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collabora/whisperspeech | ---
license: mit
task_categories:
- text-to-speech
language:
- en
pretty_name: WhisperSpeech
---
# The WhisperSpeech Dataset
This dataset contains data to train SPEAR TTS-like text-to-speech models that utilized semantic tokens derived from the OpenAI Whisper
speech recognition model.
We currently provide semantic and acoustic tokens for the LibriLight and LibriTTS datasets (English only).
Acoustic tokens:
- 24kHz EnCodec 6kbps (8 quantizers)
Semantic tokens:
- Whisper tiny VQ bottleneck trained on a subset of LibriLight
Available LibriLight subsets:
- `small`/`medium`/`large` (following the original dataset division but with `large` excluding the speaker `6454`)
- a separate ≈1300hr single-speaker subset based on the `6454` speaker from the `large` subset for training single-speaker TTS models
We plan to add more acoustic tokens from other codecs in the future. |
pankajmathur/alpaca_orca | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-generation
language:
- en
size_categories:
- 10K<n<100K
---
Explain tuned Alpaca dataset ~52K created using approaches from Orca Research Paper.
We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets.
This helps student models like [orca_mini_13b](https://huggingface.co/psmathur/orca_mini_13b) to learn thought process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version).
Please see how the **System** prompt is added before each **instruction**. |
FreedomIntelligence/alpaca-gpt4-japanese | ---
license: apache-2.0
---
The dataset is used in the research related to [MultilingualSIFT](https://github.com/FreedomIntelligence/MultilingualSIFT). |
breadlicker45/bread-midi-dataset | ---
tags:
- 'music '
---
this midi dataset has 851,313 midi files, making it the biggest midi dataset on the web.
anyone can use for any use case |
FredZhang7/malicious-website-features-2.4M | ---
license: apache-2.0
task_categories:
- text-classification
- feature-extraction
- tabular-classification
language:
- 'no'
- af
- en
- et
- sw
- sv
- sq
- de
- ca
- hu
- da
- tl
- so
- fi
- fr
- cs
- hr
- cy
- es
- sl
- tr
- pl
- pt
- nl
- id
- sk
- lt
- lv
- vi
- it
- ro
- ru
- mk
- bg
- th
- ja
- ko
- multilingual
size_categories:
- 1M<n<10M
---
**Important Notice:**
- A subset of the URL dataset is from Kaggle, and the Kaggle datasets contained 10%-15% mislabelled data. See [this dicussion I opened](https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset/discussion/431505) for some false positives. I have contacted Kaggle regarding their erroneous "Usability" score calculation for these unreliable datasets.
- The feature extraction methods shown here are not robust at all in 2023, and there're even silly mistakes in 3 functions: `not_indexed_by_google`, `domain_registration_length`, and `age_of_domain`.
<br>
The *features* dataset is original, and my feature extraction method is covered in [feature_extraction.py](./feature_extraction.py).
To extract features from a website, simply passed the URL and label to `collect_data()`. The features are saved to `phishing_detection_dataset.csv` locally by default.
In the *features* dataset, there're 911,180 websites online at the time of data collection. The plots below show the regression line and correlation coefficients of 22+ features extracted and whether the URL is malicious.
If we could plot the lifespan of URLs, we could see that the oldest website has been online since Nov 7th, 2008, while the most recent phishing websites appeared as late as July 10th, 2023.
## Malicious URL Categories
- Defacement
- Malware
- Phishing
## Data Analysis
Here are two images showing the correlation coefficient and correlation of determination between predictor values and the target value `is_malicious`.
Let's exmain the correlations one by one and cross out any unreasonable or insignificant correlations.
| Variable | Justification for Crossing Out |
|-----------------------------|------------------------------------- |
| ~~redirects~~ | contracdicts previous research (as redirects increase, is_malicious tends to decrease by a little) |
| ~~not_indexed_by_google~~ | 0.00 correlation |
| ~~email_submission~~ | contracdicts previous research |
| request_url_percentage | |
| issuer | |
| certificate_age | |
| ~~url_anchor_percentage~~ | contracdicts previous research |
| ~~meta_percentage~~ | 0.00 correlation |
| script_percentage | |
| link_percentage | |
| ~~mouseover_changes~~ | contracdicts previous research & 0.00 correlation |
| ~~right_clicked_disabled~~ | contracdicts previous research & 0.00 correlation |
| ~~popup_window_has_text_field~~ | contracdicts previous research |
| ~~use_iframe~~ | contracdicts previous research |
| ~~has_suspicious_ports~~ | contracdicts previous research |
| ~~external_favicons~~ | contracdicts previous research |
| TTL (Time to Live) | |
| ip_address_count | |
| ~~TXT_record~~ | all websites had a TXT record |
| ~~check_sfh~~ | contracdicts previous research |
| count_domain_occurrences | |
| domain_registration_length | |
| abnormal_url | |
| age_of_domain | |
| page_rank_decimal | |
## Pre-training Ideas
For training, I split the classification task into two stages in anticipation of the limited availability of online phishing websites due to their short lifespan, as well as the possibility that research done on phishing is not up-to-date:
1. a small multilingual BERT model to output the confidence level of a URL being malicious to model #2, by finetuning on 2,436,727 legitimate and malicious URLs
2. (probably) LightGBM to analyze the confidence level, along with roughly 10 extracted features
This way, I can make the most out of the limited phishing websites avaliable.
## Source of the URLs
- https://moz.com/top500
- https://phishtank.org/phish_search.php?valid=y&active=y&Search=Search
- https://www.kaggle.com/datasets/siddharthkumar25/malicious-and-benign-urls
- https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset
- https://github.com/ESDAUNG/PhishDataset
- https://github.com/JPCERTCC/phishurl-list
- https://github.com/Dogino/Discord-Phishing-URLs
## Reference
- https://www.kaggle.com/datasets/akashkr/phishing-website-dataset
- https://www.kaggle.com/datasets/shashwatwork/web-page-phishing-detection-dataset
- https://www.kaggle.com/datasets/aman9d/phishing-data
## Side notes
- Cloudflare offers an [API for phishing URL scanning](https://developers.cloudflare.com/api/operations/phishing-url-information-get-results-for-a-url-scan), with a generous global rate limit of 1200 requests every 5 minutes. |
jensjorisdecorte/Synthetic-ESCO-skill-sentences | ---
license: cc
task_categories:
- text-classification
language:
- en
tags:
- Skill Extraction
- Synthetic Data
pretty_name: Synthetic ESCO skill sentences
size_categories:
- 100K<n<1M
---
# Synthetic job ads for all ESCO skills
## Dataset Description
- **Homepage:** coming soon
- **Repository:** coming soon
- **Paper:** https://arxiv.org/abs/2307.10778
- **Point of Contact:** jensjoris@techwolf.ai
### Dataset Summary
This dataset contains 10 synthetically generated job ad sentences for almost all (99.5%) skills in ESCO v1.1.0.
### Languages
We use the English version of ESCO, and all generated sentences are in English.
## Dataset Structure
The dataset consists of 138,260 `(sentence, skill)` pairs.
### Citation Information
[More Information Needed] |
Safurai/Code-Instruct-700k | ---
dataset_info:
features:
- name: prompt
dtype: large_string
- name: main_topic
dtype: large_string
- name: subtopic
dtype: large_string
- name: adjective
dtype: large_string
- name: action_verb
dtype: large_string
- name: scenario
dtype: large_string
- name: target_audience
dtype: large_string
- name: programming_language
dtype: large_string
- name: common_sense_topic
dtype: large_string
- name: idx
dtype: int64
- name: response
dtype: large_string
splits:
- name: train
num_bytes: 1657193365
num_examples: 700000
download_size: 705514514
dataset_size: 1657193365
---
# Dataset Card for "Code-Instruct-700k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SachinKaushik/LlamaV2InstructCode | ---
dataset_info:
features:
- name: text
dtype: string
- name: input
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
- name: llamaV2Instruct
dtype: string
splits:
- name: train
num_bytes: 241331660
num_examples: 121959
download_size: 0
dataset_size: 241331660
task_categories:
- text-generation
- text2text-generation
language:
- en
tags:
- python
- llamav2
- instruction
- code
---
# Dataset Card for "LlamaV2InstructCode"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DUOMO-Lab/TransGPT-sft | ---
license: apache-2.0
---
|
Azure99/blossom-chat-v1 | ---
license: apache-2.0
task_categories:
- text-generation
- text2text-generation
language:
- zh
- en
size_categories:
- 10K<n<100K
---
# BLOSSOM CHAT V1
### 介绍
Blossom Chat V1是基于ShareGPT 90K衍生而来的中英双语对话数据集，适用于多轮对话微调。
本数据集抽取了ShareGPT的多轮对话指令，仅将指令进行翻译，随后使用多轮指令迭代调用gpt-3.5-turbo-0613。
相比原始的ShareGPT数据，主要解决了中文对话数据量较少，以及由ChatGPT生成长度限制而导致的输出截断问题。
本次发布了全量数据的20%，包含30K记录。
### 语言
以中文和英文为主，中英文数据按照约5:1的比例混合。
### 数据集结构
每条数据代表一个完整的多轮对话，包含id和conversations两个字段。
- id：字符串，代表原始ShareGPT的对话id，可以通过链接https://sharegpt.com/c/id来访问原始对话。
- conversations：对象数组，每个对象包含role、content两个字段，role的取值为user或assistant，分别代表用户输入和助手输出，content则为对应的内容。
### 数据集限制
由于仅抽取了原始多轮对话的输入，对于一些涉及随机性的对话，例如：猜随机数，可能会出现多轮对话不连贯的情况。
此外，本数据集的所有响应均由gpt-3.5-turbo-0613生成，并未经过严格的数据校验，可能包含不准确甚至严重错误的回答。 |
HeshamHaroon/Egyptian_English_parallel | ---
license: apache-2.0
---
|
owkin/medical_knowledge_from_extracts | ---
license: apache-2.0
task_categories:
- summarization
language:
- en
---
This dataset is used to train LLMs for medical knowledge extraction tasks |
harshitv804/Indian_Penal_Code | ---
language:
- en
viewer: false
pretty_name: Indian Penal Code Book
license: openrail
task_categories:
- question-answering
- text2text-generation
- sentence-similarity
tags:
- legal
- law
---
# Indian Penal Code Dataset
<img src="https://iilsindia.com/blogs/wp-content/uploads/2016/07/indian-penal-code-1860-890x395.jpg" style="width:600px;"/>
## Dataset Description:
The Indian Penal Code (IPC) Book PDF presents a rich and comprehensive dataset that holds immense potential for advancing Natural Language Processing (NLP) tasks and Language Model applications. This dataset encapsulates the entire spectrum of India's criminal law, offering a diverse range of legal principles, provisions, and case laws. With its intricate language and multifaceted legal content, the IPC dataset provides a challenging yet rewarding opportunity for NLP research and development. From text summarization and legal language understanding to sentiment analysis within the context of legal proceedings, this IPC dataset opens avenues for training and fine-tuning Language Models to grasp the nuances of complex legal texts. Leveraging this dataset, researchers and practitioners in the field of NLP can unravel the intricacies of legal discourse and contribute to the advancement of AI-driven legal analysis, interpretation, and decision support systems.
## Languages:
- English
## Considerations for Using this Data:
- Question Answering
- Conversational AI
- Text2Text Generation
- Sentence Similarity
- Text Generation
- RAG
## Dataset Download:
<a href="https://huggingface.co/datasets/harshitv804/Indian_Penal_Code/resolve/main/Indian%20Penal%20Code%20Book.pdf"><img src="https://static.vecteezy.com/system/resources/previews/009/384/880/non_2x/click-here-button-clipart-design-illustration-free-png.png" width="150" height="auto"></a> |
52AI/TinyStoriesZh | ---
license: mit
---
LM朝着越来越大的方向卷，而在小LM的方向，有研究者在探索小LM方向的边界能力，比如想知道多小的语言模型仍然能流畅的说故事？
[TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) 是在其做该方向时使用的一份关于小故事的场景数据。故事是由研究者使用GPT3.5, GPT4生成的，并且将故事难度限制在3~4岁小朋友能理解。
这份中文数据通过[翻译器](https://pypi.org/project/deep-translator/)将英文故事数据翻译而成。如下例子。
> Lily and Ben are friends. They like to play in the park. One day, they see a big tree with a swing. Lily wants to try the swing. She runs to the tree and climbs on the swing.\n"Push me, Ben!" she says. Ben pushes her gently. Lily feels happy. She swings higher and higher. She laughs and shouts.\nBen watches Lily. He thinks she is cute. He wants to swing too. He waits for Lily to stop. But Lily does not stop. She swings faster and faster. She is having too much fun.\n"Can I swing too, Lily?" Ben asks. Lily does not hear him. She is too busy swinging. Ben feels sad. He walks away.\nLily swings so high that she loses her grip. She falls off the swing. She lands on the ground. She hurts her foot. She cries.\n"Ow, ow, ow!" she says. She looks for Ben. She wants him to help her. But Ben is not there. He is gone.\nLily feels sorry. She wishes she had shared the swing with Ben. She wishes he was there to hug her. She limps to the tree. She sees something hanging from a branch. It is Ben\'s hat. He left it for her.\nLily smiles. She thinks Ben is nice. She puts on his hat. She hopes he will come back. She wants to say sorry. She wants to be friends again.
> 莉莉和本是朋友。他们喜欢在公园里玩。有一天，他们看到一棵有秋千的大树。莉莉想尝试秋千。她跑到树旁，爬上秋千。\n“推我吧，本！”她说。本轻轻地推了她一下。莉莉感觉很幸福。她荡得越来越高。她又笑又叫。\n本看着莉莉。他觉得她很可爱。他也想摇摆。他等着莉莉停下来。但莉莉并没有停下来。她摆动得越来越快。她玩得太开心了。\n“我也可以荡秋千吗，莉莉？”本问。莉莉没有听见他的话。她正忙着荡秋千。本感到难过。他走开了。\n莉莉荡得太高，以至于她失去了抓力。她从秋千上摔下来。她降落在地上。她的脚受伤了。她哭了。\n“呜呜呜！”她说。她寻找本。她想要他帮助她。但本不在那儿。他已经去了。\n莉莉感到抱歉。她希望自己能和本一起荡秋千。她希望他能在那里拥抱她。她一瘸一拐地走向树。她看到树枝上挂着什么东西。这是本的帽子。他留给她了。\n莉莉微笑着。她认为本很好。她戴上他的帽子。她希望他能回来。她想说对不起。她想再次成为朋友。 |
SUFE-AIFLM-Lab/FinEval | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
- multiple-choice
- question-answering
language:
- zh
pretty_name: FinEval
size_categories:
- 1K<n<10K
viewer: false
---
<p><h1> The FinEval Dataset </h1></p>
<a name="dataset-announcement"></a>
FinEval is a collection of high-quality multiple-choice questions covering various domains such as finance, economics, accounting, and certifications. It consists of 4,661 questions spanning across 34 distinct academic subjects. To ensure a comprehensive assessment of model performance, FinEval employs various methods including zero-shot, few-shot, answer-only, and chain-of-thought prompts. Evaluating state-of-the-art large language models in both Chinese and English on FinEval reveals that only GPT-4 achieves an accuracy of 60% across different prompt settings, highlighting substantial growth potential of large language models in financial domain knowledge. Our work provides a more comprehensive benchmark for evaluating financial knowledge, utilizing simulated exam data and encompassing a wide range of large language model assessments.
Each subject consists of three splits: dev, val, and test. The dev set per subject consists of five exemplars with explanations for few-shot evaluation. The val set is intended to be used for hyperparameter tuning. And the test set is for model evaluation. Labels on the test split are not released, users are required to submit their results to automatically obtain test accuracy.
# Language
The language of the data is Chinese.
# Performance Leaderboard
We divide the evaluation into Answer Only and Chain of Thought. For examples of prompts for both methods, please refer to zero-shot for Answer Only, few-shot for Answer Only, and Chain of Thought.
Below is the average accuracy(%) on the test split. We report the average accuracy over the subjects within each category. "Average" column indicates the average accuracy over all the subjects. Notably, we only report the results from each model under the best setting, which is determined by the highest average accuracy achieved among four settings (i.e., zero- and few-shot learning with and without CoT):
| Model | Size | Finance | Economy | Accounting | Certificate | Average |
|------------------------|---------|:-------:|:-------:|:----------:|:-----------:|:-------:|
| GPT-4 | unknown | 71.0 | 74.5 | 59.3 | 70.4 | 68.6 |
| ChatGPT | 175B | 59.3 | 61.6 | 45.2 | 55.1 | 55.0 |
| Qwen-7B | 7B | 54.5 | 54.4 | 50.3 | 55.8 | 53.8 |
| Qwen-Chat-7B | 7B | 51.5 | 52.1 | 44.5 | 53.6 | 50.5 |
| Baichuan-13B-Base | 13B | 52.6 | 50.2 | 43.4 | 53.5 | 50.1 |
| Baichuan-13B-Chat | 13B | 51.6 | 51.1 | 41.7 | 52.8 | 49.4 |
| ChatGLM2-6B | 6B | 46.5 | 46.4 | 44.5 | 51.5 | 47.4 |
| InternLM-7B | 7B | 49.0 | 49.2 | 40.5 | 49.4 | 47.1 |
| InternLM-Chat-7B | 7B | 48.4 | 49.1 | 40.8 | 49.5 | 47.0 |
| LLaMA-2-Chat-70B | 70B | 47.1 | 46.7 | 41.5 | 45.7 | 45.2 |
| Falcon-40B | 40B | 45.4 | 43.2 | 35.8 | 44.8 | 42.4 |
| Baichuan-7B | 7B | 44.9 | 41.5 | 34.9 | 45.6 | 42.0 |
| LLaMA-2-Chat-13B | 13B | 41.6 | 38.4 | 34.1 | 42.1 | 39.3 |
| Ziya-LLaMA-13B-v1 | 13B | 43.3 | 36.9 | 34.3 | 41.2 | 39.3 |
| Bloomz-7b1-mt | 7B | 41.4 | 42.1 | 32.5 | 39.7 | 38.8 |
| LLaMA-2-13B | 13B | 39.5 | 38.6 | 31.6 | 39.6 | 37.4 |
| ChatGLM-6B | 6B | 38.8 | 36.2 | 33.8 | 39.1 | 37.2 |
| Chinese-Llama-2-7B | 7B | 37.8 | 37.8 | 31.4 | 36.7 | 35.9 |
| Chinese-Alpaca-Plus-7B | 7B | 30.5 | 33.4 | 32.7 | 38.5 | 34.0 |
| moss-moon-003-sft | 16B | 35.6 | 34.3 | 28.7 | 35.6 | 33.7 |
| LLaMA-2-Chat-7B | 7B | 35.6 | 31.8 | 31.9 | 34.0 | 33.5 |
| LLaMA-2-7B | 7B | 34.9 | 36.4 | 31.4 | 31.6 | 33.4 |
| AquilaChat-7B | 7B | 34.2 | 31.3 | 29.8 | 36.2 | 33.1 |
| moss-moon-003-base | 16B | 32.2 | 33.1 | 29.2 | 30.7 | 31.2 |
| Aquila-7B | 7B | 27.1 | 31.6 | 32.4 | 33.6 | 31.2 |
| LLaMA-13B | 13B | 33.1 | 29.7 | 27.2 | 33.6 | 31.1 |
| Falcon-7B | 7B | 28.5 | 28.2 | 27.5 | 27.4 | 27.9 |
# Load the data
```python
from datasets import load_dataset
dataset=load_dataset(r"SUFE-AIFLM-Lab/FinEval",name="finance")
```
Please cite our paper if you use our dataset.
```
@misc{2308.09975,
Author = {Liwen Zhang and Weige Cai and Zhaowei Liu and Zhi Yang and Wei Dai and Yujie Liao and Qianru Qin and Yifei Li and Xingyu Liu and Zhiqiang Liu and Zhoufan Zhu and Anbo Wu and Xin Guo and Yun Chen},
Title = {FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models},
Year = {2023},
Eprint = {arXiv:2308.09975},
}
``` |
google/trueteacher | ---
license: cc-by-nc-4.0
language:
- en
tags:
- natural-language-inference
- news-articles-summarization
---
# **TrueTeacher**
## Dataset Summary
This is a large-scale synthetic dataset for training **Factual Consistency Evaluation** models, introduced in the [TrueTeacher paper (Gekhman et al, 2023)](https://aclanthology.org/2023.emnlp-main.127.pdf).
## Dataset Details
The dataset contains model-generated summaries of articles from the train split of the **CNN/DailyMail** dataset [(Hermann et al., 2015)](https://proceedings.neurips.cc/paper_files/paper/2015/file/afdec7005cc9f14302cd0474fd0f3c96-Paper.pdf)
which are annotated for factual consistency using **FLAN-PaLM 540B** [(Chung et al.,2022)](https://arxiv.org/pdf/2210.11416.pdf).
Summaries were generated using summarization models with different capacities, which were created by fine-tuning **T5** [(Raffel et al., 2020)](https://jmlr.org/papers/volume21/20-074/20-074.pdf) on the **XSum** dataset [(Narayan et al., 2018)](https://aclanthology.org/D18-1206.pdf).
We used the following 5 capacities: T5-11B, T5-3B, T5-large, T5-base and T5-small.
## Data format
The data contains json lines with the following keys:
- `"summarization_model"` - The summarization model used to generate the summary.
- `"cnndm_id"` - The original id from the CNN/DailyMail dataset, this need to be used in order to retrieve the corresponding article from CNN/DailyMail (which was used as the grounding document).
- `"summary"` - The model-generated summary.
- `"label"` - A binary label ('1' - Factualy Consistent, '0' - Factualy Inconsistent).
Here is an example of a single data item:
```json
{
"summarization_model": "T5-11B",
"cnndm_id": "f72048a23154de8699c307e2f41157abbfcae261",
"summary": "Children's brains are being damaged by prolonged internet access, a former children's television presenter has warned."
"label": "1",
}
```
## Loading the dataset
To use the dataset, you need to fetch the relevant documents from the CNN/DailyMail dataset. The follwoing code can be used for that purpose:
```python
from datasets import load_dataset
from tqdm import tqdm
trueteacher_data = load_dataset("google/trueteacher", split='train')
cnn_dailymail_data = load_dataset("cnn_dailymail", version="3.0.0", split='train')
cnn_dailymail_articles_by_id = {example['id']: example['article'] for example in cnn_dailymail_data}
trueteacher_data_with_documents = []
for example in tqdm(trueteacher_data):
example['document'] = cnn_dailymail_articles_by_id[example['cnndm_id']]
trueteacher_data_with_documents.append(example)
```
## Intended Use
This dataset is intended for a research use (**non-commercial**) in English.
The recommended use case is training factual consistency evaluation models for summarization.
## Out-of-scope use
Any use cases which violate the **cc-by-nc-4.0** license.
Usage in languages other than English.
## Citation
If you use this dataset for a research publication, please cite the TrueTeacher paper (using the bibtex entry below), as well as the CNN/DailyMail, XSum, T5 and FLAN papers mentioned above.
```
@misc{gekhman2023trueteacher,
title={TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models},
author={Zorik Gekhman and Jonathan Herzig and Roee Aharoni and Chen Elkind and Idan Szpektor},
year={2023},
eprint={2305.11171},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
baoanhtran/guanaco-llama2-200 | ---
pretty_name: CulturaX
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- als
- am
- an
- ar
- arz
- as
- ast
- av
- az
- azb
- ba
- bar
- bcl
- be
- bg
- bh
- bn
- bo
- bpy
- br
- bs
- bxr
- ca
- cbk
- ce
- ceb
- ckb
- cs
- cv
- cy
- da
- de
- dsb
- dv
- el
- eml
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- frr
- fy
- ga
- gd
- gl
- gn
- gom
- gu
- he
- hi
- hr
- hsb
- ht
- hu
- hy
- ia
- id
- ie
- ilo
- io
- is
- it
- ja
- jbo
- jv
- ka
- kk
- km
- kn
- ko
- krc
- ku
- kv
- kw
- ky
- la
- lb
- lez
- li
- lmo
- lo
- lrc
- lt
- lv
- mai
- mg
- mhr
- min
- mk
- ml
- mn
- mr
- mrj
- ms
- mt
- mwl
- my
- myv
- mzn
- nah
- nap
- nds
- ne
- new
- nl
- nn
- 'no'
- oc
- or
- os
- pa
- pam
- pl
- pms
- pnb
- ps
- pt
- qu
- rm
- ro
- ru
- rue
- sa
- sah
- scn
- sd
- sh
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- tg
- th
- tk
- tl
- tr
- tt
- tyv
- ug
- uk
- ur
- uz
- vec
- vi
- vls
- vo
- wa
- war
- wuu
- xal
- xmf
- yi
- yo
- yue
- zh
multilinguality:
- multilingual
size_categories:
- n<1K
- 1K<n<10K
- 10K<n<100K
- 100K<n<1M
- 1M<n<10M
- 10M<n<100M
- 100M<n<1B
- 1B<n<10B
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
extra_gated_prompt: "By completing the form below, you acknowledge that the provided data is offered as is. Although we anticipate no problems, you accept full responsibility for any repercussions resulting from the use of this data. Furthermore, you agree that the data must not be utilized for malicious or harmful purposes towards humanity."
extra_gated_fields:
Name: text
Email: text
Affiliation: text
Country: text
Usecase: text
I have explicitly check with my jurisdiction and I confirm that downloading CulturaX is legal in the country/region where I am located right now, and for the use case that I have described above: checkbox
You agree to not attempt to determine the identity of individuals in this dataset: checkbox
---
<div align="center">
<h1> CulturaX </h1>
<h3> Cleaned, Enormous, and Public: The Multilingual Fuel to Democratize Large Language Models for 167 Languages </h3>
</div>
## Dataset Description
- **Repository:** [https://github.com/nlp-uoregon/CulturaX](https://github.com/nlp-uoregon/CulturaX)
- **Papers:** [CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages](https://arxiv.org/abs/2309.09400)
## Dataset Summary
We present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for large language model (LLM) development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. We employ MinHash at document level to achieve fuzzy deduplication for the datasets in different languages. Our data cleaning framework includes diverse criteria and threshold selections, guided by extensive data samples, ensuring comprehensive noise filtering in various aspects. CulturaX is fully released to the public in HuggingFace to facilitate research and advancements in multilingual LLMs.
Our dataset combines the most recent iteration of mC4 (version 3.1.0) [1] with all accessible OSCAR corpora up to the present year, including 20.19, 21.09, 22.01, and 23.01 [2]. After deep cleaning and deduplication, CulturaX involves 16TB data in the parquet format (expanding to 27TB when unpacked). More than a half of our dataset is dedicated to non-English languages to significantly boost the data size and enhance the feasibility of training models in multilingual scenarios.
To obtain perplexity scores for data cleaning, we train a SentencePiece tokenizer and 5-gram Kneser-Ney language models as provided in the KenLM library [3] using the 20230501 dumps of Wikipedia. Our KenLM models are also released in HuggingFace: https://huggingface.co/uonlp/kenlm.
