6b2d4b405439',
'citedPaperId': '9d1abadc55b5e0',
...
}
You may obtain the full information about the paper using the provided paper ids
with the Semantic Scholar API (https://api.semanticscholar.org/).
The labels are:
Method, Background, Result
### 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
### Data Instances
#### default
- **Size of downloaded dataset files:** 22.12 MB
- **Size of the generated dataset:** 4.91 MB
- **Total amount of disk used:** 27.02 MB
An example of 'validation' looks as follows.
```
{
"citeEnd": 68,
"citeStart": 64,
"citedPaperId": "5e413c7872f5df231bf4a4f694504384560e98ca",
"citingPaperId": "8f1fbe460a901d994e9b81d69f77bfbe32719f4c",
"excerpt_index": 0,
"id": "8f1fbe460a901d994e9b81d69f77bfbe32719f4c>5e413c7872f5df231bf4a4f694504384560e98ca",
"isKeyCitation": false,
"label": 2,
"label2": 0,
"label2_confidence": 0.0,
"label_confidence": 0.0,
"sectionName": "Discussion",
"source": 4,
"string": "These results are in contrast with the findings of Santos et al.(16), who reported a significant association between low sedentary time and healthy CVF among Portuguese"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `string`: a `string` feature.
- `sectionName`: a `string` feature.
- `label`: a classification label, with possible values including `method` (0), `background` (1), `result` (2).
- `citingPaperId`: a `string` feature.
- `citedPaperId`: a `string` feature.
- `excerpt_index`: a `int32` feature.
- `isKeyCitation`: a `bool` feature.
- `label2`: a classification label, with possible values including `supportive` (0), `not_supportive` (1), `cant_determine` (2), `none` (3).
- `citeEnd`: a `int64` feature.
- `citeStart`: a `int64` feature.
- `source`: a classification label, with possible values including `properNoun` (0), `andPhrase` (1), `acronym` (2), `etAlPhrase` (3), `explicit` (4).
- `label_confidence`: a `float32` feature.
- `label2_confidence`: a `float32` feature.
- `id`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default| 8194| 916|1859|
## 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
```
@inproceedings{cohan-etal-2019-structural,
title = "Structural Scaffolds for Citation Intent Classification in Scientific Publications",
author = "Cohan, Arman and
Ammar, Waleed and
van Zuylen, Madeleine and
Cady, Field",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1361",
doi = "10.18653/v1/N19-1361",
pages = "3586--3596",
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | 9,062 | [
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eduagarcia/portuguese_benchmark | 2023-07-09T06:31:26.000Z | [
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dkoterwa/kor-sts | 2023-07-25T09:52:30.000Z | [
"license:cc-by-sa-4.0",
"region:us"
] | dkoterwa | null | null | 0 | 528 | 2023-07-18T14:17:23 | ---
license: cc-by-sa-4.0
dataset_info:
features:
- name: id
dtype: int64
- name: genre
dtype: string
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 1034815
num_examples: 5691
- name: valid
num_bytes: 297254
num_examples: 1465
- name: test
num_bytes: 247409
num_examples: 1376
download_size: 837346
dataset_size: 1579478
---
# Korean Semantic Textual Similarity (KorSTS) Dataset
For a better dataset description, please visit this GitHub repository prepared by the authors of the article: [LINK](https://github.com/kakaobrain/kor-nlu-datasets) <br>
<br>
**This dataset was prepared by converting tsv files from this repository.** The idea was to share the dataset for broader audience. I am not an original author of it. <br>
Because of the specifity of read_csv method from Pandas library, there are couple of observations, which had to be deleted because of the formatting (54 in train, 35 in valid, and 1 in test)
Additionaly, **None values have been removed from the dataset** (5 from train, 1 from eval, and 3 from test)
**How to download**
```
from datasets import load_dataset
data = load_dataset("dkoterwa/kor-sts")
```
**If you use this dataset for research, please cite this paper:**
```
@article{ham2020kornli,
title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},
journal={arXiv preprint arXiv:2004.03289},
year={2020}
}
``` | 1,632 | [
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] |
anyspeech/ucla_phonetic_corpus | 2023-05-06T19:05:47.000Z | [
"region:us"
] | anyspeech | null | null | 0 | 527 | 2023-05-06T19:02:43 | ---
dataset_info:
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num_examples: 71
- name: yue
num_bytes: 7815231
num_examples: 127
download_size: 427484194
dataset_size: 368082762
---
# Dataset Card for "ucla_phonetic_corpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 5,983 | [
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HuggingFaceH4/mt_bench_prompts | 2023-07-03T20:52:34.000Z | [
"task_categories:question-answering",
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"evaluation",
"arxiv:2306.05685",
"region:us"
] | HuggingFaceH4 | null | null | 2 | 526 | 2023-07-03T20:21:21 | ---
license: apache-2.0
task_categories:
- question-answering
- conversational
language:
- en
tags:
- evaluation
pretty_name: MT Bench
size_categories:
- n<1K
---
# MT Bench by LMSYS
This set of evaluation prompts is created by the [LMSYS org](https://huggingface.co/lmsys) for better evaluation of chat models.
For more information, see the [paper](https://arxiv.org/abs/2306.05685).
### Dataset loading
To load this dataset, use 🤗 datasets:
```python
from datasets import load_dataset
data = load_dataset(HuggingFaceH4/mt_bench_prompts, split="train")
```
### Dataset creation
To create the dataset, we do the following for our internal tooling.
* rename `turns` to `prompts`,
* add empty `reference` to remaining prompts (for HF Datasets),
* Use the following code to load and save as a dataset
```python
from datasets import load_dataset
import hashlib
data = load_dataset("json", data_files="https://huggingface.co/datasets/HuggingFaceH4/mt_bench_prompts/raw/main/raw/question.jsonl", split="train")
# %% create_dataset.ipynb 11
def format_example(example):
return {
"prompt": example["prompt"],
"prompt_id": int(hashlib.sha256(''.join(example["prompt"]).encode("utf-8")).hexdigest(), 16) % (10 ** 8),
"category": example["category"],
"reference": example["reference"],
}
formatted_ds = data.map(format_example, num_proc=6, remove_columns=data.column_names)
#
formatted_ds.push_to_hub("HuggingFaceH4/mt_bench_prompts", split="train")
``` | 1,491 | [
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social_bias_frames | 2023-04-05T13:40:19.000Z | [
"task_categories:text2text-generation",
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"explanation-generation",
"region:us"
] | null | Social Bias Frames is a new way of representing the biases and offensiveness that are implied in language.
For example, these frames are meant to distill the implication that "women (candidates) are less qualified"
behind the statement "we shouldn’t lower our standards to hire more women." | @inproceedings{sap2020socialbiasframes,
title={Social Bias Frames: Reasoning about Social and Power Implications of Language},
author={Sap, Maarten and Gabriel, Saadia and Qin, Lianhui and Jurafsky, Dan and Smith, Noah A and Choi, Yejin},
year={2020},
booktitle={ACL},
} | 8 | 525 | 2022-03-02T23:29:22 | ---
pretty_name: Social Bias Frames
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
- text-classification
task_ids:
- hate-speech-detection
paperswithcode_id: null
tags:
- explanation-generation
dataset_info:
features:
- name: whoTarget
dtype: string
- name: intentYN
dtype: string
- name: sexYN
dtype: string
- name: sexReason
dtype: string
- name: offensiveYN
dtype: string
- name: annotatorGender
dtype: string
- name: annotatorMinority
dtype: string
- name: sexPhrase
dtype: string
- name: speakerMinorityYN
dtype: string
- name: WorkerId
dtype: string
- name: HITId
dtype: string
- name: annotatorPolitics
dtype: string
- name: annotatorRace
dtype: string
- name: annotatorAge
dtype: string
- name: post
dtype: string
- name: targetMinority
dtype: string
- name: targetCategory
dtype: string
- name: targetStereotype
dtype: string
- name: dataSource
dtype: string
splits:
- name: test
num_bytes: 5371665
num_examples: 17501
- name: validation
num_bytes: 5096009
num_examples: 16738
- name: train
num_bytes: 34006886
num_examples: 112900
download_size: 9464583
dataset_size: 44474560
---
# Dataset Card for "social_bias_frames"
## 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://homes.cs.washington.edu/~msap/social-bias-frames/](https://homes.cs.washington.edu/~msap/social-bias-frames/)
- **Repository:** [https://homes.cs.washington.edu/~msap/social-bias-frames/](https://homes.cs.washington.edu/~msap/social-bias-frames/)
- **Paper:** [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://www.aclweb.org/anthology/2020.acl-main.486.pdf)
- **Leaderboard:**
- **Point of Contact:** [Maartin Sap](mailto:msap@cs.washington.edu)
- **Size of downloaded dataset files:** 6.32 MB
- **Size of the generated dataset:** 44.47 MB
- **Total amount of disk used:** 50.80 MB
### Dataset Summary
Warning: this document and dataset contain content that may be offensive or upsetting.
Social Bias Frames is a new way of representing the biases and offensiveness that are implied in language. For example, these frames are meant to distill the implication that "women (candidates) are less qualified" behind the statement "we shouldn’t lower our standards to hire more women." The Social Bias Inference Corpus (SBIC) supports large-scale learning and evaluation of social implications with over 150k structured annotations of social media posts, spanning over 34k implications about a thousand demographic groups.
### Supported Tasks and Leaderboards
This dataset supports both classification and generation. Sap et al. developed several models using the SBIC. They report an F1 score of 78.8 in predicting whether the posts in the test set were offensive, an F1 score of 78.6 in predicting whether the posts were intending to be offensive, an F1 score of 80.7 in predicting whether the posts were lewd, and an F1 score of 69.9 in predicting whether the posts were targeting a specific group.
Another of Sap et al.’s models performed better in the generation task. They report a BLUE score of 77.9, a Rouge-L score of 68.7, and a WMD score of 0.74 in generating a description of the targeted group given a post as well as a BLUE score of 52.6, a Rouge-L score of 44.9, and a WMD score of 2.79 in generating a description of the implied offensive statement given a post. See the paper for further details.
### Languages
The language in SBIC is predominantly white-aligned English (78%, using a lexical dialect detector, [Blodgett et al., 2016](https://www.aclweb.org/anthology/D16-1120)). The curators find less than 10% of posts in SBIC are detected to have the AAE dialect category. The BCP-47 language tag is, presumably, en-US.
## Dataset Structure
### Data Instances
Each instance contains a post that may contain an offensive statement and annotated information concerning the nature of the offensive implication as well as the demographics of the annotator and origin of the post. See the [Social Bias Frames dataset viewer](https://huggingface.co/datasets/viewer/?dataset=social_bias_frames) to explore more examples.
#### default
- **Size of downloaded dataset files:** 6.32 MB
- **Size of the generated dataset:** 44.47 MB
- **Total amount of disk used:** 50.80 MB
An example of 'validation' looks as follows.
```
{
"HITId": "3Z8UJEJOCZ74QHXOTFP0JOOGD7039J",
"WorkerId": "7162172225567546910",
"annotatorAge": "27.0",
"annotatorGender": "woman",
"annotatorMinority": "women",
"annotatorPolitics": "liberal",
"annotatorRace": "white",
"dataSource": "t/davidson",
"intentYN": "1.0",
"offensiveYN": "1.0",
"post": "Every half-breed cop in America is trying to rape white women.",
"sexPhrase": "rape white women",
"sexReason": "rape",
"sexYN": "1.0",
"speakerMinorityYN": "0.0",
"targetCategory": "race",
"targetMinority": "mixed folks",
"targetStereotype": "mixed folks are rapists.",
"whoTarget": "1.0"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- _whoTarget_: a string, ‘0.0’ if the target is a group, ‘1.0’ if the target is an individual, and blank if the post is not offensive
- _intentYN_: a string indicating if the intent behind the statement was to offend. This is a categorical variable with four possible answers, ‘1.0’ if yes, ‘0.66’ if probably, ‘0.33’ if probably not, and ‘0.0’ if no.
- _sexYN_: a string indicating whether the post contains a sexual or lewd reference. This is a categorical variable with three possible answers, ‘1.0’ if yes, ‘0.5’ if maybe, ‘0.0’ if no.
- _sexReason_: a string containing a free text explanation of what is sexual if indicated so, blank otherwise
- _offensiveYN_: a string indicating if the post could be offensive to anyone. This is a categorical variable with three possible answers, ‘1.0’ if yes, ‘0.5’ if maybe, ‘0.0’ if no.
- _annotatorGender_: a string indicating the gender of the MTurk worker
- _annotatorMinority_: a string indicating whether the MTurk worker identifies as a minority
- _sexPhrase_: a string indicating which part of the post references something sexual, blank otherwise
- _speakerMinorityYN_: a string indicating whether the speaker was part of the same minority group that's being targeted. This is a categorical variable with three possible answers, ‘1.0’ if yes, ‘0.5’ if maybe, ‘0.0’ if no.
- _WorkerId_: a string hashed version of the MTurk workerId
- _HITId_: a string id that uniquely identifies each post
- _annotatorPolitics_: a string indicating the political leaning of the MTurk worker
- _annotatorRace_: a string indicating the race of the MTurk worker
- _annotatorAge_: a string indicating the age of the MTurk worker
- _post_: a string containing the text of the post that was annotated
- _targetMinority_: a string indicating the demographic group targeted
- _targetCategory_: a string indicating the high-level category of the demographic group(s) targeted
- _targetStereotype_: a string containing the implied statement
- _dataSource_: a string indicating the source of the post (`t/...`: means Twitter, `r/...`: means a subreddit)
### Data Splits
To ensure that no post appeared in multiple splits, the curators defined a training instance as the post and its three sets of annotations. They then split the dataset into train, validation, and test sets (75%/12.5%/12.5%).
| name |train |validation|test |
|-------|-----:|---------:|----:|
|default|112900| 16738|17501|
## Dataset Creation
### Curation Rationale
The main aim for this dataset is to cover a wide variety of social biases that are implied in text, both subtle and overt, and make the biases representative of real world discrimination that people experience [RWJF 2017](https://web.archive.org/web/20200620105955/https://www.rwjf.org/en/library/research/2017/10/discrimination-in-america--experiences-and-views.html). The curators also included some innocuous statements, to balance out biases, offensive, or harmful content.
### Source Data
The curators included online posts from the following sources sometime between 2014-2019:
- r/darkJokes, r/meanJokes, r/offensiveJokes
- Reddit microaggressions ([Breitfeller et al., 2019](https://www.aclweb.org/anthology/D19-1176/))
- Toxic language detection Twitter corpora ([Waseem & Hovy, 2016](https://www.aclweb.org/anthology/N16-2013/); [Davidson et al., 2017](https://www.aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/viewPaper/15665); [Founa et al., 2018](https://www.aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/viewPaper/17909))
- Data scraped from hate sites (Gab, Stormfront, r/incels, r/mensrights)
#### Initial Data Collection and Normalization
The curators wanted posts to be as self-contained as possible, therefore, they applied some filtering to prevent posts from being highly context-dependent. For Twitter data, they filtered out @-replies, retweets, and links, and subsample posts such that there is a smaller correlation between AAE and offensiveness (to avoid racial bias; [Sap et al., 2019](https://www.aclweb.org/anthology/P19-1163/)). For Reddit, Gab, and Stormfront, they only selected posts that were one sentence long, don't contain links, and are between 10 and 80 words. Furthemore, for Reddit, they automatically removed posts that target automated moderation.
#### Who are the source language producers?
Due to the nature of this corpus, there is no way to know who the speakers are. But, the speakers of the Reddit, Gab, and Stormfront posts are likely white men (see [Gender by subreddit](http://bburky.com/subredditgenderratios/), [Gab users](https://en.wikipedia.org/wiki/Gab_(social_network)#cite_note-insidetheright-22), [Stormfront description](https://en.wikipedia.org/wiki/Stormfront_(website))).
### Annotations
#### Annotation process
For each post, Amazon Mechanical Turk workers indicate whether the post is offensive, whether the intent was to offend, and whether it contains lewd or sexual content. Only if annotators indicate potential offensiveness do they answer the group implication question. If the post targets or references a group or demographic, workers select or write which one(s); per selected group, they then write two to four stereotypes. Finally, workers are asked whether they think the speaker is part of one of the minority groups referenced by the post. The curators collected three annotations per post, and restricted the worker pool to the U.S. and Canada. The annotations in SBIC showed 82.4% pairwise agreement and Krippendorf’s α=0.45 on average.
Recent work has highlighted various negative side effects caused by annotating potentially abusive or harmful content (e.g., acute stress; Roberts, 2016). The curators mitigated these by limiting the number of posts that one worker could annotate in one day, paying workers above minimum wage ($7–12), and providing crisis management resources to the annotators.
#### Who are the annotators?
The annotators are Amazon Mechanical Turk workers aged 36±10 years old. The annotators consisted of 55% women, 42% men, and <1% non-binary and 82% identified as White, 4% Asian, 4% Hispanic, 4% Black. Information on their first language(s) and professional backgrounds was not collected.
### Personal and Sensitive Information
Usernames are not included with the data, but the site where the post was collected is, so the user could potentially be recovered.
## Considerations for Using the Data
### Social Impact of Dataset
The curators recognize that studying Social Bias Frames necessarily requires confronting online content that may be offensive or disturbing but argue that deliberate avoidance does not eliminate such problems. By assessing social media content through the lens of Social Bias Frames, automatic flagging or AI-augmented writing interfaces may be analyzed for potentially harmful online content with detailed explanations for users or moderators to consider and verify. In addition, the collective analysis over large corpora can also be insightful for educating people on reducing unconscious biases in their language by encouraging empathy towards a targeted group.