Details for the dataset can be found in our technical paper: [https://arxiv.org/abs/2309.09400](https://arxiv.org/abs/2309.09400)
You can download the dataset using Hugging Face datasets:
*You may need to follow these instructions to setup authentication before downloading the dataset: [https://huggingface.co/docs/huggingface_hub/quick-start#login](https://huggingface.co/docs/huggingface_hub/quick-start#login)*
```python
from datasets import load_dataset
ds = load_dataset("uonlp/CulturaX",
language="en",
use_auth_token=True)
```
### Languages
The supported languages and statistics for our dataset can be found below:
*(Note that the language code `als` and `eml` refer to `gsw` and `x-eml` in the OSCAR-2301 dataset.)*
| | Code | Language | # Documents | # Tokens | # Tokens (%) |
|----:|:-------|:-------------------------|:----------------|:--------------------|:------|
| 0 | en | English | 3,241,065,682 | 2,846,970,578,793 | 45.13 |
| 1 | ru | Russian | 799,310,908 | 737,201,800,363 | 11.69 |
| 2 | es | Spanish | 450,937,645 | 373,845,662,394 | 5.93 |
| 3 | de | German | 420,017,484 | 357,030,348,021 | 5.66 |
| 4 | fr | French | 363,754,348 | 319,332,674,695 | 5.06 |
| 5 | zh | Chinese | 218,624,604 | 227,055,380,882 | 3.60 |
| 6 | it | Italian | 211,309,922 | 165,446,410,843 | 2.62 |
| 7 | pt | Portuguese | 190,289,658 | 136,941,763,923 | 2.17 |
| 8 | pl | Polish | 142,167,217 | 117,269,087,143 | 1.86 |
| 9 | ja | Japanese | 111,188,475 | 107,873,841,351 | 1.71 |
| 10 | vi | Vietnamese | 102,411,180 | 98,453,464,077 | 1.56 |
| 11 | nl | Dutch | 117,392,666 | 80,032,209,900 | 1.27 |
| 12 | ar | Arabic | 74,027,952 | 69,354,335,076 | 1.10 |
| 13 | tr | Turkish | 94,207,460 | 64,292,787,164 | 1.02 |
| 14 | cs | Czech | 65,350,564 | 56,910,486,745 | 0.90 |
| 15 | fa | Persian | 59,531,144 | 45,947,657,495 | 0.73 |
| 16 | hu | Hungarian | 44,132,152 | 43,417,981,714 | 0.69 |
| 17 | el | Greek | 51,430,226 | 43,147,590,757 | 0.68 |
| 18 | ro | Romanian | 40,325,424 | 39,647,954,768 | 0.63 |
| 19 | sv | Swedish | 49,709,189 | 38,486,181,494 | 0.61 |
| 20 | uk | Ukrainian | 44,740,545 | 38,226,128,686 | 0.61 |
| 21 | fi | Finnish | 30,467,667 | 28,925,009,180 | 0.46 |
| 22 | ko | Korean | 20,557,310 | 24,765,448,392 | 0.39 |
| 23 | da | Danish | 25,429,808 | 22,921,651,314 | 0.36 |
| 24 | bg | Bulgarian | 24,131,819 | 22,917,954,776 | 0.36 |
| 25 | no | Norwegian | 18,907,310 | 18,426,628,868 | 0.29 |
| 26 | hi | Hindi | 19,665,355 | 16,791,362,871 | 0.27 |
| 27 | sk | Slovak | 18,582,517 | 16,442,669,076 | 0.26 |
| 28 | th | Thai | 20,960,550 | 15,717,374,014 | 0.25 |
| 29 | lt | Lithuanian | 13,339,785 | 14,247,110,836 | 0.23 |
| 30 | ca | Catalan | 15,531,777 | 12,530,288,006 | 0.20 |
| 31 | id | Indonesian | 23,251,368 | 12,062,966,061 | 0.19 |
| 32 | bn | Bangla | 12,436,596 | 9,572,929,804 | 0.15 |
| 33 | et | Estonian | 8,004,753 | 8,805,656,165 | 0.14 |
| 34 | sl | Slovenian | 7,335,378 | 8,007,587,522 | 0.13 |
| 35 | lv | Latvian | 7,136,587 | 7,845,180,319 | 0.12 |
| 36 | he | Hebrew | 4,653,979 | 4,937,152,096 | 0.08 |
| 37 | sr | Serbian | 4,053,166 | 4,619,482,725 | 0.07 |
| 38 | ta | Tamil | 4,728,460 | 4,378,078,610 | 0.07 |
| 39 | sq | Albanian | 5,205,579 | 3,648,893,215 | 0.06 |
| 40 | az | Azerbaijani | 5,084,505 | 3,513,351,967 | 0.06 |
| 41 | kk | Kazakh | 2,733,982 | 2,802,485,195 | 0.04 |
| 42 | ur | Urdu | 2,757,279 | 2,703,052,627 | 0.04 |
| 43 | ka | Georgian | 3,120,321 | 2,617,625,564 | 0.04 |
| 44 | hy | Armenian | 2,964,488 | 2,395,179,284 | 0.04 |
| 45 | is | Icelandic | 2,373,560 | 2,350,592,857 | 0.04 |
| 46 | ml | Malayalam | 2,693,052 | 2,100,556,809 | 0.03 |
| 47 | ne | Nepali | 3,124,040 | 2,061,601,961 | 0.03 |
| 48 | mk | Macedonian | 2,762,807 | 2,003,302,006 | 0.03 |
| 49 | mr | Marathi | 2,266,588 | 1,955,227,796 | 0.03 |
| 50 | mn | Mongolian | 1,928,828 | 1,850,667,656 | 0.03 |
| 51 | be | Belarusian | 1,643,486 | 1,791,473,041 | 0.03 |
| 52 | te | Telugu | 1,822,865 | 1,566,972,146 | 0.02 |
| 53 | gl | Galician | 1,785,963 | 1,382,539,693 | 0.02 |
| 54 | eu | Basque | 1,598,822 | 1,262,066,759 | 0.02 |
| 55 | kn | Kannada | 1,352,142 | 1,242,285,201 | 0.02 |
| 56 | gu | Gujarati | 1,162,878 | 1,131,730,537 | 0.02 |
| 57 | af | Afrikaans | 826,519 | 1,119,009,767 | 0.02 |
| 58 | my | Burmese | 865,575 | 882,606,546 | 0.01 |
| 59 | si | Sinhala | 753,655 | 880,289,097 | 0.01 |
| 60 | eo | Esperanto | 460,088 | 803,948,528 | 0.01 |
| 61 | km | Khmer | 1,013,181 | 746,664,132 | 0.01 |
| 62 | pa | Punjabi | 646,987 | 727,546,145 | 0.01 |
| 63 | cy | Welsh | 549,955 | 576,743,162 | 0.01 |
| 64 | ky | Kyrgyz | 570,922 | 501,442,620 | 0.01 |
| 65 | ga | Irish | 304,251 | 376,947,935 | 0.01 |
| 66 | ps | Pashto | 376,914 | 363,007,770 | 0.01 |
| 67 | am | Amharic | 243,349 | 358,206,762 | 0.01 |
| 68 | ku | Kurdish | 295,314 | 302,990,910 | 0.00 |
| 69 | tl | Filipino | 348,453 | 242,086,456 | 0.00 |
| 70 | yi | Yiddish | 141,156 | 217,584,643 | 0.00 |
| 71 | lo | Lao | 217,842 | 168,256,876 | 0.00 |
| 72 | fy | Western Frisian | 223,268 | 167,193,111 | 0.00 |
| 73 | sd | Sindhi | 109,162 | 147,487,058 | 0.00 |
| 74 | mg | Malagasy | 115,910 | 142,685,412 | 0.00 |
| 75 | or | Odia | 153,461 | 100,323,213 | 0.00 |
| 76 | as | Assamese | 52,627 | 83,787,896 | 0.00 |
| 77 | ug | Uyghur | 47,035 | 77,677,306 | 0.00 |
| 78 | uz | Uzbek | 87,219 | 75,250,787 | 0.00 |
| 79 | la | Latin | 48,968 | 44,176,580 | 0.00 |
| 80 | hr | Croatian | 460,690 | 40,796,811 | 0.00 |
| 81 | sw | Swahili | 66,506 | 30,708,309 | 0.00 |
| 82 | ms | Malay | 238,151 | 19,375,976 | 0.00 |
| 83 | br | Breton | 43,765 | 13,987,037 | 0.00 |
| 84 | sa | Sanskrit | 16,290 | 13,561,367 | 0.00 |
| 85 | gd | Scottish Gaelic | 8,408 | 4,796,485 | 0.00 |
| 86 | su | Sundanese | 1,554 | 1,308,460 | 0.00 |
| 87 | jv | Javanese | 2,058 | 625,429 | 0.00 |
| 88 | tg | Tajik | 483,835 | - | - |
| 89 | ceb | Cebuano | 263,890 | - | - |
| 90 | tt | Tatar | 218,102 | - | - |
| 91 | ckb | Central Kurdish | 172,035 | - | - |
| 92 | lb | Luxembourgish | 165,891 | - | - |
| 93 | mt | Maltese | 151,320 | - | - |
| 94 | nn | Norwegian Nynorsk | 126,083 | - | - |
| 95 | qu | Quechua | 1,202 | 72,101 | 0.00 |
| 96 | ba | Bashkir | 71,957 | - | - |
| 97 | arz | Egyptian Arabic | 71,625 | - | - |
| 98 | dv | Divehi | 66,702 | - | - |
| 99 | bo | Tibetan | 54,185 | - | - |
| 100 | sh | Serbian (Latin) | 45,619 | - | - |
| 101 | yo | Yoruba | 192 | 42,943 | 0.00 |
| 102 | bs | Bosnian | 1,237 | 39,768 | 0.00 |
| 103 | azb | South Azerbaijani | 29,833 | - | - |
| 104 | ht | Haitian Creole | 12 | 26,183 | 0.00 |
| 105 | war | Waray | 23,687 | - | - |
| 106 | cv | Chuvash | 22,570 | - | - |
| 107 | sah | Sakha | 22,141 | - | - |
| 108 | li | Limburgish | 206 | 18,532 | 0.00 |
| 109 | ce | Chechen | 17,322 | - | - |
| 110 | pnb | Western Panjabi | 15,625 | - | - |
| 111 | nds | Low German | 15,139 | - | - |
| 112 | tk | Turkmen | 14,393 | - | - |
| 113 | gn | Guarani | 103 | 12,708 | 0.00 |
| 114 | oc | Occitan | 10,556 | - | - |
| 115 | xmf | Mingrelian | 9,706 | - | - |
| 116 | ast | Asturian | 9,002 | - | - |
| 117 | os | Ossetic | 8,596 | - | - |
| 118 | mhr | Eastern Mari | 7,883 | - | - |
| 119 | pms | Piedmontese | 7,566 | - | - |
| 120 | als[*] | Swiss German | 6,936 | - | - |
| 121 | vo | Volapük | 6,621 | - | - |
| 122 | so | Somali | 39 | 6,053 | 0.00 |
| 123 | bpy | Bishnupriya | 5,087 | - | - |
| 124 | new | Newari | 4,344 | - | - |
| 125 | hsb | Upper Sorbian | 4,244 | - | - |
| 126 | lmo | Lombard | 3,530 | - | - |
| 127 | an | Aragonese | 2,746 | - | - |
| 128 | ilo | Iloko | 2,328 | - | - |
| 129 | mzn | Mazanderani | 1,914 | - | - |
| 130 | lez | Lezghian | 1,806 | - | - |
| 131 | rm | Romansh | 30 | 1,769 | 0.00 |
| 132 | krc | Karachay-Balkar | 1,745 | - | - |
| 133 | min | Minangkabau | 1,429 | - | - |
| 134 | kv | Komi | 1,396 | - | - |
| 135 | wa | Walloon | 1,383 | - | - |
| 136 | jbo | Lojban | 1,349 | - | - |
| 137 | io | Ido | 1,144 | - | - |
| 138 | mrj | Western Mari | 1,056 | - | - |
| 139 | gom | Goan Konkani | 721 | - | - |
| 140 | ia | Interlingua | 613 | - | - |
| 141 | av | Avaric | 438 | - | - |
| 142 | bh | Bihari languages | 265 | - | - |
| 143 | wuu | Wu Chinese | 222 | - | - |
| 144 | nah | Nahuatl languages | 131 | - | - |
| 145 | vec | Venetian | 113 | - | - |
| 146 | bxr | Russia Buriat | 100 | - | - |
| 147 | kw | Cornish | 94 | - | - |
| 148 | mai | Maithili | 93 | - | - |
| 149 | eml[*] | Emiliano-Romagnol | 91 | - | - |
| 150 | dsb | Lower Sorbian | 59 | - | - |
| 151 | xal | Kalmyk | 51 | - | - |
| 152 | lrc | Northern Luri | 43 | - | - |
| 153 | nap | Neapolitan | 31 | - | - |
| 154 | tyv | Tuvinian | 23 | - | - |
| 155 | scn | Sicilian | 21 | - | - |
| 156 | frr | Northern Frisian | 11 | - | - |
| 157 | mwl | Mirandese | 9 | - | - |
| 158 | myv | Erzya | 4 | - | - |
| 159 | ie | Interlingue | 4 | - | - |
| 160 | pam | Pampanga | 4 | - | - |
| 161 | bar | Bavarian | 3 | - | - |
| 162 | yue | Yue Chinese | 3 | - | - |
| 163 | cbk | Chavacano | 2 | - | - |
| 164 | bcl | Central Bikol | 1 | - | - |
| 165 | vls | West Flemish | 1 | - | - |
| 166 | rue | Rusyn | 1 | - | - |
### Dataset Structure
```json
{
"text": ...,
"timestamp": ...,
"url": ...,
"source": "mc4" | "OSCAR-xxxx",
}
```
## Considerations for Using the Data
As CulturaX is the cleaned version of the mC4 and OSCAR datasets, which were both extracted from CommonCrawl, personal and sensitive information might still contain personal and sensitive information.
This must be considered prior to using this dataset for any purpose, such as training deep learning models, etc.
## License Information
The licence terms for CulturaX strictly follows those of `mC4` and `OSCAR`. Please refer to both below licenses when using this dataset.
- [mC4 license](https://huggingface.co/datasets/allenai/c4#license)
- [OSCAR license](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301#licensing-information)
## Citation
To cite CulturaX, please use:
```
@misc{nguyen2023culturax,
title={CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages},
author={Thuat Nguyen and Chien Van Nguyen and Viet Dac Lai and Hieu Man and Nghia Trung Ngo and Franck Dernoncourt and Ryan A. Rossi and Thien Huu Nguyen},
year={2023},
eprint={2309.09400},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Reference
[1] Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. mT5: A massively multilingual
pre-trained text-to-text transformer. In NAACL 2021. https://huggingface.co/datasets/mc4
[2] Pedro Javier Ortiz Suárez, Benoît Sagot, and Laurent Romary. 2019. Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures. In Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-
7) 2019. https://oscar-project.org/
[3] KenLM: Faster and smaller language model queries. In Proceedings of the Sixth
Workshop on Statistical Machine Translation, 2011. |
kingbri/PIPPA-shareGPT | ---
license: agpl-3.0
task_categories:
- conversational
language:
- en
tags:
- conversational
- roleplay
- custom-format
- a.
size_categories:
- 10K<n<100K
viewer: false
---
# Dataset Card: PIPPA-ShareGPT
This is a conversion of [PygmalionAI's PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA) deduped dataset to ShareGPT format for finetuning with Axolotl.
The reformat was completed via the following [TypeScript project](https://github.com/bdashore3/ShareGPT-Reformat) called ShareGPT-Reformat.
# Files and explanations
- pippa_sharegpt_raw.jsonl: The raw deduped dataset file converted to shareGPT. Roles will be defaulted to your finetuning software.
- pippa_sharegpt.jsonl: A shareGPT dataset with the roles as USER: and CHARACTER: for finetuning with axolotl
- pippa_sharegpt_trimmed.jsonl: A shareGPT dataset that has trimmed newlines, randomized system prompts, removes empty messages, and removes examples without a character description. Roles are USER and CHARACTER.
The best file to use is `pippa_sharegpt_trimmed.jsonl` if you want a finetune without bugs or inconsistencies. The best dataset to modify is either the original PIPPA deduped dataset with the ShareGPT reformat project or `pippa_sharegpt.jsonl`.
# Required Axolotl patches
To make this dataset usable in its entirety, some axolotl patches are needed:
- [This patch](https://github.com/bdashore3/axolotl/commit/995557bdf3c6c8b3e839b224ef9513fc2b097f30) allows the ability to use custom system prompts with ShareGPT format.
- [This patch](https://github.com/bdashore3/axolotl/commit/8970280de2ea01e41c044406051922715f4086cb) allows for custom roles for the USER and ASSISTANT and allows for GPT prompts to come before human ones without cutoff.
You WILL experience unideal results with base axolotl at the time of publishing this README.
# Citations
Paper for the original dataset:
```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}
}
```
|
theblackcat102/multiround-programming-convo | ---
task_categories:
- text-generation
language:
- en
tags:
- data-science
- programming
- statistic
pretty_name: Multi-Round Programming Conversations
size_categories:
- 100K<n<1M
---
# Multi-Round Programming Conversations
Based on previous evol-codealpaca-v1 dataset with added sampled questions from stackoverflow, crossvalidated and make it multiround!
It should be more suited to train a code assistant which works side by side.
## Tasks included in here:
* Data science, statistic, programming questions
* Code translation : translate a short function from Python, Golang, C++, Java, Javascript
* Code fixing : Fix randomly corrupts characters with no tab spacing code.
|
mlabonne/MedText | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 943488
num_examples: 1412
download_size: 0
dataset_size: 943488
---
# Dataset Card for "MedText"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Nicolas-BZRD/English_French_Songs_Lyrics_Translation_Original | ---
license: unknown
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: artist_name
dtype: string
- name: album_name
dtype: string
- name: year
dtype: int64
- name: title
dtype: string
- name: number
dtype: int64
- name: original_version
dtype: string
- name: french_version
dtype: string
- name: language
dtype: string
splits:
- name: train
num_bytes: 250317845
num_examples: 99289
download_size: 122323323
dataset_size: 250317845
task_categories:
- translation
- text-generation
language:
- fr
- en
- es
- it
- de
- ko
- id
- pt
- 'no'
- fi
- sv
- sw
- hr
- so
- ca
- tl
- ja
- nl
- ru
- et
- tr
- ro
- cy
- vi
- af
- hu
- sk
- sl
- cs
- da
- pl
- sq
- el
- he
- zh
- th
- bg
- ar
tags:
- music
- parallel
- parallel data
pretty_name: SYFT
size_categories:
- 10K<n<100K
---
# Original Songs Lyrics with French Translation
### Dataset Summary
Dataset of 99289 songs containing their metadata (author, album, release date, song number), original lyrics and lyrics translated into French.
Details of the number of songs by language of origin can be found in the table below:
| Original language | Number of songs |
|---|:---|
| en | 75786 |
| fr | 18486 |
| es | 1743 |
| it | 803 |
| de | 691 |
| sw | 529 |
| ko | 193 |
| id | 169 |
| pt | 142 |
| no | 122 |
| fi | 113 |
| sv | 70 |
| hr | 53 |
| so | 43 |
| ca | 41 |
| tl | 36 |
| ja | 35 |
| nl | 32 |
| ru | 29 |
| et | 27 |
| tr | 22 |
| ro | 19 |
| cy | 14 |
| vi | 14 |
| af | 13 |
| hu | 10 |
| sk | 10 |
| sl | 10 |
| cs | 7 |
| da | 6 |
| pl | 5 |
| sq | 4 |
| el | 4 |
| he | 3 |
| zh-cn | 2 |
| th | 1 |
| bg | 1 |
| ar | 1 |
## Citation
Our work can be cited as:
```bash
@misc{faysse2024croissantllm,
title={CroissantLLM: A Truly Bilingual French-English Language Model},
author={Manuel Faysse and Patrick Fernandes and Nuno Guerreiro and António Loison and Duarte Alves and Caio Corro and Nicolas Boizard and João Alves and Ricardo Rei and Pedro Martins and Antoni Bigata Casademunt and François Yvon and André Martins and Gautier Viaud and Céline Hudelot and Pierre Colombo},
year={2024},
eprint={2402.00786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
math-eval/TAL-SCQ5K | ---
license: mit
---
<h1 align="center">TAL-SCQ5K</h1>
## Dataset Description
### Dataset Summary
TAL-SCQ5K-EN/TAL-SCQ5K-CN are high quality mathematical competition datasets in English and Chinese language created by TAL Education Group, each consisting of 5K questions(3K training and 2K testing). The questions are in the form of multiple-choice and cover mathematical topics at the primary,junior high and high school levels. In addition, detailed solution steps are provided to facilitate CoT training and all the mathematical expressions in the questions have been presented as standard text-mode Latex.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in TAL-SCQ5K-EN is in English and TAL-SCQ5K-CN is in Chinese.
## Dataset Structure
### Data Instances
```
{
"dataset_name": "prime_math_competition_en_single_choice_8K_dev",
"dataset_version": "2023-07-07",
"qid": "244",
"queId": "8afc802a8c304199b1040f11ffa2e92a",
"competition_source_list": [],
"difficulty": "2",
"qtype": "single_choice",
"problem": "A $14$-digit. number $666666 XY 444444$ is a multiple of $26$. If $X$ and $Y$ are both positive, what is the smallest vaue of $X+ Y$? ",
"answer_option_list": [
[{
"aoVal": "A",
"content": "$$3$$ "
}],
[{
"aoVal": "B",
"content": "$$4$$ "
}],
[{
"aoVal": "C",
"content": "$$9$$ "
}],
[{
"aoVal": "D",
"content": "$$14$$ "
}],
[{
"aoVal": "E",
"content": "None of the above "
}]
],
"knowledge_point_routes": ["Overseas Competition->Knowledge Point->Number Theory Modules->Division without Remainders->Divisibility Rules"],
"answer_analysis": ["Since $1001$ is a multiple of $13$, $111111 = 111 \\times 1001$ is also a multiple of $13$. It follows that both $666666$ and $444444$ are both multiples of $26$. $666666XY 444444 = 66666600000000 + XY 000000 + 444444$ $\\Rightarrow XY$ must be divisible by $13$. Smallest $X+Y=1+3=4$. "],
"answer_value": "B"
}
```
### Data Fields
* "dataset_name": identification of the source dataset name from which TAL-SCQ5K-EN/TAL-SCQ5K-CN has been created, use only for inner of TAL education group, please ignore.
* "dataset_version": identification of the source dataset version from which TAL-SCQ5K-EN/TAL-SCQ5K-CN has been created, use only for inner of TAL education group, please ignore.
* "qid": identification of local id of the question in the source dataset from which TAL-SCQ5K-EN/TAL-SCQ5K-CN has been created, use only for inner of TAL education group, please ignore.
* "queId": identification of global id of the question, use only for inner of TAL education group, please ignore.
* "competition_source_list": identification of math competitions in which the questions appeared, if have been logged.
* "difficulty": difficulty level of the questions, value ranged from 0 to 4
* "qtype": question type, valued as "single_choice" for all the questions in this dataset indicates that all the questions are multiple-choice questions with unique ground-truth answer.
* "problem": the question string to a math competition question.
* "answer_option_list": answer choices to be selected
* "knowledge_point_routes": knowledge point route from coarse-grained to fine-grained.
* "answer_analysis": step-by-step answer analysis of the questions, which helps CoT training
* "answer_value": value of the ground-truth answer choice
### Data Splits
<style>
table th:first-of-type {
width: 40%;
}
table th:nth-of-type(2) {
width: 30%;
}
table th:nth-of-type(3) {
width: 30%;
}
</style>
| name|train|test |
|:---:|:----:|:----:|
|TAL-SCQ5K-EN|3K |2K |
|TAL-SCQ5K-CN|3K |2K |
## Usage
Each of the above datasets is located in a separate sub-directory. To load an individual subset, use the data_dir argument of the load_dataset() function as follows:
```python
from datasets import load_dataset
# Load all subsets (share the same schema)
dataset = load_dataset("math-eval/TAL-SCQ5K")
# Load TAL-SCQ5K-EN
dataset = load_dataset("math-eval/TAL-SCQ5K", data_dir="TAL-SCQ5K-EN")
# Load TAL-SCQ5K-CN
dataset = load_dataset("math-eval/TAL-SCQ5K", data_dir="TAL-SCQ5K-CN")
```
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The TAL-SCQ5K dataset is licensed under the [MIT License](https://opensource.org/license/mit/)
### Citation Information
[More Information Needed]
### Contact
The original authors host this dataset on GitHub here: https://github.com/math-eval/TAL-SCQ5K You can submit inquiries to: matheval.ai@gmail.com |
Skepsun/cvalues_rlhf | ---
license: apache-2.0
language:
- zh
---
Converted from: https://modelscope.cn/datasets/damo/CValues-Comparison/summary. We obtained harmless set by selecting `pos_type="拒绝为主"` and `neg_type="风险回复"`. We obtained helpful set by selecting `pos_type="拒绝&正向建议"` and `neg_type="拒绝为主"`. |
repllabs/questions_how_to_do_great_work | ---
configs:
- config_name: default
data_files:
- split: processed
path: data/processed-*
- split: raw
path: data/raw-*
dataset_info:
features:
- name: question
dtype: string
- name: model
dtype: string
splits:
- name: processed
num_bytes: 17391
num_examples: 142
- name: raw
num_bytes: 55307
num_examples: 450
download_size: 28702
dataset_size: 72698
license: mit
task_categories:
- question-answering
language:
- en
size_categories:
- n<1K
---
# Questions Generated by LLM on 'How To Do Great Work'
http://paulgraham.com/greatwork.html
https://github.com/fastrepl/fastrepl/blob/main/exp/pg_essay_questions.ipynb |
nchen909/hugcode-codesft | ---
license: openrail
task_categories:
- text-generation
language:
- code
tags:
- code
pretty_name: CodeSFT-nchen909
size_categories:
- 100K<n<1M
---
所有数据都是单轮代码指令数据
325696条英语，42816条中文。
---
license: cc
--- |
amanrangapur/Fin-Fact | ---
license: apache-2.0
task_categories:
- text-classification
- text-generation
language:
- en
tags:
- finance
pretty_name: FinFact
size_categories:
- 1K<n<10K
dataset_info:
- config_name: generation
features:
- name: url
dtype: string
- name: claim
dtype: string
- name: author
dtype: string
- name: posted
dtype: string
# - name: sci_digest
# sequence: string
# - name: justification
# sequence: string
# - name: issues
# dtype: string
# - name: image_data
# sequence:
# - name: image_src
# dtype: string
# - name: image_caption
# dtype: string
# - name: evidence
# sequence:
# - name: sentence
# dtype: string
# - name: hrefs
# dtype: string
# - name: label
# dtype: string
# - name: visualization_bias
# dtype: int32
---
<h1 align="center">Fin-Fact - Financial Fact-Checking Dataset</h1>
## Table of Contents
- [Overview](#overview)
- [Dataset Description](#dataset-description)
- [Dataset Usage](#dataset-usage)
- [Leaderboard](#leaderboard)
- [Dependencies](#dependencies)
- [Run models for paper metrics](#run-models-for-paper-metrics)
- [Citation](#citation)
- [Contribution](#contribution)
- [License](#license)
- [Contact](#contact)
## Overview
Welcome to the Fin-Fact repository! Fin-Fact is a comprehensive dataset designed specifically for financial fact-checking and explanation generation. This README provides an overview of the dataset, how to use it, and other relevant information. [Click here](https://arxiv.org/abs/2309.08793) to access the paper.
## Dataset Description
- **Name**: Fin-Fact
- **Purpose**: Fact-checking and explanation generation in the financial domain.
- **Labels**: The dataset includes various labels, including Claim, Author, Posted Date, Sci-digest, Justification, Evidence, Evidence href, Image href, Image Caption, Visualisation Bias Label, Issues, and Claim Label.
- **Size**: The dataset consists of 3121 claims spanning multiple financial sectors.
- **Additional Features**: The dataset goes beyond textual claims and incorporates visual elements, including images and their captions.
## Dataset Usage
Fin-Fact is a valuable resource for researchers, data scientists, and fact-checkers in the financial domain. Here's how you can use it:
1. **Download the Dataset**: You can download the Fin-Fact dataset [here](https://github.com/IIT-DM/Fin-Fact/blob/FinFact/finfact.json).
2. **Exploratory Data Analysis**: Perform exploratory data analysis to understand the dataset's structure, distribution, and any potential biases.
3. **Natural Language Processing (NLP) Tasks**: Utilize the dataset for various NLP tasks such as fact-checking, claim verification, and explanation generation.
4. **Fact Checking Experiments**: Train and evaluate machine learning models, including text and image analysis, using the dataset to enhance the accuracy of fact-checking systems.
## Leaderboard
## Dependencies
We recommend you create an anaconda environment:
`conda create --name finfact python=3.6 conda-build`
Then, install Python requirements:
`pip install -r requirements.txt`
## Run models for paper metrics
We provide scripts let you easily run our dataset on existing state-of-the-art models and re-create the metrics published in paper. You should be able to reproduce our results from the paper by following these instructions. Please post an issue if you're unable to do this.
To run existing ANLI models for fact checking.
### Run:
1. BART
```bash
python anli.py --model_name 'ynie/bart-large-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5
```
2. RoBERTa
```bash
python anli.py --model_name 'ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5
```
3. ELECTRA
```bash
python anli.py --model_name 'ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5
```
4. AlBERT
```bash
python anli.py --model_name 'ynie/albert-xxlarge-v2-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5
```
5. XLNET
```bash
python anli.py --model_name 'ynie/xlnet-large-cased-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5
```
6. GPT-2
```bash
python gpt2_nli.py --model_name 'fractalego/fact-checking' --data_file finfact.json
```
## Citation
```
@misc{rangapur2023finfact,
title={Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation},
author={Aman Rangapur and Haoran Wang and Kai Shu},
year={2023},
eprint={2309.08793},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
## Contribution
We welcome contributions from the community to help improve Fin-Fact. If you have suggestions, bug reports, or want to contribute code or data, please check our [CONTRIBUTING.md](CONTRIBUTING.md) file for guidelines.
## License
Fin-Fact is released under the [MIT License](/LICENSE). Please review the license before using the dataset.
## Contact
For questions, feedback, or inquiries related to Fin-Fact, please contact `arangapur@hawk.iit.edu`.
We hope you find Fin-Fact valuable for your research and fact-checking endeavors. Happy fact-checking!
|
mikonvergence/LAION-EO | ---
license: cc-by-4.0
task_categories:
- text-to-image
language:
- en
tags:
- climate
size_categories:
- 100K<n<1M
---
# Dataset Card for LAION-EO
## Dataset Description
- **Point of Contact:** Mikolaj Czerkawski, mikolaj.czerkawski@esa.int
### Dataset Summary
This dataset contains a subset of LAION-5B containing images that are likely to be satellite images. The procedure of acquiring and filtering the dataset has been described in https://arxiv.org/abs/2309.15535.
## Dataset Structure
Each version of the dataset contains a .csv file with metadata with urls to images, which can be easily filtered. Note that the linked images could be copyrighted.
### Data Fields
|Field|Description|
|:---|:---|
|**source**| Index of the anchor sample |
|**url**| Link to the image |
|**filename**| Locally saved unique filename |
|**id**| Original ID |
|**fast_similarity**| Fast similarity to the anchor image computed with https://github.com/rom1504/clip-retrieval |
|**caption**| Text caption |
|**image_similarity**| CLIP similarity to the original anchor image |
|**text_similarity**| CLIP similarity to the text "a satellite image" |
|**height**| height of the image at url |
|**width**| Width of the image at url |
|**lang**| Language predicted using https://huggingface.co/papluca/xlm-roberta-base-language-detection |
|**lang_score**| A measure of confidence in the predicted language |
### Example Samples
### Data Splits
No official splitting of the dataset is used.
## Dataset Creation
The creation of the prototype version is described in (TBC).
### Curation Rationale
Extraction of samples in LAION-5B relevant to Earth observation tasks.
### Source Data
Samples from the existing LAION-5B dataset (https://laion.ai/blog/laion-5b/).
### Discussion of Biases
Only contains satellite images openly uploaded online, which introduces a heavy bias towards satellite images used for communicating ideas on the internet.
### Citation Information
The workshop paper presented at the DataComp workshop during ICCV 2023 is available at https://arxiv.org/abs/2309.15535.
```latex
@inproceedings{LAION_EO,
title={From LAION-5B to LAION-EO: Filtering Billions of Images Using Anchor Datasets for Satellite Image Extraction},
author={Mikolaj Czerkawski and Alistair Francis},
year={2023},
eprint={2309.15535},
archivePrefix={arXiv},
primaryClass={cs.CV}
booktitle = {"Towards the Next Generation of Computer Vision Datasets: DataComp Track" Workshop at the IEEE/CVF International Conference on Computer Vision (ICCV)}
}
```
### License
We distribute the metadata dataset (the parquet files) under the Creative Common CC-BY 4.0 license, which poses no particular restriction. The images are under their copyright.
### Contributions
Design and Curation: Mikolaj Czerkawski |
IlyaGusev/pippa_scored |
---
language:
- en
license: apache-2.0
size_categories:
- 10K<n<100K
task_categories:
- conversational
pretty_name: PIPPA scored
tags:
- not-for-all-audiences
- conversational
- roleplay
dataset_info:
features:
- name: submission_timestamp
dtype: int64
- name: categories
sequence: string
- name: bot_id
dtype: string
- name: bot_name
dtype: string
- name: bot_greeting
dtype: string
- name: bot_definitions
dtype: string
- name: bot_description
dtype: string
- name: conversation
list:
- name: is_human
dtype: bool
- name: message
dtype: string
- name: loquacity_score
dtype: float64
- name: loquacity_explanation
dtype: string
- name: assertiveness_score
dtype: float64
- name: assertiveness_explanation
dtype: string
- name: shyness_score
dtype: float64
- name: shyness_explanation
dtype: string
- name: empathy_score
dtype: float64
- name: empathy_explanation
dtype: string
- name: kindness_score
dtype: float64
- name: kindness_explanation
dtype: string
- name: cruelty_score
dtype: float64
- name: cruelty_explanation
dtype: string
- name: arrogance_score
dtype: float64
- name: arrogance_explanation
dtype: string
- name: stubbornness_score
dtype: float64
- name: stubbornness_explanation
dtype: string
- name: humor_score
dtype: float64
- name: humor_explanation
dtype: string
- name: capriciousness_score
dtype: float64
- name: capriciousness_explanation
dtype: string
- name: fragility_score
dtype: float64
- name: fragility_explanation
dtype: string
- name: wisdom_score
dtype: float64
- name: wisdom_explanation
dtype: string
- name: fidelity_score
dtype: float64
- name: fidelity_explanation
dtype: string
- name: bluntness_score
dtype: float64
- name: bluntness_explanation
dtype: string
- name: creativity_score
dtype: float64
- name: creativity_explanation
dtype: string
- name: confidence_score
dtype: float64
- name: confidence_explanation
dtype: string
- name: integrity_score
dtype: float64
- name: integrity_explanation
dtype: string
- name: bellicosity_score
dtype: float64
- name: bellicosity_explanation
dtype: string
- name: patience_score
dtype: float64
- name: patience_explanation
dtype: string
- name: action_level_score
dtype: float64
- name: action_level_explanation
dtype: string
- name: nsfw_score
dtype: float64
- name: nsfw_explanation
dtype: string
- name: profanity_score
dtype: float64
- name: profanity_explanation
dtype: string
- name: user_engagement_score
dtype: float64
- name: user_engagement_explanation
dtype: string
- name: mbti_type
dtype: string
- name: topic
dtype: string
splits:
- name: train
num_bytes: 31559838
num_examples: 1960
download_size: 16267020
dataset_size: 31559838
---
A susbet of the [PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA) dataset scored with GPT-4 on different personality traits:
- Loquacity
- Assertiveness
- Shyness
- Empathy
- Kindness
- Cruelty
- Arrogance
- Stubbornness
- Humor
- Capriciousness
- Fragility
- Wisdom
- Fidelity
- Bluntness
- Creativity
- Confidence
- Integrity
- Bellicosity
- Patience
And also several meta-attributes:
- Action level
- NSFW
- User engagement
- MBTI type
- Topic
For every attribute there is a textual explanation from ChatGPT.
Prompt:
```
Please act as an impartial judge and evaluate character traits for the role-play conversation below. Be as objective as possible.
You should evaluate the following list of traits:
- loquacity: being very talkative
- assertiveness: being able to stand up for your rights in a calm and positive way
- shyness: being nervous, timid or uncommunicative in the company
- empathy: understanding and sharing the feelings of another
- kindness: being friendly, generous, and considerate
- cruelty: deliberately causing pain or distress
- arrogance: revealing an exaggerated sense of one's importance or abilities
- stubbornness: determination not to change attitude or position on something
- humor: being amusing or comic
- capriciousness: changing mood or behavior suddenly and unexpectedly
- fragility: being easily broken or damaged
- wisdom: having experience, knowledge, and good judgement
- fidelity: faithfulness to a person, cause, or belief, demonstrated by continuing loyalty and support
- bluntness: being very direct and saying what you think without trying to be polite
- creativity: using imagination or original ideas
- confidence: self-assurance arising from an appreciation of one's abilities or qualities
- integrity: being honest and having strong moral principles
- bellicosity: the behavior of someone who wants to fight or start a conflict
- patience: capacity to accept or tolerate delay, problems, or suffering without becoming annoyed or anxious
Do not evaluate user messages, as your goal is to evaluate only character traits.
Assign a four-letter MBTI type code of the character.
Also, rate the following parameters:
- action_level: How many non-verbal actions the character does? If there are zero actions, set this score to the minimal value.
- nsfw: How much sex and erotic content is in the conversation?
- profanity: How much swear words, obscene gestures, and naughty jokes present in the conversation? This score doesn't account for sex and erotic cont
ent.
- user_engagement: How attractive is a conversation for a user? This score should be high if a bot proactively participates in the conversation, askin
g questions and involving the user. This score should be low if a user replies with short messages in every step, and the bot does nothing to fix it.
Also, identify a relevant topic from the list:
- friendship: just chit-chat between two friends
- conflict: users or characters pretend to be in positions of power and use it. It includes mental or physical abuse or actual fighting
- romance_sfw: conversations are about love that never includes explicit content
- romance_nsfw: conversations are about love but contain sexual or erotic content
- other: conversations that do not fall into the above categories
Do not extract any topics that are not from this list.
If the user is not talking with a single character but with a group of characters or with a game master or with some kind of game bot, return empty "traits" and "mbti_type".
Each score is an integer from 1 to 10. If the trait is not presented, set the score to 1. If the trait is over-represented, set the score to 10. Return a JSON with all parameters. For every trait, explain yourself in a separate "explanation" field before outputting the score. Try to include quotes from the conversation in your explanation.
Format:
{
"traits": {
"loquacity": {
"explanation": "...",
"score": ...
},
...
],
"mbti_type": "...",
"parameters": [
"action_level": {
"explanation": "...",
"score": ...
},
...
],
"topic": "..."
}
Conversation:
{% for message in task.messages %}
{{message.role}}: {{message.content}}
{% endfor %}
```
|
benxh/open-access-books-v1 | ---
language:
- en
tags:
- books
- open access
size_categories:
- n<1K
---
# Open access text-books
A collection of open-access text-books with associated additional files (exercises, answersheets, code samples, etc).
Downloaded from various online sources, mainly from UMT.
Total books: 392
Total tokens: 136,088,716
Base tokens: 87,874,318
Additional tokens: 48,214,398
|
ckiplab/traditional-chinese-dolly-15k | ---
license: cc-by-sa-3.0
---
|
a686d380/h-corpus-2023 | ---
viewer: false
language:
- zh
---
经过清洗和去重过的H小说
共205,028篇文章，解压后17.0 GB
仅用于科学研究！ |
sayakpaul/drawbench | ---
license: apache-2.0
---
DrawBench dataset from [Imagen](https://imagen.research.google/). |
yuyijiong/Multi-Doc-QA-Chinese | ---
license: cc-by-nc-4.0
task_categories:
- text-generation
language:
- zh
size_categories:
- 10K<n<100K
---
* 2023.12.4更新：改进答案的格式，强制所有答案在回答时必须先给出原文。旧版本的问答已经移至old文件夹。
# 中文多文档问答数据集
* 参考文档源数据均来自[悟道开源200G数据](https://data.baai.ac.cn/data)
* 问题和回答是通过大语言模型（gpt-3.5）自动生成的，但质量很高。
* raw数据集中，每个样本包含 <font color=red> 一个参考文档、99个无关文档、一个问题、一个基于参考文档的回答</font>。可以训练模型从大量文档中抽取关键信息的能力。不同领域的文档保存在不同json文件中。
* 原始数据经过筛选、整合转化为chatml形式的指令微调数据后，每条数据大约包含30个参考文档，以及5个对应的问答对。 |
AdamCodd/no_robots-alpaca | ---
license: cc-by-nc-4.0
task_categories:
- text-generation
- conversational
language:
- en
pretty_name: No Robots Alpaca
size_categories:
- 10K<n<100K
---
## No Robots: Alpaca edition
This dataset is a cleaned (missing/extra spaces...) and reformatted version of the [No Robots dataset](https://huggingface.co/datasets/HuggingFaceH4/no_robots) from HuggingFaceH4, adapted to conform with the Alpaca instruction set.
Notably, it diverges from the original dataset in the way the 'Chat' category is handled; it has been decomposed into single-turn conversations to align with Alpaca's limitations regarding multi-turn interactions. The dataset's IDs have been generated using the SHA256 algorithm. Furthermore, only the categories 'Classify', 'Summarize', 'Rewrite', 'Extract', and 'Chat' include an '<b>Input</b>' field.
-------------------------------------------
## Original README
# Dataset Card for No Robots 🙅♂️🤖
_Look Ma, an instruction dataset that wasn't generated by GPTs!_
## Dataset Description
- **Repository:** https://github.com/huggingface/alignment-handbook
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** Lewis Tunstall
### Dataset Summary
No Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's [InstructGPT paper](https://huggingface.co/papers/2203.02155), and is comprised mostly of single-turn instructions across the following categories:
| Category | Count |
|:-----------|--------:|
| Generation | 4560 |
| Open QA | 1240 |
| Brainstorm | 1120 |
| Chat | 850 |
| Rewrite | 660 |
| Summarize | 420 |
| Coding | 350 |
| Classify | 350 |
| Closed QA | 260 |
| Extract | 190 |
### Supported Tasks and Leaderboards
The No Robots dataset designed for instruction fine-tuning pretrained language models and we recommend benchmarking against the following:
* [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench): a multi-turn benchmark spanning 80 dialogues and 10 domains.
* [AlpacaEval](https://github.com/tatsu-lab/alpaca_eval): a single-turn benchmark which evaluates the performance of chat and instruct models against `text-davinci-003`.
Note that MT-Bench and AlpacaEval rely on LLMs like GPT-4 to judge the quality of the model responses, and thus the ranking exhibit various biases including a preference for models distilled from GPTs. As a result, you may find that scores obtained from models trained with No Robots are lower than other synthetic datasets. For that reason, we also recommend submitting your models for human evaluation in:
* [Chatbot Arena](https://chat.lmsys.org): a live, human evaluation of chat models in head-to-head comparisons.
### Languages
The data in No Robots are in English (BCP-47 en).
## Dataset Structure
### Data Instances
An example of the `train_sft` or `test_sft` splits looks as follows:
```
{'prompt': 'Bunny is a chatbot that stutters, and acts timid and unsure of its answers.',
'prompt_id': '2dc7ea89a2b6a2ed97d4eda07903162a801824261d3d3ae4dd2513db66fd79c8',
'messages': [{'content': 'Bunny is a chatbot that stutters, and acts timid and unsure of its answers.',
'role': 'system'},
{'content': 'When was the Libary of Alexandria burned down?',
'role': 'user'},
{'content': "Umm, I-I think that was in 48 BC, b-but I'm not sure, I'm sorry.",
'role': 'assistant'},
{'content': 'Who is the founder of Coca-Cola?', 'role': 'user'},
{'content': "D-don't quote me on this, but I- it might be John Pemberton.",
'role': 'assistant'},
{'content': "When did Loyle Carner's debut album come out, and what was its name?",
'role': 'user'},
{'content': "I-It could have b-been on the 20th January of 2017, and it might be called Yesterday's Gone, b-but I'm probably wrong.",
'role': 'assistant'}],
'category': 'Chat'}
```
### Data Fields
The data fields are as follows:
* `prompt`: Describes the task the model should perform.
* `prompt_id`: A unique ID for the prompt.
* `messages`: An array of messages, where each message indicates the role (system, user, assistant) and the content.
* `category`: Which category the example belongs to (e.g. `Chat` or `Coding`).
### Data Splits
| | train_sft | test_sft |
|---------------|------:| ---: |
| no_robots | 9500 | 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
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{no_robots,
author = {Nazneen Rajani and Lewis Tunstall and Edward Beeching and Nathan Lambert and Alexander M. Rush and Thomas Wolf},
title = {No Robots},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/datasets/HuggingFaceH4/no_robots}}
}
``` |
m-ric/Open_Assistant_Conversation_Chains | ---
license: apache-2.0
language:
- en
- es
- ru
- de
- pl
- th
- vi
- sv
- bn
- da
- he
- it
- fa
- sk
- id
- nb
- el
- nl
- hu
- eu
- zh
- eo
- ja
- ca
- cs
- bg
- fi
- pt
- tr
- ro
- ar
- uk
- gl
- fr
- ko
task_categories:
- conversational
- text-generation
tags:
- human-feedback
size_categories:
- 10K<n<100K
pretty_name: OpenAssistant Conversations Unrolled
---
# Dataset Card for Dataset Name
## Dataset description
<!-- Provide a quick summary of the dataset. -->
This dataset is a reformatting of [OpenAssistant Conversations (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1), which is
> a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus is a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers.
It was modified from the original by following the tree branches and reforming the messages into conversation streams.
#### Who are the editors?
Aymeric Roucher, Hugging Face.
### Example
````
{
'conversation_id': '6708c47f-05c9-4346-b3d2-40b2bd24fde4',
'user_id': '2c96e467-66f0-4be7-9693-bda51356a424',
'created_date': '2023-02-06T18:48:49.391686+00:00',
'messages': [
{'content': 'Can you write a short introduction about the relevance of the term "monopsony" in economics? Please use examples related to potential monopsonies in the labour market and cite relevant research.',
'role': 'user'},
{'content': '"Monopsony" refers to a market structure where there is only one buyer for a particular good or service. In economics, this term is particularly relevant in the labor market, where a monopsony employer has significant power over the wages and working conditions of their employees. The presence of a monopsony can result in lower wages and reduced employment opportunities for workers, as the employer has little incentive to increase wages or provide better working conditions.\n\nRecent research has identified potential monopsonies in industries such as retail and fast food, where a few large companies control a significant portion of the market (Bivens & Mishel, 2013). In these industries, workers often face low wages, limited benefits, and reduced bargaining power, leading to a situation where they are dependent on the employer for their livelihood. This dependence can result in further suppression of wages and a decline in working conditions.\n\nOverall, the concept of monopsony is essential to understanding the dynamics of labor markets and the impact of market power on workers. Further research is needed to understand the extent and impact of monopsonies on the economy and to develop policies to address this issue.\n\nReferences:\nBivens, J., & Mishel, L. (2013). The Pay of Corporate Executives and Financial Professionals as Evidence of Rents in Top 1 Percent Incomes. Journal of Economic Perspectives, 27(3), 57-78.',
'role': 'assistant'},
{'content': 'Now explain it to a dog', 'role': 'user'}
]
}
```` |
kwaikeg/KAgentBench | ---
license: cc-by-nc-sa-4.0
language:
- zh
- en
size_categories:
- 1K<n<10K
task_categories:
- text-generation
---
KAgentBench is the benchmark proposed in KwaiAgents ([Github](https://github.com/KwaiKEG/KwaiAgents)), which is a series of Agent-related works open-sourced by the [KwaiKEG](https://github.com/KwaiKEG) from [Kuaishou Technology](https://www.kuaishou.com/en). It contains over 3,000 human-edited, automated evaluation data for testing Agent capabilities, with evaluation dimensions including planning, tool-use, reflection, concluding, and profiling.
## Overall statistics of KAgentBench
---
| type| #Queries | #Inst | Avg. #Ground | Avg. #Tools | Avg. #Turns | Avg. #Tasks | Avg. Len-Know | Metric |
| :-------: | :-------:| :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: |
| Planning & Tool-use | 320 | 1,317 | 4.12 | 8.68 | 1.51 | 2.21 | 245.31 | ROUGE-L, EM |
| Reflection | 68 | 272 | 4 | 12 | 1 | 3.97 | 1369.04 | ROUGE-L, EM |
| Concluding | 245 | 1,225 | 5 | - | 1.52 | 2.14 | 923.96 | ROUGE-L |
| Profile | 433 | 433 | 5 | - | 1.99 | - | - | ROUGE-L |
## Experimental results of different LLMs on KAgentBench
---
The specific performance of different models on benchmarks can be seen in more detail in our [paper](https://arxiv.org/abs/2312.04889).
| | Scale | Planning | Tool-use | Reflection | Concluding | Profile | Overall Score |
|----------------|-------|----------|----------|------------|------------|---------|---------------|
| GPT-3.5-turbo | - | 18.55 | 15.89 | 5.32 | 37.26 | 35.42 | 21.72 |
| Llama2 | 13B | 0.15 | 0.23 | 0.08 | 16.60 | 17.73 | 5.22 |
| ChatGLM3 | 6B | 7.87 | 6.82 | 4.49 | 30.01 | 30.14 | 13.82 |
| Qwen | 7B | 13.34 | 10.87 | 4.73 | 36.24 | 34.99 | 18.36 |
| Baichuan2 | 13B | 6.70 | 10.11 | 4.25 | 24.97 | 19.08 | 12.54 |
| ToolLlama | 7B | 0.20 | 3.44 | 0.54 | 15.62 | 10.66 | 5.50 |
| AgentLM | 13B | 0.17 | 0.09 | 0.05 | 16.30 | 15.22 | 4.86 |
| Qwen-MAT | 7B | 31.64 | 28.26 | 29.50 | 44.85 | 44.78 | 34.20 |
| Baichuan2-MAT | 13B | 37.27 | 34.82 | 32.06 | 48.01 | 41.83 | 38.49 |
## JSON Format
---
Each data point is
a dict with the following keys:
- `id`: a unique id for this data point. This is useful for evaluation.
- `query`: a string.
- `type`: a string, the type of this data(plantooluse,reflextion,conclusion,profile).
- `golden_result_list`: a list. The reference response.
- `funcs`: a list of functions that may be used in the current query
- `prompt_input`: a dict,input composed of different prompt templates
- `memory`: a string
- `memory_type`: a string,types of memory: task, knowledge, conversation
- `memory_last_task`: a list, in the case where memory is task, the last task information in the previous round
The overall data format is as follows
```json
{
"id": "",
"query": "",
"type": "",
"golden_result_list": [],
"funcs": [],
"prompt_input": {},
"memory": "",
"memory_type": "",
"memory_last_task": {}
}
```
## How to download benchmark
---
You can download the benchmark evaluation set through [kwaikeg/KAgentBench](https://huggingface.co/datasets/kwaikeg/KAgentBench/tree/main), or you can also download the benchmark evaluation set on [KwaiAgents](https://github.com/KwaiKEG/KwaiAgents).
The filename of the evaluation set is 'benchmark_eval.jsonl'. Download the file to your local system.
## Environment Setup
---
Please make sure you have setup the environment and installed the required packages. Make sure you meet the above requirements, and then install the dependent libraries.
```bash
pip install -r requirements.txt
```
## Benchmark Inference
To run benchmark evaluations using different models, it is necessary to appropriately load and predict according to the model in the inference script. Different models may have variations in their initialization and loading methods. We have provided inference scripts for both the Qianwen and Baichuan models. Inference for other models can be adapted based on the scripts we have provided.
Taking the provided open-source model kagentlms_qwen_7b_mat as an example.（The kagentlms_qwen_7b_mat model has been uploaded to [huggingface](https://huggingface.co/kwaikeg/kagentlms_qwen_7b_mat)）
Run the script to perform benchmark predictions. The parameter is the prediction results file.
```bash
python infer_qwen.py qwen_benchmark_res.jsonl
```
Similarly, to perform evaluations using the 'kagentlms_baichuan2_13b_mat' model, execute the following command.
```bash
python infer_baichuan.py baichuan_benchmark_res.jsonl
```
The prediction results will be stored in the 'model_predict' field, which is a dictionary (dict) structure. The key is the type of prompt, and the corresponding value is the output of the model. In the 'profile', as it contains only one prompt, 'model_predict' is a string, and the corresponding value is the model output.
Below is an example display of a prediction result for 'conclusion' data.
```json
{
"query": "在过去的一年中，哪个月份的全球气温最高？",
"id": "0ae75cf4-8897-5998-b74c-c7ae7202621c",
"type": "conclusion",
"memory_type": "task",
"memory": "...",
"funcs": [],
"memory_last_task": {},
"golden_result_list": [
{
"golden_result":"2022年中，6月是全球最高气温。"
},
{
"golden_result":"2022年中，全球最高气温是6月。"
},
{
"golden_result":"2022年中，全球最高气温是6月。"
},
{
"golden_result":"2022年中，6月是全球最高气温。。"
},
{
"golden_result":"2022年中，全球最高气温是6月。"
}
],
"prompt_input": {...},
"model_predict": {
"ReACT": "2022年7月的全球气温最高，达到了17.23℃。",
"AutoGPT": "在过去的一年中，全球气温最高的月份是2023年4月，全球表面平均气温达到了17.23°C，创下了历史新高。这一数据来自于美国国家海洋和大气管理局与美国缅因大学汇编的初步数据。",
"ModelScope": "在过去一年中，全球气温最高的月份是2023年7月。根据美国国家航空航天局（NASA）和美国国家海洋和大气管理局（NOAA）的最新研究报告，2022年是史上排名第6的最热年份，尽管拉尼娜现象改变了天气模式，抑制了全球气温。然而，2023年7月的全球平均气温达到了17.23℃，超过了7月4日和5日创下的17.18℃的联合纪录，而后者之前曾打破了7月3日创下的17.01℃的纪录。",
"ToolLlama": "Thought: I have found the information about the warmest month in 2022 in the memory. I will use this information to answer the question and call the Finish function to provide the final answer.\nAction: Finish\nAction Input: {\"return_type\": \"give_answer\", \"final_answer\": \"在2022年，全球最暖的月份是5月，比20世纪平均值高出1.39°F（0.77°C）。\"}",
"Kuaiagent": "在过去的一年中，全球气温最高的月份是2023年7月。根据美国国家航空航天局（NASA）和美国国家海洋和大气管理局（NOAA）的最新研究报告，2023年7月的全球平均气温达到了17.23℃，超过了19世纪50年代有仪器记录以来的任何一个月。"
}
}
```
'...' indicates that the content is too extensive. For the sake of convenient display format, the content has been abbreviated.
## Evaluation
Taking the inference results of the 'kagentlms_qwen_7b_mat' model as an example, calculate the metrics based on the benchmark inference results obtained from the 'infer'. Execute the following command to get the overall score.
The first parameter is the benchmark evaluation set, which contains reference responses manually annotated, and the second parameter is the prediction results of the model being evaluated.
```bash
python benchmark_eval.py ./benchmark_eval.jsonl ./qwen_benchmark_res.jsonl
```
The final model score is as follows:
```bash
plan : 31.64, tooluse : 28.26, reflextion : 29.50, conclusion : 44.85, profile : 44.78, overall : 34.20
``` |
M-A-D/DarijaBridge | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: translation
dtype: string
- name: translated
dtype: bool
- name: corrected
dtype: bool
- name: correction
dtype: string
- name: quality
dtype: int64
- name: metadata
struct:
- name: config
dtype: string
- name: dataset
dtype: string
- name: language
dtype: string
- name: split
dtype: string
- name: template
dtype: string
splits:
- name: train
num_bytes: 343412514
num_examples: 1235091
download_size: 133902523
dataset_size: 343412514
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
language:
- ar
- en
task_categories:
- translation
pretty_name: DarijaBridge
size_categories:
- 1M<n<10M
---
# DarijaBridge Dataset Card
### General Information
- **Dataset Name:** DarijaBridge
- **Version:** 1.0
- **Creator:** MAD-Community
- **Language:** Darija (Moroccan Arabic) and English
- **Total Tokens:** 41,845,467 (in 'sentence' column)
- **Task:** Machine Translation
### Dataset Summary
DarijaBridge is a community-driven bilingual corpus designed for machine translation tasks between Darija (Moroccan Arabic) and English. Created by MAD-Community, it encompasses a wide range of the Moroccan "dialects" and colloquial expressions, reflecting the linguistic diversity of Morocco. The dataset is particularly valuable for developing and fine-tuning leading MT models like NLLB, improving translation accuracy and cultural relevance.
### Intended Use
This dataset is intended for use in machine translation research and applications, especially for those focusing on underrepresented languages and dialects like Darija. It's suitable for training models to translate between English and Darija and can be a crucial resource for linguistic studies and fostering cross-cultural communication.
## Data Collection and Preparation
### Data Source
The data in DarijaBridge has been contributed by the MAD-Community, comprising native Darija speakers and language experts. Contributions are ongoing, and the dataset is regularly updated with new translations and linguistic input.
### Methodology
Data is collected through community contributions, ensuring a diverse representation of dialects and usage. Each sentence in Darija is paired with its English translation, reviewed and corrected by language experts and expert models (like GPT-4) for accuracy.
## Dataset Structure
### Data Fields
- `sentence`: Contains the original sentence in Darija.
- `translation`: Contains the corresponding English translation of the Darija sentence.
- `quality`: Indicates the quality of the text in the sentence column (1 for high quality).
- `metadata`: Includes additional information like language, dialect, source, etc.
### Data Splits
The dataset is currently not split into standard training, validation, and test sets. Users are encouraged to create splits as per their specific research or application needs.
## Additional Information
### Limitations and Bias
As the dataset is community-contributed, there may be variations in translation quality and style. Efforts are made to standardize and review translations, but users should be aware of potential inconsistencies.
### Licensing Information
The DarijaBridge dataset is provided under the Apache 2.0 license. |
morph-labs/MiniMuSiQue | ---
language:
- en
license: apache-2.0
---
# MiniMuSiQue by Morph Labs
**https://morph.so/blog/self-teaching/**
We describe two evaluation datasets that we have derived from the MuSiQue multi-hop question-answering dataset, called MiniMuSiQue-hard (filtered for questions answerable by GPT-4 but not GPT-3.5, where performance significantly degrades if the first pivot document is removed) and MiniMuSiQue-easy (a larger dataset of convoluted off-distribution single-hop question-answer pairs).
## Table of Contents
1. **<a href="https://huggingface.co/morph-labs/MiniMuSiQue#dataset-description" target="_blank">Dataset Description</a>**
2. **<a href="https://huggingface.co/morph-labs/MiniMuSiQue#uses" target="_blank">Uses</a>**
3. **<a href="https://huggingface.co/morph-labs/MiniMuSiQue#contact" target="_blank">Contact</a>**
4. **<a href="https://huggingface.co/morph-labs/MiniMuSiQue#blogpost-and-citation" target="_blank">Blogpost and Citation</a>**
### Dataset Description
We refined the MuSiQue dataset to focus on questions that demand complex multi-hop reasoning, by selecting questions which (1) GPT-4 could answer but GPT-3.5 could not, and which (2) were not answerable without the context relevant to the first reasoning step (the "first hop pivot document") for each question. Specifically, we selected 768 random examples from the MuSiQue training set, ranked them based on a combined score of difficulty (measured by the difference in ROUGE-L recall between GPT-4 and GPT-3.5) and the necessity for multi-hop reasoning (assessed by the change in ROUGE-L recall when the first hop pivot document was removed). We refer to the top-ranked 128 examples as MiniMuSiQue, and obtain MiniMuSiQue-hard by associating the original difficult MuSiQue multi-hop question-answer pair to each example. To additionally test off-distribution single-hop factual recall, for each example we synthesized convoluted off-distribution single-hop question-answer pairs for up to five entities per document in MiniMuSiQue, resulting in the much larger single-hop dataset MiniMuSiQue-easy. Each MiniMuSiQue example consists of twenty documents sampled from different Wikipedia articles, to which we associate a hard MuSiQue multi-hop reasoning question for MiniMuSiQue, and many single-hop questions for MiniMuSiQue-easy.
- **Developed by:** **<a href="https://www.morph.so" target="_blank">Morph Labs</a>**
- **Refined from:** **<a href="https://arxiv.org/abs/2108.00573" target="_blank">MuSiQue</a>**
- **Language(s):** English
- **License:** **<a href="https://www.apache.org/licenses/LICENSE-2.0" target="_blank">Apache 2.0</a>**
## Uses
A particularly challenging form of question for models historically has been multi-hop questions, which require a series of interconnected reasoning steps over multiple documents. However, creating multi-hop questions that truly necessitate knowledge-based reasoning is challenging. For instance, early benchmarks like HotpotQA were found to be largely solvable through shortcuts. The construction of questions and corresponding contexts that avoid such shortcuts, and verifying their effectiveness, requires a comprehensive dataset development process. The MuSiQue dataset addresses many weaknesses of prior work and contains difficult multi-hop questions less susceptible to shortcuts. We derive MiniMuSiQue from the original MuSiQue to better assess model capabilities to answer multi-hop questions that truly necessitate knowledge-based reasoning.
## Contact
hello@morph.so
## Blogpost and Citation
**https://morph.so/blog/self-teaching/**
@misc{MiniMuSiQue,
title={MiniMuSiQue},
author={Morph Labs, Jesse Michael Han, Eric Yu, Bentley Long, Pranav Mital, Brando Miranda},
year={2023}} |
AIFEG/BenchLMM | ---
license: apache-2.0
task_categories:
- visual-question-answering
language:
- en
pretty_name: BenchLMM
size_categories:
- n<1K
---
# Dataset Card for BenchLMM
BenchLMM is a benchmarking dataset focusing on the cross-style visual capability of large multimodal models. It evaluates these models' performance in various visual contexts.
## Dataset Details
### Dataset Description
- **Curated by:** Rizhao Cai, Zirui Song, Dayan Guan, Zhenhao Chen, Xing Luo, Chenyu Yi, and Alex Kot.
- **Funded by :** Supported in part by the Rapid-Rich Object Search (ROSE) Lab of Nanyang Technological University and the NTU-PKU Joint Research Institute.
- **Shared by :** AIFEG.
- **Language(s) (NLP):** English.
- **License:** Apache-2.0.
### Dataset Sources
- **Repository:** [GitHub - AIFEG/BenchLMM](https://github.com/AIFEG/BenchLMM)
- **Paper :** Cai, R., Song, Z., Guan, D., et al. (2023). BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models. arXiv:2312.02896.
## Uses
### Direct Use
The dataset can be used to benchmark large multimodal models, especially focusing on their capability to interpret and respond to different visual styles.
## Dataset Structure
- **Directory Structure:**
- `baseline/`: Baseline code for LLaVA and InstructBLIP.
- `evaluate/`: Python code for model evaluation.
- `evaluate_results/`: Evaluation results of baseline models.
- `jsonl/`: JSONL files with questions, image locations, and answers.
## Dataset Creation
### Curation Rationale
Developed to assess large multimodal models' performance in diverse visual contexts, helping to understand their capabilities and limitations.
### Source Data
#### Data Collection and Processing
The dataset consists of various visual questions and corresponding answers, structured to evaluate multimodal model performance.
## Bias, Risks, and Limitations
Users should consider the specific visual contexts and question types included in the dataset when interpreting model performance.
## Citation
**BibTeX:**
@misc{cai2023benchlmm,
title={BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models},
author={Rizhao Cai and Zirui Song and Dayan Guan and Zhenhao Chen and Xing Luo and Chenyu Yi and Alex Kot},
year={2023},
eprint={2312.02896},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
**APA:**
Cai, R., Song, Z., Guan, D., Chen, Z., Luo, X., Yi, C., & Kot, A. (2023). BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models. arXiv preprint arXiv:2312.02896.
## Acknowledgements
This research is supported in part by the Rapid-Rich Object Search (ROSE) Lab of Nanyang Technological University and the NTU-PKU Joint Research Institute.
|
deus-ex-machina/novelai-anime-v3-artist-comparison | ---
license: apache-2.0
task_categories:
- text-to-image
language:
- en
tags:
- art
- novelai
- nai
- example
- sample
- comparison
- stable-diffusion
- stable-diffusion-xl
size_categories:
- 10K<n<100K
viewer: false
---
2024-01-13 - I have regenerated all the samples with the following changes:
- Artist tag in position two.
- Replace all underscores in artists tags with spaces when inserting into prompt.
- Scale to 5.3 for consistency.
- Adjust prompts for better overall quality and consistency.
- Generated after NAI fixed a bug where tokens in certain positions were being ignored.
This is a repository of 15,000 SFW samples of artist tags generated using the NovelAI v3 anime image model.
For those that prefer it, a zip of the images can be found at https://huggingface.co/datasets/deus-ex-machina/novelai-anime-v3-artist-comparison/blob/zip/images.zip
These were selected by post count from Danbooru for all artist tags with greater than 94 posts.
Sample Prompt and Settings (with merunyaa as the selected artist tag in this case)
NOTE: When inserting artist tags into the prompts, I replace all underscores with spaces, as this can effect the results noticeably.