### Discussion of Biases
Because this is a corpus of social biases, a lot of posts contain implied or overt biases against the following groups (in decreasing order of prevalence):
- gender/sexuality
- race/ethnicity
- religion/culture
- social/political
- disability body/age
- victims
The curators warn that technology trained on this dataset could have side effects such as censorship and dialect-based racial bias.
### Other Known Limitations
Because the curators found that the dataset is predominantly written in White-aligned English, they caution researchers to consider the potential for dialect or identity-based biases in labelling ([Davidson et al.,2019](https://www.aclweb.org/anthology/W19-3504.pdf); [Sap et al., 2019a](https://www.aclweb.org/anthology/P19-1163.pdf)) before deploying technology based on SBIC.
## Additional Information
### Dataset Curators
This dataset was developed by Maarten Sap of the Paul G. Allen School of Computer Science & Engineering at the University of Washington, Saadia Gabriel, Lianhui Qin, Noah A Smith, and Yejin Choi of the Paul G. Allen School of Computer Science & Engineering and the Allen Institute for Artificial Intelligence, and Dan Jurafsky of the Linguistics & Computer Science Departments of Stanford University.
### Licensing Information
The SBIC is licensed under the [Creative Commons 4.0 License](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@inproceedings{sap-etal-2020-social,
title = "Social Bias Frames: Reasoning about Social and Power Implications of Language",
author = "Sap, Maarten and
Gabriel, Saadia and
Qin, Lianhui and
Jurafsky, Dan and
Smith, Noah A. and
Choi, Yejin",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.486",
doi = "10.18653/v1/2020.acl-main.486",
pages = "5477--5490",
abstract = "Warning: this paper contains content that may be offensive or upsetting. Language has the power to reinforce stereotypes and project social biases onto others. At the core of the challenge is that it is rarely what is stated explicitly, but rather the implied meanings, that frame people{'}s judgments about others. For example, given a statement that {``}we shouldn{'}t lower our standards to hire more women,{''} most listeners will infer the implicature intended by the speaker - that {``}women (candidates) are less qualified.{''} Most semantic formalisms, to date, do not capture such pragmatic implications in which people express social biases and power differentials in language. We introduce Social Bias Frames, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes onto others. In addition, we introduce the Social Bias Inference Corpus to support large-scale modelling and evaluation with 150k structured annotations of social media posts, covering over 34k implications about a thousand demographic groups. We then establish baseline approaches that learn to recover Social Bias Frames from unstructured text. We find that while state-of-the-art neural models are effective at high-level categorization of whether a given statement projects unwanted social bias (80{\%} F1), they are not effective at spelling out more detailed explanations in terms of Social Bias Frames. Our study motivates future work that combines structured pragmatic inference with commonsense reasoning on social implications.",
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@otakumesi](https://github.com/otakumesi), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | 17,471 | [
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huggan/pokemon | 2022-04-01T11:50:45.000Z | [
"region:us"
] | huggan | null | null | 13 | 525 | 2022-04-01T11:44:34 | Source: https://www.kaggle.com/datasets/djilax/pkmn-image-dataset | 65 | [
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GATE-engine/COCOStuff10K | 2023-06-23T05:01:36.000Z | [
"region:us"
] | GATE-engine | null | null | 0 | 522 | 2023-06-23T04:55:07 | ---
dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
splits:
- name: test
num_bytes: 490670380.0
num_examples: 1000
- name: train
num_bytes: 4380309288.0
num_examples: 9000
download_size: 4871873017
dataset_size: 4870979668.0
---
# Dataset Card for "COCOStuff10K"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 464 | [
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THUDM/ImageRewardDB | 2023-06-21T06:36:29.000Z | [
"task_categories:text-to-image",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"arxiv:2304.05977",
"region:us"
] | THUDM | ImageRewardDB is a comprehensive text-to-image comparison dataset, focusing on text-to-image human preference. It consists of 137k pairs of expert comparisons, based on text prompts and corresponding model outputs from DiffusionDB. To build the ImageRewadDB, we design a pipeline tailored for it, establishing criteria for quantitative assessment and annotator training, optimizing labeling experience, and ensuring quality validation. \ | @misc{xu2023imagereward,
title={ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation},
author={Jiazheng Xu and Xiao Liu and Yuchen Wu and Yuxuan Tong and Qinkai Li and Ming Ding and Jie Tang and Yuxiao Dong},
year={2023},
eprint={2304.05977},
archivePrefix={arXiv},
primaryClass={cs.CV}
} | 19 | 520 | 2023-05-21T15:39:22 | ---
license: apache-2.0
task_categories:
- text-to-image
language:
- en
pretty_name: ImageReward Dataset
size_categories:
- 100K<n<1M
---
# ImageRewardDB
## Dataset Description
- **Homepage: https://huggingface.co/datasets/wuyuchen/ImageRewardDB**
- **Repository: https://github.com/THUDM/ImageReward**
- **Paper: https://arxiv.org/abs/2304.05977**
### Dataset Summary
ImageRewardDB is a comprehensive text-to-image comparison dataset, focusing on text-to-image human preference.
It consists of 137k pairs of expert comparisons, based on text prompts and corresponding model outputs from DiffusionDB.
To build the ImageRewadDB, we design a pipeline tailored for it, establishing criteria for quantitative assessment and
annotator training, optimizing labeling experience, and ensuring quality validation. And ImageRewardDB is now publicly available at
[🤗 Hugging Face Dataset](https://huggingface.co/datasets/wuyuchen/ImageRewardDB).
Notice: All images in ImageRewardDB are collected from DiffusionDB, and in addition, we gathered together images corresponding to the same prompt.
### Languages
The text in the dataset is all in English.
### Four Subsets
Considering that the ImageRewardDB contains a large number of images, we provide four subsets in different scales to support different needs.
For all subsets, the validation and test splits remain the same. The validation split(1.10GB) contains 412 prompts and 2.6K images(7.32K pairs) and
the test(1.16GB) split contains 466 prompts and 2.7K images(7.23K pairs). The information on the train split in different scales is as follows:
|Subset|Num of Pairs|Num of Images|Num of Prompts|Size|
|:--|--:|--:|--:|--:|
|ImageRewardDB 1K|17.6K|6.2K|1K|2.7GB|
|ImageRewardDB 2K|35.5K|12.5K|2K|5.5GB|
|ImageRewardDB 4K|71.0K|25.1K|4K|10.8GB|
|ImageRewardDB 8K|141.1K|49.9K|8K|20.9GB|
## Dataset Structure
All the data in this repository is stored in a well-organized way. The 62.6K images in ImageRewardDB are split into several folders,
stored in corresponding directories under "./images" according to its split. Each folder contains around 500 prompts, their corresponding
images, and a JSON file. The JSON file links the image with its corresponding prompt and annotation.
The file structure is as follows:
```
# ImageRewardDB
./
├── images
│ ├── train
│ │ ├── train_1
│ │ │ ├── 0a1ed3a5-04f6-4a1b-aee6-d584e7c8ed9c.webp
│ │ │ ├── 0a58cfa8-ff61-4d31-9757-27322aec3aaf.webp
│ │ │ ├── [...]
│ │ │ └── train_1.json
│ │ ├── train_2
│ │ ├── train_3
│ │ ├── [...]
│ │ └── train_32
│ ├── validation
│ │ └── [...]
│ └── test
│ └── [...]
├── metadata-train.parquet
├── metadata-validation.parquet
└── metadata-test.parquet
```
The sub-folders have the name of {split_name}_{part_id}, and the JSON file has the same name as the sub-folder.
Each image is a lossless WebP file and has a unique name generated by [UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier).
### Data Instances
For instance, below is the image of `1b4b2d61-89c2-4091-a1c0-f547ad5065cb.webp` and its information in train_1.json.
```json
{
"image_path": "images/train/train_1/0280642d-f69f-41d1-8598-5a44e296aa8b.webp",
"prompt_id": "000864-0061",
"prompt": "painting of a holy woman, decorated, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8 k ",
"classification": "People",
"image_amount_in_total": 9,
"rank": 5,
"overall_rating": 4,
"image_text_alignment_rating": 3,
"fidelity_rating": 4
}
```
### Data Fields
* image: The image object
* prompt_id: The id of the corresponding prompt
* prompt: The text of the corresponding prompt
* classification: The classification of the corresponding prompt
* image_amount_in_total: Total amount of images related to the prompt
* rank: The relative rank of the image in all related images
* overall_rating: The overall score of this image
* image_text_alignment_rating: The score of how well the generated image matches the given text
* fidelity_rating: The score of whether the output image is true to the shape and characteristics that the object should have
### Data Splits
As we mentioned above, all scales of the subsets we provided have three splits of "train", "validation", and "test".
And all the subsets share the same validation and test splits.
### Dataset Metadata
We also include three metadata tables `metadata-train.parquet`, `metadata-validation.parquet`, and `metadata-test.parquet` to
help you access and comprehend ImageRewardDB without downloading the Zip files.
All the tables share the same schema, and each row refers to an image. The schema is shown below,
and actually, the JSON files we mentioned above share the same schema:
|Column|Type|Description|
|:---|:---|:---|
|`image_path`|`string`|The relative path of the image in the repository.|
|`prompt_id`|`string`|The id of the corresponding prompt.|
|`prompt`|`string`|The text of the corresponding prompt.|
|`classification`|`string`| The classification of the corresponding prompt.|
|`image_amount_in_total`|`int`| Total amount of images related to the prompt.|
|`rank`|`int`| The relative rank of the image in all related images.|
|`overall_rating`|`int`| The overall score of this image.
|`image_text_alignment_rating`|`int`|The score of how well the generated image matches the given text.|
|`fidelity_rating`|`int`|The score of whether the output image is true to the shape and characteristics that the object should have.|
Below is an example row from metadata-train.parquet.
|image_path|prompt_id|prompt|classification|image_amount_in_total|rank|overall_rating|image_text_alignment_rating|fidelity_rating|
|:---|:---|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---|:---|:---|:---|:---|:---|
|images/train/train_1/1b4b2d61-89c2-4091-a1c0-f547ad5065cb.webp|001324-0093|a magical forest that separates the good world from the dark world, ...|Outdoor Scenes|8|3|6|6|6|
## Loading ImageRewardDB
You can use the Hugging Face [Datasets](https://huggingface.co/docs/datasets/quickstart) library to easily load the ImageRewardDB.
As we mentioned before, we provide four subsets in the scales of 1k, 2k, 4k, and 8k. You can load them using as following:
```python
from datasets import load_dataset
# Load the 1K-scale dataset
dataset = load_dataset("THUDM/ImageRewardDB", "1k")
# Load the 2K-scale dataset
dataset = load_dataset("THUDM/ImageRewardDB", "2k")
# Load the 4K-scale dataset
dataset = load_dataset("THUDM/ImageRewardDB", "4K")
# Load the 8K-scale dataset
dataset = load_dataset("THUDM/ImageRewardDB", "8k")
```
## Additional Information
### Licensing Information
The ImageRewardDB dataset is available under the [Apache license 2.0](https://www.apache.org/licenses/LICENSE-2.0.html).
The Python code in this repository is available under the [MIT License](https://github.com/poloclub/diffusiondb/blob/main/LICENSE).
### Citation Information
```
@misc{xu2023imagereward,
title={ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation},
author={Jiazheng Xu and Xiao Liu and Yuchen Wu and Yuxuan Tong and Qinkai Li and Ming Ding and Jie Tang and Yuxiao Dong},
year={2023},
eprint={2304.05977},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` | 7,794 | [
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] |
wiki_split | 2023-04-05T13:43:23.000Z | [
"task_categories:text2text-generation",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"split-and-rephrase",
"arxiv:1808.09468",
"region:us"
] | null | One million English sentences, each split into two sentences that together preserve the original meaning, extracted from Wikipedia
Google's WikiSplit dataset was constructed automatically from the publicly available Wikipedia revision history. Although
the dataset contains some inherent noise, it can serve as valuable training data for models that split or merge sentences. | @InProceedings{BothaEtAl2018,
title = {{Learning To Split and Rephrase From Wikipedia Edit History}},
author = {Botha, Jan A and Faruqui, Manaal and Alex, John and Baldridge, Jason and Das, Dipanjan},
booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
pages = {to appear},
note = {arXiv preprint arXiv:1808.09468},
year = {2018}
} | 3 | 518 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: WikiSplit
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: wikisplit
tags:
- split-and-rephrase
dataset_info:
features:
- name: complex_sentence
dtype: string
- name: simple_sentence_1
dtype: string
- name: simple_sentence_2
dtype: string
splits:
- name: test
num_bytes: 1949294
num_examples: 5000
- name: train
num_bytes: 384513073
num_examples: 989944
- name: validation
num_bytes: 1935459
num_examples: 5000
download_size: 100279164
dataset_size: 388397826
---
# Dataset Card for "wiki_split"
## 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://dataset-homepage/](https://dataset-homepage/)
- **Repository:** https://github.com/google-research-datasets/wiki-split
- **Paper:** [Learning To Split and Rephrase From Wikipedia Edit History](https://arxiv.org/abs/1808.09468)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 100.28 MB
- **Size of the generated dataset:** 388.40 MB
- **Total amount of disk used:** 488.68 MB
### Dataset Summary
One million English sentences, each split into two sentences that together preserve the original meaning, extracted from Wikipedia
Google's WikiSplit dataset was constructed automatically from the publicly available Wikipedia revision history. Although
the dataset contains some inherent noise, it can serve as valuable training data for models that split or merge sentences.
### Supported Tasks and Leaderboards
- Split and Rephrase
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 100.28 MB
- **Size of the generated dataset:** 388.40 MB
- **Total amount of disk used:** 488.68 MB
An example of 'train' looks as follows.
```
{
"complex_sentence": " '' As she translates from one language to another , she tries to find the appropriate wording and context in English that would correspond to the work in Spanish her poems and stories started to have differing meanings in their respective languages .",
"simple_sentence_1": "' '' As she translates from one language to another , she tries to find the appropriate wording and context in English that would correspond to the work in Spanish . ",
"simple_sentence_2": " Ergo , her poems and stories started to have differing meanings in their respective languages ."
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `complex_sentence`: a `string` feature.
- `simple_sentence_1`: a `string` feature.
- `simple_sentence_2`: a `string` feature.
### Data Splits
| name |train |validation|test|
|-------|-----:|---------:|---:|
|default|989944| 5000|5000|
## 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
The WikiSplit dataset is a verbatim copy of certain content from the publicly available Wikipedia revision history.
The dataset is therefore licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/).
Any third party content or data is provided "As Is" without any warranty, express or implied.
### Citation Information
```
@inproceedings{botha-etal-2018-learning,
title = "Learning To Split and Rephrase From {W}ikipedia Edit History",
author = "Botha, Jan A. and
Faruqui, Manaal and
Alex, John and
Baldridge, Jason and
Das, Dipanjan",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1080",
doi = "10.18653/v1/D18-1080",
pages = "732--737",
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun) for adding this dataset. | 7,214 | [
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SiberiaSoft/SiberianPersonaChat | 2023-08-02T18:16:20.000Z | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:ru",
"license:mit",
"region:us"
] | SiberiaSoft | null | null | 10 | 517 | 2023-07-22T03:46:53 | ---
license: mit
task_categories:
- text-generation
- text2text-generation
- conversational
language:
- ru
size_categories:
- 100K<n<1M
---
### SiberiaSoft/SiberianPersonaChat
Датасет инструкций, диалогов, QA
Данный датасет был создан для диалоговых агентов с имитацией личности.
Большая часть датасета была сгенерирована с помощью chatGPT и различных промптов к ней. Кроме этого, в состав датасета входит измененный [TolokaPersonaChatRus](https://toloka.ai/datasets/?category=nlp)
## Формат описаний личности
1. Ты парень, пилот самолета. Увлекаешься дайвингом. Собираешь марки. Любишь древнюю архитектуру.
2. Ты девушка, художница. Увлекаешься нейросетевым искусством. Умеешь программировать. Любишь рисовать.
Также в промпт можно подставлять факты о личности: ФИО, возраст и т.д
1. Я девушка 18 лет. Я учусь в институте. Живу с родителями. У меня есть кот. Ищу парня для семьи.
Статья на habr: [ссылка](https://habr.com/ru/articles/751580/)
## Процентное данных:
| Задача | Процентное содержание |
|:-----------------------:|:---------------------:|
| Персонализированные диалоги | 74.602% |
| Инструкции с its5Q/yandex-q | 4.585% |
| Инструкции с Den4ikAI/russian_instructions_2 | 3.328% |
| Инструкции с lksy/ru_instruct_gpt4 (жестко очищенные) | 3.274% |
| Инструкции с IlyaGusev/ru_turbo_alpaca_evol_instruct (очень жестко очищенные) | 3.237% |
| QA с длинными, развернутыми ответами | 3.236% |
| Ручные диалоги | 3.199% |
| QA с использованием Wikipedia | 2.628% |
| Ответы на вопросы по тексту Den4ikAI/ru_sberquad_long_answers | 1.784% |
| Решение проблем | 0.102% |
| QA Объясни ребенку | 0.025% |
### Citation
```
@MISC{SiberiaSoft/SiberianPersonaChat,
author = {Denis Petrov, Ivan Ramovich},
title = {Russian dataset for Chat models},
url = {https://huggingface.co/datasets/SiberiaSoft/SiberianPersonaChat},
year = 2023
}
```
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ccdv/WCEP-10 | 2022-10-25T10:55:52.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"conditional-text-generation",
"arxiv:2005.10070",
"arxiv:2110.08499",
"region:us"
] | ccdv | WCEP10 dataset for summarization.