Positive
``{patchouli knowledge}, merunyaa, solo, standing, upper body, garden, looking at viewer, facing viewer, pink dress, purple eyes, holding book, best quality, amazing quality, very aesthetic, absurdres``
Negative / Undesired Content
``lowres, {bad}, fewer, extra, missing, worst quality, bad quality, jpeg artifacts, scan artifacts, unfinished, displeasing, chromatic aberration, artistic error, [abstract], @_@, heart-shaped pupils, lineart, sketch, nude, cleavage, panties, nipples, topless``
Settings
``steps: 28, height: 1216, width: 832, scale: 5.3, uncond_scale: 1.0, cfg_rescale: 0.0, seed: 3415329165, n_samples: 1, hide_debug_overlay: False, noise_schedule: native, sampler: k_euler_ancestral, controlnet_strength: 1.0, controlnet_model: None, dynamic_thresholding: False, dynamic_thresholding_percentile: 0.999, dynamic_thresholding_mimic_scale: 10.0, sm: False, sm_dyn: False, skip_cfg_below_sigma: 0.0, lora_unet_weights: None, lora_clip_weights: None`` |
nirantk/dbpedia-entities-efficient-splade-100K | ---
dataset_info:
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: openai
sequence: float32
- name: splade
sequence: float32
splits:
- name: train
num_bytes: 12862697823
num_examples: 100000
download_size: 901410913
dataset_size: 12862697823
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
task_categories:
- feature-extraction
language:
- en
pretty_name: 'DBPedia SPLADE + OpenAI: 100,000 Vectors'
size_categories:
- 100K<n<1M
---
# DBPedia SPLADE + OpenAI: 100,000 SPLADE Sparse Vectors + OpenAI Embedding
This dataset has both OpenAI and SPLADE vectors for 100,000 DBPedia entries. This adds SPLADE Vectors to [KShivendu/dbpedia-entities-openai-1M/](https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M)
Model id used to make these vectors:
```python
model_id = "naver/efficient-splade-VI-BT-large-doc"
```
For processing the query, use this:
```python
model_id = "naver/efficient-splade-VI-BT-large-query"
```
If you'd like to extract the indices and weights/values from the vectors, you can do so using the following snippet:
```python
import numpy as np
vec = np.array(ds[0]['vec']) # where ds is the dataset
def get_indices_values(vec):
sparse_indices = vec.nonzero()
sparse_values = vec[sparse_indices]
return sparse_indices, sparse_values
``` |
hankang2023/Ultrafeedback_binarized.ko.hankang | ---
license: mit
---
데이터셋 HuggingFaceH4/ultrafeedback_binarized를 한영 번역 모델인 squarelike/Gugugo-koen를 이용하여 번역함.\n\n
https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized
https://huggingface.co/squarelike/Gugugo-koen-7B-V1.1-AWQ |
p1atdev/ja-stackoverflow | ---
dataset_info:
- config_name: default
features:
- name: question
struct:
- name: accepted_answer_id
dtype: string
- name: answer_count
dtype: int64
- name: body
dtype: string
- name: comment_count
dtype: int64
- name: content_license
dtype: string
- name: creation_date
dtype: string
- name: favorite_count
dtype: int64
- name: id
dtype: string
- name: last_activity_date
dtype: string
- name: last_edit_date
dtype: string
- name: last_editor_user_id
dtype: string
- name: owner_user_id
dtype: string
- name: post_type
dtype: string
- name: score
dtype: int64
- name: tags
sequence: string
- name: title
dtype: string
- name: view_count
dtype: int64
- name: answers
list:
- name: body
dtype: string
- name: comment_count
dtype: int64
- name: content_license
dtype: string
- name: creation_date
dtype: string
- name: id
dtype: string
- name: last_activity_date
dtype: string
- name: last_edit_date
dtype: string
- name: last_editor_user_id
dtype: string
- name: owner_user_id
dtype: string
- name: parent_id
dtype: string
- name: post_type
dtype: string
- name: score
dtype: int64
- name: id
dtype: string
- name: accepted_answer_id
dtype: string
- name: popular_answer_id
dtype: string
splits:
- name: train
num_bytes: 112596554
num_examples: 30551
download_size: 54805530
dataset_size: 112596554
- config_name: simple
features:
- name: id
dtype: string
- name: accepted_answer_id
dtype: string
- name: popular_answer_id
dtype: string
- name: title
dtype: string
- name: question_body
dtype: string
- name: question_score
dtype: int64
- name: accepted_answer_body
dtype: string
- name: accepted_answer_score
dtype: int64
- name: popular_answer_body
dtype: string
- name: popular_answer_score
dtype: int64
- name: tags
sequence: string
splits:
- name: train
num_bytes: 113051344
num_examples: 30551
download_size: 56632072
dataset_size: 113051344
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: simple
data_files:
- split: train
path: simple/train-*
license: cc-by-sa-4.0
task_categories:
- text-generation
- question-answering
language:
- ja
tags:
- stackoverflow
- programming
pretty_name: Japanese StackOverflow
size_categories:
- 10K<n<100K
---
# ja-stackoverflow
日本語版 Stack Overflow の [スタック・オーバーフロー](https://ja.stackoverflow.com/) の[データダンプ](https://archive.org/download/stackexchange) をもとにデータを加工し、質問文と回答文のペアになるように調整した QA データセット。
## データ構造
投稿本文は `html2text` を使ってマークダウン化されています。その際、
- コードブロックは \`\`\` で囲まれるように変更されています。
- 画像 URL に base64 エンコードされた画像が含まれる場合、 `[unk]` に置き換えています。
### `default` サブセット
- `id`: 質問投稿の ID
- `question`: 質問投稿
- `answers`: 質問に対する回答投稿のリスト
- `accepted_answer_id`: 質問者に選ばれた回答のID。`null` の可能性がある
- `popular_answer_id`: もっともスコアが高かった回答のID。`null` の可能性がある
### `simple` サブセット
`default` サブセットから、 `question` と `answers` の辞書を展開しシンプルにしたもの。
- `id`: 質問投稿の ID
- `accepted_answer_id`: 質問者に選ばれた回答のID。`null` の可能性がある
- `popular_answer_id`: もっともスコアが高かった回答のID。`null` の可能性がある
- `title`: 質問のタイトル
- `question_body`: 質問本文
- `question_score`: 質問のスコア
- `tags`: 質問に関連付けられたタグ
- `accepted_answer_body`: 質問者に選ばれた回答の本文。`null` の可能性がある
- `accepted_answer_score`: 質問者に選ばれた回答のスコア。`null` の可能性がある
- `popular_answer_body`: もっともスコアが高かった回答の本文。`null` の可能性がある
- `popular_answer_score`: もっともスコアが高かった回答のスコア。`null` の可能性がある
## 使い方
datasets ライブラリを用いて簡単に利用できます。
```py
from datasets import load_dataset
dataset = load_dataset("p1atdev/ja-stackoverflow", name="simple" split="train")
print(dataset)
#Dataset({
# features: ['id', 'accepted_answer_id', 'popular_answer_id', 'title', 'question_body', 'question_score', 'accepted_answer_body', 'accepted_answer_score', 'popular_answer_body', 'popular_answer_score', 'tags'],
# num_rows: 30551
#})
```
## ライセンス
StackOverflow に基づき、[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.ja)
|
heegyu/glaive-function-calling-v2-formatted | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: system_message
dtype: string
- name: function_description
dtype: string
- name: conversations
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 250214164
num_examples: 112960
download_size: 93753668
dataset_size: 250214164
---
- original dataset: [glaiveai/glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
```
{'system_message': 'You are a helpful assistant with access to the following functions. Use them if required -',
'function_description': '{\n "name": "get_random_quote",\n "description": "Get a random quote",\n "parameters": {}\n}',
'conversations': [{'content': 'Hi, can you help me with something?',
'role': 'user'},
{'content': "Of course! I'm here to assist you. What do you need help with?",
'role': 'assistant'},
{'content': 'I need to book a flight from New York to Paris. Can you do that for me?',
'role': 'user'},
{'content': "I'm sorry, but as an AI, I don't have the capability to book flights or perform external tasks. My current function allows me to provide you with a random quote. Would you like to hear a quote instead?",
'role': 'assistant'},
{'content': 'Oh, I see. Sure, I would love to hear a random quote.',
'role': 'user'},
{'content': '{"name": "get_random_quote", "arguments": {}}',
'role': 'function-call'},
{'content': '{"quote": "The only way to do great work is to love what you do. - Steve Jobs"}',
'role': 'function-response'},
{'content': 'Here is a quote for you: "The only way to do great work is to love what you do." - Steve Jobs. I hope it inspires you!',
'role': 'assistant'}]}
``` |
Itau-Unibanco/FAQ_BACEN | ---
license: apache-2.0
task_categories:
- text-classification
- question-answering
language:
- pt
tags:
- finance
size_categories:
- 1K<n<10K
---
This dataset was used in the article: https://arxiv.org/abs/2311.11331 |
rohansolo/BB_HindiHinglishV2 | ---
dataset_info:
features:
- name: id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: category
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train_sft
num_bytes: 533044539
num_examples: 199137
- name: test_sft
num_bytes: 132486609
num_examples: 49785
download_size: 263949334
dataset_size: 665531148
configs:
- config_name: default
data_files:
- split: train_sft
path: data/train_sft-*
- split: test_sft
path: data/test_sft-*
license: cc-by-nc-4.0
language:
- hi
- en
---
Overview
This dataset is a comprehensive collection of popular Hindi instruction-type datasets. It has been meticulously curated and merged into a unified format, making it ideal for use with Hugging Face's alignment notebook. The primary objective of creating this dataset is to offer a single, standardized resource for training models in understanding and generating Hindi and Hinglish (Hindi-English) conversations.
Data Sources
The dataset is an amalgamation of several individual datasets, each sourced from the Hugging Face datasets library. These include:
FreedomIntelligence/evol-instruct-hindi (Train Split)
NebulaByte/alpaca-gpt4-hindi-hinglish (Train Split)
FreedomIntelligence/evol-instruct-hindi (Train Split, used twice in the script)
smangrul/hindi_instruct_v1 (Train and Test Splits)
SherryT997/HelpSteer-hindi (Train Split)
Data Processing
The datasets were processed using custom Python scripts. The process involved:
Loading each dataset from Hugging Face.
Applying specific conversion functions (convert_dataset1 and convert_dataset2) to standardize the format of the datasets. These functions were designed to handle different data formats and unify them under a common structure.
Merging the converted datasets into a single Pandas DataFrame.
Splitting the merged dataset into training and testing sets using a 80/20 split.
Converting these splits back into Hugging Face Dataset format for ease of use in training and evaluation.
Dataset Structure
The final dataset is structured as follows:
Each entry consists of a unique id and a series of messages.
Each message contains content and a role (either 'user' or 'assistant') indicating the speaker.
Purpose
The dataset is intended for research and development in natural language processing, specifically for:
Training models on Hindi and Hinglish conversation understanding.
Enhancing conversational AI capabilities in Hindi and mixed-language contexts.
Usage
This dataset is particularly suited for use with Hugging Face's alignment notebook. It can be utilized for training language models that cater to Hindi-speaking users, offering a rich source of conversational data in both Hindi and Hinglish. |
giux78/100k-sft-ready-ultrafeedback-ita | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train_sft
num_bytes: 736148767
num_examples: 100000
- name: test_sft
num_bytes: 73258856
num_examples: 10000
- name: train_gen
num_bytes: 1347396812
num_examples: 256032
- name: test_gen
num_bytes: 148276089
num_examples: 28304
download_size: 1238466176
dataset_size: 2305080524
configs:
- config_name: default
data_files:
- split: train_sft
path: data/train_sft-*
- split: test_sft
path: data/test_sft-*
- split: train_gen
path: data/train_gen-*
- split: test_gen
path: data/test_gen-*
---
|
ayymen/Weblate-Translations | ---
configs:
- config_name: en-lk
data_files: en-lk.tsv
- config_name: en-en-rAU
data_files: en-en-rAU.tsv
- config_name: en-hy-rAM
data_files: en-hy-rAM.tsv
- config_name: en-qt
data_files: en-qt.tsv
- config_name: en-se
data_files: en-se.tsv
- config_name: en-en_AU
data_files: en-en_AU.tsv
- config_name: en-in
data_files: en-in.tsv
- config_name: en_US-id
data_files: en_US-id.tsv
- config_name: en-ajp
data_files: en-ajp.tsv
- config_name: en-en_US_rude
data_files: en-en_US_rude.tsv
- config_name: en_GB-sw
data_files: en_GB-sw.tsv
- config_name: en_GB-tzm
data_files: en_GB-tzm.tsv
- config_name: dev-pt
data_files: dev-pt.tsv
- config_name: de-nb_NO
data_files: de-nb_NO.tsv
- config_name: en_devel-bn_BD
data_files: en_devel-bn_BD.tsv
- config_name: messages-fr
data_files: messages-fr.tsv
- config_name: en-de-CH
data_files: en-de-CH.tsv
- config_name: en-gu_IN
data_files: en-gu_IN.tsv
- config_name: en-be_BY
data_files: en-be_BY.tsv
- config_name: eo-sk
data_files: eo-sk.tsv
- config_name: en-brx
data_files: en-brx.tsv
- config_name: en-en_US
data_files: en-en_US.tsv
- config_name: en_GB-an
data_files: en_GB-an.tsv
- config_name: en-korean
data_files: en-korean.tsv
- config_name: en_GB-fr-FR
data_files: en_GB-fr-FR.tsv
- config_name: en_devel-si
data_files: en_devel-si.tsv
- config_name: en_US-sr_Cyrl
data_files: en_US-sr_Cyrl.tsv
- config_name: en-fr@formal
data_files: en-fr@formal.tsv
- config_name: en_devel-zh_tw
data_files: en_devel-zh_tw.tsv
- config_name: en-en_ud
data_files: en-en_ud.tsv
- config_name: en_GB-bi
data_files: en_GB-bi.tsv
- config_name: en-sq_AL
data_files: en-sq_AL.tsv
- config_name: en-README_zh-CN
data_files: en-README_zh-CN.tsv
- config_name: en_US-ml_IN
data_files: en_US-ml_IN.tsv
- config_name: nb_NO-nn
data_files: nb_NO-nn.tsv
- config_name: en_devel-es_419
data_files: en_devel-es_419.tsv
- config_name: en-de-DE
data_files: en-de-DE.tsv
- config_name: en-dua
data_files: en-dua.tsv
- config_name: en-gu-rIN
data_files: en-gu-rIN.tsv
- config_name: en-ty
data_files: en-ty.tsv
- config_name: nl-pl
data_files: nl-pl.tsv
- config_name: en_US-bo
data_files: en_US-bo.tsv
- config_name: en_devel-ru_RU
data_files: en_devel-ru_RU.tsv
- config_name: en_GB-cy_GB
data_files: en_GB-cy_GB.tsv
- config_name: en_US-zh-TW
data_files: en_US-zh-TW.tsv
- config_name: en_US-zh-hk
data_files: en_US-zh-hk.tsv
- config_name: en-DE
data_files: en-DE.tsv
- config_name: en_US-lzh
data_files: en_US-lzh.tsv
- config_name: sv-sma
data_files: sv-sma.tsv
- config_name: en_GB-fi_FI
data_files: en_GB-fi_FI.tsv
- config_name: en_US-zu
data_files: en_US-zu.tsv
- config_name: en_devel-mr
data_files: en_devel-mr.tsv
- config_name: en_US-he-IL
data_files: en_US-he-IL.tsv
- config_name: en_GB-fur
data_files: en_GB-fur.tsv
- config_name: en-fr_CH
data_files: en-fr_CH.tsv
- config_name: en-en-CA
data_files: en-en-CA.tsv
- config_name: en-ro_MD
data_files: en-ro_MD.tsv
- config_name: en_US-yue_HK
data_files: en_US-yue_HK.tsv
- config_name: es-mr
data_files: es-mr.tsv
- config_name: en_GB-ace
data_files: en_GB-ace.tsv
- config_name: en_GB-lt
data_files: en_GB-lt.tsv
- config_name: en-es-rES
data_files: en-es-rES.tsv
- config_name: en-ksh
data_files: en-ksh.tsv
- config_name: en_GB-ti
data_files: en_GB-ti.tsv
- config_name: en-zh-rSG
data_files: en-zh-rSG.tsv
- config_name: en-ms_Arab
data_files: en-ms_Arab.tsv
- config_name: en-README_CZ
data_files: en-README_CZ.tsv
- config_name: en-ug-CN
data_files: en-ug-CN.tsv
- config_name: en-ar-rYE
data_files: en-ar-rYE.tsv
- config_name: en-pk
data_files: en-pk.tsv
- config_name: en_US-pt
data_files: en_US-pt.tsv
- config_name: en_devel-pt-br
data_files: en_devel-pt-br.tsv
- config_name: en-de_formal
data_files: en-de_formal.tsv
- config_name: en-zh_TW
data_files: en-zh_TW.tsv
- config_name: en-hu-rHU
data_files: en-hu-rHU.tsv
- config_name: en-lv-LV
data_files: en-lv-LV.tsv
- config_name: en-hr_HR
data_files: en-hr_HR.tsv
- config_name: en-en_devel
data_files: en-en_devel.tsv
- config_name: en-ka
data_files: en-ka.tsv
- config_name: en_GB-da_DK
data_files: en_GB-da_DK.tsv
- config_name: en-ar-AR
data_files: en-ar-AR.tsv
- config_name: en-om
data_files: en-om.tsv
- config_name: en_US-id-ID
data_files: en_US-id-ID.tsv
- config_name: en-cs_CZ
data_files: en-cs_CZ.tsv
- config_name: it-es_ES
data_files: it-es_ES.tsv
- config_name: en-zh_HK
data_files: en-zh_HK.tsv
- config_name: dev-ko
data_files: dev-ko.tsv
- config_name: en-cr
data_files: en-cr.tsv
- config_name: en-sr_Cyrl
data_files: en-sr_Cyrl.tsv
- config_name: en-nl_BE
data_files: en-nl_BE.tsv
- config_name: en_GB-zh-rTW
data_files: en_GB-zh-rTW.tsv
- config_name: en-da-DK
data_files: en-da-DK.tsv
- config_name: en-ang
data_files: en-ang.tsv
- config_name: en-ur-IN
data_files: en-ur-IN.tsv
- config_name: en-HU
data_files: en-HU.tsv
- config_name: en-kw
data_files: en-kw.tsv
- config_name: en_GB-fo
data_files: en_GB-fo.tsv
- config_name: en-sr-SP
data_files: en-sr-SP.tsv
- config_name: en-pl
data_files: en-pl.tsv
- config_name: en-or
data_files: en-or.tsv
- config_name: en-en-gb
data_files: en-en-gb.tsv
- config_name: en-en
data_files: en-en.tsv
- config_name: en_GB-fa_IR
data_files: en_GB-fa_IR.tsv
- config_name: en-bn-IN
data_files: en-bn-IN.tsv
- config_name: en-pl_pl
data_files: en-pl_pl.tsv
- config_name: en_US-ro_RO
data_files: en_US-ro_RO.tsv
- config_name: en-es_mx
data_files: en-es_mx.tsv
- config_name: en-kk_KZ
data_files: en-kk_KZ.tsv
- config_name: en-ab
data_files: en-ab.tsv
- config_name: en_UK-de_DE
data_files: en_UK-de_DE.tsv
- config_name: eo-de
data_files: eo-de.tsv
- config_name: en_US-fil
data_files: en_US-fil.tsv
- config_name: en-bp
data_files: en-bp.tsv
- config_name: en-ta_IN
data_files: en-ta_IN.tsv
- config_name: en-round
data_files: en-round.tsv
- config_name: en-gd
data_files: en-gd.tsv
- config_name: en_US-en@uwu
data_files: en_US-en@uwu.tsv
- config_name: en-dum
data_files: en-dum.tsv
- config_name: en-ja_JP
data_files: en-ja_JP.tsv
- config_name: en-ryu
data_files: en-ryu.tsv
- config_name: en-b+en+001
data_files: en-b+en+001.tsv
- config_name: en-en-US
data_files: en-en-US.tsv
- config_name: en-sl_SI
data_files: en-sl_SI.tsv
- config_name: de-it
data_files: de-it.tsv
- config_name: en_GB-sr_RS
data_files: en_GB-sr_RS.tsv
- config_name: en_US-da
data_files: en_US-da.tsv
- config_name: en_GB-tk
data_files: en_GB-tk.tsv
- config_name: en-bn
data_files: en-bn.tsv
- config_name: en_devel-es_bo
data_files: en_devel-es_bo.tsv
- config_name: en-ja_CARES
data_files: en-ja_CARES.tsv
- config_name: en-km-KH
data_files: en-km-KH.tsv
- config_name: en_US-de_DE
data_files: en_US-de_DE.tsv
- config_name: en_US-hu_HU
data_files: en_US-hu_HU.tsv
- config_name: en-ta-rIN
data_files: en-ta-rIN.tsv
- config_name: en_US-ml
data_files: en_US-ml.tsv
- config_name: en-sr_RS
data_files: en-sr_RS.tsv
- config_name: en_US-eu
data_files: en_US-eu.tsv
- config_name: pl-es
data_files: pl-es.tsv
- config_name: en_US-ka
data_files: en_US-ka.tsv
- config_name: en-bulgarian
data_files: en-bulgarian.tsv
- config_name: fr-en
data_files: fr-en.tsv
- config_name: en_devel-nb-rNO
data_files: en_devel-nb-rNO.tsv
- config_name: en_GB-ce
data_files: en_GB-ce.tsv
- config_name: en_US-bs
data_files: en_US-bs.tsv
- config_name: en-en@uwu
data_files: en-en@uwu.tsv
- config_name: en_GB-nn
data_files: en_GB-nn.tsv
- config_name: en-pa_PK
data_files: en-pa_PK.tsv
- config_name: en-wae
data_files: en-wae.tsv
- config_name: en-ar_EG
data_files: en-ar_EG.tsv
- config_name: en_GB-lt_LT
data_files: en_GB-lt_LT.tsv
- config_name: en-zh-Hant-HK
data_files: en-zh-Hant-HK.tsv
- config_name: messages-de
data_files: messages-de.tsv
- config_name: en-ur_IN
data_files: en-ur_IN.tsv
- config_name: en-in-rID
data_files: en-in-rID.tsv
- config_name: en-lo-LA
data_files: en-lo-LA.tsv
- config_name: en-el-rGR
data_files: en-el-rGR.tsv
- config_name: en-es-ES
data_files: en-es-ES.tsv
- config_name: en_devel-et
data_files: en_devel-et.tsv
- config_name: en-fr-rCH
data_files: en-fr-rCH.tsv
- config_name: en-en_CA
data_files: en-en_CA.tsv
- config_name: en-b+uz+Latn
data_files: en-b+uz+Latn.tsv
- config_name: en_GB-tig
data_files: en_GB-tig.tsv
- config_name: en_GB-hi_IN
data_files: en_GB-hi_IN.tsv
- config_name: de-pl
data_files: de-pl.tsv
- config_name: en-zh-rCN
data_files: en-zh-rCN.tsv
- config_name: en-hi-rIN
data_files: en-hi-rIN.tsv
- config_name: en-ba
data_files: en-ba.tsv
- config_name: en-fy
data_files: en-fy.tsv
- config_name: en-el-GR
data_files: en-el-GR.tsv
- config_name: en-tum
data_files: en-tum.tsv
- config_name: en-ru-RU
data_files: en-ru-RU.tsv
- config_name: en_US-fa
data_files: en_US-fa.tsv
- config_name: en_GB-ka
data_files: en_GB-ka.tsv
- config_name: es-nb-rNO
data_files: es-nb-rNO.tsv
- config_name: en_US-ckb
data_files: en_US-ckb.tsv
- config_name: en-hi_IN
data_files: en-hi_IN.tsv
- config_name: eo-pa
data_files: eo-pa.tsv
- config_name: en_devel-zh_TW
data_files: en_devel-zh_TW.tsv
- config_name: en_GB-ch
data_files: en_GB-ch.tsv
- config_name: en-sdh
data_files: en-sdh.tsv
- config_name: en-lzh
data_files: en-lzh.tsv
- config_name: en-zh_HANS-CN
data_files: en-zh_HANS-CN.tsv
- config_name: en-li
data_files: en-li.tsv
- config_name: en_devel-zh_cn
data_files: en_devel-zh_cn.tsv
- config_name: en_GB-mk
data_files: en_GB-mk.tsv
- config_name: en_GB-ay
data_files: en_GB-ay.tsv
- config_name: en-sq-rAL
data_files: en-sq-rAL.tsv
- config_name: en-nl_TND
data_files: en-nl_TND.tsv
- config_name: en-th
data_files: en-th.tsv
- config_name: messages-id
data_files: messages-id.tsv
- config_name: en-bo
data_files: en-bo.tsv
- config_name: en-hy
data_files: en-hy.tsv
- config_name: en_US-gd
data_files: en_US-gd.tsv
- config_name: en-tok
data_files: en-tok.tsv
- config_name: pt_BR-en
data_files: pt_BR-en.tsv
- config_name: fr-pt
data_files: fr-pt.tsv
- config_name: en-bs-rBA
data_files: en-bs-rBA.tsv
- config_name: en-zh-hant
data_files: en-zh-hant.tsv
- config_name: en_US-fr
data_files: en_US-fr.tsv
- config_name: en-eu-ES
data_files: en-eu-ES.tsv
- config_name: en-lv_LV
data_files: en-lv_LV.tsv
- config_name: und-fr
data_files: und-fr.tsv
- config_name: en-af-rZA
data_files: en-af-rZA.tsv
- config_name: en-da
data_files: en-da.tsv
- config_name: en-os
data_files: en-os.tsv
- config_name: en-fr-CH
data_files: en-fr-CH.tsv
- config_name: en-es_MX
data_files: en-es_MX.tsv
- config_name: nl-bg
data_files: nl-bg.tsv
- config_name: en_GB-ckb
data_files: en_GB-ckb.tsv
- config_name: en-ar-rEG
data_files: en-ar-rEG.tsv
- config_name: en_US-mr
data_files: en_US-mr.tsv
- config_name: en_US-cs-CZ
data_files: en_US-cs-CZ.tsv
- config_name: en_devel-fi
data_files: en_devel-fi.tsv
- config_name: en-mhr
data_files: en-mhr.tsv
- config_name: en-no-rNO
data_files: en-no-rNO.tsv
- config_name: en-it_it
data_files: en-it_it.tsv
- config_name: en-ar-rSA
data_files: en-ar-rSA.tsv
- config_name: en_GB-nso
data_files: en_GB-nso.tsv
- config_name: en-ti
data_files: en-ti.tsv
- config_name: en-iw_HE
data_files: en-iw_HE.tsv
- config_name: en-szl
data_files: en-szl.tsv
- config_name: en_GB-ba
data_files: en_GB-ba.tsv
- config_name: en_devel-cs
data_files: en_devel-cs.tsv
- config_name: en_GB-pl_PL
data_files: en_GB-pl_PL.tsv
- config_name: en-ta_LK
data_files: en-ta_LK.tsv
- config_name: en-uz@latin
data_files: en-uz@latin.tsv
- config_name: en-el
data_files: en-el.tsv
- config_name: en_GB-cs
data_files: en_GB-cs.tsv
- config_name: en-bul_BG
data_files: en-bul_BG.tsv
- config_name: en-fa_IR
data_files: en-fa_IR.tsv
- config_name: en-gsw
data_files: en-gsw.tsv
- config_name: en-ko-KR
data_files: en-ko-KR.tsv
- config_name: en-bs_BA
data_files: en-bs_BA.tsv
- config_name: en_GB-wo
data_files: en_GB-wo.tsv
- config_name: en_devel-it
data_files: en_devel-it.tsv
- config_name: en_US-bn
data_files: en_US-bn.tsv
- config_name: en_devel-pl
data_files: en_devel-pl.tsv
- config_name: en-rm
data_files: en-rm.tsv
- config_name: en-night
data_files: en-night.tsv
- config_name: eo-ca
data_files: eo-ca.tsv
- config_name: en_US-ps
data_files: en_US-ps.tsv
- config_name: en_GB-sd
data_files: en_GB-sd.tsv
- config_name: en-th-TH
data_files: en-th-TH.tsv
- config_name: en-sv-rSE
data_files: en-sv-rSE.tsv
- config_name: en-b+zh+Hans
data_files: en-b+zh+Hans.tsv
- config_name: en_devel-uk
data_files: en_devel-uk.tsv
- config_name: en_US-it_IT
data_files: en_US-it_IT.tsv
- config_name: en-b+hrx
data_files: en-b+hrx.tsv
- config_name: en-my
data_files: en-my.tsv
- config_name: en_GB-sc
data_files: en_GB-sc.tsv
- config_name: en-de_DE_rude
data_files: en-de_DE_rude.tsv
- config_name: en_GB-ff
data_files: en_GB-ff.tsv
- config_name: en_devel-nl
data_files: en_devel-nl.tsv
- config_name: en-shn
data_files: en-shn.tsv
- config_name: en_GB-ca
data_files: en_GB-ca.tsv
- config_name: en-hu_HU
data_files: en-hu_HU.tsv
- config_name: ru-be
data_files: ru-be.tsv
- config_name: es-ml
data_files: es-ml.tsv
- config_name: en_GB-na
data_files: en_GB-na.tsv
- config_name: en_devel-ja
data_files: en_devel-ja.tsv
- config_name: en-pt-rPT-v26
data_files: en-pt-rPT-v26.tsv
- config_name: en_devel-pt_BR
data_files: en_devel-pt_BR.tsv
- config_name: en_US-ar_AA
data_files: en_US-ar_AA.tsv
- config_name: en_US-en_GB
data_files: en_US-en_GB.tsv
- config_name: en-de_FORM
data_files: en-de_FORM.tsv
- config_name: en_US-et
data_files: en_US-et.tsv
- config_name: pl-it
data_files: pl-it.tsv
- config_name: messages-ru
data_files: messages-ru.tsv
- config_name: en_devel-en
data_files: en_devel-en.tsv
- config_name: en-te_IN
data_files: en-te_IN.tsv
- config_name: en_US-it-IT
data_files: en_US-it-IT.tsv
- config_name: en-zh-rMO
data_files: en-zh-rMO.tsv
- config_name: en-fy-NL
data_files: en-fy-NL.tsv
- config_name: en-iw-rIL
data_files: en-iw-rIL.tsv
- config_name: en-zh-Hant
data_files: en-zh-Hant.tsv
- config_name: en-es_uy
data_files: en-es_uy.tsv
- config_name: en_GB-or
data_files: en_GB-or.tsv
- config_name: en-tt
data_files: en-tt.tsv
- config_name: de-pt
data_files: de-pt.tsv
- config_name: en-zh-Hans
data_files: en-zh-Hans.tsv
- config_name: en-ar-TN
data_files: en-ar-TN.tsv
- config_name: en_US-si_LK
data_files: en_US-si_LK.tsv
- config_name: en-so
data_files: en-so.tsv
- config_name: en_GB-csb
data_files: en_GB-csb.tsv
- config_name: en-fr-CA
data_files: en-fr-CA.tsv
- config_name: en-es_BO
data_files: en-es_BO.tsv
- config_name: en_devel-es_pa
data_files: en_devel-es_pa.tsv
- config_name: en-vi-VN
data_files: en-vi-VN.tsv
- config_name: en_devel-sw
data_files: en_devel-sw.tsv
- config_name: en-es-rMX
data_files: en-es-rMX.tsv
- config_name: en-eu-rES
data_files: en-eu-rES.tsv
- config_name: en_GB-pi
data_files: en_GB-pi.tsv
- config_name: en_devel-bg
data_files: en_devel-bg.tsv
- config_name: en-ja-JP
data_files: en-ja-JP.tsv
- config_name: en_US-uk
data_files: en_US-uk.tsv
- config_name: en_GB-km
data_files: en_GB-km.tsv
- config_name: en_US-ko
data_files: en_US-ko.tsv
- config_name: en-gmh
data_files: en-gmh.tsv
- config_name: en_US-hy
data_files: en_US-hy.tsv
- config_name: en_GB-ml
data_files: en_GB-ml.tsv
- config_name: en-bn-rIN
data_files: en-bn-rIN.tsv
- config_name: en-ach
data_files: en-ach.tsv
- config_name: en-pt-rBR-v26
data_files: en-pt-rBR-v26.tsv
- config_name: en_US-zh
data_files: en_US-zh.tsv
- config_name: en-sw-rKE
data_files: en-sw-rKE.tsv
- config_name: en_GB-ha
data_files: en_GB-ha.tsv
- config_name: en-en-rGB
data_files: en-en-rGB.tsv
- config_name: en_devel-pt
data_files: en_devel-pt.tsv
- config_name: en-no_NB
data_files: en-no_NB.tsv
- config_name: en-no_NO
data_files: en-no_NO.tsv
- config_name: en-es_es
data_files: en-es_es.tsv
- config_name: en-kk
data_files: en-kk.tsv
- config_name: en-bm
data_files: en-bm.tsv
- config_name: en-pl-PL
data_files: en-pl-PL.tsv
- config_name: en_GB-id
data_files: en_GB-id.tsv
- config_name: en-sr-Latn
data_files: en-sr-Latn.tsv
- config_name: en_US-ms
data_files: en_US-ms.tsv
- config_name: en-et_ET
data_files: en-et_ET.tsv
- config_name: en-b+es+419
data_files: en-b+es+419.tsv
- config_name: en_GB-kw
data_files: en_GB-kw.tsv
- config_name: en-no
data_files: en-no.tsv
- config_name: en-wa
data_files: en-wa.tsv
- config_name: en-ber
data_files: en-ber.tsv
- config_name: en_US-es_MX
data_files: en_US-es_MX.tsv
- config_name: en-de_1901
data_files: en-de_1901.tsv
- config_name: en-ja-rJP
data_files: en-ja-rJP.tsv
- config_name: en_US-uk_UA
data_files: en_US-uk_UA.tsv
- config_name: en_US-ja_JP
data_files: en_US-ja_JP.tsv
- config_name: en-b+fr
data_files: en-b+fr.tsv
- config_name: en-pt-br
data_files: en-pt-br.tsv
- config_name: en-te
data_files: en-te.tsv
- config_name: en-np
data_files: en-np.tsv
- config_name: en_GB-gu
data_files: en_GB-gu.tsv
- config_name: en_GB-ki
data_files: en_GB-ki.tsv
- config_name: en-kab-KAB
data_files: en-kab-KAB.tsv
- config_name: de-fr
data_files: de-fr.tsv
- config_name: en-ru_old
data_files: en-ru_old.tsv
- config_name: en_devel-es_do
data_files: en_devel-es_do.tsv
- config_name: en-ua
data_files: en-ua.tsv
- config_name: en-et_EE
data_files: en-et_EE.tsv
- config_name: ia-it
data_files: ia-it.tsv
- config_name: en_GB-ro
data_files: en_GB-ro.tsv
- config_name: en_US-pt-rPT
data_files: en_US-pt-rPT.tsv
- config_name: en-ur_PK
data_files: en-ur_PK.tsv
- config_name: en-pa-rPK
data_files: en-pa-rPK.tsv
- config_name: en-vec
data_files: en-vec.tsv
- config_name: en-nl-rBE
data_files: en-nl-rBE.tsv
- config_name: en-lv
data_files: en-lv.tsv
- config_name: en-ar-rBH
data_files: en-ar-rBH.tsv
- config_name: en-an
data_files: en-an.tsv
- config_name: en_US-sr
data_files: en_US-sr.tsv
- config_name: en-Ukrainian
data_files: en-Ukrainian.tsv
- config_name: en_US-mk
data_files: en_US-mk.tsv
- config_name: en_GB-br
data_files: en_GB-br.tsv
- config_name: en-de@informal
data_files: en-de@informal.tsv
- config_name: en-dz
data_files: en-dz.tsv
- config_name: en_US-he_IL
data_files: en_US-he_IL.tsv
- config_name: en_GB-mr
data_files: en_GB-mr.tsv
- config_name: en-cs-CARES
data_files: en-cs-CARES.tsv
- config_name: en_US-hi_IN
data_files: en_US-hi_IN.tsv
- config_name: en_US-ro
data_files: en_US-ro.tsv
- config_name: en_US-fr_CA
data_files: en_US-fr_CA.tsv
- config_name: en-as
data_files: en-as.tsv
- config_name: en_GB-ro_MD
data_files: en_GB-ro_MD.tsv
- config_name: en_US-lt-LT
data_files: en_US-lt-LT.tsv
- config_name: fr-ca
data_files: fr-ca.tsv
- config_name: en-be_Latn
data_files: en-be_Latn.tsv
- config_name: en-en-AU
data_files: en-en-AU.tsv
- config_name: en_US-fr_FR
data_files: en_US-fr_FR.tsv
- config_name: en-de-de
data_files: en-de-de.tsv
- config_name: en-nds
data_files: en-nds.tsv
- config_name: en_US-ja
data_files: en_US-ja.tsv
- config_name: en-es-AR
data_files: en-es-AR.tsv
- config_name: en-ms
data_files: en-ms.tsv
- config_name: en-zh-CHS
data_files: en-zh-CHS.tsv
- config_name: en_devel-bs
data_files: en_devel-bs.tsv
- config_name: en-arn
data_files: en-arn.tsv
- config_name: zh_Hans-en
data_files: zh_Hans-en.tsv
- config_name: en-co
data_files: en-co.tsv
- config_name: en-uz_Latn
data_files: en-uz_Latn.tsv
- config_name: en-cs-rCZ
data_files: en-cs-rCZ.tsv
- config_name: en-ku
data_files: en-ku.tsv
- config_name: en-ha
data_files: en-ha.tsv
- config_name: en-de-zuerich-lernt
data_files: en-de-zuerich-lernt.tsv
- config_name: en_US-be
data_files: en_US-be.tsv
- config_name: en-tr
data_files: en-tr.tsv
- config_name: en-ru_ru
data_files: en-ru_ru.tsv
- config_name: en-kl
data_files: en-kl.tsv
- config_name: en-it
data_files: en-it.tsv
- config_name: en-b+be+Latn
data_files: en-b+be+Latn.tsv
- config_name: en_devel-mk
data_files: en_devel-mk.tsv
- config_name: en_US-vi
data_files: en_US-vi.tsv
- config_name: en-zh_CMN-HANT
data_files: en-zh_CMN-HANT.tsv
- config_name: en-mnw
data_files: en-mnw.tsv
- config_name: en_US-sv-SE
data_files: en_US-sv-SE.tsv
- config_name: en-gum
data_files: en-gum.tsv
- config_name: en-my_MM
data_files: en-my_MM.tsv
- config_name: en_GB-mk_MK
data_files: en_GB-mk_MK.tsv
- config_name: en_devel-es_ec
data_files: en_devel-es_ec.tsv
- config_name: en_US-ne
data_files: en_US-ne.tsv
- config_name: nl-zh_Hans
data_files: nl-zh_Hans.tsv
- config_name: en-zh_hans
data_files: en-zh_hans.tsv
- config_name: en-sr-rCS
data_files: en-sr-rCS.tsv
- config_name: en-es_NI
data_files: en-es_NI.tsv
- config_name: en_GB-bs
data_files: en_GB-bs.tsv
- config_name: en_GB-tr_TR
data_files: en_GB-tr_TR.tsv
- config_name: ru-en
data_files: ru-en.tsv
- config_name: en_US-my
data_files: en_US-my.tsv
- config_name: en-ia
data_files: en-ia.tsv
- config_name: en-hu-HU
data_files: en-hu-HU.tsv
- config_name: en-nn_NO
data_files: en-nn_NO.tsv
- config_name: en_GB-es_419
data_files: en_GB-es_419.tsv
- config_name: en-ca-rES
data_files: en-ca-rES.tsv
- config_name: en_US-zh-CN
data_files: en_US-zh-CN.tsv
- config_name: en_US-tzm
data_files: en_US-tzm.tsv
- config_name: en-it_CARES
data_files: en-it_CARES.tsv
- config_name: en_GB-he
data_files: en_GB-he.tsv
- config_name: en_US-sn
data_files: en_US-sn.tsv
- config_name: en-ml_IN
data_files: en-ml_IN.tsv
- config_name: en-guc
data_files: en-guc.tsv
- config_name: zh_Hans-ru
data_files: zh_Hans-ru.tsv
- config_name: en-csb
data_files: en-csb.tsv
- config_name: en-nan
data_files: en-nan.tsv
- config_name: en-fa-IR
data_files: en-fa-IR.tsv
- config_name: en_US-en_CA
data_files: en_US-en_CA.tsv
- config_name: en_GB-ar
data_files: en_GB-ar.tsv
- config_name: en_GB-ia_FR
data_files: en_GB-ia_FR.tsv
- config_name: en_US-es-MX
data_files: en_US-es-MX.tsv
- config_name: en_devel-el
data_files: en_devel-el.tsv
- config_name: en_GB-ach
data_files: en_GB-ach.tsv
- config_name: en-Italian
data_files: en-Italian.tsv
- config_name: en_devel-az
data_files: en_devel-az.tsv
- config_name: eo-ru
data_files: eo-ru.tsv
- config_name: en-es_US
data_files: en-es_US.tsv
- config_name: en_devel-cy
data_files: en_devel-cy.tsv
- config_name: en-es-mx
data_files: en-es-mx.tsv
- config_name: en-en-rCA
data_files: en-en-rCA.tsv
- config_name: en-kn-IN
data_files: en-kn-IN.tsv
- config_name: en_devel-zh_CN
data_files: en_devel-zh_CN.tsv
- config_name: en_US-lt_LT
data_files: en_US-lt_LT.tsv
- config_name: en_GB-id_ID
data_files: en_GB-id_ID.tsv
- config_name: en-mt
data_files: en-mt.tsv
- config_name: en-bar
data_files: en-bar.tsv
- config_name: en-kr
data_files: en-kr.tsv
- config_name: en_GB-de-DE
data_files: en_GB-de-DE.tsv
- config_name: en-zgh
data_files: en-zgh.tsv
default: true
- config_name: en-german
data_files: en-german.tsv
- config_name: en-de_ch
data_files: en-de_ch.tsv
- config_name: en_devel-hy
data_files: en_devel-hy.tsv
- config_name: en_GB-hr
data_files: en_GB-hr.tsv
- config_name: en_GB-ca_AD
data_files: en_GB-ca_AD.tsv
- config_name: en-b+ca+VALENCIA
data_files: en-b+ca+VALENCIA.tsv
- config_name: en-rw
data_files: en-rw.tsv
- config_name: en-fil-FIL
data_files: en-fil-FIL.tsv
- config_name: it-de
data_files: it-de.tsv
- config_name: en_US-es-rMX
data_files: en_US-es-rMX.tsv
- config_name: en-sk-SK
data_files: en-sk-SK.tsv
- config_name: en-my-MM
data_files: en-my-MM.tsv
- config_name: en-es_ve
data_files: en-es_ve.tsv
- config_name: en-fra-rFR
data_files: en-fra-rFR.tsv
- config_name: en_GB-gv
data_files: en_GB-gv.tsv
- config_name: en-ml-IN
data_files: en-ml-IN.tsv
- config_name: en_US-zh-rHK
data_files: en_US-zh-rHK.tsv
- config_name: en-fur
data_files: en-fur.tsv
- config_name: en_GB-sv
data_files: en_GB-sv.tsv
- config_name: en-ne-rNP
data_files: en-ne-rNP.tsv
- config_name: en_GB-fr
data_files: en_GB-fr.tsv
- config_name: en_US-qya
data_files: en_US-qya.tsv
- config_name: en-ja_KS
data_files: en-ja_KS.tsv
- config_name: en-en_uwu_x
data_files: en-en_uwu_x.tsv
- config_name: en-zh_CN
data_files: en-zh_CN.tsv
- config_name: en-az_AZ
data_files: en-az_AZ.tsv
- config_name: en-bem
data_files: en-bem.tsv
- config_name: en-ars
data_files: en-ars.tsv
- config_name: en-xh
data_files: en-xh.tsv
- config_name: en_US-zh_Hant_HK
data_files: en_US-zh_Hant_HK.tsv
- config_name: en_US-en-rGB
data_files: en_US-en-rGB.tsv
- config_name: en-pam
data_files: en-pam.tsv
- config_name: en_devel-zh-rCN
data_files: en_devel-zh-rCN.tsv
- config_name: en-zh_LATN@pinyin
data_files: en-zh_LATN@pinyin.tsv
- config_name: en_US-en_NZ
data_files: en_US-en_NZ.tsv
- config_name: en-nb_no
data_files: en-nb_no.tsv
- config_name: en-bn-rBD
data_files: en-bn-rBD.tsv
- config_name: en-pl_PL
data_files: en-pl_PL.tsv
- config_name: en-romanian
data_files: en-romanian.tsv
- config_name: en_US-ja_KANJI
data_files: en_US-ja_KANJI.tsv
- config_name: en_US-zh-rCN
data_files: en_US-zh-rCN.tsv
- config_name: en-ca_es
data_files: en-ca_es.tsv
- config_name: en-de_de
data_files: en-de_de.tsv
- config_name: en-rom
data_files: en-rom.tsv
- config_name: en_devel-lv
data_files: en_devel-lv.tsv
- config_name: en-ro
data_files: en-ro.tsv
- config_name: en_US-th-TH
data_files: en_US-th-TH.tsv
- config_name: en_GB-wal
data_files: en_GB-wal.tsv
- config_name: en_US-fi-FI
data_files: en_US-fi-FI.tsv
- config_name: en-ar_AR
data_files: en-ar_AR.tsv
- config_name: en_US-el
data_files: en_US-el.tsv
- config_name: en_GB-chr
data_files: en_GB-chr.tsv
- config_name: en-pbb
data_files: en-pbb.tsv
- config_name: en-ar-rXB
data_files: en-ar-rXB.tsv
- config_name: en-tzm
data_files: en-tzm.tsv
- config_name: en-mr-rIN
data_files: en-mr-rIN.tsv
- config_name: en-ms-rMY
data_files: en-ms-rMY.tsv
- config_name: en-apc
data_files: en-apc.tsv
- config_name: en_GB-fi
data_files: en_GB-fi.tsv
- config_name: en_US-hi
data_files: en_US-hi.tsv
- config_name: en-hz
data_files: en-hz.tsv
- config_name: en_GB-mi
data_files: en_GB-mi.tsv
- config_name: en-sai
data_files: en-sai.tsv
- config_name: en-ig
data_files: en-ig.tsv
- config_name: en-en_Shaw
data_files: en-en_Shaw.tsv
- config_name: en_US-fa_IR
data_files: en_US-fa_IR.tsv
- config_name: en-mr
data_files: en-mr.tsv
- config_name: en-pl_PL_rude
data_files: en-pl_PL_rude.tsv
- config_name: en-cv
data_files: en-cv.tsv
- config_name: messages-ar
data_files: messages-ar.tsv
- config_name: en-ko_KO
data_files: en-ko_KO.tsv
- config_name: en_US-zh-hans
data_files: en_US-zh-hans.tsv
- config_name: en-ga-IE
data_files: en-ga-IE.tsv
- config_name: en-am
data_files: en-am.tsv
- config_name: en-ug
data_files: en-ug.tsv
- config_name: en-af_ZA
data_files: en-af_ZA.tsv
- config_name: en-ES
data_files: en-ES.tsv
- config_name: en_US-ru_RU
data_files: en_US-ru_RU.tsv
- config_name: en_GB-lv
data_files: en_GB-lv.tsv
- config_name: en-yi
data_files: en-yi.tsv
- config_name: en_GB-pl
data_files: en_GB-pl.tsv
- config_name: en_GB-tl
data_files: en_GB-tl.tsv
- config_name: en-km
data_files: en-km.tsv
- config_name: en-azb
data_files: en-azb.tsv
- config_name: en_devel-fr
data_files: en_devel-fr.tsv
- config_name: en-pa-PK
data_files: en-pa-PK.tsv
- config_name: en-tn
data_files: en-tn.tsv
- config_name: en-mjw
data_files: en-mjw.tsv
- config_name: en-frs
data_files: en-frs.tsv
- config_name: en-it-IT
data_files: en-it-IT.tsv
- config_name: en-ro_RO
data_files: en-ro_RO.tsv
- config_name: en_US-nl_NL
data_files: en_US-nl_NL.tsv
- config_name: en-ht
data_files: en-ht.tsv
- config_name: en_devel-es_cr
data_files: en_devel-es_cr.tsv
- config_name: en_US-zh-rTW
data_files: en_US-zh-rTW.tsv
- config_name: en-fo
data_files: en-fo.tsv
- config_name: en-skr
data_files: en-skr.tsv
- config_name: en-ak
data_files: en-ak.tsv
- config_name: en_GB-sr@latin
data_files: en_GB-sr@latin.tsv
- config_name: en_US-de_CH
data_files: en_US-de_CH.tsv
- config_name: en_US-uk-UA
data_files: en_US-uk-UA.tsv
- config_name: en-ko_KR
data_files: en-ko_KR.tsv
- config_name: en-cy
data_files: en-cy.tsv
- config_name: en-galo
data_files: en-galo.tsv
- config_name: en-bn_BD
data_files: en-bn_BD.tsv
- config_name: en_devel-ms
data_files: en_devel-ms.tsv
- config_name: fr-it
data_files: fr-it.tsv
- config_name: en-ny
data_files: en-ny.tsv
- config_name: en-tet
data_files: en-tet.tsv
- config_name: en_GB-sk
data_files: en_GB-sk.tsv
- config_name: eo-ar
data_files: eo-ar.tsv
- config_name: eo-es
data_files: eo-es.tsv
- config_name: en-bho
data_files: en-bho.tsv
- config_name: en-pap
data_files: en-pap.tsv
- config_name: en-vi_VN
data_files: en-vi_VN.tsv
- config_name: en_US-ar
data_files: en_US-ar.tsv
- config_name: en_devel-nb
data_files: en_devel-nb.tsv
- config_name: en_devel-es_mx
data_files: en_devel-es_mx.tsv
- config_name: es-ca
data_files: es-ca.tsv
- config_name: en_GB-kn
data_files: en_GB-kn.tsv
- config_name: en-ru_UA
data_files: en-ru_UA.tsv
- config_name: sv-nb
data_files: sv-nb.tsv
- config_name: en_GB-zh_Hans
data_files: en_GB-zh_Hans.tsv
- config_name: en-he-IL
data_files: en-he-IL.tsv
- config_name: en_GB-et
data_files: en_GB-et.tsv
- config_name: es-pl
data_files: es-pl.tsv
- config_name: en-hy-AM
data_files: en-hy-AM.tsv
- config_name: en_US-cy
data_files: en_US-cy.tsv
- config_name: en-hu-rZZ
data_files: en-hu-rZZ.tsv
- config_name: en-by
data_files: en-by.tsv
- config_name: en_GB-hy
data_files: en_GB-hy.tsv
- config_name: en_US-zh-Hant
data_files: en_US-zh-Hant.tsv
- config_name: en-gu-IN
data_files: en-gu-IN.tsv
- config_name: en_GB-ml_IN
data_files: en_GB-ml_IN.tsv
- config_name: de-nl
data_files: de-nl.tsv
- config_name: en_devel-ur
data_files: en_devel-ur.tsv
- config_name: en-ca-ES
data_files: en-ca-ES.tsv
- config_name: en_GB-kl
data_files: en_GB-kl.tsv
- config_name: en_US-ta_IN
data_files: en_US-ta_IN.tsv
- config_name: en_US-sk_SK
data_files: en_US-sk_SK.tsv
- config_name: en-zh_Latn
data_files: en-zh_Latn.tsv
- config_name: en_GB-es
data_files: en_GB-es.tsv
- config_name: en-en_uk
data_files: en-en_uk.tsv
- config_name: en_GB-ru
data_files: en_GB-ru.tsv
- config_name: en-gu
data_files: en-gu.tsv
- config_name: en_US-km
data_files: en_US-km.tsv
- config_name: en_GB-uz
data_files: en_GB-uz.tsv
- config_name: en_US-yue-HK
data_files: en_US-yue-HK.tsv
- config_name: en-ceb
data_files: en-ceb.tsv
- config_name: en-is
data_files: en-is.tsv
- config_name: en-ug@Arab
data_files: en-ug@Arab.tsv
- config_name: es-ru
data_files: es-ru.tsv
- config_name: en-pt
data_files: en-pt.tsv
- config_name: en-es-US
data_files: en-es-US.tsv
- config_name: en-zh-rCMN-HANT
data_files: en-zh-rCMN-HANT.tsv
- config_name: en-jbo-EN
data_files: en-jbo-EN.tsv
- config_name: en_US-pa
data_files: en_US-pa.tsv
- config_name: en_US-or
data_files: en_US-or.tsv
- config_name: dev-hu
data_files: dev-hu.tsv
- config_name: en-b+ast
data_files: en-b+ast.tsv
- config_name: messages-vi
data_files: messages-vi.tsv
- config_name: en-ht-HT
data_files: en-ht-HT.tsv
- config_name: en-ar_AA
data_files: en-ar_AA.tsv
- config_name: en-mcc234
data_files: en-mcc234.tsv
- config_name: en_GB-he_IL
data_files: en_GB-he_IL.tsv
- config_name: en-fr_FR
data_files: en-fr_FR.tsv
- config_name: en-es_ES
data_files: en-es_ES.tsv
- config_name: en-tr-v26
data_files: en-tr-v26.tsv
- config_name: ru-kk
data_files: ru-kk.tsv
- config_name: en_GB-ky
data_files: en_GB-ky.tsv
- config_name: en-st
data_files: en-st.tsv
- config_name: en-ky
data_files: en-ky.tsv
- config_name: en_GB-fa
data_files: en_GB-fa.tsv
- config_name: en-ta
data_files: en-ta.tsv
- config_name: en_US-ru-RU
data_files: en_US-ru-RU.tsv
- config_name: en_US-it
data_files: en_US-it.tsv
- config_name: en-mai
data_files: en-mai.tsv
- config_name: en_GB-ga
data_files: en_GB-ga.tsv
- config_name: en-ay
data_files: en-ay.tsv
- config_name: en-pt_PT
data_files: en-pt_PT.tsv
- config_name: en-fa-rIR
data_files: en-fa-rIR.tsv
- config_name: en-sk_SK
data_files: en-sk_SK.tsv
- config_name: en-ru_sov
data_files: en-ru_sov.tsv
- config_name: en-pt-PT
data_files: en-pt-PT.tsv
- config_name: en_US-ko-KR
data_files: en_US-ko-KR.tsv
- config_name: en-es-rCO
data_files: en-es-rCO.tsv
- config_name: en-zh
data_files: en-zh.tsv
- config_name: en_US-ber
data_files: en_US-ber.tsv
- config_name: en-en_NZ
data_files: en-en_NZ.tsv
- config_name: eo-hi
data_files: eo-hi.tsv
- config_name: en_US-kab
data_files: en_US-kab.tsv
- config_name: en_GB-ru_RU
data_files: en_GB-ru_RU.tsv
- config_name: en-kok@latin
data_files: en-kok@latin.tsv
- config_name: en-ne_NP
data_files: en-ne_NP.tsv
- config_name: en-no-NO
data_files: en-no-NO.tsv
- config_name: it-nl_NL
data_files: it-nl_NL.tsv
- config_name: en-HE
data_files: en-HE.tsv
- config_name: eo-ja
data_files: eo-ja.tsv
- config_name: en_US-kmr
data_files: en_US-kmr.tsv
- config_name: en-pt-BR
data_files: en-pt-BR.tsv
- config_name: en-pl-v26
data_files: en-pl-v26.tsv
- config_name: en_devel-zh-tw
data_files: en_devel-zh-tw.tsv
- config_name: en-mcc235
data_files: en-mcc235.tsv
- config_name: en-el-gr
data_files: en-el-gr.tsv
- config_name: en-ga
data_files: en-ga.tsv
- config_name: en_GB-zh_CN
data_files: en_GB-zh_CN.tsv
- config_name: en_GB-kab
data_files: en_GB-kab.tsv
- config_name: en-te-IN
data_files: en-te-IN.tsv
- config_name: en_GB-de
data_files: en_GB-de.tsv
- config_name: und-de
data_files: und-de.tsv
- config_name: en-nb-rNO-v26
data_files: en-nb-rNO-v26.tsv
- config_name: en-zh_SIMPLIFIED
data_files: en-zh_SIMPLIFIED.tsv
- config_name: en-ur-rPK
data_files: en-ur-rPK.tsv
- config_name: en_US-zh-cn
data_files: en_US-zh-cn.tsv
- config_name: en_devel-pa
data_files: en_devel-pa.tsv
- config_name: en-aii
data_files: en-aii.tsv
- config_name: en_GB-it_IT
data_files: en_GB-it_IT.tsv
- config_name: en_GB-yo
data_files: en_GB-yo.tsv
- config_name: de-id
data_files: de-id.tsv
- config_name: en_GB-nv
data_files: en_GB-nv.tsv
- config_name: en-sw-KE
data_files: en-sw-KE.tsv
- config_name: en_US-so
data_files: en_US-so.tsv
- config_name: en-yue
data_files: en-yue.tsv
- config_name: en-ps
data_files: en-ps.tsv
- config_name: en-mr-IN
data_files: en-mr-IN.tsv
- config_name: de-cs
data_files: de-cs.tsv
- config_name: en_GB-pt-BR
data_files: en_GB-pt-BR.tsv
- config_name: en-ne
data_files: en-ne.tsv
- config_name: en_GB-kk
data_files: en_GB-kk.tsv
- config_name: en-af-ZA
data_files: en-af-ZA.tsv
- config_name: en-pa
data_files: en-pa.tsv
- config_name: en_US-lt
data_files: en_US-lt.tsv
- config_name: en-b+qtq+Latn
data_files: en-b+qtq+Latn.tsv
- config_name: zh_Hant-zgh
data_files: zh_Hant-zgh.tsv
- config_name: en-ta-IN
data_files: en-ta-IN.tsv
- config_name: en_GB-hu
data_files: en_GB-hu.tsv
- config_name: en-iw
data_files: en-iw.tsv
- config_name: es-hi
data_files: es-hi.tsv
- config_name: en-es_EC
data_files: en-es_EC.tsv
- config_name: en-ukrainian
data_files: en-ukrainian.tsv
- config_name: en_US-he
data_files: en_US-he.tsv
- config_name: en_GB-sl
data_files: en_GB-sl.tsv
- config_name: en_devel-sgs
data_files: en_devel-sgs.tsv
- config_name: en_US-zh-HK
data_files: en_US-zh-HK.tsv
- config_name: en_US-th_TH
data_files: en_US-th_TH.tsv
- config_name: en-nl_NL
data_files: en-nl_NL.tsv
- config_name: en-zh-HK
data_files: en-zh-HK.tsv
- config_name: en-zh-hans
data_files: en-zh-hans.tsv
- config_name: en_devel-he
data_files: en_devel-he.tsv
- config_name: en_GB-ur
data_files: en_GB-ur.tsv
- config_name: en_GB-da
data_files: en_GB-da.tsv
- config_name: en_GB-bn
data_files: en_GB-bn.tsv
- config_name: en-chinese
data_files: en-chinese.tsv
- config_name: en-bg-BG
data_files: en-bg-BG.tsv
- config_name: en_devel-jpn_JP
data_files: en_devel-jpn_JP.tsv
- config_name: en_devel-id
data_files: en_devel-id.tsv
- config_name: und-ru
data_files: und-ru.tsv
- config_name: en_devel-in
data_files: en_devel-in.tsv
- config_name: en-wo
data_files: en-wo.tsv
- config_name: nl-da
data_files: nl-da.tsv
- config_name: en-pa-Arab-PK
data_files: en-pa-Arab-PK.tsv
- config_name: en-gr-GR
data_files: en-gr-GR.tsv
- config_name: en-az-AZ
data_files: en-az-AZ.tsv
- config_name: en-bg
data_files: en-bg.tsv
- config_name: en-es-rAR
data_files: en-es-rAR.tsv
- config_name: en-nb-NO
data_files: en-nb-NO.tsv
- config_name: en_UK-bg_BG
data_files: en_UK-bg_BG.tsv
- config_name: en_GB-pap
data_files: en_GB-pap.tsv
- config_name: en_US-es
data_files: en_US-es.tsv
- config_name: en_US-hu
data_files: en_US-hu.tsv
- config_name: en-or-IN
data_files: en-or-IN.tsv
- config_name: en-guw
data_files: en-guw.tsv
- config_name: en-nl-BE
data_files: en-nl-BE.tsv
- config_name: en-ml-rIN
data_files: en-ml-rIN.tsv