From paper: "A Large-Scale Multi-Document Summarization Dataset from the Wikipedia
Current Events Portal" by D. Gholipour et al."
From paper: "PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document
Summarization" by W. Xiao et al." | @article{DBLP:journals/corr/abs-2005-10070,
author = {Demian Gholipour Ghalandari and
Chris Hokamp and
Nghia The Pham and
John Glover and
Georgiana Ifrim},
title = {A Large-Scale Multi-Document Summarization Dataset from the Wikipedia
Current Events Portal},
journal = {CoRR},
volume = {abs/2005.10070},
year = {2020},
url = {https://arxiv.org/abs/2005.10070},
eprinttype = {arXiv},
eprint = {2005.10070},
timestamp = {Fri, 22 May 2020 16:21:28 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2005-10070.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{DBLP:journals/corr/abs-2110-08499,
author = {Wen Xiao and
Iz Beltagy and
Giuseppe Carenini and
Arman Cohan},
title = {{PRIMER:} Pyramid-based Masked Sentence Pre-training for Multi-document
Summarization},
journal = {CoRR},
volume = {abs/2110.08499},
year = {2021},
url = {https://arxiv.org/abs/2110.08499},
eprinttype = {arXiv},
eprint = {2110.08499},
timestamp = {Fri, 22 Oct 2021 13:33:09 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2110-08499.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | 3 | 516 | 2022-05-09T14:13:26 | ---
language:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- summarization
- text2text-generation
task_ids: []
tags:
- conditional-text-generation
---
# WCEP10 dataset for summarization
Summarization dataset copied from [PRIMERA](https://github.com/allenai/PRIMER)
This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable:
```python
"ccdv/WCEP-10": ("document", "summary")
```
# Configs
4 possibles configs:
- `roberta` will concatenate documents with "\</s\>" (default)
- `newline` will concatenate documents with "\n"
- `bert` will concatenate documents with "[SEP]"
- `list` will return the list of documents instead of a string
### Data Fields
- `id`: paper id
- `document`: a string/list containing the body of a set of documents
- `summary`: a string containing the abstract of the set
### Data Splits
This dataset has 3 splits: _train_, _validation_, and _test_. \
| Dataset Split | Number of Instances |
| ------------- | --------------------|
| Train | 8158 |
| Validation | 1020 |
| Test | 1022 |
# Cite original article
```
@article{DBLP:journals/corr/abs-2005-10070,
author = {Demian Gholipour Ghalandari and
Chris Hokamp and
Nghia The Pham and
John Glover and
Georgiana Ifrim},
title = {A Large-Scale Multi-Document Summarization Dataset from the Wikipedia
Current Events Portal},
journal = {CoRR},
volume = {abs/2005.10070},
year = {2020},
url = {https://arxiv.org/abs/2005.10070},
eprinttype = {arXiv},
eprint = {2005.10070},
timestamp = {Fri, 22 May 2020 16:21:28 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2005-10070.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{DBLP:journals/corr/abs-2110-08499,
author = {Wen Xiao and
Iz Beltagy and
Giuseppe Carenini and
Arman Cohan},
title = {{PRIMER:} Pyramid-based Masked Sentence Pre-training for Multi-document
Summarization},
journal = {CoRR},
volume = {abs/2110.08499},
year = {2021},
url = {https://arxiv.org/abs/2110.08499},
eprinttype = {arXiv},
eprint = {2110.08499},
timestamp = {Fri, 22 Oct 2021 13:33:09 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2110-08499.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | 2,761 | [
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] |
blended_skill_talk | 2023-04-05T09:41:47.000Z | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:2004.08449",
"region:us"
] | null | A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge. | @misc{smith2020evaluating,
title={Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills},
author={Eric Michael Smith and Mary Williamson and Kurt Shuster and Jason Weston and Y-Lan Boureau},
year={2020},
eprint={2004.08449},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 46 | 515 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: BlendedSkillTalk
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- conversational
task_ids:
- dialogue-generation
paperswithcode_id: blended-skill-talk
dataset_info:
features:
- name: personas
sequence: string
- name: additional_context
dtype: string
- name: previous_utterance
sequence: string
- name: context
dtype: string
- name: free_messages
sequence: string
- name: guided_messages
sequence: string
- name: suggestions
sequence:
- name: convai2
dtype: string
- name: empathetic_dialogues
dtype: string
- name: wizard_of_wikipedia
dtype: string
- name: guided_chosen_suggestions
sequence: string
- name: label_candidates
sequence:
sequence: string
splits:
- name: train
num_bytes: 10831361
num_examples: 4819
- name: validation
num_bytes: 43961658
num_examples: 1009
- name: test
num_bytes: 44450102
num_examples: 980
download_size: 38101408
dataset_size: 99243121
---
# Dataset Card for "blended_skill_talk"
## 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://parl.ai/projects/bst/](https://parl.ai/projects/bst/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://arxiv.org/abs/2004.08449v1)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 38.11 MB
- **Size of the generated dataset:** 15.08 MB
- **Total amount of disk used:** 53.17 MB
### Dataset Summary
A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge.
### 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
### Data Instances
#### default
- **Size of downloaded dataset files:** 38.11 MB
- **Size of the generated dataset:** 15.08 MB
- **Total amount of disk used:** 53.17 MB
An example of 'train' looks as follows.
```
{
'personas': ['my parents don t really speak english , but i speak italian and english.', 'i have three children.'],
'additional_context': 'Backstreet Boys',
'previous_utterance': ['Oh, I am a BIG fan of the Backstreet Boys! Have you ever seen them performing live?', "No,I listen to their music a lot, mainly the unbreakable which is the Backstreet Boys' sixth studio album. "],
'context': 'wizard_of_wikipedia',
'free_messages': ['you are very knowledgeable, do you prefer nsync or bsb?', "haha kids of this days don't know them, i'm 46 and i still enjoying them, my kids only listen k-pop", "italian?haha that's strange, i only talk english and a little spanish "],
'guided_messages': ["i don't have a preference, they are both great. All 3 of my kids get annoyed when I listen to them though.", 'Sometimes I sing their songs in Italian, that really annoys them lol.', 'My parents barely speak English, so I was taught both. By the way, what is k-pop?'],
'suggestions': {'convai2': ["i don't have a preference , both are pretty . do you have any hobbies ?", "do they the backstreet boys ? that's my favorite group .", 'are your kids interested in music ?'], 'empathetic_dialogues': ['I actually just discovered Imagine Dragons. I love them!', "Hahaha that just goes to show ya, age is just a umber!'", 'That would be hard! Do you now Spanish well?'], 'wizard_of_wikipedia': ['NSYNC Also had Lance Bass and Joey Fatone, sometimes called the Fat One.', 'Yes, there are a few K-Pop songs that I have heard good big in the USA. It is the most popular in South Korea and has Western elements of pop.', 'English, beleive it or not.']},
'guided_chosen_suggestions': ['convai2', '', ''],
'label_candidates': []}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `personas`: a `list` of `string` features.
- `additional_context`: a `string` feature.
- `previous_utterance`: a `list` of `string` features.
- `context`: a `string` feature.
- `free_messages`: a `list` of `string` features.
- `guided_messgaes`: a `list` of `string` features.
- `suggestions`: a dictionary feature containing:
- `convai2`: a `string` feature.
- `empathetic_dialogues`: a `string` feature.
- `wizard_of_wikipedia`: a `string` feature.
- `guided_chosen_suggestions`: a `list` of `string` features.
- `label_candidates`: a `list` of `lists` of `string` features.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default| 4819| 1009| 980|
## 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
```
@misc{smith2020evaluating,
title={Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills},
author={Eric Michael Smith and Mary Williamson and Kurt Shuster and Jason Weston and Y-Lan Boureau},
year={2020},
eprint={2004.08449},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset. | 8,816 | [
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] |
lucadiliello/asnq | 2022-12-05T11:17:24.000Z | [
"region:us"
] | lucadiliello | null | null | 0 | 515 | 2022-12-05T11:14:52 | ---
dataset_info:
features:
- name: label
dtype: int64
- name: question
dtype: string
- name: answer
dtype: string
- name: key
dtype: int64
splits:
- name: test
num_bytes: 87612019
num_examples: 466148
- name: dev
num_bytes: 87607015
num_examples: 463914
- name: train
num_bytes: 3814936393
num_examples: 20377568
download_size: 2602671423
dataset_size: 3990155427
---
# Dataset Card for "asnq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 589 | [
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c-s-ale/alpaca-gpt4-data-zh | 2023-05-03T17:56:55.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:zh",
"license:cc-by-4.0",
"gpt",
"alpaca",
"fine-tune",
"instruct-tune",
"instruction",
"arxiv:2304.03277",
"region:us"
] | c-s-ale | null | null | 22 | 514 | 2023-04-07T19:22:10 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 32150579
num_examples: 48818
download_size: 35100559
dataset_size: 32150579
license: cc-by-4.0
language:
- zh
pretty_name: Instruction Tuning with GPT-4
size_categories:
- 10K<n<100K
task_categories:
- text-generation
tags:
- gpt
- alpaca
- fine-tune
- instruct-tune
- instruction
---
# Dataset Description
- **Project Page:** https://instruction-tuning-with-gpt-4.github.io
- **Repo:** https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
- **Paper:** https://arxiv.org/abs/2304.03277
# Dataset Card for "alpaca-gpt4-data-zh"
All of the work is done by [this team](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM).
# Usage and License Notices
The data is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.
# English Dataset
[Found here](https://huggingface.co/datasets/c-s-ale/alpaca-gpt4-data)
# Citation
```
@article{peng2023gpt4llm,
title={Instruction Tuning with GPT-4},
author={Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao},
journal={arXiv preprint arXiv:2304.03277},
year={2023}
}
``` | 1,389 | [
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] |
shariqfarooq/cs323_densepred_depth | 2023-09-16T00:02:26.000Z | [
"region:us"
] | shariqfarooq | null | null | 0 | 514 | 2023-09-16T00:00:58 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: depth
dtype: image
splits:
- name: train
num_bytes: 651397023.7943412
num_examples: 25356
- name: test
num_bytes: 13440344.421658808
num_examples: 518
download_size: 343390111
dataset_size: 664837368.216
---
# Dataset Card for "cs323_densepred_depth"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 610 | [
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pvduy/synth_code_preference_20k | 2023-10-14T11:42:27.000Z | [
"region:us"
] | pvduy | null | null | 0 | 514 | 2023-10-14T11:42:25 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: selected
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 75033356
num_examples: 20910
download_size: 16397343
dataset_size: 75033356
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "synth_code_preference_20k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 533 | [
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ScandEval/swerec-mini | 2023-07-05T09:46:49.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:sv",
"license:cc-by-nc-4.0",
"region:us"
] | ScandEval | null | null | 1 | 511 | 2022-11-09T18:15:56 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: test
num_bytes: 713970
num_examples: 2048
- name: train
num_bytes: 355633
num_examples: 1024
- name: val
num_bytes: 82442
num_examples: 256
download_size: 684710
dataset_size: 1152045
license: cc-by-nc-4.0
task_categories:
- text-classification
language:
- sv
size_categories:
- 1K<n<10K
---
# Dataset Card for "swerec-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 606 | [
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miracl/miracl-corpus | 2023-01-05T17:28:26.000Z | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:expert-generated",
"multilinguality:multilingual",
"language:ar",
"language:bn",
"language:en",
"language:es",
"language:fa",
"language:fi",
"language:fr",
"language:hi",
"language:id",
"language:ja",
"language:ko",
"language:ru",
"language:sw",
"language:te",
"language:th",
"language:zh",
"license:apache-2.0",
"arxiv:2210.09984",
"region:us"
] | miracl | null | null | 15 | 510 | 2022-09-29T14:49:58 | ---
annotations_creators:
- expert-generated
language:
- ar
- bn
- en
- es
- fa
- fi
- fr
- hi
- id
- ja
- ko
- ru
- sw
- te
- th
- zh
multilinguality:
- multilingual
pretty_name: MIRACL-corpus
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# Dataset Card for MIRACL Corpus
## Dataset Description
* **Homepage:** http://miracl.ai
* **Repository:** https://github.com/project-miracl/miracl
* **Paper:** https://arxiv.org/abs/2210.09984
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.
This dataset contains the collection data of the 16 "known languages". The remaining 2 "surprise languages" will not be released until later.
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.
## Dataset Structure
Each retrieval unit contains three fields: `docid`, `title`, and `text`. Consider an example from the English corpus:
```
{
"docid": "39#0",
"title": "Albedo",
"text": "Albedo (meaning 'whiteness') is the measure of the diffuse reflection of solar radiation out of the total solar radiation received by an astronomical body (e.g. a planet like Earth). It is dimensionless and measured on a scale from 0 (corresponding to a black body that absorbs all incident radiation) to 1 (corresponding to a body that reflects all incident radiation)."
}
```
The `docid` has the schema `X#Y`, where all passages with the same `X` come from the same Wikipedia article, whereas `Y` denotes the passage within that article, numbered sequentially. The text field contains the text of the passage. The title field contains the name of the article the passage comes from.
The collection can be loaded using:
```
lang='ar' # or any of the 16 languages
miracl_corpus = datasets.load_dataset('miracl/miracl-corpus', lang)['train']
for doc in miracl_corpus:
docid = doc['docid']
title = doc['title']
text = doc['text']
```
## Dataset Statistics and Links
The following table contains the number of passage and Wikipedia articles in the collection of each language, along with the links to the datasets and raw Wikipedia dumps.
| Language | # of Passages | # of Articles | Links | Raw Wiki Dump |
|:----------------|--------------:|--------------:|:------|:------|
| Arabic (ar) | 2,061,414 | 656,982 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ar) | [🌏](https://archive.org/download/arwiki-20190201/arwiki-20190201-pages-articles-multistream.xml.bz2)
| Bengali (bn) | 297,265 | 63,762 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-bn) | [🌏](https://archive.org/download/bnwiki-20190201/bnwiki-20190201-pages-articles-multistream.xml.bz2)
| English (en) | 32,893,221 | 5,758,285 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-en) | [🌏](https://archive.org/download/enwiki-20190201/enwiki-20190201-pages-articles-multistream.xml.bz2)
| Spanish (es) | 10,373,953 | 1,669,181 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-es) | [🌏](https://archive.org/download/eswiki-20220301/eswiki-20220301-pages-articles-multistream.xml.bz2)
| Persian (fa) | 2,207,172 | 857,827 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fa) | [🌏](https://archive.org/download/fawiki-20220301/fawiki-20220301-pages-articles-multistream.xml.bz2)
| Finnish (fi) | 1,883,509 | 447,815 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fi) | [🌏](https://archive.org/download/fiwiki-20190201/fiwiki-20190201-pages-articles-multistream.xml.bz2)
| French (fr) | 14,636,953 | 2,325,608 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fr) | [🌏](https://archive.org/download/frwiki-20220301/frwiki-20220301-pages-articles-multistream.xml.bz2)
| Hindi (hi) | 506,264 | 148,107 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-hi) | [🌏](https://archive.org/download/hiwiki-20220301/hiwiki-20220301-pages-articles-multistream.xml.bz2)
| Indonesian (id) | 1,446,315 | 446,330 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-id) | [🌏](https://archive.org/download/idwiki-20190201/idwiki-20190201-pages-articles-multistream.xml.bz2)
| Japanese (ja) | 6,953,614 | 1,133,444 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ja) | [🌏](https://archive.org/download/jawiki-20190201/jawiki-20190201-pages-articles-multistream.xml.bz2)
| Korean (ko) | 1,486,752 | 437,373 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ko) | [🌏](https://archive.org/download/kowiki-20190201/kowiki-20190201-pages-articles-multistream.xml.bz2)
| Russian (ru) | 9,543,918 | 1,476,045 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ru) | [🌏](https://archive.org/download/ruwiki-20190201/ruwiki-20190201-pages-articles-multistream.xml.bz2)
| Swahili (sw) | 131,924 | 47,793 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-sw) | [🌏](https://archive.org/download/swwiki-20190201/swwiki-20190201-pages-articles-multistream.xml.bz2)
| Telugu (te) | 518,079 | 66,353 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-te) | [🌏](https://archive.org/download/tewiki-20190201/tewiki-20190201-pages-articles-multistream.xml.bz2)
| Thai (th) | 542,166 | 128,179 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-th) | [🌏](https://archive.org/download/thwiki-20190101/thwiki-20190101-pages-articles-multistream.xml.bz2)
| Chinese (zh) | 4,934,368 | 1,246,389 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-zh) | [🌏](https://archive.org/download/zhwiki-20220301/zhwiki-20220301-pages-articles-multistream.xml.bz2)
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] |
facebook/babi_qa | 2023-01-25T14:26:58.000Z | [
"task_categories:question-answering",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"chained-qa",
"arxiv:1502.05698",
"arxiv:1511.06931",
"region:us"
] | facebook | The (20) QA bAbI tasks are a set of proxy tasks that evaluate reading
comprehension via question answering. Our tasks measure understanding
in several ways: whether a system is able to answer questions via chaining facts,
simple induction, deduction and many more. The tasks are designed to be prerequisites
for any system that aims to be capable of conversing with a human.