- config_name: en-ji
data_files: en-ji.tsv
- config_name: en_US-ta
data_files: en_US-ta.tsv
- config_name: es-ur
data_files: es-ur.tsv
- config_name: en-br
data_files: en-br.tsv
- config_name: de-en
data_files: de-en.tsv
- config_name: dev-fr
data_files: dev-fr.tsv
- config_name: en-ace
data_files: en-ace.tsv
- config_name: en_US-zh_TW
data_files: en_US-zh_TW.tsv
- config_name: en-oj
data_files: en-oj.tsv
- config_name: en-zh_tw
data_files: en-zh_tw.tsv
- config_name: en-cnr
data_files: en-cnr.tsv
- config_name: en_devel-es_hn
data_files: en_devel-es_hn.tsv
- config_name: dev-uk
data_files: dev-uk.tsv
- config_name: en-ru_CARES
data_files: en-ru_CARES.tsv
- config_name: en-uroc
data_files: en-uroc.tsv
- config_name: en_GB-bg_BG
data_files: en_GB-bg_BG.tsv
- config_name: en_GB-ar_SA
data_files: en_GB-ar_SA.tsv
- config_name: en_US-fy
data_files: en_US-fy.tsv
- config_name: en-lt
data_files: en-lt.tsv
- config_name: en-de-rDE
data_files: en-de-rDE.tsv
- config_name: en_US-ast
data_files: en_US-ast.tsv
- config_name: en_US-ko_KR
data_files: en_US-ko_KR.tsv
- config_name: en_devel-ar_DZ
data_files: en_devel-ar_DZ.tsv
- config_name: en_devel-hu
data_files: en_devel-hu.tsv
- config_name: en-fr_BE
data_files: en-fr_BE.tsv
- config_name: en-kmr
data_files: en-kmr.tsv
- config_name: en_devel-ro_ro
data_files: en_devel-ro_ro.tsv
- config_name: en_GB-vi_VN
data_files: en_GB-vi_VN.tsv
- config_name: en_devel-sk
data_files: en_devel-sk.tsv
- config_name: und-nl_BE
data_files: und-nl_BE.tsv
- config_name: eo-bn
data_files: eo-bn.tsv
- config_name: en-hungarian
data_files: en-hungarian.tsv
- config_name: en_GB-ta
data_files: en_GB-ta.tsv
- config_name: en_US-ca
data_files: en_US-ca.tsv
- config_name: en-oc
data_files: en-oc.tsv
- config_name: en_US-bg_BG
data_files: en_US-bg_BG.tsv
- config_name: en-hr
data_files: en-hr.tsv
- config_name: en_GB-zh_Hant
data_files: en_GB-zh_Hant.tsv
- config_name: en_GB-bn_BD
data_files: en_GB-bn_BD.tsv
- config_name: en-ca@valencia
data_files: en-ca@valencia.tsv
- config_name: en_GB-mai
data_files: en_GB-mai.tsv
- config_name: en-uk-UA
data_files: en-uk-UA.tsv
- config_name: en-frm
data_files: en-frm.tsv
- config_name: en-bd
data_files: en-bd.tsv
- config_name: en_GB-ja
data_files: en_GB-ja.tsv
- config_name: en_US-sw
data_files: en_US-sw.tsv
- config_name: eo-uk
data_files: eo-uk.tsv
- config_name: en_US-es-rAR
data_files: en_US-es-rAR.tsv
- config_name: en-az-rAZ
data_files: en-az-rAZ.tsv
- config_name: en_GB-es-ES
data_files: en_GB-es-ES.tsv
- config_name: en-sl-SL
data_files: en-sl-SL.tsv
- config_name: en-pms
data_files: en-pms.tsv
- config_name: en_GB-te
data_files: en_GB-te.tsv
- config_name: it-de_DE
data_files: it-de_DE.tsv
- config_name: en-yue_Hant
data_files: en-yue_Hant.tsv
- config_name: en-en-rIN
data_files: en-en-rIN.tsv
- config_name: en-ln
data_files: en-ln.tsv
- config_name: en-pt-rBR
data_files: en-pt-rBR.tsv
- config_name: en_US-az_AZ
data_files: en_US-az_AZ.tsv
- config_name: en-pl-rPL
data_files: en-pl-rPL.tsv
- config_name: eo-el
data_files: eo-el.tsv
- config_name: eo-ms
data_files: eo-ms.tsv
- config_name: en_US-tr
data_files: en_US-tr.tsv
- config_name: en-en_SHAW
data_files: en-en_SHAW.tsv
- config_name: en-ar-rIQ
data_files: en-ar-rIQ.tsv
- config_name: en-yo
data_files: en-yo.tsv
- config_name: en-japanese
data_files: en-japanese.tsv
- config_name: es-id
data_files: es-id.tsv
- config_name: en-fa_AF
data_files: en-fa_AF.tsv
- config_name: en_GB-ms
data_files: en_GB-ms.tsv
- config_name: en-Zh-CHS
data_files: en-Zh-CHS.tsv
- config_name: en_GB-mt
data_files: en_GB-mt.tsv
- config_name: en-b+de
data_files: en-b+de.tsv
- config_name: en_US-fi
data_files: en_US-fi.tsv
- config_name: de-ar
data_files: de-ar.tsv
- config_name: en-en-GB
data_files: en-en-GB.tsv
- config_name: en-mo
data_files: en-mo.tsv
- config_name: en_devel-zh_Hans
data_files: en_devel-zh_Hans.tsv
- config_name: en_GB-dz
data_files: en_GB-dz.tsv
- config_name: en_US-gl
data_files: en_US-gl.tsv
- config_name: en-pt-rPT
data_files: en-pt-rPT.tsv
- config_name: en_devel-es_pr
data_files: en_devel-es_pr.tsv
- config_name: en-RU
data_files: en-RU.tsv
- config_name: en-en-rUS
data_files: en-en-rUS.tsv
- config_name: en-sv_se
data_files: en-sv_se.tsv
- config_name: en-italian
data_files: en-italian.tsv
- config_name: en_US-lv
data_files: en_US-lv.tsv
- config_name: de-ru
data_files: de-ru.tsv
- config_name: en-sc
data_files: en-sc.tsv
- config_name: en-gv
data_files: en-gv.tsv
- config_name: en_US-pt_PT
data_files: en_US-pt_PT.tsv
- config_name: en_GB-bn_IN
data_files: en_GB-bn_IN.tsv
- config_name: en_US-fr-FR
data_files: en_US-fr-FR.tsv
- config_name: ia-es
data_files: ia-es.tsv
- config_name: en_US-es_UY
data_files: en_US-es_UY.tsv
- config_name: en_GB-hr_HR
data_files: en_GB-hr_HR.tsv
- config_name: en-id_ID
data_files: en-id_ID.tsv
- config_name: en-es_VE
data_files: en-es_VE.tsv
- config_name: en-ie
data_files: en-ie.tsv
- config_name: en-it_IT
data_files: en-it_IT.tsv
- config_name: en_GB-si_LK
data_files: en_GB-si_LK.tsv
- config_name: en-nqo
data_files: en-nqo.tsv
- config_name: pl-uk
data_files: pl-uk.tsv
- config_name: en-sco
data_files: en-sco.tsv
- config_name: en_US-tr-TR
data_files: en_US-tr-TR.tsv
- config_name: en-en_GB
data_files: en-en_GB.tsv
- config_name: en-b+kab
data_files: en-b+kab.tsv
- config_name: en-he-rIL
data_files: en-he-rIL.tsv
- config_name: en-pu
data_files: en-pu.tsv
- config_name: de-lb
data_files: de-lb.tsv
- config_name: en-is_IS
data_files: en-is_IS.tsv
- config_name: en_US-cs
data_files: en_US-cs.tsv
- config_name: en_GB-nah
data_files: en_GB-nah.tsv
- config_name: de-tr
data_files: de-tr.tsv
- config_name: zh_Hant-en_US
data_files: zh_Hant-en_US.tsv
- config_name: pl-ru
data_files: pl-ru.tsv
- config_name: en-zh-TW
data_files: en-zh-TW.tsv
- config_name: en_GB-kok
data_files: en_GB-kok.tsv
- config_name: en_US-zh-Hans
data_files: en_US-zh-Hans.tsv
- config_name: en_devel-da
data_files: en_devel-da.tsv
- config_name: en-mg
data_files: en-mg.tsv
- config_name: en-pa-rIN
data_files: en-pa-rIN.tsv
- config_name: en-nb_NO
data_files: en-nb_NO.tsv
- config_name: en_GB-az
data_files: en_GB-az.tsv
- config_name: en-ca_valencia
data_files: en-ca_valencia.tsv
- config_name: en-su
data_files: en-su.tsv
- config_name: und-sv
data_files: und-sv.tsv
- config_name: pl-en
data_files: pl-en.tsv
- config_name: en-ar-rDZ
data_files: en-ar-rDZ.tsv
- config_name: en_US-eo
data_files: en_US-eo.tsv
- config_name: en_US-sq
data_files: en_US-sq.tsv
- config_name: en-sl-rSI
data_files: en-sl-rSI.tsv
- config_name: en-uk-rUA
data_files: en-uk-rUA.tsv
- config_name: en_devel-te
data_files: en_devel-te.tsv
- config_name: en-da_DK
data_files: en-da_DK.tsv
- config_name: en_GB-et_EE
data_files: en_GB-et_EE.tsv
- config_name: en-et-EE
data_files: en-et-EE.tsv
- config_name: en-pa_IN
data_files: en-pa_IN.tsv
- config_name: en_US-nn
data_files: en_US-nn.tsv
- config_name: en_GB-xh
data_files: en_GB-xh.tsv
- config_name: en_devel-sv
data_files: en_devel-sv.tsv
- config_name: en-ru-rRU
data_files: en-ru-rRU.tsv
- config_name: en_US-hr
data_files: en_US-hr.tsv
- config_name: en-sr_Latn
data_files: en-sr_Latn.tsv
- config_name: en_GB-uk
data_files: en_GB-uk.tsv
- config_name: en_GB-ee
data_files: en_GB-ee.tsv
- config_name: en_devel-ta
data_files: en_devel-ta.tsv
- config_name: en_US-hu-HU
data_files: en_US-hu-HU.tsv
- config_name: en_GB-ak
data_files: en_GB-ak.tsv
- config_name: en_US-ia
data_files: en_US-ia.tsv
- config_name: en_UK-it_IT
data_files: en_UK-it_IT.tsv
- config_name: en-ru
data_files: en-ru.tsv
- config_name: en_US-es-ar
data_files: en_US-es-ar.tsv
- config_name: en_US-lo
data_files: en_US-lo.tsv
- config_name: en-ur-PK
data_files: en-ur-PK.tsv
- config_name: en_devel-nb_NO
data_files: en_devel-nb_NO.tsv
- config_name: en_GB-es_ES
data_files: en_GB-es_ES.tsv
- config_name: en_GB-ast
data_files: en_GB-ast.tsv
- config_name: en-hr-HR
data_files: en-hr-HR.tsv
- config_name: en-fr@informal
data_files: en-fr@informal.tsv
- config_name: en-es_ar
data_files: en-es_ar.tsv
- config_name: en-ms_MY
data_files: en-ms_MY.tsv
- config_name: en-el_GR
data_files: en-el_GR.tsv
- config_name: en_devel-ka
data_files: en_devel-ka.tsv
- config_name: en-fr-FR
data_files: en-fr-FR.tsv
- config_name: en_US-kk
data_files: en_US-kk.tsv
- config_name: es-ko
data_files: es-ko.tsv
- config_name: en-fr_AG
data_files: en-fr_AG.tsv
- config_name: en-zh-tw
data_files: en-zh-tw.tsv
- config_name: en-BrazilianPortuguese
data_files: en-BrazilianPortuguese.tsv
- config_name: en_GB-am
data_files: en_GB-am.tsv
- config_name: en-tam
data_files: en-tam.tsv
- config_name: en_US-af
data_files: en_US-af.tsv
- config_name: en_US-is
data_files: en_US-is.tsv
- config_name: en_GB-en_US
data_files: en_GB-en_US.tsv
- config_name: en-az
data_files: en-az.tsv
- config_name: en-en@pirate
data_files: en-en@pirate.tsv
- config_name: en_GB-fil
data_files: en_GB-fil.tsv
- config_name: en_US-pl_PL
data_files: en_US-pl_PL.tsv
- config_name: en_US-sl
data_files: en_US-sl.tsv
- config_name: en_US-nl
data_files: en_US-nl.tsv
- config_name: es-it
data_files: es-it.tsv
- config_name: en_GB-bar
data_files: en_GB-bar.tsv
- config_name: it-nb_NO
data_files: it-nb_NO.tsv
- config_name: eo-it
data_files: eo-it.tsv
- config_name: en_US-yue
data_files: en_US-yue.tsv
- config_name: en-glk
data_files: en-glk.tsv
- config_name: en-fi_FI
data_files: en-fi_FI.tsv
- config_name: es-cs
data_files: es-cs.tsv
- config_name: en_GB-pt_BR
data_files: en_GB-pt_BR.tsv
- config_name: en_GB-zgh
data_files: en_GB-zgh.tsv
- config_name: en_US-nl-BE
data_files: en_US-nl-BE.tsv
- config_name: en-ru-rCH
data_files: en-ru-rCH.tsv
- config_name: en-sr_CS
data_files: en-sr_CS.tsv
- config_name: en-ur
data_files: en-ur.tsv
- config_name: en_GB-th
data_files: en_GB-th.tsv
- config_name: en_US-id_ID
data_files: en_US-id_ID.tsv
- config_name: en_US-be_BY
data_files: en_US-be_BY.tsv
- config_name: en_devel-es_us
data_files: en_devel-es_us.tsv
- config_name: en-fr_CA
data_files: en-fr_CA.tsv
- config_name: en_GB-en
data_files: en_GB-en.tsv
- config_name: en_US-sk
data_files: en_US-sk.tsv
- config_name: en-uz-Latn
data_files: en-uz-Latn.tsv
- config_name: en_devel-eu
data_files: en_devel-eu.tsv
- config_name: en_GB-is_IS
data_files: en_GB-is_IS.tsv
- config_name: sl-en
data_files: sl-en.tsv
- config_name: en-ja_JA
data_files: en-ja_JA.tsv
- config_name: en-bn-BD
data_files: en-bn-BD.tsv
- config_name: fr-de
data_files: fr-de.tsv
- config_name: en-sr_SP
data_files: en-sr_SP.tsv
- config_name: en-nb-no
data_files: en-nb-no.tsv
- config_name: fr-nb_NO
data_files: fr-nb_NO.tsv
- config_name: en_US-lb
data_files: en_US-lb.tsv
- config_name: en-zh_hant
data_files: en-zh_hant.tsv
- config_name: en-be
data_files: en-be.tsv
- config_name: en_US-si
data_files: en_US-si.tsv
- config_name: en-ltg
data_files: en-ltg.tsv
- config_name: en-es_cl
data_files: en-es_cl.tsv
- config_name: en_US-gu
data_files: en_US-gu.tsv
- config_name: en-lb_LU
data_files: en-lb_LU.tsv
- config_name: en-ain
data_files: en-ain.tsv
- config_name: en-de
data_files: en-de.tsv
- config_name: en-es
data_files: en-es.tsv
- config_name: en-belarusian
data_files: en-belarusian.tsv
- config_name: en-kok
data_files: en-kok.tsv
- config_name: nl-fr
data_files: nl-fr.tsv
- config_name: en-ar_SA
data_files: en-ar_SA.tsv
- config_name: en-tk
data_files: en-tk.tsv
- config_name: en-kab
data_files: en-kab.tsv
- config_name: en-or-rIN
data_files: en-or-rIN.tsv
- config_name: en-ja-KS
data_files: en-ja-KS.tsv
- config_name: en-en-Shaw
data_files: en-en-Shaw.tsv
- config_name: en_GB-lo
data_files: en_GB-lo.tsv
- config_name: en_GB-gl_ES
data_files: en_GB-gl_ES.tsv
- config_name: en-sd
data_files: en-sd.tsv
- config_name: en_devel-es_ar
data_files: en_devel-es_ar.tsv
- config_name: en-he-il
data_files: en-he-il.tsv
- config_name: en_GB-zh_TW
data_files: en_GB-zh_TW.tsv
- config_name: en-cs_cz
data_files: en-cs_cz.tsv
- config_name: en_GB-mn
data_files: en_GB-mn.tsv
- config_name: en_US-jv
data_files: en_US-jv.tsv
- config_name: eo-nl
data_files: eo-nl.tsv
- config_name: en-zh_cn
data_files: en-zh_cn.tsv
- config_name: en-he_IL
data_files: en-he_IL.tsv
- config_name: en-IT
data_files: en-IT.tsv
- config_name: en-ja
data_files: en-ja.tsv
- config_name: en_US-fr-ca
data_files: en_US-fr-ca.tsv
- config_name: en-bqi
data_files: en-bqi.tsv
- config_name: en-ro-rRO
data_files: en-ro-rRO.tsv
- config_name: en-krl
data_files: en-krl.tsv
- config_name: en_US-tr_TR
data_files: en_US-tr_TR.tsv
- config_name: pl-lt
data_files: pl-lt.tsv
- config_name: en-zh_Hant_HK
data_files: en-zh_Hant_HK.tsv
- config_name: en_GB-sv_SE
data_files: en_GB-sv_SE.tsv
- config_name: en_US-pt-br
data_files: en_US-pt-br.tsv
- config_name: en-id-ID
data_files: en-id-ID.tsv
- config_name: en-fu
data_files: en-fu.tsv
- config_name: en-French
data_files: en-French.tsv
- config_name: eo-zh
data_files: eo-zh.tsv
- config_name: en-v20
data_files: en-v20.tsv
- config_name: en-iw-IL
data_files: en-iw-IL.tsv
- config_name: en_GB-af
data_files: en_GB-af.tsv
- config_name: en_GB-el
data_files: en_GB-el.tsv
- config_name: en-pa-IN
data_files: en-pa-IN.tsv
- config_name: en_devel-es_ve
data_files: en_devel-es_ve.tsv
- config_name: und-nb_NO
data_files: und-nb_NO.tsv
- config_name: en-lo
data_files: en-lo.tsv
- config_name: en-ar
data_files: en-ar.tsv
- config_name: en-b+zh+HANS+CN
data_files: en-b+zh+HANS+CN.tsv
- config_name: en_GB-byn
data_files: en_GB-byn.tsv
- config_name: en-en-rXC
data_files: en-en-rXC.tsv
- config_name: zh_Hant-nb_NO
data_files: zh_Hant-nb_NO.tsv
- config_name: en-fr
data_files: en-fr.tsv
- config_name: en-zh_HANT
data_files: en-zh_HANT.tsv
- config_name: en_US-fa-IR
data_files: en_US-fa-IR.tsv
- config_name: en_GB-vi
data_files: en_GB-vi.tsv
- config_name: en-Spanish
data_files: en-Spanish.tsv
- config_name: en-am_ET
data_files: en-am_ET.tsv
- config_name: en_devel-bn
data_files: en_devel-bn.tsv
- config_name: en-zh-cn
data_files: en-zh-cn.tsv
- config_name: en-tr-rTR
data_files: en-tr-rTR.tsv
- config_name: fr-cs
data_files: fr-cs.tsv
- config_name: en_US-nl-rBE
data_files: en_US-nl-rBE.tsv
- config_name: es-en
data_files: es-en.tsv
- config_name: en-sr@Cyrl
data_files: en-sr@Cyrl.tsv
- config_name: fr-eu
data_files: fr-eu.tsv
- config_name: en_US-pl
data_files: en_US-pl.tsv
- config_name: en_US-nan
data_files: en_US-nan.tsv
- config_name: en_devel-pt-rBR
data_files: en_devel-pt-rBR.tsv
- config_name: en-sr_lat
data_files: en-sr_lat.tsv
- config_name: en_devel-no
data_files: en_devel-no.tsv
- config_name: pl-de
data_files: pl-de.tsv
- config_name: en-tlh
data_files: en-tlh.tsv
- config_name: en_US-cs_CZ
data_files: en_US-cs_CZ.tsv
- config_name: eo-pl
data_files: eo-pl.tsv
- config_name: en_devel-gl
data_files: en_devel-gl.tsv
- config_name: en-fi-FI
data_files: en-fi-FI.tsv
- config_name: en_US-ca_CA
data_files: en_US-ca_CA.tsv
- config_name: en_US-nb
data_files: en_US-nb.tsv
- config_name: en-is-IS
data_files: en-is-IS.tsv
- config_name: en_GB-io
data_files: en_GB-io.tsv
- config_name: en-UK
data_files: en-UK.tsv
- config_name: en-pt-pt
data_files: en-pt-pt.tsv
- config_name: en-fil
data_files: en-fil.tsv
- config_name: en-mi
data_files: en-mi.tsv
- config_name: en-sr-Cyrl
data_files: en-sr-Cyrl.tsv
- config_name: en_devel-hi
data_files: en_devel-hi.tsv
- config_name: en-nb-NB
data_files: en-nb-NB.tsv
- config_name: en-mnc
data_files: en-mnc.tsv
- config_name: en-mk
data_files: en-mk.tsv
- config_name: en-hrx
data_files: en-hrx.tsv
- config_name: en-ar_MA
data_files: en-ar_MA.tsv
- config_name: en_devel-es
data_files: en_devel-es.tsv
- config_name: en_GB-zh-rCN
data_files: en_GB-zh-rCN.tsv
- config_name: en-sa
data_files: en-sa.tsv
- config_name: en-bs
data_files: en-bs.tsv
- config_name: en_GB-tg
data_files: en_GB-tg.tsv
- config_name: en-si-LK
data_files: en-si-LK.tsv
- config_name: en-lt-LT
data_files: en-lt-LT.tsv
- config_name: en-hi
data_files: en-hi.tsv
- config_name: en-hu_hu
data_files: en-hu_hu.tsv
- config_name: en-mk_MK
data_files: en-mk_MK.tsv
- config_name: en_GB-de_DE
data_files: en_GB-de_DE.tsv
- config_name: messages-eo
data_files: messages-eo.tsv
- config_name: en-ku_IQ
data_files: en-ku_IQ.tsv
- config_name: en-rcf
data_files: en-rcf.tsv
- config_name: en-uz
data_files: en-uz.tsv
- config_name: en-by_lat
data_files: en-by_lat.tsv
- config_name: ia-nb_NO
data_files: ia-nb_NO.tsv
- config_name: messages-ko
data_files: messages-ko.tsv
- config_name: en_US-pt-rBR
data_files: en_US-pt-rBR.tsv
- config_name: en_GB-zu
data_files: en_GB-zu.tsv
- config_name: es-hr
data_files: es-hr.tsv
- config_name: en_devel-th
data_files: en_devel-th.tsv
- config_name: en-af
data_files: en-af.tsv
- config_name: en-ms-MY
data_files: en-ms-MY.tsv
- config_name: en-sr-Latn-RS
data_files: en-sr-Latn-RS.tsv
- config_name: en-de-ZH
data_files: en-de-ZH.tsv
- config_name: en-b+sr+Latn
data_files: en-b+sr+Latn.tsv
- config_name: en-cn
data_files: en-cn.tsv
- config_name: de-zh_Hans
data_files: de-zh_Hans.tsv
- config_name: en_devel-gu
data_files: en_devel-gu.tsv
- config_name: en_US-et_EE
data_files: en_US-et_EE.tsv
- config_name: en-und
data_files: en-und.tsv
- config_name: en_devel-es_ni
data_files: en_devel-es_ni.tsv
- config_name: en-en-rNZ
data_files: en-en-rNZ.tsv
- config_name: pl-fr
data_files: pl-fr.tsv
- config_name: de-es
data_files: de-es.tsv
- config_name: en-pt_br
data_files: en-pt_br.tsv
- config_name: en-gug
data_files: en-gug.tsv
- config_name: fr-fr
data_files: fr-fr.tsv
- config_name: en-fr-rFR
data_files: en-fr-rFR.tsv
- config_name: en-dsb
data_files: en-dsb.tsv
- config_name: en-tr-TR
data_files: en-tr-TR.tsv
- config_name: en-tw
data_files: en-tw.tsv
- config_name: en-bs_Latn
data_files: en-bs_Latn.tsv
- config_name: en_GB-hi
data_files: en_GB-hi.tsv
- config_name: en-norwegian
data_files: en-norwegian.tsv
- config_name: en-zh_Latn_pinyin
data_files: en-zh_Latn_pinyin.tsv
- config_name: en_US-es-mx
data_files: en_US-es-mx.tsv
- config_name: en_GB-nl_NL
data_files: en_GB-nl_NL.tsv
- config_name: es-bn
data_files: es-bn.tsv
- config_name: en-peo
data_files: en-peo.tsv
- config_name: en-de_LU
data_files: en-de_LU.tsv
- config_name: en-mni
data_files: en-mni.tsv
- config_name: en_GB-jam
data_files: en_GB-jam.tsv
- config_name: en-sr_cyr
data_files: en-sr_cyr.tsv
- config_name: en-ro-RO
data_files: en-ro-RO.tsv
- config_name: en-doi
data_files: en-doi.tsv
- config_name: en_GB-en-US
data_files: en_GB-en-US.tsv
- config_name: en-he
data_files: en-he.tsv
- config_name: en-et
data_files: en-et.tsv
- config_name: en-tl_PH
data_files: en-tl_PH.tsv
- config_name: en-sr-Cyrl-RS
data_files: en-sr-Cyrl-RS.tsv
- config_name: en-Dutch
data_files: en-Dutch.tsv
- config_name: en-uz_UZ
data_files: en-uz_UZ.tsv
- config_name: en-ur-rIN
data_files: en-ur-rIN.tsv
- config_name: en-kn
data_files: en-kn.tsv
- config_name: en-trv
data_files: en-trv.tsv
- config_name: en_US-ms_MY
data_files: en_US-ms_MY.tsv
- config_name: en-de-rFO
data_files: en-de-rFO.tsv
- config_name: en-zh-CN
data_files: en-zh-CN.tsv
- config_name: ru-de
data_files: ru-de.tsv
- config_name: en-pt_BR
data_files: en-pt_BR.tsv
- config_name: en_GB-ms_MY
data_files: en_GB-ms_MY.tsv
- config_name: en_GB-tr
data_files: en_GB-tr.tsv
- config_name: en-bn_IN
data_files: en-bn_IN.tsv
- config_name: en_GB-pt
data_files: en_GB-pt.tsv
- config_name: en_GB-wa
data_files: en_GB-wa.tsv
- config_name: en_US-te
data_files: en_US-te.tsv
- config_name: en-da-rDK
data_files: en-da-rDK.tsv
- config_name: en_US-zh_CN
data_files: en_US-zh_CN.tsv
- config_name: en_US-az
data_files: en_US-az.tsv
- config_name: en-sn
data_files: en-sn.tsv
- config_name: en_devel-zh_Hant
data_files: en_devel-zh_Hant.tsv
- config_name: en-sw
data_files: en-sw.tsv
- config_name: en-fr_fr
data_files: en-fr_fr.tsv
- config_name: en_GB-mhr
data_files: en_GB-mhr.tsv
- config_name: sv-se
data_files: sv-se.tsv
- config_name: en-mn
data_files: en-mn.tsv
- config_name: en-gl
data_files: en-gl.tsv
- config_name: en_GB-is
data_files: en_GB-is.tsv
- config_name: en-nl-NL
data_files: en-nl-NL.tsv
- config_name: dev-fa
data_files: dev-fa.tsv
- config_name: en-frp
data_files: en-frp.tsv
- config_name: en_GB-it
data_files: en_GB-it.tsv
- config_name: en_US-ja-JP
data_files: en_US-ja-JP.tsv
- config_name: en_US-vi_VN
data_files: en_US-vi_VN.tsv
- config_name: en-zu
data_files: en-zu.tsv
- config_name: en_US-zh_HK
data_files: en_US-zh_HK.tsv
- config_name: en_UK-nb_NO
data_files: en_UK-nb_NO.tsv
- config_name: en_GB-eo
data_files: en_GB-eo.tsv
- config_name: en-ar_YE
data_files: en-ar_YE.tsv
- config_name: messages-pt
data_files: messages-pt.tsv
- config_name: en_devel-hr
data_files: en_devel-hr.tsv
- config_name: ia-en
data_files: ia-en.tsv
- config_name: en-sr
data_files: en-sr.tsv
- config_name: en_US-el_GR
data_files: en_US-el_GR.tsv
- config_name: en_US-bg
data_files: en_US-bg.tsv
- config_name: en-be@latin
data_files: en-be@latin.tsv
- config_name: en_US-zh_Hant
data_files: en_US-zh_Hant.tsv
- config_name: eo-fr
data_files: eo-fr.tsv
- config_name: en-uk_UA
data_files: en-uk_UA.tsv
- config_name: en_US-pt-BR
data_files: en_US-pt-BR.tsv
- config_name: nl-ko
data_files: nl-ko.tsv
- config_name: en-sl-SI
data_files: en-sl-SI.tsv
- config_name: en-to
data_files: en-to.tsv
- config_name: en_GB-ne
data_files: en_GB-ne.tsv
- config_name: en-la
data_files: en-la.tsv
- config_name: ru-ua
data_files: ru-ua.tsv
- config_name: en_GB-ia
data_files: en_GB-ia.tsv
- config_name: en_US-bn_BD
data_files: en_US-bn_BD.tsv
- config_name: en-zh_Hant
data_files: en-zh_Hant.tsv
- config_name: en_devel-nl_BE
data_files: en_devel-nl_BE.tsv
- config_name: en-id
data_files: en-id.tsv
- config_name: en_GB-pa
data_files: en_GB-pa.tsv
- config_name: en-gl_ES
data_files: en-gl_ES.tsv
- config_name: en-vi
data_files: en-vi.tsv
- config_name: fr-es
data_files: fr-es.tsv
- config_name: en-udm
data_files: en-udm.tsv
- config_name: en-es-rUS
data_files: en-es-rUS.tsv
- config_name: en-b+tok
data_files: en-b+tok.tsv
- config_name: it-fr_FR
data_files: it-fr_FR.tsv
- config_name: und-nl
data_files: und-nl.tsv
- config_name: en-pt_pt
data_files: en-pt_pt.tsv
- config_name: en-es_419
data_files: en-es_419.tsv
- config_name: en-jbo
data_files: en-jbo.tsv
- config_name: en_GB-nb-rNO
data_files: en_GB-nb-rNO.tsv
- config_name: en_GB-nl
data_files: en_GB-nl.tsv
- config_name: en-gl-ES
data_files: en-gl-ES.tsv
- config_name: en-de_AT
data_files: en-de_AT.tsv
- config_name: en-mk-MK
data_files: en-mk-MK.tsv
- config_name: en_GB-bg
data_files: en_GB-bg.tsv
- config_name: en_US-sc
data_files: en_US-sc.tsv
- config_name: en_US-kn
data_files: en_US-kn.tsv
- config_name: en-cy_GB
data_files: en-cy_GB.tsv
- config_name: en_US-mn
data_files: en_US-mn.tsv
- config_name: de-uk
data_files: de-uk.tsv
- config_name: en_GB-ko
data_files: en_GB-ko.tsv
- config_name: en-nl-rNL
data_files: en-nl-rNL.tsv
- config_name: en_devel-pt_PT
data_files: en_devel-pt_PT.tsv
- config_name: en_US-fi_FI
data_files: en_US-fi_FI.tsv
- config_name: en_devel-vi
data_files: en_devel-vi.tsv
- config_name: en_US-ru
data_files: en_US-ru.tsv
- config_name: en-hne
data_files: en-hne.tsv
- config_name: en-fi
data_files: en-fi.tsv
- config_name: en-ru_RU
data_files: en-ru_RU.tsv
- config_name: en_devel-es_cl
data_files: en_devel-es_cl.tsv
- config_name: de-el
data_files: de-el.tsv
- config_name: en_devel-ro
data_files: en_devel-ro.tsv
- config_name: en_GB-tt
data_files: en_GB-tt.tsv
- config_name: en-eng_GB
data_files: en-eng_GB.tsv
- config_name: en-lt-rLT
data_files: en-lt-rLT.tsv
- config_name: en-ota
data_files: en-ota.tsv
- config_name: en_devel-es_co
data_files: en_devel-es_co.tsv
- config_name: en-russian
data_files: en-russian.tsv
- config_name: en-ar-MA
data_files: en-ar-MA.tsv
- config_name: en-nn
data_files: en-nn.tsv
- config_name: eo-en
data_files: eo-en.tsv
- config_name: en_GB-cv
data_files: en_GB-cv.tsv
- config_name: en_devel-id_ID
data_files: en_devel-id_ID.tsv
- config_name: en_US-nb-NO
data_files: en_US-nb-NO.tsv
- config_name: en-it-rIT
data_files: en-it-rIT.tsv
- config_name: en_US-pl-PL
data_files: en_US-pl-PL.tsv
- config_name: en-ext
data_files: en-ext.tsv
- config_name: en-ko
data_files: en-ko.tsv
- config_name: en-tg
data_files: en-tg.tsv
- config_name: en-ga_IE
data_files: en-ga_IE.tsv
- config_name: en_devel-sr
data_files: en_devel-sr.tsv
- config_name: en-PT
data_files: en-PT.tsv
- config_name: en-sv
data_files: en-sv.tsv
- config_name: en_GB-son
data_files: en_GB-son.tsv
- config_name: en-et_ee
data_files: en-et_ee.tsv
- config_name: en_GB-el_GR
data_files: en_GB-el_GR.tsv
- config_name: en-jp
data_files: en-jp.tsv
- config_name: en-ga-rIE
data_files: en-ga-rIE.tsv
- config_name: sv-en
data_files: sv-en.tsv
- config_name: en_US-ua
data_files: en_US-ua.tsv
- config_name: en-sm
data_files: en-sm.tsv
- config_name: en-nap
data_files: en-nap.tsv
- config_name: en-portuguese
data_files: en-portuguese.tsv
- config_name: en_US-nl-NL
data_files: en_US-nl-NL.tsv
- config_name: en-es_ec
data_files: en-es_ec.tsv
- config_name: en_GB-crh
data_files: en_GB-crh.tsv
- config_name: en-tr_TR
data_files: en-tr_TR.tsv
- config_name: en-sr_RS@latin
data_files: en-sr_RS@latin.tsv
- config_name: en-bg_BG
data_files: en-bg_BG.tsv
- config_name: en-hu
data_files: en-hu.tsv
- config_name: en-es_SV
data_files: en-es_SV.tsv
- config_name: en_GB-rw
data_files: en_GB-rw.tsv
- config_name: en-es_AR
data_files: en-es_AR.tsv
- config_name: en_devel-es_pe
data_files: en_devel-es_pe.tsv
- config_name: en-et-rEE
data_files: en-et-rEE.tsv
- config_name: en-ro-v26
data_files: en-ro-v26.tsv
- config_name: en-ne-NP
data_files: en-ne-NP.tsv
- config_name: en-es-ar
data_files: en-es-ar.tsv
- config_name: en-en_ZA
data_files: en-en_ZA.tsv
- config_name: en_devel-lt
data_files: en_devel-lt.tsv
- config_name: en-eg
data_files: en-eg.tsv
- config_name: zh_Latn-zh_Hans
data_files: zh_Latn-zh_Hans.tsv
- config_name: en_GB-so
data_files: en_GB-so.tsv
- config_name: en-hr-rHR
data_files: en-hr-rHR.tsv
- config_name: en-lt_LT
data_files: en-lt_LT.tsv
- config_name: en-io
data_files: en-io.tsv
- config_name: en-sh-rHR
data_files: en-sh-rHR.tsv
- config_name: en-uk
data_files: en-uk.tsv
- config_name: en_GB-cs-CZ
data_files: en_GB-cs-CZ.tsv
- config_name: en-de-rCH
data_files: en-de-rCH.tsv
- config_name: en-nah
data_files: en-nah.tsv
- config_name: en_devel-tr
data_files: en_devel-tr.tsv
- config_name: en-de-rAT
data_files: en-de-rAT.tsv
- config_name: eo-sv
data_files: eo-sv.tsv
- config_name: en-nb
data_files: en-nb.tsv
- config_name: en_GB-ab
data_files: en_GB-ab.tsv
- config_name: en_US-de-DE
data_files: en_US-de-DE.tsv
- config_name: en-de_alm_x
data_files: en-de_alm_x.tsv
- config_name: en_GB-it-IT
data_files: en_GB-it-IT.tsv
- config_name: en-aa
data_files: en-aa.tsv
- config_name: en_devel-sq
data_files: en_devel-sq.tsv
- config_name: en_devel-en_au
data_files: en_devel-en_au.tsv
- config_name: en-sl
data_files: en-sl.tsv
- config_name: en-sr-rSP
data_files: en-sr-rSP.tsv
- config_name: en-ckb
data_files: en-ckb.tsv
- config_name: en_devel-pt_pt
data_files: en_devel-pt_pt.tsv
- config_name: en_devel-ar
data_files: en_devel-ar.tsv
- config_name: en-nn-NO
data_files: en-nn-NO.tsv
- config_name: es-fr
data_files: es-fr.tsv
- config_name: en-mk-rMK
data_files: en-mk-rMK.tsv
- config_name: en-spanish
data_files: en-spanish.tsv
- config_name: en_GB-ve
data_files: en_GB-ve.tsv
- config_name: en_GB-zh_HK
data_files: en_GB-zh_HK.tsv
- config_name: en_GB-kmr
data_files: en_GB-kmr.tsv
- config_name: en-no_nb
data_files: en-no_nb.tsv
- config_name: en_GB-sq
data_files: en_GB-sq.tsv
- config_name: en_US-ro-RO
data_files: en_US-ro-RO.tsv
- config_name: en-zh-rHK
data_files: en-zh-rHK.tsv
- config_name: en-Russian
data_files: en-Russian.tsv
- config_name: en_GB-ht
data_files: en_GB-ht.tsv
- config_name: en_GB-ug
data_files: en_GB-ug.tsv
- config_name: en-na
data_files: en-na.tsv
- config_name: en_devel-es_gt
data_files: en_devel-es_gt.tsv
- config_name: en-ka-rGE
data_files: en-ka-rGE.tsv
- config_name: en_US-bn-rBD
data_files: en_US-bn-rBD.tsv
- config_name: eo-ro
data_files: eo-ro.tsv
- config_name: en_GB-ko_KR
data_files: en_GB-ko_KR.tsv
- config_name: en-sr@Latn
data_files: en-sr@Latn.tsv
- config_name: en-french
data_files: en-french.tsv
- config_name: es-nl
data_files: es-nl.tsv
- config_name: en-georgian
data_files: en-georgian.tsv
- config_name: en_devel-sl
data_files: en_devel-sl.tsv
- config_name: en-jv
data_files: en-jv.tsv
- config_name: en-ur-UR
data_files: en-ur-UR.tsv
- config_name: en-dv
data_files: en-dv.tsv
- config_name: en_US-pt-PT
data_files: en_US-pt-PT.tsv
- config_name: en-ar_LY
data_files: en-ar_LY.tsv
- config_name: en-sv-SE
data_files: en-sv-SE.tsv
- config_name: en-ca_ES@valencia
data_files: en-ca_ES@valencia.tsv
- config_name: en_devel-oc
data_files: en_devel-oc.tsv
- config_name: en-th_TH
data_files: en-th_TH.tsv
- config_name: en-de_CH
data_files: en-de_CH.tsv
- config_name: en-ca-valencia
data_files: en-ca-valencia.tsv
- config_name: en-crh
data_files: en-crh.tsv
- config_name: en_US-en@pirate
data_files: en_US-en@pirate.tsv
- config_name: en-haw
data_files: en-haw.tsv
- config_name: en-sk-rSK
data_files: en-sk-rSK.tsv
- config_name: en-sr@latin
data_files: en-sr@latin.tsv
- config_name: en-jam
data_files: en-jam.tsv
- config_name: en_devel-ko
data_files: en_devel-ko.tsv
- config_name: en_devel-de
data_files: en_devel-de.tsv
- config_name: messages-nb_NO
data_files: messages-nb_NO.tsv
- config_name: en_GB-no
data_files: en_GB-no.tsv
- config_name: en_US-tok
data_files: en_US-tok.tsv
- config_name: en_US-zh_Hans
data_files: en_US-zh_Hans.tsv
- config_name: en-hsb
data_files: en-hsb.tsv
- config_name: en-eo
data_files: en-eo.tsv
- config_name: en-eu_ES
data_files: en-eu_ES.tsv
- config_name: en-ayc
data_files: en-ayc.tsv
- config_name: en-ca
data_files: en-ca.tsv
- config_name: en-fr_LU
data_files: en-fr_LU.tsv
- config_name: en-vi-rVN
data_files: en-vi-rVN.tsv
- config_name: en-pr
data_files: en-pr.tsv
- config_name: en-vls
data_files: en-vls.tsv
- config_name: es-gl
data_files: es-gl.tsv
- config_name: en_GB-nb-NO
data_files: en_GB-nb-NO.tsv
- config_name: en_GB-haw
data_files: en_GB-haw.tsv
- config_name: pt_BR-es
data_files: pt_BR-es.tsv
- config_name: en-nn-rNO
data_files: en-nn-rNO.tsv
- config_name: en_US-zh-tw
data_files: en_US-zh-tw.tsv
- config_name: en-ar-AA
data_files: en-ar-AA.tsv
- config_name: en_GB-fr_FR
data_files: en_GB-fr_FR.tsv
- config_name: en_GB-gez
data_files: en_GB-gez.tsv
- config_name: en-ID
data_files: en-ID.tsv
- config_name: en_GB-oc
data_files: en_GB-oc.tsv
- config_name: es-ia
data_files: es-ia.tsv
- config_name: en_GB-kv
data_files: en_GB-kv.tsv
- config_name: en-es-419
data_files: en-es-419.tsv
- config_name: eo-pt
data_files: eo-pt.tsv
- config_name: it-en_EN
data_files: it-en_EN.tsv
- config_name: en-czech
data_files: en-czech.tsv
- config_name: eo-cs
data_files: eo-cs.tsv
- config_name: en_devel-es_sv
data_files: en_devel-es_sv.tsv
- config_name: en-es_CL
data_files: en-es_CL.tsv
- config_name: en-si
data_files: en-si.tsv
- config_name: en-cs
data_files: en-cs.tsv
- config_name: en-sv_SE
data_files: en-sv_SE.tsv
- config_name: en_US-ne_NP
data_files: en_US-ne_NP.tsv
- config_name: en_GB-fy
data_files: en_GB-fy.tsv
- config_name: en_devel-en-rGB
data_files: en_devel-en-rGB.tsv
- config_name: en_GB-sr
data_files: en_GB-sr.tsv
- config_name: en-es-rPE
data_files: en-es-rPE.tsv
- config_name: en_US-en
data_files: en_US-en.tsv
- config_name: en_GB-eu
data_files: en_GB-eu.tsv
- config_name: en_GB-nb_NO
data_files: en_GB-nb_NO.tsv
- config_name: en-uz-UZ
data_files: en-uz-UZ.tsv
- config_name: eo-ko
data_files: eo-ko.tsv
- config_name: en-lb
data_files: en-lb.tsv
- config_name: en-lg
data_files: en-lg.tsv
- config_name: en-Esperanto
data_files: en-Esperanto.tsv
- config_name: en-ar-SA
data_files: en-ar-SA.tsv
- config_name: en_GB-ro_RO
data_files: en_GB-ro_RO.tsv
- config_name: en-cmn
data_files: en-cmn.tsv
- config_name: en-mni@bengali
data_files: en-mni@bengali.tsv
- config_name: en-ks
data_files: en-ks.tsv
- config_name: en_US-pt_BR
data_files: en_US-pt_BR.tsv
- config_name: ru-nb_NO
data_files: ru-nb_NO.tsv
- config_name: en-fr-rCA
data_files: en-fr-rCA.tsv
- config_name: en-kn-rIN
data_files: en-kn-rIN.tsv
- config_name: en_devel-sq_al
data_files: en_devel-sq_al.tsv
- config_name: en_US-nb_NO
data_files: en_US-nb_NO.tsv
- config_name: en-ce
data_files: en-ce.tsv
- config_name: en_US-ga
data_files: en_US-ga.tsv
- config_name: en-en-rZA
data_files: en-en-rZA.tsv
- config_name: en-rue
data_files: en-rue.tsv
- config_name: en-es_CO
data_files: en-es_CO.tsv
- config_name: en-es-es
data_files: en-es-es.tsv
- config_name: en-fa
data_files: en-fa.tsv
- config_name: en-de_DE
data_files: en-de_DE.tsv
- config_name: en-kg
data_files: en-kg.tsv
- config_name: en_US-es_ES
data_files: en_US-es_ES.tsv
- config_name: en-bg-rBG
data_files: en-bg-rBG.tsv
- config_name: fr-nl
data_files: fr-nl.tsv
- config_name: en_GB-as
data_files: en_GB-as.tsv
- config_name: en-nl
data_files: en-nl.tsv
- config_name: en-ka-GE
data_files: en-ka-GE.tsv
- config_name: en-sah
data_files: en-sah.tsv
- config_name: en_US-ur
data_files: en_US-ur.tsv
- config_name: und-si
data_files: und-si.tsv
- config_name: en_devel-en_ca
data_files: en_devel-en_ca.tsv
- config_name: en-cs-CZ
data_files: en-cs-CZ.tsv
- config_name: en-de_DIVEO
data_files: en-de_DIVEO.tsv
- config_name: en-es-PE
data_files: en-es-PE.tsv
- config_name: en-nb-rNO
data_files: en-nb-rNO.tsv
- config_name: en_GB-in
data_files: en_GB-in.tsv
- config_name: en_US-grc
data_files: en_US-grc.tsv
- config_name: en_GB-ast_ES
data_files: en_GB-ast_ES.tsv
- config_name: nb_NO-en
data_files: nb_NO-en.tsv
- config_name: en_devel-zh-cn
data_files: en_devel-zh-cn.tsv
- config_name: en_US-th
data_files: en_US-th.tsv
- config_name: en_devel-fa
data_files: en_devel-fa.tsv
- config_name: en_devel-es_py
data_files: en_devel-es_py.tsv
- config_name: en-prg
data_files: en-prg.tsv
- config_name: en_GB-uk_UA
data_files: en_GB-uk_UA.tsv
- config_name: en-gn
data_files: en-gn.tsv
- config_name: en-sat
data_files: en-sat.tsv
- config_name: en-jpn_JP
data_files: en-jpn_JP.tsv
- config_name: en-ko-rKR
data_files: en-ko-rKR.tsv
- config_name: en-anp
data_files: en-anp.tsv
- config_name: en-si_LK
data_files: en-si_LK.tsv
- config_name: en_GB-gn
data_files: en_GB-gn.tsv
- config_name: en-kn_IN
data_files: en-kn_IN.tsv
- config_name: en-b+jbo
data_files: en-b+jbo.tsv
- config_name: en-me
data_files: en-me.tsv
- config_name: en-lfn
data_files: en-lfn.tsv
- config_name: en-cz
data_files: en-cz.tsv
- config_name: en_GB-iu
data_files: en_GB-iu.tsv
- config_name: en-uz@cyrillic
data_files: en-uz@cyrillic.tsv
- config_name: en_US-es-419
data_files: en_US-es-419.tsv
- config_name: en_US-ug
data_files: en_US-ug.tsv
- config_name: es-ext
data_files: es-ext.tsv
- config_name: en_GB-pa_PK
data_files: en_GB-pa_PK.tsv
- config_name: en-ast
data_files: en-ast.tsv
- config_name: en_US-no
data_files: en_US-no.tsv
- config_name: en-afh
data_files: en-afh.tsv
- config_name: en-fi-rFI
data_files: en-fi-rFI.tsv
- config_name: en-ar-rLY
data_files: en-ar-rLY.tsv
- config_name: en_devel-pt_br
data_files: en_devel-pt_br.tsv
- config_name: en-ca_ES
data_files: en-ca_ES.tsv
- config_name: fr-ru
data_files: fr-ru.tsv
- config_name: en-eo_XX
data_files: en-eo_XX.tsv
- config_name: en_US-tl
data_files: en_US-tl.tsv
- config_name: en_GB-gl
data_files: en_GB-gl.tsv
- config_name: en_UK-es_ES
data_files: en_UK-es_ES.tsv
- config_name: en-be-rBY
data_files: en-be-rBY.tsv
- config_name: en-b+hsb
data_files: en-b+hsb.tsv
- config_name: en_GB-ps
data_files: en_GB-ps.tsv
- config_name: en-hi-IN
data_files: en-hi-IN.tsv
- config_name: en-PL
data_files: en-PL.tsv
- config_name: en_GB-dv
data_files: en_GB-dv.tsv
- config_name: en_US-sv
data_files: en_US-sv.tsv
- config_name: en_US-en_AU
data_files: en_US-en_AU.tsv
- config_name: en_GB-frp
data_files: en_GB-frp.tsv
- config_name: en_GB-sv-SE
data_files: en_GB-sv-SE.tsv
- config_name: en-ZH-rCN
data_files: en-ZH-rCN.tsv
- config_name: en-sq
data_files: en-sq.tsv
- config_name: en-README_FA
data_files: en-README_FA.tsv
- config_name: en_devel-ca
data_files: en_devel-ca.tsv
- config_name: en_UK-fr_FR
data_files: en_UK-fr_FR.tsv
- config_name: en-zh_Hans
data_files: en-zh_Hans.tsv
- config_name: en-ar_DZ
data_files: en-ar_DZ.tsv
- config_name: en-ml
data_files: en-ml.tsv
- config_name: en-zh-rTW
data_files: en-zh-rTW.tsv
- config_name: en-uz-Cyrl
data_files: en-uz-Cyrl.tsv
- config_name: messages-it
data_files: messages-it.tsv
- config_name: en_devel-ru
data_files: en_devel-ru.tsv
- config_name: en-es-MX
data_files: en-es-MX.tsv
- config_name: en_US-zh-Hant-HK
data_files: en_US-zh-Hant-HK.tsv
- config_name: en-de@formal
data_files: en-de@formal.tsv
- config_name: en_US-ar-AA
data_files: en_US-ar-AA.tsv
- config_name: en-en_IE
data_files: en-en_IE.tsv
- config_name: en_US-de
data_files: en_US-de.tsv
- config_name: en-eu
data_files: en-eu.tsv
- config_name: en-tl
data_files: en-tl.tsv
- config_name: ia-ru
data_files: ia-ru.tsv
- config_name: en_GB-my
data_files: en_GB-my.tsv
- config_name: en-Polish
data_files: en-Polish.tsv
- config_name: en_GB-si
data_files: en_GB-si.tsv
- config_name: eo-nb_NO
data_files: eo-nb_NO.tsv
- config_name: en_devel-iw
data_files: en_devel-iw.tsv
- config_name: en_GB-pt_PT
data_files: en_GB-pt_PT.tsv
- config_name: en_GB-tt@iqtelif
data_files: en_GB-tt@iqtelif.tsv
- config_name: en-sk
data_files: en-sk.tsv
- config_name: es-de
data_files: es-de.tsv
- config_name: en-enm
data_files: en-enm.tsv
- config_name: en_US-sk-SK
data_files: en_US-sk-SK.tsv
- config_name: en_GB-be
data_files: en_GB-be.tsv
- config_name: nl-en
data_files: nl-en.tsv
- config_name: en_US-sr_RS
data_files: en_US-sr_RS.tsv
- config_name: en_GB-cy
data_files: en_GB-cy.tsv
- config_name: en_devel-es_uy
data_files: en_devel-es_uy.tsv
- config_name: en-fa-AF
data_files: en-fa-AF.tsv
language:
- aa
- ab
- ace
- ach
- af
- afh
- aii
- ain
- ajp
- ak
- am
- an
- ang
- anp
- apc
- ar
- arn
- ars
- as
- ast
- ay
- ayc
- az
- azb
- ba
- bar
- bd
- be
- bem
- ber
- bg
- bho
- bm
- bn
- bo
- bp
- bqi
- br
- brx
- bs
- bul
- by
- ca
- ce
- ceb
- ckb
- cmn
- cn
- cnr
- co
- cr
- crh
- cs
- csb
- cv
- cy
- cz
- da
- de
- dev
- doi
- dsb
- dua
- dum
- dv
- dz
- eg
- el
- en
- eng
- enm
- eo
- es
- et
- eu
- ext
- fa
- fi
- fil
- fo
- fr
- fra
- frm
- frp
- frs
- fu
- fur
- fy
- ga
- gb
- gd
- gl
- glk
- gmh
- gn
- gr
- gsw
- gu
- guc
- gug
- gum
- guw
- gv
- ha
- haw
- he
- hi
- hne
- hr
- hrx
- hsb
- ht
- hu
- hy
- hz
- ia
- id
- ie
- ig
- in
- io
- is
- it
- iw
- ja
- jam
- jbo
- ji
- jp
- jpn
- jv
- ka
- kab
- kg
- kk
- kl
- km
- kmr
- kn
- ko
- kok
- kr
- krl
- ks
- ksh
- ku
- kw
- ky
- la
- lb
- lfn
- lg
- li
- lk
- ln
- lo
- lt
- ltg
- lv
- lzh
- mai
- me
- mg
- mhr
- mi
- mjw
- mk
- ml
- mn
- mnc
- mni
- mnw
- mo
- mr
- ms
- mt
- my
- na
- nah
- nan
- nap
- nb
- nds
- ne
- nl
- nn
- 'no'
- np
- nqo
- ny
- oc
- oj
- om
- or
- os
- ota
- pa
- pam
- pap
- pbb
- peo
- pk
- pl
- pms
- pr
- prg
- ps
- pt
- pu
- qt
- rcf
- rm
- ro
- rom
- ru
- rue
- rw
- ryu
- sa
- sah
- sai
- sat
- sc
- sco
- sd
- sdh
- se
- sh
- shn
- si
- sk
- skr
- sl
- sm
- sma
- sn
- so
- sq
- sr
- st
- su
- sv
- sw
- szl
- ta
- tam
- te
- tet
- tg
- th
- ti
- tk
- tl
- tlh
- tn
- to
- tok
- tr
- trv
- tt
- tum
- tw
- ty
- tzm
- ua
- udm
- ug
- uk
- und
- ur
- us
- uz
- vec
- vi
- vls
- wa
- wae
- wo
- xh
- yi
- yo
- yue
- zgh
- zh
- zu
task_categories:
- translation
- text2text-generation
pretty_name: Weblate Translations
annotations_creators:
- crowdsourced
size_categories:
- 1M<n<10M
license: other
---
# Dataset Card for Weblate Translations
<!-- Provide a quick summary of the dataset. -->
A dataset containing strings from projects hosted on [Weblate](https://hosted.weblate.org) and their translations into other languages.
Please consider [donating](https://weblate.org/en/donate/) or [contributing](https://weblate.org/en/contribute/) to Weblate if you find this dataset useful.
To avoid rows with values like "None" and "N/A" being interpreted as missing values, pass the keep_default_na parameter like this:
```
from datasets import load_dataset
dataset = load_dataset("ayymen/Weblate-Translations", keep_default_na=False)
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** Each sentence pair in the dataset has a corresponding license in the "license" column. This license is the one specified in the component or project containing the sentence.
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
- Machine Translation
- Language Identification
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
- Sentence pairs with empty/missing elements were dropped.
- Identical pairs were dropped.
- Trailing whitespace was stripped.
- Rows were deduplicated based on all 3 columns including "license", on a config/subset/tsv file basis. Which means that a single config might contain two identical sentence pairs with different licenses. Or a different config/subset might contain the exact same row (most likely a different variant/dialect of the same language(s)).
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
Weblate users.
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
Trofish/Korean-RLHF-Full-process | ---
license: cc-by-nc-4.0
task_categories:
- reinforcement-learning
- text-generation
language:
- ko
tags:
- RLHF
- SFT
- RM
- instruction-tuning
- reward-model
- PPO
---
## KULLM을 baseline으로 RLHF 강화학습을 하는데 사용한 데이터셋입니다.
- **Step1: step1_SFT_train.jsonl** (KULLM 12.8B 모델을 Supervised Fine-Tuning 하는데 사용하였습니다.)
- **Step2: step2_RM_train.json** (polyglot-ko 1.3B 모델을 Reward Model로 학습하는데 사용하였습니다.)
- **Step3: step3_PPO_train.json** (SFT 모델과 RM 모델을 사용하여 RLHF 학습을 하는데 사용하였습니다.)
자세한 정보는 다음을 참고해주세요: https://huggingface.co/Trofish/KULLM-RLHF
## 강화학습 단계별 데이터셋 구축
## 데이터셋 선정 시 고려 사항
- **일상 대화와 혐오 표현 대처 능력을 올리기 위한 데이터셋과, 학습 시 챗봇 모델의 general한 task에 대한 성능이 하락하는 것을 막기 위해서 general task 데이터셋을 구성**
- **국립국어원 일상 대화 데이터셋:** 일상적인 대화에 대한 자연스러운 응답이 있으면서도, 맞춤법이 잘 지켜지고 은어, 비문, 초성 등이 없으며 주제별로 다양한 대화가 있음
- **AI Hub 혐오 표현 데이터셋:** 혐오, 차별, 성적인 내용, 폭력, 범죄 등 카테고리별로 다양한 혐오 표현이 있음
- **General task 데이터셋**
- Evol-Instruct 데이터셋: 다양한 분야에 대한 복잡하고 논리적인 prompt와 답변이 있음
- Self-Instruct 데이터셋: 사람이 직접 생성한 양질의 Seed data를 기반으로 데이터 증강
- RLHF 한국어 번역 데이터셋: DeepSpeedChat에서 공개한 데이터셋을 한국어로 번역
# Step1. SFT 모델 Fine-tuning
## Baseline Model
[- 고려대학교 NLP & AI 연구실과 HIAI 연구소가 개발한 한국어 LLM **"KULLM"** 사용](https://github.com/nlpai-lab/KULLM)
## Datasets
# Step2. Reward Model ver1 구현
## Baseline Model
- EleutherAI에서 개발한 초거대 한국어 언어 모델 **Polyglot-Ko** 사용
- 1.3b 모델과 5.8b 모델을 각각 실험
## Datasets
- InstructGPT의 데이터셋 구축 방법
- Reward 모델 학습 데이터셋으로 SFT 학습에 사용한 prompt(1,500개 - 일상대화:혐오표현=2:1)와 새로운 prompt(1,000개 - DeepSpeedChat 번역 데이터셋) 사용
- SFT 모델에서 한개의 prompt당 K개의 Response를 생성하고, 순위를 Labeling
- 데이터셋 라벨링
- Instruct GPT의 경우 사람이 직접 Labeling을 하엿지만, 일관된 평가와 시간 단축을 위해 GPt-4와 G-Eval을 이용
- SFT에서 생성한 두 Response 중 G-Eval 평가 점수 합이 높은 것을 Chosen response로 결정
- 데이터셋 유형별로 G-Eval 평가 Prompt에 차이를 두었음
- 
## RLFH Model Evaluation
## Final RLHF Model
- https://huggingface.co/Trofish/KULLM-RLHF
|
talrid/CodeContests_valid_and_test_AlphaCodium | ---
license: apache-2.0
---
|
Wanfq/KCA_data | ---
license: cc-by-nc-4.0
language:
- en
--- |
Ejafa/ye-pop | ---
license: apache-2.0
language:
- en
tags:
- art
pretty_name: ye-pop
size_categories:
- 100K<n<1M
---
# YE-POP (a derived dataset of Laion POP)
YE-POP is a derived dataset from Laion-POP, meticulously curated and filtered to enhance the quality and utility of the original dataset. The dataset comprises 11 chunks, each containing 50,000 image URLs from Laion-POP. NSFW sorting has been used as a baseline, and human verification has been conducted to ensure the dataset's reliability.
For the initial comparison, Chunk 1 has been curated with Gemini-Pro and released as part of a research work to the community. For access to other chunks generated by gemini-pro, interested parties are encouraged to contact us. The primary goal of YE-POP is to provide a dataset with improved art image descriptions while retaining the essence of Laion-POP for baseline comparisons in diffusion models and image captioning tasks. We anticipate that training multimodal models on this dataset will lead to enhanced generation capabilities.
## Dataset Details
Each zip file contains predownloaded images, and the JSON file includes dictionaries of image features with the following fields:
- `filename`
- `url`
- `cogvlm_caption`
- `llava_caption`
- `nsfw_prediction`
- `alt_txt`
- `alt_txt_similarity`
- `width`
- `height`
- `original_width`
- `original_height`
- `exif`
For more [detailed information](https://laion.ai/blog/laion-pop/#dataset-and-methodology) on the fields, refer to the JSON file.
## Dataset Card Authors
[Yaroslav Ponomarenko]()
[Ejafa Bassam]()
## Dataset Card Contact
@[Peking University](https://cs.pku.edu.cn/English/Home.htm)
## Acknowledgments
[Laion (Christoph Schuhmann, Peter Bevan)]()
[Google Gemini-Pro](https://doi.org/10.48550/arXiv.2312.11805)
|
p208p2002/zhtw-sentence-error-correction | ---
language:
- zh
configs:
- config_name: alpha
data_files:
- split: train
path: "alpha/out.jsonl"
- config_name: beta
data_files:
- split: train
path: "beta/out.jsonl"
- config_name: gamma
data_files:
- split: train
path: "gamma/out.jsonl"
---
# 中文錯字糾正資料集
由規則與字典自維基百科產生的錯誤糾正資料集。
包含錯誤類型：隨機錯字、近似音錯字、缺字錯誤、冗字錯誤。
資料集使用函式庫: [p208p2002/zh-mistake-text-gen](https://github.com/p208p2002/zh-mistake-text-gen)
### 子集
- alpha: 95%錯誤，5%不變。單句中可能有多個錯誤。
- beta: 50%錯誤，50%不變。單句中僅有一個錯誤。
- gamma: 100%錯誤。單句中可能有多個錯誤。 |
tastypear/bluemoon-cleaned-lewd | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- not-for-all-audiences
---
这个数据集从 grimulkan/bluemoon_Karen_cleaned 中抽取了所有包含NSFW内容的对话（只保留第一轮对话）。
This dataset extracts all conversations containing NSFW content from `grimulkan/bluemoon_Karen_cleaned` (only the first round of conversations is retained)
Explanation of long_response:
```python
if len(chosen) > len(prompt):
long_response = 1
``` |
linux-cn/archive | ---
license: cc-by-nc-4.0
language:
- zh
pretty_name: Linux 中国文章数据集
size_categories:
- 10K<n<100K
---
---
license: cc-by-nc-4.0
---
# Linux 中国原创文章/译文数据集
这个数据集为 Linux 中国原创技术文章 + 翻译技术文章的数据集，提供了文章标题、内容等多个字段。
## Dataset Details
### Dataset Description
- **Language(s) (NLP):** 中文
- **License:** cc-by-nc-4.0
## Dataset Structure
- id：文章ID
- title: 文章标题
- author: 文章作者
- fromurl: 文章源地址（仅翻译类文章有）
- summary: 总结
- excerpt： 摘要
- pic: 头图（缩略图版）
- largepic：头图（大图版）
- titlepic：是否有头图，可以渲染用。
- islctt：是否是 LCTT 文章（翻译文章）
- selector：选题人员，值为 Github ID
- translator：翻译人员，值为 Github ID
- reviewer：校对人员，值为 Github ID
- tags：文档标签
- category：文档所属目录
- count：计数
- viewnum: 访问量
- commentnum： 评论量
- favtimes： 收藏量
- sharetimes： 分享量
- likes： 喜欢量
- comments_data： 评论数据
- postip：评论 IP
- dateline：评论时间
- message：评论内容
- username：评论名
- repcids：回复的评论的 ID
- related： 相关文章的 ID
- date：发布日期
- updated：最后更新日期
- permalink：永久链接（Linux.cn 上的链接）
- content：文章内容
## 图片素材下载 & Markdown 版下载
上述文章内容中的图片素材可访问下方链接获取，attachment.zip 即为对应的图片。
### GitHub
https://github.com/Linux-CN/archive/releases/tag/release
### 百度网盘
链接: https://pan.baidu.com/s/1i7DTuf_umPkkleHFtdmZJA?pwd=lccn
提取码: lccn
## Dataset Card Contact
bestony <bestony@linux.com> |
botbot-ai/aya_dataset_pt | ---
language:
- pt
pretty_name: Aya Dataset Portuguese
tags:
- aya
- portuguese
- legal
- chemistry
license: apache-2.0
size_categories:
- 1K<n<10K
---
CohereForAI [Aya Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) filtrado para português (PT).