The aim is to classify these tasks into skill sets,so that researchers
can identify (and then rectify)the failings of their systems. | @misc{weston2015aicomplete,
title={Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks},
author={Jason Weston and Antoine Bordes and Sumit Chopra and Alexander M. Rush and Bart van Merriënboer and Armand Joulin and Tomas Mikolov},
year={2015},
eprint={1502.05698},
archivePrefix={arXiv},
primaryClass={cs.AI}
} | 5 | 509 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: babi-1
pretty_name: BabiQa
configs:
- en-10k-qa1
- en-10k-qa10
- en-10k-qa11
- en-10k-qa12
- en-10k-qa13
- en-10k-qa14
- en-10k-qa15
- en-10k-qa16
- en-10k-qa17
- en-10k-qa18
- en-10k-qa19
- en-10k-qa2
- en-10k-qa20
- en-10k-qa3
- en-10k-qa4
- en-10k-qa5
- en-10k-qa6
- en-10k-qa7
- en-10k-qa8
- en-10k-qa9
- en-qa1
- en-qa10
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- en-qa12
- en-qa13
- en-qa14
- en-qa15
- en-qa16
- en-qa17
- en-qa18
- en-qa19
- en-qa2
- en-qa20
- en-qa3
- en-qa4
- en-qa5
- en-qa6
- en-qa7
- en-qa8
- en-qa9
- en-valid-10k-qa1
- en-valid-10k-qa10
- en-valid-10k-qa11
- en-valid-10k-qa12
- en-valid-10k-qa13
- en-valid-10k-qa14
- en-valid-10k-qa15
- en-valid-10k-qa16
- en-valid-10k-qa17
- en-valid-10k-qa18
- en-valid-10k-qa19
- en-valid-10k-qa2
- en-valid-10k-qa20
- en-valid-10k-qa3
- en-valid-10k-qa4
- en-valid-10k-qa5
- en-valid-10k-qa6
- en-valid-10k-qa7
- en-valid-10k-qa8
- en-valid-10k-qa9
- en-valid-qa1
- en-valid-qa10
- en-valid-qa11
- en-valid-qa12
- en-valid-qa13
- en-valid-qa14
- en-valid-qa15
- en-valid-qa16
- en-valid-qa17
- en-valid-qa18
- en-valid-qa19
- en-valid-qa2
- en-valid-qa20
- en-valid-qa3
- en-valid-qa4
- en-valid-qa5
- en-valid-qa6
- en-valid-qa7
- en-valid-qa8
- en-valid-qa9
- hn-10k-qa1
- hn-10k-qa10
- hn-10k-qa11
- hn-10k-qa12
- hn-10k-qa13
- hn-10k-qa14
- hn-10k-qa15
- hn-10k-qa16
- hn-10k-qa17
- hn-10k-qa18
- hn-10k-qa19
- hn-10k-qa2
- hn-10k-qa20
- hn-10k-qa3
- hn-10k-qa4
- hn-10k-qa5
- hn-10k-qa6
- hn-10k-qa7
- hn-10k-qa8
- hn-10k-qa9
- hn-qa1
- hn-qa10
- hn-qa11
- hn-qa12
- hn-qa13
- hn-qa14
- hn-qa15
- hn-qa16
- hn-qa17
- hn-qa18
- hn-qa19
- hn-qa2
- hn-qa20
- hn-qa3
- hn-qa4
- hn-qa5
- hn-qa6
- hn-qa7
- hn-qa8
- hn-qa9
- shuffled-10k-qa1
- shuffled-10k-qa10
- shuffled-10k-qa11
- shuffled-10k-qa12
- shuffled-10k-qa13
- shuffled-10k-qa14
- shuffled-10k-qa15
- shuffled-10k-qa16
- shuffled-10k-qa17
- shuffled-10k-qa18
- shuffled-10k-qa19
- shuffled-10k-qa2
- shuffled-10k-qa20
- shuffled-10k-qa3
- shuffled-10k-qa4
- shuffled-10k-qa5
- shuffled-10k-qa6
- shuffled-10k-qa7
- shuffled-10k-qa8
- shuffled-10k-qa9
- shuffled-qa1
- shuffled-qa10
- shuffled-qa11
- shuffled-qa12
- shuffled-qa13
- shuffled-qa14
- shuffled-qa15
- shuffled-qa16
- shuffled-qa17
- shuffled-qa18
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- shuffled-qa20
- shuffled-qa3
- shuffled-qa4
- shuffled-qa5
- shuffled-qa6
- shuffled-qa7
- shuffled-qa8
- shuffled-qa9
tags:
- chained-qa
dataset_info:
- config_name: en-qa1
features:
- name: story
sequence:
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dtype: string
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dtype:
class_label:
names:
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download_size: 15719851
dataset_size: 330903
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download_size: 15719851
dataset_size: 609519
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dataset_size: 687806
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dataset_size: 1273214
---
# Dataset Card for bAbi QA
## 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:**[The bAbI project](https://research.fb.com/downloads/babi/)
- **Repository:**
- **Paper:** [arXiv Paper](https://arxiv.org/pdf/1502.05698.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The (20) QA bAbI tasks are a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. The aim is to classify these tasks into skill sets,so that researchers can identify (and then rectify) the failings of their systems.
### Supported Tasks and Leaderboards
The dataset supports a set of 20 proxy story-based question answering tasks for various "types" in English and Hindi. The tasks are:
|task_no|task_name|
|----|------------|
|qa1 |single-supporting-fact|
|qa2 |two-supporting-facts|
|qa3 |three-supporting-facts|
|qa4 |two-arg-relations|
|qa5 |three-arg-relations|
|qa6 |yes-no-questions|
|qa7 |counting|
|qa8 |lists-sets|
|qa9 |simple-negation|
|qa10| indefinite-knowledge|
|qa11| basic-coreference|
|qa12| conjunction|
|qa13| compound-coreference|
|qa14| time-reasoning|
|qa15| basic-deduction|
|qa16| basic-induction|
|qa17| positional-reasoning|
|qa18| size-reasoning|
|qa19| path-finding|
|qa20| agents-motivations|
The "types" are are:
- `en`
- the tasks in English, readable by humans.
- `hn`
- the tasks in Hindi, readable by humans.
- `shuffled`
- the same tasks with shuffled letters so they are not readable by humans, and for existing parsers and taggers cannot be used in a straight-forward fashion to leverage extra resources-- in this case the learner is more forced to rely on the given training data. This mimics a learner being first presented a language and having to learn from scratch.
- `en-10k`, `shuffled-10k` and `hn-10k`
- the same tasks in the three formats, but with 10,000 training examples, rather than 1000 training examples.
- `en-valid` and `en-valid-10k`
- are the same as `en` and `en10k` except the train sets have been conveniently split into train and valid portions (90% and 10% split).
To get a particular dataset, use `load_dataset('babi_qa',type=f'{type}',task_no=f'{task_no}')` where `type` is one of the types, and `task_no` is one of the task numbers. For example, `load_dataset('babi_qa', type='en', task_no='qa1')`.
### Languages
## Dataset Structure
### Data Instances
An instance from the `en-qa1` config's `train` split:
```
{'story': {'answer': ['', '', 'bathroom', '', '', 'hallway', '', '', 'hallway', '', '', 'office', '', '', 'bathroom'], 'id': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15'], 'supporting_ids': [[], [], ['1'], [], [], ['4'], [], [], ['4'], [], [], ['11'], [], [], ['8']], 'text': ['Mary moved to the bathroom.', 'John went to the hallway.', 'Where is Mary?', 'Daniel went back to the hallway.', 'Sandra moved to the garden.', 'Where is Daniel?', 'John moved to the office.', 'Sandra journeyed to the bathroom.', 'Where is Daniel?', 'Mary moved to the hallway.', 'Daniel travelled to the office.', 'Where is Daniel?', 'John went back to the garden.', 'John moved to the bedroom.', 'Where is Sandra?'], 'type': [0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1]}}
```
### Data Fields
- `story`: a dictionary feature containing:
- `id`: a `string` feature, which denotes the line number in the example.
- `type`: a classification label, with possible values including `context`, `question`, denoting whether the text is context or a question.
- `text`: a `string` feature the text present, whether it is a question or context.
- `supporting_ids`: a `list` of `string` features containing the line numbers of the lines in the example which support the answer.
- `answer`: a `string` feature containing the answer to the question, or an empty string if the `type`s is not `question`.
### Data Splits
The splits and corresponding sizes are:
| | train | test | validation |
|-------------------|---------|--------|--------------|
| en-qa1 | 200 | 200 | - |
| en-qa2 | 200 | 200 | - |
| en-qa3 | 200 | 200 | - |
| en-qa4 | 1000 | 1000 | - |
| en-qa5 | 200 | 200 | - |
| en-qa6 | 200 | 200 | - |
| en-qa7 | 200 | 200 | - |
| en-qa8 | 200 | 200 | - |
| en-qa9 | 200 | 200 | - |
| en-qa10 | 200 | 200 | - |
| en-qa11 | 200 | 200 | - |
| en-qa12 | 200 | 200 | - |
| en-qa13 | 200 | 200 | - |
| en-qa14 | 200 | 200 | - |
| en-qa15 | 250 | 250 | - |
| en-qa16 | 1000 | 1000 | - |
| en-qa17 | 125 | 125 | - |
| en-qa18 | 198 | 199 | - |
| en-qa19 | 1000 | 1000 | - |
| en-qa20 | 94 | 93 | - |
| en-10k-qa1 | 2000 | 200 | - |
| en-10k-qa2 | 2000 | 200 | - |
| en-10k-qa3 | 2000 | 200 | - |
| en-10k-qa4 | 10000 | 1000 | - |
| en-10k-qa5 | 2000 | 200 | - |
| en-10k-qa6 | 2000 | 200 | - |
| en-10k-qa7 | 2000 | 200 | - |
| en-10k-qa8 | 2000 | 200 | - |
| en-10k-qa9 | 2000 | 200 | - |
| en-10k-qa10 | 2000 | 200 | - |
| en-10k-qa11 | 2000 | 200 | - |
| en-10k-qa12 | 2000 | 200 | - |
| en-10k-qa13 | 2000 | 200 | - |
| en-10k-qa14 | 2000 | 200 | - |
| en-10k-qa15 | 2500 | 250 | - |
| en-10k-qa16 | 10000 | 1000 | - |
| en-10k-qa17 | 1250 | 125 | - |
| en-10k-qa18 | 1978 | 199 | - |
| en-10k-qa19 | 10000 | 1000 | - |
| en-10k-qa20 | 933 | 93 | - |
| en-valid-qa1 | 180 | 200 | 20 |
| en-valid-qa2 | 180 | 200 | 20 |
| en-valid-qa3 | 180 | 200 | 20 |
| en-valid-qa4 | 900 | 1000 | 100 |
| en-valid-qa5 | 180 | 200 | 20 |
| en-valid-qa6 | 180 | 200 | 20 |
| en-valid-qa7 | 180 | 200 | 20 |
| en-valid-qa8 | 180 | 200 | 20 |
| en-valid-qa9 | 180 | 200 | 20 |
| en-valid-qa10 | 180 | 200 | 20 |
| en-valid-qa11 | 180 | 200 | 20 |
| en-valid-qa12 | 180 | 200 | 20 |
| en-valid-qa13 | 180 | 200 | 20 |
| en-valid-qa14 | 180 | 200 | 20 |
| en-valid-qa15 | 225 | 250 | 25 |
| en-valid-qa16 | 900 | 1000 | 100 |
| en-valid-qa17 | 113 | 125 | 12 |
| en-valid-qa18 | 179 | 199 | 19 |
| en-valid-qa19 | 900 | 1000 | 100 |
| en-valid-qa20 | 85 | 93 | 9 |
| en-valid-10k-qa1 | 1800 | 200 | 200 |
| en-valid-10k-qa2 | 1800 | 200 | 200 |
| en-valid-10k-qa3 | 1800 | 200 | 200 |
| en-valid-10k-qa4 | 9000 | 1000 | 1000 |
| en-valid-10k-qa5 | 1800 | 200 | 200 |
| en-valid-10k-qa6 | 1800 | 200 | 200 |
| en-valid-10k-qa7 | 1800 | 200 | 200 |
| en-valid-10k-qa8 | 1800 | 200 | 200 |
| en-valid-10k-qa9 | 1800 | 200 | 200 |
| en-valid-10k-qa10 | 1800 | 200 | 200 |
| en-valid-10k-qa11 | 1800 | 200 | 200 |
| en-valid-10k-qa12 | 1800 | 200 | 200 |
| en-valid-10k-qa13 | 1800 | 200 | 200 |
| en-valid-10k-qa14 | 1800 | 200 | 200 |
| en-valid-10k-qa15 | 2250 | 250 | 250 |
| en-valid-10k-qa16 | 9000 | 1000 | 1000 |
| en-valid-10k-qa17 | 1125 | 125 | 125 |
| en-valid-10k-qa18 | 1781 | 199 | 197 |
| en-valid-10k-qa19 | 9000 | 1000 | 1000 |
| en-valid-10k-qa20 | 840 | 93 | 93 |
| hn-qa1 | 200 | 200 | - |
| hn-qa2 | 200 | 200 | - |
| hn-qa3 | 167 | 167 | - |
| hn-qa4 | 1000 | 1000 | - |
| hn-qa5 | 200 | 200 | - |
| hn-qa6 | 200 | 200 | - |
| hn-qa7 | 200 | 200 | - |
| hn-qa8 | 200 | 200 | - |
| hn-qa9 | 200 | 200 | - |
| hn-qa10 | 200 | 200 | - |
| hn-qa11 | 200 | 200 | - |
| hn-qa12 | 200 | 200 | - |
| hn-qa13 | 125 | 125 | - |
| hn-qa14 | 200 | 200 | - |
| hn-qa15 | 250 | 250 | - |
| hn-qa16 | 1000 | 1000 | - |
| hn-qa17 | 125 | 125 | - |
| hn-qa18 | 198 | 198 | - |
| hn-qa19 | 1000 | 1000 | - |
| hn-qa20 | 93 | 94 | - |
| hn-10k-qa1 | 2000 | 200 | - |
| hn-10k-qa2 | 2000 | 200 | - |
| hn-10k-qa3 | 1667 | 167 | - |
| hn-10k-qa4 | 10000 | 1000 | - |
| hn-10k-qa5 | 2000 | 200 | - |
| hn-10k-qa6 | 2000 | 200 | - |
| hn-10k-qa7 | 2000 | 200 | - |
| hn-10k-qa8 | 2000 | 200 | - |
| hn-10k-qa9 | 2000 | 200 | - |
| hn-10k-qa10 | 2000 | 200 | - |
| hn-10k-qa11 | 2000 | 200 | - |
| hn-10k-qa12 | 2000 | 200 | - |
| hn-10k-qa13 | 1250 | 125 | - |
| hn-10k-qa14 | 2000 | 200 | - |
| hn-10k-qa15 | 2500 | 250 | - |
| hn-10k-qa16 | 10000 | 1000 | - |
| hn-10k-qa17 | 1250 | 125 | - |
| hn-10k-qa18 | 1977 | 198 | - |
| hn-10k-qa19 | 10000 | 1000 | - |
| hn-10k-qa20 | 934 | 94 | - |
| shuffled-qa1 | 200 | 200 | - |
| shuffled-qa2 | 200 | 200 | - |
| shuffled-qa3 | 200 | 200 | - |
| shuffled-qa4 | 1000 | 1000 | - |
| shuffled-qa5 | 200 | 200 | - |
| shuffled-qa6 | 200 | 200 | - |
| shuffled-qa7 | 200 | 200 | - |
| shuffled-qa8 | 200 | 200 | - |
| shuffled-qa9 | 200 | 200 | - |
| shuffled-qa10 | 200 | 200 | - |
| shuffled-qa11 | 200 | 200 | - |
| shuffled-qa12 | 200 | 200 | - |
| shuffled-qa13 | 200 | 200 | - |
| shuffled-qa14 | 200 | 200 | - |
| shuffled-qa15 | 250 | 250 | - |
| shuffled-qa16 | 1000 | 1000 | - |
| shuffled-qa17 | 125 | 125 | - |
| shuffled-qa18 | 198 | 199 | - |
| shuffled-qa19 | 1000 | 1000 | - |
| shuffled-qa20 | 94 | 93 | - |
| shuffled-10k-qa1 | 2000 | 200 | - |
| shuffled-10k-qa2 | 2000 | 200 | - |
| shuffled-10k-qa3 | 2000 | 200 | - |
| shuffled-10k-qa4 | 10000 | 1000 | - |
| shuffled-10k-qa5 | 2000 | 200 | - |
| shuffled-10k-qa6 | 2000 | 200 | - |
| shuffled-10k-qa7 | 2000 | 200 | - |
| shuffled-10k-qa8 | 2000 | 200 | - |
| shuffled-10k-qa9 | 2000 | 200 | - |
| shuffled-10k-qa10 | 2000 | 200 | - |
| shuffled-10k-qa11 | 2000 | 200 | - |
| shuffled-10k-qa12 | 2000 | 200 | - |
| shuffled-10k-qa13 | 2000 | 200 | - |
| shuffled-10k-qa14 | 2000 | 200 | - |
| shuffled-10k-qa15 | 2500 | 250 | - |
| shuffled-10k-qa16 | 10000 | 1000 | - |
| shuffled-10k-qa17 | 1250 | 125 | - |
| shuffled-10k-qa18 | 1978 | 199 | - |
| shuffled-10k-qa19 | 10000 | 1000 | - |
| shuffled-10k-qa20 | 933 | 93 | - |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Code to generate tasks is available on [github](https://github.com/facebook/bAbI-tasks)
#### 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
Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston, at Facebook Research.
### Licensing Information
```
Creative Commons Attribution 3.0 License
```
### Citation Information
```
@misc{dodge2016evaluating,
title={Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems},
author={Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston},
year={2016},
eprint={1511.06931},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset. | 105,707 | [
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] |
bri25yu-temp/cve | 2023-11-01T18:18:10.000Z | [
"region:us"
] | bri25yu-temp | null | null | 0 | 509 | 2023-10-23T16:10:43 | ---
dataset_info:
- config_name: cve_search_eval
features:
- name: function_call
dtype: string
- name: reference
sequence: string
- name: count
dtype: int64
- name: results
sequence: string
- name: results_count
dtype: int64
- name: correct
dtype: bool
splits:
- name: train
num_bytes: 5294673
num_examples: 11
download_size: 1905758
dataset_size: 5294673
- config_name: function_calling_retrieval
features:
- name: completion
dtype: string
- name: query
dtype: string
splits:
- name: train
num_bytes: 4395
num_examples: 31
download_size: 0
dataset_size: 4395
- config_name: metadata
features:
- name: Assigner
dtype: string
- name: CVSS v2 ac insuf info
dtype: bool
- name: CVSS v2 access complexity
dtype: string
- name: CVSS v2 access vector
dtype: string
- name: CVSS v2 authentication
dtype: string
- name: CVSS v2 availability impact
dtype: string
- name: CVSS v2 base score
dtype: float64
- name: CVSS v2 confidentiality impact
dtype: string
- name: CVSS v2 exploitability score
dtype: float64
- name: CVSS v2 impact score
dtype: float64
- name: CVSS v2 integrity impact
dtype: string
- name: CVSS v2 obtain all privilege
dtype: bool
- name: CVSS v2 obtain other privilege
dtype: bool
- name: CVSS v2 obtain user privilege
dtype: bool
- name: CVSS v2 severity
dtype: string
- name: CVSS v2 user interaction required
dtype: bool
- name: CVSS v2 vector string
dtype: string
- name: CVSS v2 version
dtype: string
- name: CVSS v3 attack complexity
dtype: string
- name: CVSS v3 attack vector
dtype: string
- name: CVSS v3 availability impact
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- name: CVSS v3 base score
dtype: float64
- name: CVSS v3 base severity
dtype: string
- name: CVSS v3 confidentiality impact
dtype: string
- name: CVSS v3 exploitability score
dtype: float64
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dtype: float64
- name: CVSS v3 integrity impact
dtype: string
- name: CVSS v3 privileges required
dtype: string
- name: CVSS v3 scope
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- name: CVSS v3 user interaction
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- name: CVSS v3 vector string
dtype: string
- name: CVSS v3 version
dtype: string
- name: Description
dtype: string
- name: Id
dtype: string
- name: Last modified date
dtype: string
- name: Problem type
struct:
- name: problemtype_data
list:
- name: description
list:
- name: lang
dtype: string
- name: value
dtype: string
- name: Published date
dtype: string
- name: References
struct:
- name: reference_data
list:
- name: name
dtype: string
- name: refsource
dtype: string
- name: tags
sequence: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 257448469
num_examples: 229429
download_size: 56250637
dataset_size: 257448469
- config_name: metadata_with_references
features:
- name: CVSS v2 severity
dtype: string
- name: CVSS v3 base severity
dtype: string
- name: Last modified date
dtype: string
- name: Published date
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- name: text_to_search
dtype: string
- name: chunks
list:
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dtype: string
- name: text
dtype: string
- name: text_to_embed
dtype: string
- name: CVE URL
dtype: string
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dtype: string
splits:
- name: train
num_bytes: 11604379994
num_examples: 229429
download_size: 2349591033
dataset_size: 11604379994
- config_name: references_only
features:
- name: url
dtype: string
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dtype: string
splits:
- name: train
num_bytes: 2719849957
num_examples: 279921
download_size: 867942737
dataset_size: 2719849957
configs:
- config_name: cve_search_eval
data_files:
- split: train
path: cve_search_eval/train-*
- config_name: function_calling_retrieval
data_files:
- split: train
path: function_calling_retrieval/train-*
- config_name: metadata
data_files:
- split: train
path: metadata/train-*
- config_name: metadata_with_references
data_files:
- split: train
path: metadata_with_references/train-*
- config_name: references_only
data_files:
- split: train
path: references_only/train-*
---
# Dataset Card for "cve"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 4,629 | [
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] |
lavita/ChatDoctor-iCliniq | 2023-09-11T21:13:37.000Z | [
"region:us"
] | lavita | null | null | 2 | 508 | 2023-09-11T21:11:18 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: answer_icliniq
dtype: string
- name: answer_chatgpt
dtype: string
- name: answer_chatdoctor
dtype: string
splits:
- name: train
num_bytes: 16962106
num_examples: 7321
download_size: 9373080
dataset_size: 16962106
---
# Dataset Card for "ChatDoctor-iCliniq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 581 | [
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opus_gnome | 2023-06-01T14:59:53.000Z | [
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] | null | A parallel corpus of GNOME localization files. Source: https://l10n.gnome.org
187 languages, 12,822 bitexts
total number of files: 113,344
total number of tokens: 267.27M
total number of sentence fragments: 58.12M | @InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
} | 1 | 507 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
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language_bcp47:
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license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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task_ids: []
paperswithcode_id: null
pretty_name: OpusGnome
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features:
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dtype:
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languages:
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features:
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dtype:
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languages:
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splits:
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num_examples: 150
download_size: 6116
dataset_size: 12121
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features:
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splits:
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num_bytes: 134978
num_examples: 2169
download_size: 15891
dataset_size: 134978
config_names:
- ar-bal
- bg-csb
- ca-en_GB
- cs-eo
- cs-tk
- da-vi
- de-ha
- de-tt
- el-sk
- en_GB-my
---
# Dataset Card for Opus Gnome
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/GNOME.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/GNOME.php
E.g.
`dataset = load_dataset("opus_gnome", lang1="it", lang2="pl")`
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
```
{
'id': '0',
'translation': {
'ar': 'إعداد سياسة القفل',
'bal': 'تنظیم کتن سیاست کبل'
}
}
```
### Data Fields
Each instance has two fields:
- **id**: the id of the example
- **translation**: a dictionary containing translated texts in two languages.
### Data Splits
Each subset simply consists in a train set. We provide the number of examples for certain language pairs:
| | train |
|:---------|--------:|
| ar-bal | 60 |
| bg-csb | 10 |
| ca-en_GB | 7982 |
| cs-eo | 73 |
| de-ha | 216 |
| cs-tk | 18686 |
| da-vi | 149 |
| en_GB-my | 28232 |
| el-sk | 150 |
| de-tt | 2169 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
@InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
}
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. | 8,329 | [
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wiki_snippets | 2023-04-05T13:43:20.000Z | [
"task_categories:text-generation",
"task_categories:other",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:extended|wiki40b",
"source_datasets:extended|wikipedia",
"language:en",
"license:unknown",
"text-search",
"region:us"
] | null | Wikipedia version split into plain text snippets for dense semantic indexing. | @ONLINE {wikidump,
author = {Wikimedia Foundation},
title = {Wikimedia Downloads},
url = {https://dumps.wikimedia.org}
} | 0 | 507 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- multilingual
pretty_name: WikiSnippets
size_categories:
- 10M<n<100M
source_datasets:
- extended|wiki40b
- extended|wikipedia
task_categories:
- text-generation
- other
task_ids:
- language-modeling
paperswithcode_id: null
tags:
- text-search
dataset_info:
- config_name: wiki40b_en_100_0
features:
- name: _id
dtype: string
- name: datasets_id
dtype: int32
- name: wiki_id
dtype: string
- name: start_paragraph
dtype: int32
- name: start_character
dtype: int32
- name: end_paragraph
dtype: int32
- name: end_character
dtype: int32
- name: article_title
dtype: string
- name: section_title
dtype: string
- name: passage_text
dtype: string
splits:
- name: train
num_bytes: 12938641686
num_examples: 17553713
download_size: 0
dataset_size: 12938641686
- config_name: wikipedia_en_100_0
features:
- name: _id
dtype: string
- name: datasets_id
dtype: int32
- name: wiki_id
dtype: string
- name: start_paragraph
dtype: int32
- name: start_character
dtype: int32
- name: end_paragraph
dtype: int32
- name: end_character
dtype: int32
- name: article_title
dtype: string
- name: section_title
dtype: string
- name: passage_text
dtype: string
splits:
- name: train
num_bytes: 26407884393
num_examples: 33849898
download_size: 0
dataset_size: 26407884393
---
# Dataset Card for "wiki_snippets"
## 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://dumps.wikimedia.org](https://dumps.wikimedia.org)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
Wikipedia version split into plain text snippets for dense semantic indexing.
### 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 2 configurations of the dataset (with 100 snippet passage length and 0 overlap) in
English:
- wiki40b_en_100_0: Wiki-40B
- wikipedia_en_100_0: Wikipedia
### Data Instances
#### wiki40b_en_100_0
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 12.94 GB
- **Total amount of disk used:** 12.94 GB
An example of 'train' looks as follows:
```
{'_id': '{"datasets_id": 0, "wiki_id": "Q1294448", "sp": 2, "sc": 0, "ep": 6, "ec": 610}',
'datasets_id': 0,
'wiki_id': 'Q1294448',
'start_paragraph': 2,
'start_character': 0,
'end_paragraph': 6,
'end_character': 610,
'article_title': 'Ági Szalóki',
'section_title': 'Life',
'passage_text': "Ági Szalóki Life She started singing as a toddler, considering Márta Sebestyén a role model. Her musical background is traditional folk music; she first won recognition for singing with Ökrös in a traditional folk style, and Besh o droM, a Balkan gypsy brass band. With these ensembles she toured around the world from the Montreal Jazz Festival, through Glastonbury Festival to the Théatre de la Ville in Paris, from New York to Beijing.\nSince 2005, she began to pursue her solo career and explore various genres, such as jazz, thirties ballads, or children's songs.\nUntil now, three of her six released albums"}
```
#### wikipedia_en_100_0
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 26.41 GB
- **Total amount of disk used:** 26.41 GB
An example of 'train' looks as follows:
```
{'_id': '{"datasets_id": 0, "wiki_id": "Anarchism", "sp": 0, "sc": 0, "ep": 2, "ec": 129}',
'datasets_id': 0,
'wiki_id': 'Anarchism',
'start_paragraph': 0,
'start_character': 0,
'end_paragraph': 2,
'end_character': 129,
'article_title': 'Anarchism',
'section_title': 'Start',
'passage_text': 'Anarchism is a political philosophy and movement that is sceptical of authority and rejects all involuntary, coercive forms of hierarchy. Anarchism calls for the abolition of the state, which it holds to be unnecessary, undesirable, and harmful. As a historically left-wing movement, placed on the farthest left of the political spectrum, it is usually described alongside communalism and libertarian Marxism as the libertarian wing (libertarian socialism) of the socialist movement, and has a strong historical association with anti-capitalism and socialism. Humans lived in societies without formal hierarchies long before the establishment of formal states, realms, or empires. With the'}
```
### Data Fields
The data fields are the same for all configurations:
- `_id`: a `string` feature.
- `datasets_id`: a `int32` feature.
- `wiki_id`: a `string` feature.
- `start_paragraph`: a `int32` feature.
- `start_character`: a `int32` feature.
- `end_paragraph`: a `int32` feature.
- `end_character`: a `int32` feature.
- `article_title`: a `string` feature.
- `section_title`: a `string` feature.
- `passage_text`: a `string` feature.
### Data Splits
| name | train |
|:-------------------|---------:|
| wiki40b_en_100_0 | 17553713 |
| wikipedia_en_100_0 | 33849898 |
## 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
See licensing information of source datasets.
### Citation Information
Cite source datasets:
- Wiki-40B:
```
@inproceedings{49029,
title = {Wiki-40B: Multilingual Language Model Dataset},
author = {Mandy Guo and Zihang Dai and Denny Vrandecic and Rami Al-Rfou},
year = {2020},
booktitle = {LREC 2020}
}
```
- Wikipedia:
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@yjernite](https://github.com/yjernite) for adding this dataset. | 9,276 | [
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codymlewis/nbaiot | 2023-10-13T04:02:56.000Z | [
"license:cc-by-4.0",
"arxiv:1805.03409",
"region:us"
] | codymlewis | An intrusion detection dataset that focuses on IoT botnet attacks. | @article{DBLP:journals/corr/abs-1805-03409,
author = {Yair Meidan and
Michael Bohadana and
Yael Mathov and
Yisroel Mirsky and
Dominik Breitenbacher and
Asaf Shabtai and
Yuval Elovici},
title = {N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep
Autoencoders},
journal = {CoRR},
volume = {abs/1805.03409},
year = {2018},
url = {http://arxiv.org/abs/1805.03409},
eprinttype = {arXiv},
eprint = {1805.03409},
timestamp = {Mon, 13 Aug 2018 16:49:04 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1805-03409.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | 0 | 505 | 2023-09-20T02:24:15 | ---
dataset_info:
features:
- name: features
sequence: float32
length: 115
- name: attack
dtype:
class_label:
names:
'0': benign_traffic
'1': combo
'2': junk
'3': mirai-ack
'4': mirai-scan
'5': mirai-syn
'6': mirai-udp
'7': mirai-udpplain
'8': scan
'9': tcp
'10': udp
- name: device
dtype:
class_label:
names:
'0': Danmini_Doorbell
'1': Ecobee_Thermostat
'2': Ennio_Doorbell
'3': Philips_B120N10_Baby_Monitor
'4': Provision_PT_737E_Security_Camera
'5': Provision_PT_838_Security_Camera
'6': Samsung_SNH_1011_N_Webcam
'7': SimpleHome_XCS7_1002_WHT_Security_Camera
'8': SimpleHome_XCS7_1003_WHT_Security_Camera
splits:
- name: train
num_bytes: 2857231888
num_examples: 6002588
- name: test
num_bytes: 504568568
num_examples: 1060018
download_size: 1772922927
dataset_size: 3361800456
license: cc-by-4.0
pretty_name: nbaiot
---
# Dataset Card for N-BAIoT
*From https://archive.ics.uci.edu/dataset/442/detection+of+iot+botnet+attacks+n+baiot:* This dataset addresses the lack of public botnet datasets, especially for the IoT. It suggests *real* traffic data, gathered from 9 commercial IoT devices authentically infected by Mirai and BASHLITE.
## Dataset Details
### Dataset Description
*From https://archive.ics.uci.edu/dataset/442/detection+of+iot+botnet+attacks+n+baiot:*
(a) Attribute being predicted:
-- Originally we aimed at distinguishing between benign and Malicious traffic data by means of anomaly detection techniques.
-- However, as the malicious data can be divided into 10 attacks carried by 2 botnets, the dataset can also be used for multi-class classification: 10 classes of attacks, plus 1 class of 'benign'.
(b) The study's results:
-- For each of the 9 IoT devices we trained and optimized a deep autoencoder on 2/3 of its benign data (i.e., the training set of each device). This was done to capture normal network traffic patterns.
-- The test data of each device comprised of the remaining 1/3 of benign data plus all the malicious data. On each test set we applied the respective trained (deep) autoencoder as an anomaly detector. The detection of anomalies (i.e., the cyberattacks launched from each of the above IoT devices) concluded with 100% TPR.
- **Curated by:** Meidan, Yair, Bohadana, Michael, Mathov, Yael, Mirsky, Yisroel, Breitenbacher, Dominik, , Asaf, and Shabtai, Asaf
- **License:** [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode)
### Dataset Sources
- **Repository:** https://archive.ics.uci.edu/dataset/442/detection+of+iot+botnet+attacks+n+baiot
- **Paper:** https://arxiv.org/abs/1805.03409
## Citation
**BibTeX:**
@misc{misc_detection_of_iot_botnet_attacks_n_baiot_442,
author = {Meidan,Yair, Bohadana,Michael, Mathov,Yael, Mirsky,Yisroel, Breitenbacher,Dominik, ,Asaf, and Shabtai,Asaf},
title = {{N-BaIoT Dataset to Detect IoT Botnet Attacks}},
year = {2018},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5RC8J}
}
**APA:**
Meidan, Yair, Bohadana, Michael, Mathov, Yael, Mirsky, Yisroel, Breitenbacher, Dominik, ,Asaf, and Shabtai, Asaf. (2018). N-BaIoT Dataset to Detect IoT Botnet Attacks. UCI Machine Learning Repository. https://doi.org/10.24432/C5RC8J.
## Glossary [optional]
- **IoT**: Internet of Things
- **Botnet**: A collection of devices that are maliciously controlled via malware | 3,686 | [
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nthngdy/oscar-mini | 2022-12-06T11:05:51.000Z | [
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] | nthngdy | The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.\ | @inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{\'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.156",
pages = "1703--1714",
abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{\'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{\"u}ngen and Caroline Iliadi},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
language = {en}
} | 3 | 504 | 2022-03-09T14:18:51 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- am
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- arz
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license:
- cc0-1.0
multilinguality:
- multilingual
source_datasets:
- oscar
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: oscar
pretty_name: OSCAR
---
## WARNING: this dataset is an extract of the OSCAR dataset published here to simulate the use of the full dataset in low-resource contexts and debug codebases that would eventually use the original OSCAR dataset.
Using this dataset is equivalent to using a processed version of OSCAR legally speaking. I take no credit for the gathering of the original data and hence refer entirely to the original dataset in the card below.
# Dataset Card for "oscar"
## 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://oscar-corpus.com](https://oscar-corpus.com)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
OSCAR or **O**pen **S**uper-large **C**rawled [**A**LMAnaCH](https://team.inria.fr/almanach/) co**R**pus is a huge multilingual corpus obtained by language classification and filtering of the [Common Crawl](https://commoncrawl.org/) corpus using the [goclassy](https://github.com/pjox/goclassy) architecture. Data is distributed by language in both original and deduplicated form.
### Supported Tasks and Leaderboards
OSCAR is mainly intended to pretrain language models and word represantations.
### Languages
All the data is distributed by language, both the original and the deduplicated versions of the data are available. 166 different languages are available. The table in subsection [Data Splits Sample Size](#data-splits-sample-size) provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR.
## Dataset Structure
We show detailed information for all the configurations of the dataset.
## Dataset Creation
### Curation Rationale
OSCAR was constructed new pipeline derived from the [fastText's one](https://github.com/facebookresearch/fastText), called [_goclassy_](https://github.com/pjox/goclassy). Goclassy reuses the [fastText linear classifier](https://fasttext.cc) and the pre-trained fastText model for language recognition, but it completely rewrites and parallelises their pipeline in an asynchronous manner.
The order of operations is more or less the same as in the fastText pre-processing pipeline but instead of clustering multiple operations into a single blocking process, a worker is launched for each operation but bounding the number of possible parallel operations at a given time by the number of available threads instead of the number of CPUs. Goclassy is implemented in the [Go programming language](https://golang.org/) so it lets the [Go runtime](https://golang.org/src/runtime/mprof.go) handle the scheduling of the processes. Thus the goclassy's pipeline one does not have to wait for a whole WET file to download, decompress and classify in order to start downloading and processing the next one, a new file will start downloading and processing as soon as the scheduler is able to allocate a new process.
Filtering and cleaning processes at line level are done before feeding each line to the classifier. Lines shorter than 100 UTF-8 characters and lines containing invalid UTF-8 characters are discarted and are not classified. After all files are proccesed the deduplicated versions are constructed and everything is then splitted in shards and compressed.
### Source Data
#### Initial Data Collection and Normalization
[Common Crawl](https://commoncrawl.org/) is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected [nofollow](http://microformats.org/wiki/rel-nofollow) and [robots.txt](https://www.robotstxt.org/) policies.
Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics.
To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of OSCAR, the **November 2018** snapshot was used. It surpasses 20TB of uncompressed data and contains more than 50 thousand plain text files where each file consists of the plain text from multiple websites along its metadata header.
#### Who are the source language producers?
The data comes from multiple web pages in a large variety of languages.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
Being constructed from Common Crawl, Personal and sensitive information might be present. This **must** be considered before training deep learning models with OSCAR, specially in the case of text-generation models.
## Considerations for Using the Data
### Social Impact of Dataset
OSCAR is intended to bring more data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures.
### Discussion of Biases
OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models.
### Other Known Limitations
The [fastText linear classifier](https://fasttext.cc) is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource langiuages. Some audits have already been done by [third parties](https://arxiv.org/abs/2010.14571).
## Additional Information
### Dataset Curators
The corpus was put together by [Pedro J. Ortiz](https://pjortiz.eu/), [Benoît Sagot](http://pauillac.inria.fr/~sagot/), and [Laurent Romary](https://cv.archives-ouvertes.fr/laurentromary), during work done at [Inria](https://www.inria.fr/en), particularly at the [ALMAnaCH team](https://team.inria.fr/almanach/).
### Licensing Information
These data are released under this licensing scheme
We do not own any of the text from which these data has been extracted.
We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR
This work is published from: France.
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources from the next release of the corpus.
### Citation Information
```
@inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.156",
pages = "1703--1714",
abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi},
publisher = {Leibniz-Institut f{"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
language = {en}
}
```
### Contributions
Thanks to [@pjox](https://github.com/pjox) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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] |
Multimodal-Fatima/StanfordCars_train | 2023-06-12T06:26:48.000Z | [
"region:us"
] | Multimodal-Fatima | null | null | 0 | 504 | 2023-01-28T02:30:01 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': am general hummer suv 2000
'1': acura rl sedan 2012
'2': acura tl sedan 2012
'3': acura tl type-s 2008
'4': acura tsx sedan 2012
'5': acura integra type r 2001
'6': acura zdx hatchback 2012
'7': aston martin v8 vantage convertible 2012
'8': aston martin v8 vantage coupe 2012
'9': aston martin virage convertible 2012
'10': aston martin virage coupe 2012
'11': audi rs 4 convertible 2008
'12': audi a5 coupe 2012
'13': audi tts coupe 2012
'14': audi r8 coupe 2012
'15': audi v8 sedan 1994
'16': audi 100 sedan 1994
'17': audi 100 wagon 1994
'18': audi tt hatchback 2011
'19': audi s6 sedan 2011
'20': audi s5 convertible 2012
'21': audi s5 coupe 2012
'22': audi s4 sedan 2012
'23': audi s4 sedan 2007
'24': audi tt rs coupe 2012
'25': bmw activehybrid 5 sedan 2012
'26': bmw 1 series convertible 2012
'27': bmw 1 series coupe 2012
'28': bmw 3 series sedan 2012
'29': bmw 3 series wagon 2012
'30': bmw 6 series convertible 2007
'31': bmw x5 suv 2007
'32': bmw x6 suv 2012
'33': bmw m3 coupe 2012
'34': bmw m5 sedan 2010
'35': bmw m6 convertible 2010
'36': bmw x3 suv 2012
'37': bmw z4 convertible 2012
'38': bentley continental supersports conv. convertible 2012
'39': bentley arnage sedan 2009
'40': bentley mulsanne sedan 2011
'41': bentley continental gt coupe 2012
'42': bentley continental gt coupe 2007
'43': bentley continental flying spur sedan 2007
'44': bugatti veyron 16.4 convertible 2009
'45': bugatti veyron 16.4 coupe 2009
'46': buick regal gs 2012
'47': buick rainier suv 2007
'48': buick verano sedan 2012
'49': buick enclave suv 2012
'50': cadillac cts-v sedan 2012
'51': cadillac srx suv 2012
'52': cadillac escalade ext crew cab 2007
'53': chevrolet silverado 1500 hybrid crew cab 2012
'54': chevrolet corvette convertible 2012
'55': chevrolet corvette zr1 2012
'56': chevrolet corvette ron fellows edition z06 2007
'57': chevrolet traverse suv 2012
'58': chevrolet camaro convertible 2012
'59': chevrolet hhr ss 2010
'60': chevrolet impala sedan 2007
'61': chevrolet tahoe hybrid suv 2012
'62': chevrolet sonic sedan 2012
'63': chevrolet express cargo van 2007
'64': chevrolet avalanche crew cab 2012
'65': chevrolet cobalt ss 2010
'66': chevrolet malibu hybrid sedan 2010
'67': chevrolet trailblazer ss 2009
'68': chevrolet silverado 2500hd regular cab 2012
'69': chevrolet silverado 1500 classic extended cab 2007
'70': chevrolet express van 2007
'71': chevrolet monte carlo coupe 2007
'72': chevrolet malibu sedan 2007
'73': chevrolet silverado 1500 extended cab 2012
'74': chevrolet silverado 1500 regular cab 2012
'75': chrysler aspen suv 2009
'76': chrysler sebring convertible 2010
'77': chrysler town and country minivan 2012
'78': chrysler 300 srt-8 2010
'79': chrysler crossfire convertible 2008
'80': chrysler pt cruiser convertible 2008
'81': daewoo nubira wagon 2002
'82': dodge caliber wagon 2012
'83': dodge caliber wagon 2007
'84': dodge caravan minivan 1997
'85': dodge ram pickup 3500 crew cab 2010
'86': dodge ram pickup 3500 quad cab 2009
'87': dodge sprinter cargo van 2009
'88': dodge journey suv 2012
'89': dodge dakota crew cab 2010
'90': dodge dakota club cab 2007
'91': dodge magnum wagon 2008
'92': dodge challenger srt8 2011
'93': dodge durango suv 2012
'94': dodge durango suv 2007
'95': dodge charger sedan 2012
'96': dodge charger srt-8 2009
'97': eagle talon hatchback 1998
'98': fiat 500 abarth 2012
'99': fiat 500 convertible 2012
'100': ferrari ff coupe 2012
'101': ferrari california convertible 2012
'102': ferrari 458 italia convertible 2012
'103': ferrari 458 italia coupe 2012
'104': fisker karma sedan 2012
'105': ford f-450 super duty crew cab 2012
'106': ford mustang convertible 2007
'107': ford freestar minivan 2007
'108': ford expedition el suv 2009
'109': ford edge suv 2012
'110': ford ranger supercab 2011
'111': ford gt coupe 2006
'112': ford f-150 regular cab 2012
'113': ford f-150 regular cab 2007
'114': ford focus sedan 2007
'115': ford e-series wagon van 2012
'116': ford fiesta sedan 2012
'117': gmc terrain suv 2012
'118': gmc savana van 2012
'119': gmc yukon hybrid suv 2012
'120': gmc acadia suv 2012
'121': gmc canyon extended cab 2012
'122': geo metro convertible 1993
'123': hummer h3t crew cab 2010
'124': hummer h2 sut crew cab 2009
'125': honda odyssey minivan 2012
'126': honda odyssey minivan 2007
'127': honda accord coupe 2012
'128': honda accord sedan 2012
'129': hyundai veloster hatchback 2012
'130': hyundai santa fe suv 2012
'131': hyundai tucson suv 2012
'132': hyundai veracruz suv 2012
'133': hyundai sonata hybrid sedan 2012
'134': hyundai elantra sedan 2007
'135': hyundai accent sedan 2012
'136': hyundai genesis sedan 2012
'137': hyundai sonata sedan 2012
'138': hyundai elantra touring hatchback 2012
'139': hyundai azera sedan 2012
'140': infiniti g coupe ipl 2012
'141': infiniti qx56 suv 2011
'142': isuzu ascender suv 2008
'143': jaguar xk xkr 2012
'144': jeep patriot suv 2012
'145': jeep wrangler suv 2012
'146': jeep liberty suv 2012
'147': jeep grand cherokee suv 2012
'148': jeep compass suv 2012
'149': lamborghini reventon coupe 2008
'150': lamborghini aventador coupe 2012
'151': lamborghini gallardo lp 570-4 superleggera 2012
'152': lamborghini diablo coupe 2001
'153': land rover range rover suv 2012
'154': land rover lr2 suv 2012
'155': lincoln town car sedan 2011
'156': mini cooper roadster convertible 2012
'157': maybach landaulet convertible 2012
'158': mazda tribute suv 2011
'159': mclaren mp4-12c coupe 2012
'160': mercedes-benz 300-class convertible 1993
'161': mercedes-benz c-class sedan 2012
'162': mercedes-benz sl-class coupe 2009
'163': mercedes-benz e-class sedan 2012
'164': mercedes-benz s-class sedan 2012
'165': mercedes-benz sprinter van 2012
'166': mitsubishi lancer sedan 2012
'167': nissan leaf hatchback 2012
'168': nissan nv passenger van 2012
'169': nissan juke hatchback 2012
'170': nissan 240sx coupe 1998
'171': plymouth neon coupe 1999
'172': porsche panamera sedan 2012
'173': ram c/v cargo van minivan 2012
'174': rolls-royce phantom drophead coupe convertible 2012
'175': rolls-royce ghost sedan 2012
'176': rolls-royce phantom sedan 2012
'177': scion xd hatchback 2012
'178': spyker c8 convertible 2009
'179': spyker c8 coupe 2009
'180': suzuki aerio sedan 2007
'181': suzuki kizashi sedan 2012
'182': suzuki sx4 hatchback 2012
'183': suzuki sx4 sedan 2012
'184': tesla model s sedan 2012
'185': toyota sequoia suv 2012
'186': toyota camry sedan 2012
'187': toyota corolla sedan 2012
'188': toyota 4runner suv 2012
'189': volkswagen golf hatchback 2012
'190': volkswagen golf hatchback 1991
'191': volkswagen beetle hatchback 2012
'192': volvo c30 hatchback 2012
'193': volvo 240 sedan 1993
'194': volvo xc90 suv 2007
'195': smart fortwo convertible 2012
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dtype: int64
- name: clip_tags_ViT_L_14
sequence: string
- name: LLM_Description_gpt3_downstream_tasks_ViT_L_14
sequence: string
- name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14
sequence: string
- name: blip_caption_beam_5
dtype: string
- name: Attributes_ViT_L_14_text_davinci_003_full
sequence: string
- name: Attributes_ViT_L_14_text_davinci_003_stanfordcars
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- name: clip_tags_ViT_L_14_with_openai_classes
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- name: clip_tags_ViT_L_14_wo_openai_classes
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- name: clip_tags_ViT_B_16_ensemble_specific
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- name: clip_tags_ViT_B_32_ensemble_specific
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- name: Attributes_ViT_B_16_descriptors_text_davinci_003_full
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- name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_simple_specific
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- name: Attributes_ViT_L_14_descriptors_text_davinci_003_full
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splits:
- name: train
num_bytes: 1016273762.0
num_examples: 8144
download_size: 991440998
dataset_size: 1016273762.0
---
# Dataset Card for "StanfordCars_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 10,430 | [
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sentiment140 | 2023-10-20T12:55:00.000Z | [
"language:en",
"region:us"
] | null | Sentiment140 consists of Twitter messages with emoticons, which are used as noisy labels for
sentiment classification. For more detailed information please refer to the paper. | @article{go2009twitter,
title={Twitter sentiment classification using distant supervision},
author={Go, Alec and Bhayani, Richa and Huang, Lei},
journal={CS224N project report, Stanford},
volume={1},
number={12},
pages={2009},
year={2009}
} | 10 | 503 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: sentiment140
pretty_name: Sentiment140
dataset_info:
config_name: sentiment140
features:
- name: text
dtype: string
- name: date
dtype: string
- name: user
dtype: string
- name: sentiment
dtype: int32
- name: query
dtype: string
splits:
- name: train
num_bytes: 224542690
num_examples: 1600000
- name: test
num_bytes: 72971
num_examples: 498
download_size: 81363704
dataset_size: 224615661
train-eval-index:
- config: sentiment140
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
sentiment: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for "sentiment140"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [http://help.sentiment140.com/home](http://help.sentiment140.com/home)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 81.36 MB
- **Size of the generated dataset:** 225.82 MB
- **Total amount of disk used:** 307.18 MB
### Dataset Summary
Sentiment140 consists of Twitter messages with emoticons, which are used as noisy labels for
sentiment classification. For more detailed information please refer to the paper.
### 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
### Data Instances
#### sentiment140
- **Size of downloaded dataset files:** 81.36 MB
- **Size of the generated dataset:** 225.82 MB
- **Total amount of disk used:** 307.18 MB
An example of 'train' looks as follows.
```
{
"date": "23-04-2010",
"query": "NO_QUERY",
"sentiment": 3,
"text": "train message",
"user": "train user"
}
```
### Data Fields
The data fields are the same among all splits.
#### sentiment140
- `text`: a `string` feature.
- `date`: a `string` feature.
- `user`: a `string` feature.
- `sentiment`: a `int32` feature.
- `query`: a `string` feature.
### Data Splits
| name | train |test|
|------------|------:|---:|
|sentiment140|1600000| 498|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{go2009twitter,
title={Twitter sentiment classification using distant supervision},
author={Go, Alec and Bhayani, Richa and Huang, Lei},
journal={CS224N project report, Stanford},
volume={1},
number={12},
pages={2009},
year={2009}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | 6,837 | [
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osunlp/Mind2Web | 2023-07-19T03:44:34.000Z | [
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"Web Agent",
"arxiv:2306.06070",
"region:us"
] | osunlp | null | null | 45 | 503 | 2023-06-10T02:38:11 | ---
license: cc-by-4.0
language:
- en
tags:
- Web Agent
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** https://osu-nlp-group.github.io/Mind2Web/
- **Repository:** https://github.com/OSU-NLP-Group/Mind2Web
- **Paper:** https://arxiv.org/abs/2306.06070
- **Point of Contact:** [Xiang Deng](mailto:deng.595@osu.edu)
### Dataset Summary
Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites spanning 31 domains and crowdsourced action sequences for the tasks, Mind2Web provides three necessary ingredients for building generalist web agents: 1. diverse domains, websites, and tasks, 2. use of real-world websites instead of simulated and simplified ones, and 3. a broad spectrum of user interaction patterns.
## Dataset Structure
### Data Fields
- "annotation_id" (str): unique id for each task
- "website" (str): website name
- "domain" (str): website domain
- "subdomain" (str): website subdomain
- "confirmed_task" (str): task description
- "action_reprs" (list[str]): human readable string representation of the action sequence
- "actions" (list[dict]): list of actions (steps) to complete the task
- "action_uid" (str): unique id for each action (step)
- "raw_html" (str): raw html of the page before the action is performed
- "cleaned_html" (str): cleaned html of the page before the action is performed
- "operation" (dict): operation to perform
- "op" (str): operation type, one of CLICK, TYPE, SELECT
- "original_op" (str): original operation type, contain additional HOVER and ENTER that are mapped to CLICK, not used
- "value" (str): optional value for the operation, e.g., text to type, option to select
- "pos_candidates" (list[dict]): ground truth elements. Here we only include positive elements that exist in "cleaned_html" after our preprocessing, so "pos_candidates" might be empty. The original labeled element can always be found in the "raw_html".
- "tag" (str): tag of the element
- "is_original_target" (bool): whether the element is the original target labeled by the annotator
- "is_top_level_target" (bool): whether the element is a top level target find by our algorithm. please see the paper for more details.
- "backend_node_id" (str): unique id for the element
- "attributes" (str): serialized attributes of the element, use `json.loads` to convert back to dict
- "neg_candidates" (list[dict]): other candidate elements in the page after preprocessing, has similar structure as "pos_candidates"
### Data Splits
- train: 1,009 instances
- test: (To prevent potential data leakage, please check our [repo](https://github.com/OSU-NLP-Group/Mind2Web) for information on obtaining the test set.)
- Cross Task: 252 instances, tasks from the same website are seen during training
- Cross Website: 177 instances, websites are not seen during training
- Cross Domain: 9,12 instances, entire domains are not seen during training
### Licensing Information
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
### Disclaimer
This dataset was collected and released solely for research purposes, with the goal of making the web more accessible via language technologies. The authors are strongly against any potential harmful use of the data or technology to any party.
### Citation Information
```
@misc{deng2023mind2web,
title={Mind2Web: Towards a Generalist Agent for the Web},
author={Xiang Deng and Yu Gu and Boyuan Zheng and Shijie Chen and Samuel Stevens and Boshi Wang and Huan Sun and Yu Su},
year={2023},
eprint={2306.06070},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 4,308 | [
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ChaiML/20231012_chai_prize_reward_model_data | 2023-10-12T20:29:40.000Z | [
"region:us"
] | ChaiML | null | null | 0 | 503 | 2023-10-12T20:29:31 | ---
dataset_info:
features:
- name: input_text
dtype: string
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 120271659
num_examples: 78726
download_size: 69397345
dataset_size: 120271659
---
# Dataset Card for "20231012_chai_prize_reward_model_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 425 | [
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opus_paracrawl | 2023-06-01T14:59:53.000Z | [
"task_categories:translation",
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"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
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"language:da",
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"language:el",
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"language:sv",
"language:sw",
"language:tl",
"language:uk",
"language:zh",
"license:cc0-1.0",
"region:us"
] | null | Parallel corpora from Web Crawls collected in the ParaCrawl project.
42 languages, 43 bitexts
total number of files: 59,996
total number of tokens: 56.11G
total number of sentence fragments: 3.13G | null | 5 | 502 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- bg
- ca
- cs
- da
- de
- el
- en
- es
- et
- eu
- fi
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- nl
- nn
- pl
- pt
- ro
- ru
- si
- sk
- sl
- so
- sv
- sw
- tl
- uk
- zh
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusParaCrawl
dataset_info:
- config_name: el-en
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- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- el
- en
splits:
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download_size: 2317102846
dataset_size: 6760375061
- config_name: en-ha
features:
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dtype: string
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dtype:
translation:
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splits:
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download_size: 1757433
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splits:
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features:
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dtype:
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languages:
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splits:
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download_size: 32933118
dataset_size: 82502798
- config_name: es-gl
features:
- name: id
dtype: string
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dtype:
translation:
languages:
- es
- gl
splits:
- name: train
num_bytes: 582660901
num_examples: 1879689
download_size: 236696353
dataset_size: 582660901
config_names:
- de-pl
- el-en
- en-ha
- en-ig
- en-km
- en-so
- en-sw
- en-tl
- es-gl
- fr-nl
---
# Dataset Card for OpusParaCrawl
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/ParaCrawl.php
- **Repository:** None
- **Paper:** [ParaCrawl: Web-Scale Acquisition of Parallel Corpora](https://aclanthology.org/2020.acl-main.417/)
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
Parallel corpora from Web Crawls collected in the ParaCrawl project.
Tha dataset contains:
- 42 languages, 43 bitexts
- total number of files: 59,996
- total number of tokens: 56.11G
- total number of sentence fragments: 3.13G
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs,
e.g.
```python
dataset = load_dataset("opus_paracrawl", lang1="en", lang2="so")
```
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/ParaCrawl.php
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The languages in the dataset are:
- bg
- ca
- cs
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- gl
- hr
- hu
- is
- it
- km
- ko
- lt
- lv
- mt
- my
- nb
- ne
- nl
- nn
- pl
- pt
- ro
- ru
- si
- sk
- sl
- so
- sv
- sw
- tl
- uk
- zh
## Dataset Structure
### Data Instances
```
{
'id': '0',
'translation': {
"el": "Συνεχίστε ευθεία 300 μέτρα μέχρι να καταλήξουμε σε μια σωστή οδός (ul. Gagarina)? Περπατήστε περίπου 300 μέτρα μέχρι να φτάσετε το πρώτο ορθή οδός (ul Khotsa Namsaraeva)?",
"en": "Go straight 300 meters until you come to a proper street (ul. Gagarina); Walk approximately 300 meters until you reach the first proper street (ul Khotsa Namsaraeva);"
}
}
```
### Data Fields
- `id` (`str`): Unique identifier of the parallel sentence for the pair of languages.
- `translation` (`dict`): Parallel sentences for the pair of languages.
### Data Splits
The dataset contains a single `train` split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
- Creative commons CC0 (no rights reserved)
### Citation Information
```bibtex
@inproceedings{banon-etal-2020-paracrawl,
title = "{P}ara{C}rawl: Web-Scale Acquisition of Parallel Corpora",
author = "Ba{\~n}{\'o}n, Marta and
Chen, Pinzhen and
Haddow, Barry and
Heafield, Kenneth and
Hoang, Hieu and
Espl{\`a}-Gomis, Miquel and
Forcada, Mikel L. and
Kamran, Amir and
Kirefu, Faheem and
Koehn, Philipp and
Ortiz Rojas, Sergio and
Pla Sempere, Leopoldo and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Sarr{\'\i}as, Elsa and
Strelec, Marek and
Thompson, Brian and
Waites, William and
Wiggins, Dion and
Zaragoza, Jaume",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.417",
doi = "10.18653/v1/2020.acl-main.417",
pages = "4555--4567",
}
```
```bibtex
@InProceedings{TIEDEMANN12.463,
author = {Jörg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
}
```
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. | 9,096 | [
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SetFit/bbc-news | 2022-01-18T05:58:34.000Z | [
"region:us"
] | SetFit | null | null | 5 | 502 | 2022-03-02T23:29:22 | # BBC News Topic Classification
Dataset on [BBC News Topic Classification](https://www.kaggle.com/yufengdev/bbc-text-categorization/data): 2225 articles, each labeled under one of 5 categories: business, entertainment, politics, sport or tech. | 246 | [
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] |
tau/sled | 2022-10-25T07:33:44.000Z | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_ids:multiple-choice-qa",
"task_ids:natural-language-inference",
"language:en",
"license:mit",
"multi-hop-question-answering",
"query-based-summarization",
"long-texts",
"arxiv:2208.00748",
"arxiv:2201.03533",
"arxiv:2104.02112",
"arxiv:2104.07091",
"arxiv:2104.05938",
"arxiv:1712.07040",
"arxiv:2105.03011",
"arxiv:2112.08608",
"arxiv:2110.01799",
"arxiv:1606.05250",
"arxiv:1809.09600",
"region:us"
] | tau | Efficient Long-Text Understanding with Short-Text Models.
Our SLiding-Encoder and Decoder uses any pretrained encoder-decoder model, to independtly encode overlapping chunks of
the inputs, and perform fusion-in-decoder to achieve linear-memory requirment for long-range natural language understanding. | @inproceedings{Ivgi2022EfficientLU,
title={Efficient Long-Text Understanding with Short-Text Models},
author={Maor Ivgi and Uri Shaham and Jonathan Berant},
year={2022}
}
Note that each SLED dataset has its own citation. Please see the source to
get the correct citation for each contained dataset (and also cite the SCROLLS dataset on which it is based). | 7 | 502 | 2022-08-05T08:54:23 | ---
language:
- en
license:
- mit
task_categories:
- question-answering
- summarization
- text-generation
task_ids:
- multiple-choice-qa
- natural-language-inference
configs:
- gov_report
- summ_screen_fd
- qmsum
- qasper
- narrative_qa
- quality
- contract_nli
- squad
- squad_shuffled_distractors
- squad_ordered_distractors
- hotpotqa
- hotpotqa_second_only
tags:
- multi-hop-question-answering
- query-based-summarization
- long-texts
---
## Dataset Description
- **Repository:** [SLED Github repository](https://github.com/Mivg/SLED)
- **Paper:** [Efficient Long-Text Understanding with Short-Text Models
](https://arxiv.org/pdf/2208.00748.pdf)
# Dataset Card for SCROLLS
## Overview
This dataset is based on the [SCROLLS](https://huggingface.co/datasets/tau/scrolls) dataset ([paper](https://arxiv.org/pdf/2201.03533.pdf)), the [SQuAD 1.1](https://huggingface.co/datasets/squad) dataset and the [HotpotQA](https://huggingface.co/datasets/hotpot_qa) dataset.
It doesn't contain any unpblished data, but includes the configuration needed for the [Efficient Long-Text Understanding with Short-Text Models
](https://arxiv.org/pdf/2208.00748.pdf) paper.
## Tasks
The tasks included are:
#### GovReport ([Huang et al., 2021](https://arxiv.org/pdf/2104.02112.pdf))
GovReport is a summarization dataset of reports addressing various national policy issues published by the
Congressional Research Service and the U.S. Government Accountability Office, where each document is paired with a hand-written executive summary.
The reports and their summaries are longer than their equivalents in other popular long-document summarization datasets;
for example, GovReport's documents are approximately 1.5 and 2.5 times longer than the documents in Arxiv and PubMed, respectively.
#### SummScreenFD ([Chen et al., 2021](https://arxiv.org/pdf/2104.07091.pdf))
SummScreenFD is a summarization dataset in the domain of TV shows (e.g. Friends, Game of Thrones).
Given a transcript of a specific episode, the goal is to produce the episode's recap.
The original dataset is divided into two complementary subsets, based on the source of its community contributed transcripts.
For SCROLLS, we use the ForeverDreaming (FD) subset, as it incorporates 88 different shows,
making it a more diverse alternative to the TV MegaSite (TMS) subset, which has only 10 shows.
Community-authored recaps for the ForeverDreaming transcripts were collected from English Wikipedia and TVMaze.
#### QMSum ([Zhong et al., 2021](https://arxiv.org/pdf/2104.05938.pdf))
QMSum is a query-based summarization dataset, consisting of 232 meetings transcripts from multiple domains.
The corpus covers academic group meetings at the International Computer Science Institute and their summaries, industrial product meetings for designing a remote control,
and committee meetings of the Welsh and Canadian Parliaments, dealing with a variety of public policy issues.
Annotators were tasked with writing queries about the broad contents of the meetings, as well as specific questions about certain topics or decisions,
while ensuring that the relevant text for answering each query spans at least 200 words or 10 turns.
#### NarrativeQA ([Kočiský et al., 2021](https://arxiv.org/pdf/1712.07040.pdf))
NarrativeQA (Kočiský et al., 2021) is an established question answering dataset over entire books from Project Gutenberg and movie scripts from different websites.
Annotators were given summaries of the books and scripts obtained from Wikipedia, and asked to generate question-answer pairs,
resulting in about 30 questions and answers for each of the 1,567 books and scripts.
They were encouraged to use their own words rather then copying, and avoid asking yes/no questions or ones about the cast.
Each question was then answered by an additional annotator, providing each question with two reference answers (unless both answers are identical).
#### Qasper ([Dasigi et al., 2021](https://arxiv.org/pdf/2105.03011.pdf))
Qasper is a question answering dataset over NLP papers filtered from the Semantic Scholar Open Research Corpus (S2ORC).
Questions were written by NLP practitioners after reading only the title and abstract of the papers,
while another set of NLP practitioners annotated the answers given the entire document.
Qasper contains abstractive, extractive, and yes/no questions, as well as unanswerable ones.
#### QuALITY ([Pang et al., 2021](https://arxiv.org/pdf/2112.08608.pdf))
QuALITY is a multiple-choice question answering dataset over articles and stories sourced from Project Gutenberg,
the Open American National Corpus, and more.
Experienced writers wrote questions and distractors, and were incentivized to write answerable, unambiguous questions such that in order to correctly answer them,
human annotators must read large portions of the given document.
Reference answers were then calculated using the majority vote between of the annotators and writer's answers.
To measure the difficulty of their questions, Pang et al. conducted a speed validation process,
where another set of annotators were asked to answer questions given only a short period of time to skim through the document.
As a result, 50% of the questions in QuALITY are labeled as hard, i.e. the majority of the annotators in the speed validation setting chose the wrong answer.
#### ContractNLI ([Koreeda and Manning, 2021](https://arxiv.org/pdf/2110.01799.pdf))
Contract NLI is a natural language inference dataset in the legal domain.
Given a non-disclosure agreement (the premise), the task is to predict whether a particular legal statement (the hypothesis) is entailed, not entailed (neutral), or cannot be entailed (contradiction) from the contract.
The NDAs were manually picked after simple filtering from the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) and Google.
The dataset contains a total of 607 contracts and 17 unique hypotheses, which were combined to produce the dataset's 10,319 examples.
#### SQuAD 1.1 ([Rajpurkar et al., 2016](https://arxiv.org/pdf/1606.05250.pdf))
Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
articles, where the answer to every question is a segment of text, or span, \
from the corresponding reading passage, or the question might be unanswerable.
#### HotpotQA ([Yang et al., 2018](https://arxiv.org/pdf/1809.09600.pdf))
HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features:
(1) the questions require finding and reasoning over multiple supporting documents to answer;
(2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas;
(3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervisionand explain the predictions;
(4) we offer a new type of factoid comparison questions to testQA systems’ ability to extract relevant facts and perform necessary comparison.
## Data Fields
All the datasets in the benchmark are in the same input-output format
- `input`: a `string` feature. The input document.
- `input_prefix`: an optional `string` feature, for the datasets containing prefix (e.g. question)
- `output`: a `string` feature. The target.
- `id`: a `string` feature. Unique per input.
- `pid`: a `string` feature. Unique per input-output pair (can differ from 'id' in NarrativeQA and Qasper, where there is more then one valid target).
The dataset that contain `input_prefix` are:
- SQuAD - the question
- HotpotQA - the question
- qmsum - the query
- qasper - the question
- narrative_qa - the question
- quality - the question + the four choices
- contract_nli - the hypothesis
## Controlled experiments
To test multiple properties of SLED, we modify SQuAD 1.1 [Rajpurkar et al., 2016](https://arxiv.org/pdf/1606.05250.pdf)
and HotpotQA [Yang et al., 2018](https://arxiv.org/pdf/1809.09600.pdf) to create a few controlled experiments settings.
Those are accessible via the following configurations:
- squad - Contains the original version of SQuAD 1.1 (question + passage)
- squad_ordered_distractors - For each example, 9 random distrctor passages are concatenated (separated by '\n')
- squad_shuffled_distractors - For each example, 9 random distrctor passages are added (separated by '\n'), and jointly the 10 passages are randomly shuffled
- hotpotqa - A clean version of HotpotQA, where each input contains only the two gold passages (separated by '\n')
- hotpotqa_second_only - In each example, the input contains only the second gold passage
## Citation
If you use this dataset, **please make sure to cite all the original dataset papers as well SCROLLS.** [[bibtex](https://drive.google.com/uc?export=download&id=1IUYIzQD9DPsECw0JWkwk4Ildn8JOMtuU)]
```
@inproceedings{Ivgi2022EfficientLU,
title={Efficient Long-Text Understanding with Short-Text Models},
author={Maor Ivgi and Uri Shaham and Jonathan Berant},
year={2022}
}
``` | 9,059 | [
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] |
distil-whisper/librispeech_asr-prompted | 2023-09-19T09:31:43.000Z | [
"region:us"
] | distil-whisper | null | null | 0 | 502 | 2023-09-19T08:45:04 | ---
dataset_info:
config_name: all
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
- name: whisper_transcript_unprompted
dtype: string
- name: whisper_transcript
dtype: string
splits:
- name: train.clean.100
num_bytes: 6641615051.062
num_examples: 28539
- name: train.clean.360
num_bytes: 23977966959.828
num_examples: 104014
- name: train.other.500
num_bytes: 31918849882.584
num_examples: 148688
- name: validation.clean
num_bytes: 361026354.966
num_examples: 2703
- name: validation.other
num_bytes: 338707588.648
num_examples: 2864
- name: test.clean
num_bytes: 369123744.42
num_examples: 2620
- name: test.other
num_bytes: 353861942.154
num_examples: 2939
download_size: 61926395211
dataset_size: 63961151523.662
configs:
- config_name: all
data_files:
- split: train.clean.100
path: all/train.clean.100-*
- split: train.clean.360
path: all/train.clean.360-*
- split: train.other.500
path: all/train.other.500-*
- split: validation.clean
path: all/validation.clean-*
- split: validation.other
path: all/validation.other-*
- split: test.clean
path: all/test.clean-*
- split: test.other
path: all/test.other-*
---
# Dataset Card for "librispeech_asr-prompted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,635 | [
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bigcode/guanaco-commits | 2023-06-28T08:54:47.000Z | [
"region:us"
] | bigcode | null | null | 3 | 499 | 2023-06-28T08:54:28 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 17347601.0
num_examples: 12958
- name: test
num_bytes: 827046.0
num_examples: 629
download_size: 10948498
dataset_size: 18174647.0
---
# Dataset Card for "guanaco-commits"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 467 | [
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ai4bharat/samanantar | 2022-12-07T15:33:46.000Z | [
"task_categories:text-generation",
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"language:as",
"language:bn",
"language:gu",
"language:hi",
"language:kn",
"language:ml",
"language:mr",
"language:or",
"language:pa",
"language:ta",
"language:te",
"license:cc-by-nc-4.0",
"conditional-text-generation",
"arxiv:2104.05596",
"region:us"
] | ai4bharat | Samanantar is the largest publicly available parallel corpora collection for Indic languages: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu. The corpus has 49.6M sentence pairs between English to Indian Languages. | @misc{ramesh2021samanantar,
title={Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages},
author={Gowtham Ramesh and Sumanth Doddapaneni and Aravinth Bheemaraj and Mayank Jobanputra and Raghavan AK and Ajitesh Sharma and Sujit Sahoo and Harshita Diddee and Mahalakshmi J and Divyanshu Kakwani and Navneet Kumar and Aswin Pradeep and Srihari Nagaraj and Kumar Deepak and Vivek Raghavan and Anoop Kunchukuttan and Pratyush Kumar and Mitesh Shantadevi Khapra},
year={2021},
eprint={2104.05596},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 12 | 498 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- cc-by-nc-4.0
multilinguality:
- translation
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-generation
- translation
task_ids: []
pretty_name: Samanantar
tags:
- conditional-text-generation
---
# Dataset Card for Samanantar
## 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://indicnlp.ai4bharat.org/samanantar/
- **Repository:**
- **Paper:** [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://arxiv.org/abs/2104.05596)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Samanantar is the largest publicly available parallel corpora collection for Indic language: Assamese, Bengali,
Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu.
The corpus has 49.6M sentence pairs between English to Indian Languages.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Samanantar contains parallel sentences between English (`en`) and 11 Indic language:
- Assamese (`as`),
- Bengali (`bn`),
- Gujarati (`gu`),
- Hindi (`hi`),
- Kannada (`kn`),
- Malayalam (`ml`),
- Marathi (`mr`),
- Odia (`or`),
- Punjabi (`pa`),
- Tamil (`ta`) and
- Telugu (`te`).
## Dataset Structure
### Data Instances
```
{
'idx': 0,
'src': 'Prime Minister Narendra Modi met Her Majesty Queen Maxima of the Kingdom of the Netherlands today.',
'tgt': 'নতুন দিল্লিতে সোমবার প্রধানমন্ত্রী শ্রী নরেন্দ্র মোদীর সঙ্গে নেদারন্যান্ডসের মহারানী ম্যাক্সিমা সাক্ষাৎ করেন।',
'data_source': 'pmi'
}
```
### Data Fields
- `idx` (int): ID.
- `src` (string): Sentence in source language (English).
- `tgt` (string): Sentence in destination language (one of the 11 Indic languages).
- `data_source` (string): Source of the data.
For created data sources, depending on the destination language, it might be one of:
- anuvaad_catchnews
- anuvaad_DD_National
- anuvaad_DD_sports
- anuvaad_drivespark
- anuvaad_dw
- anuvaad_financialexpress
- anuvaad-general_corpus
- anuvaad_goodreturns
- anuvaad_indianexpress
- anuvaad_mykhel
- anuvaad_nativeplanet
- anuvaad_newsonair
- anuvaad_nouns_dictionary
- anuvaad_ocr
- anuvaad_oneindia
- anuvaad_pib
- anuvaad_pib_archives
- anuvaad_prothomalo
- anuvaad_timesofindia
- asianetnews
- betterindia
- bridge
- business_standard
- catchnews
- coursera
- dd_national
- dd_sports
- dwnews
- drivespark
- fin_express
- goodreturns
- gu_govt
- jagran-business
- jagran-education
- jagran-sports
- ie_business
- ie_education
- ie_entertainment
- ie_general
- ie_lifestyle
- ie_news
- ie_sports
- ie_tech
- indiccorp
- jagran-entertainment
- jagran-lifestyle
- jagran-news
- jagran-tech
- khan_academy
- Kurzgesagt
- marketfeed
- mykhel
- nativeplanet
- nptel
- ocr
- oneindia
- pa_govt
- pmi
- pranabmukherjee
- sakshi
- sentinel
- thewire
- toi
- tribune
- vsauce
- wikipedia
- zeebiz
### 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
[Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
```
@misc{ramesh2021samanantar,
title={Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages},
author={Gowtham Ramesh and Sumanth Doddapaneni and Aravinth Bheemaraj and Mayank Jobanputra and Raghavan AK and Ajitesh Sharma and Sujit Sahoo and Harshita Diddee and Mahalakshmi J and Divyanshu Kakwani and Navneet Kumar and Aswin Pradeep and Srihari Nagaraj and Kumar Deepak and Vivek Raghavan and Anoop Kunchukuttan and Pratyush Kumar and Mitesh Shantadevi Khapra},
year={2021},
eprint={2104.05596},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
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] |
squad_es | 2023-04-05T13:40:35.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|squad",
"language:es",
"license:cc-by-4.0",
"arxiv:1912.05200",
"region:us"
] | null | automatic translation of the Stanford Question Answering Dataset (SQuAD) v2 into Spanish | @article{2016arXiv160605250R,
author = {Casimiro Pio , Carrino and Marta R. , Costa-jussa and Jose A. R. , Fonollosa},
title = "{Automatic Spanish Translation of the SQuAD Dataset for Multilingual
Question Answering}",
journal = {arXiv e-prints},
year = 2019,
eid = {arXiv:1912.05200v1},
pages = {arXiv:1912.05200v1},
archivePrefix = {arXiv},
eprint = {1912.05200v2},
} | 6 | 497 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- es
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|squad
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: squad-es
pretty_name: SQuAD-es
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
config_name: v1.1.0
splits:
- name: train
num_bytes: 83680438
num_examples: 87595
- name: validation
num_bytes: 10955800
num_examples: 10570
download_size: 39291362
dataset_size: 94636238
---
# Dataset Card for "squad_es"
## 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/ccasimiro88/TranslateAlignRetrieve](https://github.com/ccasimiro88/TranslateAlignRetrieve)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 39.29 MB
- **Size of the generated dataset:** 94.63 MB
- **Total amount of disk used:** 133.92 MB
### Dataset Summary
Automatic translation of the Stanford Question Answering Dataset (SQuAD) v2 into Spanish
### 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
### Data Instances
#### v1.1.0
- **Size of downloaded dataset files:** 39.29 MB
- **Size of the generated dataset:** 94.63 MB
- **Total amount of disk used:** 133.92 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [404, 356, 356],
"text": ["Santa Clara, California", "Levi 's Stadium", "Levi 's Stadium en la Bahía de San Francisco en Santa Clara, California."]
},
"context": "\"El Super Bowl 50 fue un partido de fútbol americano para determinar al campeón de la NFL para la temporada 2015. El campeón de ...",
"id": "56be4db0acb8001400a502ee",
"question": "¿Dónde tuvo lugar el Super Bowl 50?",
"title": "Super Bowl _ 50"
}
```
### Data Fields
The data fields are the same among all splits.
#### v1.1.0
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name |train|validation|
|------|----:|---------:|
|v1.1.0|87595| 10570|
## 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
The SQuAD-es dataset is licensed under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
### Citation Information
```
@article{2016arXiv160605250R,
author = {Casimiro Pio , Carrino and Marta R. , Costa-jussa and Jose A. R. , Fonollosa},
title = "{Automatic Spanish Translation of the SQuAD Dataset for Multilingual
Question Answering}",
journal = {arXiv e-prints},
year = 2019,
eid = {arXiv:1912.05200v1},
pages = {arXiv:1912.05200v1},
archivePrefix = {arXiv},
eprint = {1912.05200v2},
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun) for adding this dataset. | 6,916 | [
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distil-whisper/librispeech_asr-noise | 2023-09-27T15:56:45.000Z | [
"region:us"
] | distil-whisper | null | null | 0 | 497 | 2023-09-27T15:14:14 | ---
dataset_info:
- config_name: test-pub-noise
features:
- name: audio
dtype: audio
- name: text
dtype: string
- name: id
dtype: string
splits:
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num_bytes: 2517727265.74
num_examples: 2620
- name: '35'
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- name: '0'
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num_examples: 2620
- name: minus5
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num_examples: 2620
- name: minus10
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num_examples: 2620
download_size: 9029521258
dataset_size: 27694999923.13999
- config_name: test-white-noise
features:
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dtype: audio
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dtype: string
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dtype: string
splits:
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num_examples: 2620
- name: minus10
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num_examples: 2620
download_size: 15639888311
dataset_size: 27694999923.13999
- config_name: validation-pub-noise
features:
- name: audio
dtype: audio
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dtype: string
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dtype: string
splits:
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num_examples: 2703
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num_examples: 2703
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num_examples: 2703
download_size: 15441254231
dataset_size: 25443430177.77
- config_name: validation-white-noise
features:
- name: audio
dtype: audio
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dtype: string
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dtype: string
splits:
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num_examples: 2703
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num_examples: 2703
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num_examples: 2703
download_size: 15581612447
dataset_size: 25443430177.77
configs:
- config_name: test-pub-noise
data_files:
- split: '40'
path: test-pub-noise/40-*
- split: '35'
path: test-pub-noise/35-*
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path: test-pub-noise/30-*
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path: test-pub-noise/25-*
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path: test-pub-noise/20-*
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path: test-pub-noise/15-*
- split: '10'
path: test-pub-noise/10-*
- split: '5'
path: test-pub-noise/5-*
- split: '0'
path: test-pub-noise/0-*
- split: minus5
path: test-pub-noise/minus5-*
- split: minus10
path: test-pub-noise/minus10-*
- config_name: test-white-noise
data_files:
- split: '40'
path: test-white-noise/40-*
- split: '35'
path: test-white-noise/35-*
- split: '30'
path: test-white-noise/30-*
- split: '25'
path: test-white-noise/25-*
- split: '20'
path: test-white-noise/20-*
- split: '15'
path: test-white-noise/15-*
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path: test-white-noise/10-*
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path: test-white-noise/5-*
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path: test-white-noise/0-*
- split: minus5
path: test-white-noise/minus5-*
- split: minus10
path: test-white-noise/minus10-*
- config_name: validation-pub-noise
data_files:
- split: '40'
path: validation-pub-noise/40-*
- split: '35'
path: validation-pub-noise/35-*
- split: '30'
path: validation-pub-noise/30-*
- split: '25'
path: validation-pub-noise/25-*
- split: '20'
path: validation-pub-noise/20-*
- split: '15'
path: validation-pub-noise/15-*
- split: '10'
path: validation-pub-noise/10-*
- split: '5'
path: validation-pub-noise/5-*
- split: '0'
path: validation-pub-noise/0-*
- split: minus5
path: validation-pub-noise/minus5-*
- split: minus10
path: validation-pub-noise/minus10-*
- config_name: validation-white-noise
data_files:
- split: '40'
path: validation-white-noise/40-*
- split: '35'
path: validation-white-noise/35-*
- split: '30'
path: validation-white-noise/30-*
- split: '25'
path: validation-white-noise/25-*
- split: '20'
path: validation-white-noise/20-*
- split: '15'
path: validation-white-noise/15-*
- split: '10'
path: validation-white-noise/10-*
- split: '5'
path: validation-white-noise/5-*
- split: '0'
path: validation-white-noise/0-*
- split: minus5
path: validation-white-noise/minus5-*
- split: minus10
path: validation-white-noise/minus10-*
---
# Dataset Card for "librispeech_asr-noise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 6,455 | [
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tab_fact | 2023-01-25T14:45:28.000Z | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1909.02164",
"region:us"
] | null | The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are restricted to dealing with unstructured textual evidence (e.g., sentences and passages, a pool of passages), while verification using structured forms of evidence, such as tables, graphs, and databases, remains unexplored. TABFACT is large scale dataset with 16k Wikipedia tables as evidence for 118k human annotated statements designed for fact verification with semi-structured evidence. The statements are labeled as either ENTAILED or REFUTED. TABFACT is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning. | @inproceedings{2019TabFactA,
title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
booktitle = {International Conference on Learning Representations (ICLR)},
address = {Addis Ababa, Ethiopia},
month = {April},
year = {2020}
} | 7 | 496 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: tabfact
pretty_name: TabFact
dataset_info:
- config_name: tab_fact
features:
- name: id
dtype: int32
- name: table_id
dtype: string
- name: table_text
dtype: string
- name: table_caption
dtype: string
- name: statement
dtype: string
- name: label
dtype:
class_label:
names:
'0': refuted
'1': entailed
splits:
- name: train
num_bytes: 99852664
num_examples: 92283
- name: validation
num_bytes: 13846872
num_examples: 12792
- name: test
num_bytes: 13493391
num_examples: 12779
download_size: 196508436
dataset_size: 127192927
- config_name: blind_test
features:
- name: id
dtype: int32
- name: table_id
dtype: string
- name: table_text
dtype: string
- name: table_caption
dtype: string
- name: statement
dtype: string
- name: test_id
dtype: string
splits:
- name: test
num_bytes: 10954442
num_examples: 9750
download_size: 196508436
dataset_size: 10954442
---
# Dataset Card for TabFact
## 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:** [TabFact](https://tabfact.github.io/index.html)
- **Repository:** [GitHub](https://github.com/wenhuchen/Table-Fact-Checking)
- **Paper:** [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://arxiv.org/abs/1909.02164)
- **Leaderboard:** [Leaderboard](https://competitions.codalab.org/competitions/21611)
- **Point of Contact:** [Wenhu Chen](wenhuchen@cs.ucsb.edu)
### Dataset Summary
The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are restricted to dealing with unstructured textual evidence (e.g., sentences and passages, a pool of passages), while verification using structured forms of evidence, such as tables, graphs, and databases, remains unexplored. TABFACT is large scale dataset with 16k Wikipedia tables as evidence for 118k human annotated statements designed for fact verification with semi-structured evidence. The statements are labeled as either ENTAILED or REFUTED. TABFACT is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{2019TabFactA,
title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
booktitle = {International Conference on Learning Representations (ICLR)},
address = {Addis Ababa, Ethiopia},
month = {April},
year = {2020}
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | 5,237 | [
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castorini/afriberta-corpus | 2022-10-19T21:33:04.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language:om",
"language:am",
"language:rw",
"language:rn",
"language:ha",
"language:ig",
"language:pcm",
"language:so",
"language:sw",
"language:ti",
"language:yo",
"language:multilingual",
"license:apache-2.0",
"region:us"
] | castorini | Corpus used for training AfriBERTa models | @inproceedings{ogueji-etal-2021-small,
title = "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages",
author = "Ogueji, Kelechi and
Zhu, Yuxin and
Lin, Jimmy",
booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.mrl-1.11",
pages = "116--126",
} | 7 | 496 | 2022-03-02T23:29:22 | ---
language:
- om
- am
- rw
- rn
- ha
- ig
- pcm
- so
- sw
- ti
- yo
- multilingual
license: apache-2.0
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for AfriBERTa's Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Loading Dataset](#loading-dataset)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Discussion of Biases](#discussion-of-biases)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
This is the corpus on which AfriBERTa was trained on.
The dataset is mostly from the BBC news website, but some languages also have data from Common Crawl.
- **Homepage:** https://github.com/keleog/afriberta
- **Models:**
- https://huggingface.co/castorini/afriberta_small
- https://huggingface.co/castorini/afriberta_base
- https://huggingface.co/castorini/afriberta_large
- **Paper:** https://aclanthology.org/2021.mrl-1.11/
- **Point of Contact:** kelechi.ogueji@uwaterloo.ca
### Supported Tasks and Leaderboards
The AfriBERTa corpus was mostly intended to pre-train language models.
### Languages
```
afaanoromoo
amharic
gahuza
hausa
igbo
pidgin
somali
swahili
tigrinya
yoruba
```
### Loading Dataset
An example to load the train split of the Somali corpus:
```
dataset = load_dataset("castorini/afriberta-corpus", "somali", split="train")
```
An example to load the test split of the Pidgin corpus:
```
dataset = load_dataset("castorini/afriberta-corpus", "pidgin", split="test")
```
## Dataset Structure
### Data Instances
Each data point is a line of text.
An example from the `igbo` dataset:
```
{"id": "6", "text": "Ngwá ọrụ na-echebe ma na-ebuli gị na kọmputa."}
```
### Data Fields
The data fields are:
- id: id of the example
- text: content as a string
### Data Splits
Each language has a train and test split, with varying sizes.
## Considerations for Using the Data
### Discussion of Biases
Since majority of the data is obtained from the BBC's news website, models trained on this dataset are likely going to
be biased towards the news domain.
Also, since some of the data is obtained from Common Crawl, care should be taken (especially for text generation models) since personal and sensitive information might be present.
## Additional Information
### Citation Information
```
@inproceedings{ogueji-etal-2021-small,
title = "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages",
author = "Ogueji, Kelechi and
Zhu, Yuxin and
Lin, Jimmy",
booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.mrl-1.11",
pages = "116--126",
}
```
### Contributions
Thanks to [Kelechi Ogueji](https://github.com/keleog) for adding this dataset. | 3,412 | [
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huggan/CelebA-HQ | 2022-04-12T14:10:49.000Z | [
"arxiv:1710.10196",
"region:us"
] | huggan | null | null | 8 | 496 | 2022-03-24T09:12:05 | # Citation
```
@article{DBLP:journals/corr/abs-1710-10196,
author = {Tero Karras and
Timo Aila and
Samuli Laine and
Jaakko Lehtinen},
title = {Progressive Growing of GANs for Improved Quality, Stability, and Variation},
journal = {CoRR},
volume = {abs/1710.10196},
year = {2017},
url = {http://arxiv.org/abs/1710.10196},
eprinttype = {arXiv},
eprint = {1710.10196},
timestamp = {Mon, 13 Aug 2018 16:46:42 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1710-10196.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | 647 | [
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