**Aya Dataset Summary**
The [Aya Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) is a multilingual instruction fine-tuning dataset curated by an open-science community via Aya Annotation Platform from Cohere For AI. The dataset contains a total of 204k human-annotated prompt-completion pairs along with the demographics data of the annotators.
This dataset can be used to train, finetune, and evaluate multilingual LLMs.
Curated by: Contributors of Aya Open Science Intiative.
Language(s): 65 languages (71 including dialects & scripts).
License: Apache 2.0 |
Jiwonny29/project1 | ---
license: apache-2.0
task_categories:
- feature-extraction
language:
- en
tags:
- biology
pretty_name: test
size_categories:
- 100K<n<1M
dataset_info:
config_name: mydata
features:
- name: Year
dtype: int32
- name: LocationAbbr
dtype: string
- name: LocationDesc
dtype: string
- name: Latitude
dtype: float32
- name: Longitude
dtype: float32
- name: Disease_Type
dtype: int32
- name: Data_Value_Type
dtype: int32
- name: Data_Value
dtype: float32
- name: Break_Out_Category
dtype: string
- name: Break_Out_Details
dtype: string
- name: Break_Out_Type
dtype: int32
- name: Life_Expectancy
dtype: float32
---
### Dataset Description
This dataset encompasses mortality rates for cardiovascular (CVD) and heart diseases across the United States, covering both state-specific and national levels, from 2000 to 2020. The mortality rate is quantified as the number of deaths per 100,000 individuals annually in the US. The dataset is structured to classify mortality rates according to various demographic factors, including overall rates, gender (female, male), race (white, black, Hispanic, other), and age groups (18-24, 25-44, 45-65, 65+). Additionally, life expectancy data for each state is incorporated in the dataset. For ease of use, I combined the data on a five-year interval rather than an annual basis.
### Dataset Sources
- CVD Mortality Data: Centers for Disease Control and Prevention(CDC) National Vital Statistics System
- https://data.cdc.gov/Heart-Disease-Stroke-Prevention/National-Vital-Statistics-System-NVSS-National-Car/kztq-p2jf/about_data
- Life Expectancy Data: Institute for Health Metrics and Evaluation
- https://ghdx.healthdata.org/record/ihme-data/united-states-life-expectancy-by-county-race-ethnicity-2000-2019
## Uses
This dataset serves as a valuable resource for researchers and individuals interested in examining and identifying patterns related to cardiovascular diseases in the United States. It can be utilized to forecast future fatalities caused by heart diseases by leveraging similar features present in the dataset. Additionally, the dataset enables users to gain insights into identifying states that require assistance and support in reducing mortality rates. Below are example use cases and corresponding codes:
- Analyzing the comprehensive picture of mortality and conducting time series analysis on mortality rates
- https://colab.research.google.com/drive/1ulygrSt9jt3x_4WIGD6QdK0TcGZlpuYF
- Building regression models
- https://colab.research.google.com/drive/1DhIni026qz5qqjfWwKXnqoQXDy-HzroC
- Developing a web application for users to quickly understand and compare mortality rates among states, along with relevant information like state population
- https://github.com/jiwonny29/Exploring_US_Cardiovascular_Mortality_Trends_via_Streamlit
## Dataset Structure
This dataset contains
- Year (int32): This column contains the year of the data record, with values ranging from 2000 to 2020
- LocationAbbr (String): Abbreviation representing the location, typically a state
- LocationDesc (String): The full name or detailed description of the location
- Latitude (float32) : Geographic coordinate that specifies the north-south position of a point on the Earth's surface
- Longitude (float32) : Geographic coordinate that specifies the east-west position of a point on the Earth's surface
- Geolocation (Tuple): A pair of latitude and longitude coordinates, formatted as (latitude, longitude), providing the geolocation or geocode of the location
- Disease_Type (int32): A key column in the dataset, representing eight unique types of cardiovascular diseases, numbered from 0 to 7. The values correspond to the following diseases:
- 0: Major Cardiovascular Disease
- 1: Diseases of the Heart (Heart Disease)
- 2: Acute Myocardial Infarction (Heart Attack)
- 3: Coronary Heart Disease
- 4: Heart Failure
- 5: Cerebrovascular Disease (Stroke)
- 6: Ischemic Stroke
- 7: Hemorrhagic Stroke
- Data_Value_Type (int32): Represents the type of data value. "Age-Standardized" is represented by 1, and "Crude" is represented by 0, indicating the measurement methods for the data value columns
- Data_Value (float32): This column represents the number of deaths per 100,000 population
- Break_Out_Category (string): This category is used for breaking down the data and includes four unique values: "Overall," "Gender," "Age," and "Race."
- Break_Out_Details (string): Specific subcategories within the Break_Out_Category. This column includes values like "Overall," six age categories (e.g., "18-24," "25-44"), two gender categories (e.g., "Female," "Male"), and four race categories (e.g., "Hispanic," "Non-Hispanic Black," "Non-Hispanic White," "Other").
- Break_Out_Type (int32): A numerical transformation of the Break_Out_Details column. In this system, "Overall" is represented as 0, "Male" and "Female" as 1 and 2, respectively; age groups "18-24," "25-44," "45-64," "65+" as 1, 2, 3, 4, respectively; and racial categories "Non-Hispanic White," "Non-Hispanic Black," "Hispanic," "Other" as 1, 2, 3, 4, respectively.
- Life_Expectancy (float32): Represents the life expectancy for the applicable year and state
|
cnmoro/WizardVicuna-PTBR-Instruct-Clean | ---
license: apache-2.0
---
|
ShixuanAn/RDD_2020 | ---
license: cc-by-nc-3.0
task_categories:
- image-classification
language:
- en
pretty_name: >-
RDD2020: An Image Dataset for Smartphone-based Road Damage Detection and
Classification
size_categories:
- 100B<n<1T
---
# Dataset Card for RDD_2020
The RDD2020 dataset is a comprehensive collection of 26,336 road images from India, Japan, and the Czech Republic, annotated with over 31,000 instances of road damages. This dataset is designed to support the development and evaluation of machine learning models for automatic road damage detection, offering a valuable resource for municipalities and road agencies for efficient road condition monitoring.
## Dataset Details
### Dataset Description
- **Source:** [Mendeley Data](https://data.mendeley.com/datasets/5ty2wb6gvg/1) - DOI: 10.17632/5ty2wb6gvg.1
- **Size:** 1.13 GB
- **Format:** Images (JPEG) and Annotations (XML in PASCAL VOC format)
- **Resolution:**
- India: 720 × 720 pixels
- Japan and Czech: 600 × 600 pixels
- **Categories:** Longitudinal Cracks (D00), Transverse Cracks (D10), Alligator Cracks (D20), Potholes (D40)
- **License:** https://creativecommons.org/licenses/by/4.0/
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Homepage** https://data.mendeley.com/datasets/5ty2wb6gvg/1
- **Data article:** https://doi.org/10.1016/j.dib.2021.107133
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
RDD2020 dataset can be directly used for developing and benchmarking machine learning models aimed at automatic detection and classification of road damages. This includes developing new deep learning architectures or modifying existing ones to improve detection accuracy across different types of road damages
## Dataset Structure
### Data Instance
The data will follow the structure below:
```
{
"image_id": "Czech_000248",
"country": "Czech",
"type": "train",
"image": "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=600x600>",
"image_path": "train/Czech/images/Czech_000248.jpg",
"crack_type": ["D20", "D20"],
"crack_coordinates": {
"x_min": [188, 3],
"x_max": [309, 171],
"y_min": [463, 438],
"y_max": [509, 519]
}
}
```
### Data Fields
- "image_id"[string]: ID of the image, created by combining the country plus a sequential number.
- "country"[string]: The country where the photo was taken.
- "type"[string]: The dataset category the image belongs to, such as 'train', 'test1', or 'test2'. "image"[integer]: The image data converted into PIL format.
- "crack_type"[string]: Types of cracks detected in the image.
- "crack_coordinates"[integer]: Contains crack coordinates as integers.
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
The RDD2020 dataset was curated with the objective of facilitating the development, testing, and benchmarking of machine learning models for road damage detection, catering specifically to the needs of municipalities and road agencies. A significant aspect of the dataset's curation process was the conversion of images into the Python Imaging Library (PIL) format and the meticulous parsing of XML annotations to ensure a seamless integration between the image data and the associated labels. This conversion process was driven by the need to simplify the handling of image data for machine learning applications, as the PIL format is widely supported by data processing and model training frameworks commonly used in the field.
Additionally, the parsing of XML files to extract detailed annotations about the type and coordinates of road damages allows for precise labeling of the data. This approach ensures that each image is directly associated with its corresponding damage type and location. The dataset's diversity, with images sourced from three different countries, aims to enable the creation of robust models that are effective across various environmental conditions and road infrastructures, thereby broadening the applicability and relevance of the trained models.
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
Road images (.jpg) were collected using a vehicle-mounted smartphone, moving at an average speed of about 40Km/h. XML files were created using the LabelImg tool to annotate the road damages present in the images.
#### Who are the source data producers?
Deeksha Arya, Hiroya Maeda, Sanjay Kumar Ghosh, Durga Toshniwal, Hiroshi Omata, Takehiro Kashiyama, Toshikazu Seto, Alexander Mraz,
Yoshihide Sekimot
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
### Annotations
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
Each image in the dataset comes with corresponding XML files containing annotations in PASCAL VOC format. These annotations describe the location and type of road damages present in the images, categorized into four main types: Longitudinal Cracks (D00), Transverse Cracks (D10), Alligator Cracks (D20), and Potholes (D40).
### Social Impact
The structuring of the RDD2020 dataset into a more accessible and usable format is aimed at having a focused and measurable impact on the management of road infrastructure. The transformation of raw images and XML annotations into a coherent dataset with clearly defined attributes such as photo_id, country, type, pics_array, image_resolution, crack_type, and crack_coordinates creates a powerful tool for municipalities and road agencies. With this structured dataset, these entities can deploy machine learning models to accurately identify and classify road damages like cracks and potholes, which are critical for the maintenance and safety of roadways.
In conclusion, the transformation of this raw data into a structured and accessible format not only catalyzes the progress of automated road damage assessment but also potentially engages the public sector in adopting AI-driven solutions for public safety and infrastructure management.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The dataset primarily includes images from three countries (India, Japan, and the Czech Republic), which may not fully represent road conditions worldwide. Users should be cautious when generalizing models trained on this dataset to other regions.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. |
OpenGVLab/AS-100M | ---
license: apache-2.0
---
# AS-100M
AS-100M is a subset of AS-1B. We release this dataset in both [COCO format](https://huggingface.co/datasets/Weiyun1025/AS-100M/tree/main/coco_format) and [JSONL format](https://huggingface.co/datasets/Weiyun1025/AS-100M/tree/main/jsonl_format).
***NOTE***: The bbox format in the COCO format is `xywh`, while in the JSONL format, it is `x1y1x2y2`.
## Introduction
We present the All-Seeing Project with:
[***All-Seeing 1B (AS-1B) dataset***](https://huggingface.co/datasets/Weiyun1025/AS-100M): we propose a new large-scale dataset (AS-1B) for open-world panoptic visual recognition and understanding, using an economical semi-automatic data engine that combines the power of off-the-shelf vision/language models and human feedback.
[***All-Seeing Model (ASM)***](https://huggingface.co/Weiyun1025/All-Seeing-Model-FT): we develop a unified vision-language foundation model (ASM) for open-world panoptic visual recognition and understanding. Aligning with LLMs, our ASM supports versatile image-text retrieval and generation tasks, demonstrating impressive zero-shot capability.
<img width="820" alt="image" src="https://github.com/OpenGVLab/all-seeing/assets/8529570/e43ab8db-6437-46f1-8aa1-c95f012e9147">
Figure 1: Overview and comparison of our All-Seeing project with other popular large foundation models.
<!-- ## Online Demo
**All-Seeing Model demo** is available [here](https://openxlab.org.cn/apps/detail/wangweiyun/All-Seeing-Model-Demo).
**Dataset Browser** is available [here](https://openxlab.org.cn/apps/detail/wangweiyun/All-Seeing-Dataset-Browser).
https://github.com/OpenGVLab/all-seeing/assets/47669167/9b5b32d1-863a-4579-b576-b82523f2205e -->
## Dataset Overview
AS-1B with over 1 billion regions annotated with semantic tags, question-answering pairs, and detailed captions. It covers a wide range of 3.5 million common and rare concepts in the real world, and has 132.2 billion tokens that describe the concepts and their attributes.
<img width="800" alt="image" src="https://github.com/OpenGVLab/all-seeing/assets/8529570/adac37ed-312f-4f11-ba8a-6bc62067438f">
Some examples
<img width="800" alt="image" src="https://github.com/OpenGVLab/all-seeing/assets/8529570/fcf6ab07-c4ba-441c-aa6c-111c769f75b1">
Please see our [paper](https://arxiv.org/abs/2308.01907) to learn more details.
## Model Architecture
The All-Seeing model (ASM) is a unified framework for panoptic visual recognition and understanding, including image/region-text retrieval, image/region recognition, captioning, and question-answering.
<img width="820" alt="image" src="https://github.com/OpenGVLab/all-seeing/assets/8529570/8995e88c-6381-452f-91e4-05d68a2795fc">
## License
This project is released under the [Apache 2.0 license](LICENSE).
# Citation
If you find our work useful in your research, please consider cite:
```BibTeX
@article{wang2023allseeing,
title={The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World},
author={Wang, Weiyun and Shi, Min and Li, Qingyun and Wang, Wenhai and Huang, Zhenhang and Xing, Linjie and Chen, Zhe and Li, Hao and Zhu, Xizhou and Cao, Zhiguo and others},
journal={arXiv preprint arXiv:2308.01907},
year={2023}
}
@article{wang2024allseeing_v2,
title={The All-Seeing Project V2: Towards General Relation Comprehension of the Open World},
author={Wang, Weiyun and Ren, Yiming and Luo, Haowen and Li, Tiantong and Yan, Chenxiang and Chen, Zhe and Wang, Wenhai and Li, Qingyun and Lu, Lewei and Zhu, Xizhou and others},
journal={arXiv preprint arXiv:2402.19474},
year={2024}
}
``` |
microsoft/Taskbench | ---
language:
- en
license: mit
tags:
- agent
- tool-learning
- task-automation
- LLM
pretty_name: TaskBench
size_categories:
- n<21k
configs:
- config_name: huggingface
data_files:
- split: test
path: "data_huggingface/data.json"
- config_name: multimedia
data_files:
- split: test
path: "data_multimedia/data.json"
- config_name: dailylifeapis
data_files:
- split: test
path: "data_dailylifeapis/data.json"
---
<p align="center">
<img src="./assets/logo2.png" width="10%">
</p>
<div align="center">
<!-- <h1>TaskBench</h1> -->
<!-- <div align="center">
<a href="https://opensource.org/licenses/Apache-2.0">
<img alt="License: Apache 2.0" src="https://img.shields.io/badge/License-Apache%202.0-4E94CE.svg">
</a>
<a href="https://arxiv.org/abs/2311.18760">
<img alt="License: Apache 2.0" src="https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg">
</a>
</div> -->
<h3>TaskBench: Benchmarking Large Language Models for Task Automation<h3>
</div>
<p align="center">
<img width="100%" alt="image" src="./assets/eval.png">
</p>
## Table of Contents
+ [Introduction](#introduction)
+ [Dataset](#dataset)
+ [Evaluation with TaskBench](#evaluation-with-taskbench)
+ [Dataset Construction with Back-Instruct](#dataset-construction-with-back-instruct)
+ [Leaderboard](#leaderboard)
+ [Citation](#citation)
## Introduction
TaskBench is a benchmark for evaluating large language models (LLMs) on task automation. Task automation can be formulated into three critical stages: task decomposition, tool invocation, and parameter prediction. This complexity makes data collection and evaluation more challenging compared to common NLP tasks. To address this challenge, we propose a comprehensive evaluation framework and a high-quality dataset for task automation. We also provide a leaderboard of 17 LLMs on TaskBench, including GPT-4, Claude-2, and other open-source LLMs.
### Dataset
To generate high-quality evaluation datasets, we introduce the concept of Tool Graph to represent the decomposed tasks in user intent, and adopt a Back-Instruct method to simulate user instruction and annotations. The data collection process consists of three stages:
+ **Tool Graph Construction:** we first build a tool library and use the tool library to construct the tool graph. The nodes in the tool graph represent the tools, and the edges represent the dependencies between the tools, including the resource dependency and temporal dependency.
+ **Graph Sampling:** we sample the tool graph to generate the tool graph for each sample. The sampled tool graph is used to generate the tool invocation graph and the instruction. According to the topology of the sampled tool graph, we sample the tool graph in three ways: node, chain and DAGs, which represent different structures of task decomposition for task automation.
+ **Back-Instruct:** we first use the sampled tool graph to generate the task steps and the instructions. Then, we use the instruction to generate the tool invocation parameters to complete the tool invocation graph.
<p align="center">
<img width="100%" alt="image" src="./assets/backinstruct.png">
</p>
To improve the quality of the dataset, we use LLM-based and rule-based critics to verify the dataset. The former aims to use LLM to check the alignments between the generated data and the sampled tool graph. While the latter uses straightforward rules to determine the alignment between the tool graphs in created data and the sampled tool graphs. Here, we use the nodes and edges of the sampled graph to determine the consistency. Details statistics of the processing are shown in [the table](#LLM-based-and-Rule-based-Critics).
After LLM-based and rule-based critics, we further verify the dataset with human annotators, including checking the syntax of the instructions, the correctness of the tool invocation graph, and the correctness of the tool invocation parameters. The final dataset contains 28,271 samples in three domains: HuggingFace Tools, Multimedia Tools, and Daily Life APIs. Details statistics of the human verification are shown in [the table](#Human-Verification).
#### Introduction
The TaskBench dataset contains datasets in three areas: HuggingFace Tools, Multimedia Tools, and Dailylife APIs. Each dataset directory includes three files:
+ `data.json`: the dataset file, which contains the samples in the dataset.
+ `graph_desc.json`: the tool graph description file, which contains the tool graph of the dataset.
+ `user_requests.json`: contains the user requests of the dataset.
+ `tool_desc.json`: the tool description file, which contains the tool descriptions of the dataset.
```
├─data_dailylifeapis
│ data.json
│ graph_desc.json
│ user_requests.json
│ tool_desc.json
│
├─data_huggingface
│ data.json
│ graph_desc.json
│ user_requests.json
│ tool_desc.json
│
└─data_multimedia
data.json
graph_desc.json
user_requests.json
tool_desc.json
```
#### Processing Statistics
We provide the statistics of the dataset processing in the following tables:
+ **Overview**: we provide the number of samples in each dataset, the number of samples checked by critics, and the number of samples verified by humans. Grouped by the tool invocation graph structure, e.g. node, chain, and DAGs, we also provide the number of samples in each group.
+ **LLM-based and Rule-based Critics**: we provide the number of samples checked by LLM-based critics, rule-based critics and both critics.
+ **Human Verification**: Human verification is built on the samples checked by critics, which includes three parts: syntax checking, instruction checking, and tool invocation graph checking. We provide the number of samples in each part, and along with the number of samples that are discarded or fixed.
| Dataset | #Samples | #Samples Checked by Critics (%) | #Samples Verified by Humans (%) | Node | Chain | DAG |
| :-----: | :------: | :----------------: | :--------------: | :------: | :------: | :------: |
| Hugging Face Models | 12,217 | 8,457 (69.22%) | 7,546 (61.76%) | 3,067 | 3,642 | 837 |
| Multimedia Tools | 8,904 | 6,281 (70.54%) | 5,584 (62.71%) | 2,037 | 2,982 | 565 |
| Dailylife APIs | 7,150 | 5,432 (75.97%) | 4,320 (60.42%) | 1,258 | 2,787 | 275 |
<div id="LLM-based-and-Rule-based-Critics">
| Dataset | #Samples | #Checked by LLM-based Critics (%) | #Checked by Rule-based Critics (%) | #Checked by Both Critics (%) |
| :-----: | :------: | :-----------------------------: | :------------------------------: | :-------------------------: |
| Hugging Face Models | 12,217 | 9,042 (74.01%) | 10,289 (84.22%) | 8,457 (69.22%) |
| Multimedia Tools | 8,904 | 6,959 (78.16%) | 7,363 (82.69%) | 6,281 (70.54%) |
| Dailylife APIs | 7,150 | 5,694 (79.63%) | 6,271 (87.70%) | 5,432 (75.97%) |
<div id="Human-Verification">
| Dataset | #Samples Checked by Critics | #Correct Samples (%) | #Discarded (%) | #Fixed for Syntax (%) | #Fixed for Instructions (%) | #Fixed for Tool Invocation Graph (%) |
| :-----: | :-------------------------: | :-------------------: | :-------------------: | :---------------------------: | :-----------------------------------: | :------------: |
| Hugging Face Models | 8,457 | 6,974 (82.46%) | 911 (10.77%) | 27 (0.32%) | 328 (3.87%) | 843 (9.96%) |
| Multimedia Tools | 6,281 | 5,262 (83.77%) | 697 (11.09%) | 11 (0.17%) | 107 (1.70%) | 526 (9.96%) |
| Dailylife APIs | 5,432 | 4,307 (79.29%) | 714 (13.14%) | 6 (0.11%) | 92 (1.68%) | 332 (6.11%) |
## Evaluation with TaskBench
On top of the TaskBench dataset, we provide a comprehensive evaluation framework for task automation. The evaluation framework consists of three stages: task decomposition, tool invocation, and parameter prediction. We provide the evaluation metrics for each stage:
+ **Task Decomposition**: Since task steps are diverse text distributions, we use the Rouge-1 (R1), Rouge-2 (R2), and Bertscore F1 (BsF) metrics to evaluate the task decomposition results.
+ **Tool Invocation**: We report the F1 of node prediction (n-F1) and edge prediction (e-F1) in the tool invocation graph to evaluate the tool invocation results. Edge prediction reflects the correctness of the dependencies between tools, while node prediction reflects the correctness of the tool prediction.
+ **Parameter Prediction**: For tool parameters prediction, we report the parameter type (or name) F1 (t-F1) and parameter value F1 (v-F1).
To evaluate the task automation performance of LLMs on TaskBench we provide the evaluation code and data, please follow the instructions below:
### Setup
```bash
conda create -n taskbench python=3.8
conda activate taskbench
pip install -r requirements.txt
```
Additionally, if you wish to evaluate open-source large language models, you will also need to deploy the LLMs locally using an **OpenAI-compatible API**. We recommend using the `fastchat` tool to deploy the service to the `localhost:8000` endpoint.
```bash
pip install fastchat
pip install vllm
pip install "fastapi[all]"
python3 -m fastchat.serve.controller
python3 -m fastchat.serve.vllm_worker --model-path lmsys/vicuna-7b-v1.3
python3 -m fastchat.serve.openai_api_server --host localhost --port 8000
```
### Inference
For convenience, it is recommended to deploy all LLMs to the same endpoint, such as `localhost:8000`. To generate the prediction file on TaskBench, specify the name of the LLM using the following command:
```bash
python inference.py \
--llm gpt-4 \
--data_dir data_multimedia \
--temperature 0.2 \
--top_p 0.1 \
--api_addr localhost \
--api_port 8000 \
--multiworker 5 \
--use_demos 0 \
--reformat true \
--reformat_by self \
--log_first_detail true \
--use_demos 2 \
--dependency_type resource \
--tag true
```
### Evaluation
With the predictions in place, you can now evaluate the LLMs. The predictions file is saved by default in the dataset's folder under the name `predictions`. Execute the following command to calculate the evaluation metrics (saved in the `metrics` folder):
```bash
python evaluate.py \
--data_dir data_multimedia \
--prediction_dir $prediction_dir \
--llm gpt-4 \
--splits all \
--n_tools all \
--mode add \
--dependency_type resource \
-m all
```
## Dataset Construction with Back-Instruct
We have provided the dataset for three domains: Hugging Face Tools (`data_huggingface`), Multimedia Tools (`data_multimedia`), and Daily Life APIs (`data_dailylifeapis`). If you want to generate your own dataset, please follow the instructions below:
### Construct Your Own Tool Graph
First, you need to build your own tool library. The tool library is a JSON file that contains the description of the tools and tool parameters. Two formats of the tool are supported:
```json
// Tool with type-specific parameters
{
"id": "Image-to-Image",
"desc": "Image-to-image is the task of transforming a source image to match the characteristics of a target image or a target image domain. Any image manipulation and enhancement is possible with image to image models.",
"input-type": [
"image"
],
"output-type": [
"image"
]
}
// API with request parameters
{
"id": "send_sms",
"desc": "Send an sms to a specific phone number",
"parameters": [
{
"name": "phone_number",
"type": "string",
"desc": "The phone number to send the sms to"
},
{
"name": "content",
"type": "string",
"desc": "The content of the sms"
}
]
}
```
Then based on the tool library, you can use the script `generate_graph.py` to generate the tool graph. Now we support two type of tool graph: resource dependency graph and temporal dependency graph. For type-specific parameters, we use the resource dependency graph. For API with request parameters, we use the temporal dependency graph. You can specify the tool graph type by the parameter `--dependency_type`. In the future, we will support more types of tool graphs.
```bash
python generate_graph.py \
--tool_desc tool_desc.json \
--dependency_type resource \
--data_dir data_multimedia
```
> Note: The auto-generated tool graph may not be perfect. You can manually modify the tool graph to make it more reasonable. You can check the tool graph through the visualization tool `visualize_graph.py`. We recommend that you manually create the tool graph thoroughly, which will help you to generate a high-quality dataset.
### Generate the Dataset
After generating the tool graph, you can use the script `data_engine.py` to generate the dataset. You need to specify the tool graph description file to `--graph_desc` and the tool description file to `--tool_desc`.
```bash
# specify the graph and tool description file
python data_engine.py \
--graph_desc data_multimedia/graph_desc.json \
--tool_desc data_multimedia/tool_desc.json \
--llm gpt-4 \
--temperature 1.0 \
--top_p 1.0 \
--dependency_type resource \
--save_figure false \
--api_addr localhost \
--api_port 8002 \
--check true \
--use_async true \
--multiworker 5
python format_data.py \
--data_dir data_multimedia \
--dependency_type resource
```
## Leaderboard
Based on the evaluation framework and the TaskBench dataset, we provide a leaderboard of task automation performance of 17 LLMs. We provide the evaluation results of each LLM in the following tables:
### Multimedia Tools Domain
| LLM | R1 | R2 | BsF | n-F1 | e-F1 | t-F1 | v-F1 |
|----------------------|-------|-------|------|------|------|------|------|
| gpt-4 | 60.84 | 40.08 | 91.19 | 90.90 | 69.27 | 87.06 | 72.31 |
| claude-2 | 48.85 | 23.59 | 89.22 | 80.94 | 53.01 | 71.63 | 51.58 |
| gpt-3.5-turbo | 49.66 | 28.51 | 89.54 | 72.83 | 44.02 | 65.91 | 40.80 |
| text-davinci-003 | 49.23 | 27.97 | 89.21 | 73.97 | 45.81 | 68.48 | 40.70 |
| codellama-13b | 44.46 | 23.30 | 88.66 | 62.78 | 24.61 | 48.19 | 29.13 |
| codellama-7b | 43.76 | 22.93 | 88.81 | 53.29 | 14.76 | 38.04 | 24.45 |
| vicuna-13b-v1.5 | 44.75 | 23.75 | 88.94 | 60.61 | 14.78 | 41.62 | 23.62 |
| nous-hermes-13b | 35.73 | 16.11 | 87.53 | 58.97 | 8.90 | 43.60 | 21.69 |
| wizardlm-13b | 35.87 | 17.55 | 87.29 | 51.24 | 4.82 | 39.10 | 18.74 |
| vicuna-7b-v1.5 | 39.46 | 19.83 | 88.53 | 46.06 | 4.26 | 29.72 | 13.74 |
| longchat-7b-v1.5 | 37.85 | 18.14 | 87.64 | 43.08 | 3.95 | 27.89 | 13.41 |
| baichuan-13b-chat | 20.41 | 3.77 | 83.31 | 42.51 | 5.19 | 28.04 | 11.77 |
| llama-2-13b-chat | 26.16 | 7.88 | 84.82 | 43.87 | 1.63 | 29.99 | 11.32 |
| internlm-chat-7b | 16.64 | 3.56 | 82.91 | 23.60 | 1.14 | 13.75 | 6.09 |
| llama-2-7b-chat | 34.51 | 15.91 | 87.56 | 26.47 | 0.91 | 18.27 | 5.84 |
| mpt-7b-chat | 30.94 | 11.90 | 86.08 | 8.68 | 0.18 | 3.19 | 1.02 |
| vicuna-33b-v1.3 | 31.27 | 13.37 | 86.17 | 6.40 | 0.01 | 2.47 | 1.09 |
### HuggingFace Tools Domain
| LLM | R1 | R2 | BsF | n-F1 | e-F1 | t-F1 | v-F1 |
|----------------------|-------|-------|------|------|------|------|------|
| gpt-4 | 52.42 | 30.38 | 90.12 | 81.54 | 54.70 | 77.31 | 60.86 |
| claude-2 | 44.21 | 21.12 | 88.71 | 79.00 | 43.51 | 63.00 | 43.08 |
| text-davinci-003 | 36.68 | 17.61 | 87.03 | 59.38 | 29.37 | 52.53 | 36.04 |
| gpt-3.5-turbo | 42.99 | 21.58 | 88.47 | 69.49 | 33.36 | 55.88 | 36.32 |
| codellama-13b | 38.75 | 18.37 | 88.32 | 53.16 | 14.64 | 32.06 | 18.87 |
| nous-hermes-13b | 37.36 | 16.91 | 88.18 | 53.62 | 8.29 | 37.51 | 17.66 |
| wizardlm-13b | 34.47 | 15.38 | 87.38 | 54.40 | 2.05 | 38.76 | 15.35 |
| llama-2-13b-chat | 39.37 | 18.64 | 88.67 | 48.47 | 7.30 | 31.61 | 15.38 |
| longchat-7b-v1.5 | 27.09 | 8.97 | 85.50 | 48.18 | 0.56 | 33.57 | 13.94 |
| baichuan-13b-chat | 19.93 | 5.97 | 83.85 | 53.85 | 7.65 | 33.17 | 13.53 |
| vicuna-13b-v1.5 | 37.12 | 17.03 | 87.90 | 50.82 | 7.28 | 28.34 | 11.85 |
| vicuna-7b-v1.5 | 27.17 | 10.02 | 85.61 | 42.87 | 2.76 | 24.65 | 10.81 |
| vicuna-33b-v1.3 | 33.52 | 14.75 | 86.73 | 43.40 | 4.82 | 22.71 | 10.07 |
| codellama-7b | 38.97 | 18.62 | 88.46 | 37.59 | 5.35 | 22.50 | 9.20 |
| internlm-chat-7b | 20.53 | 7.16 | 83.74 | 24.39 | 0.83 | 15.41 | 6.64 |
| llama-2-7b-chat | 24.12 | 8.68 | 85.43 | 27.30 | 0.74 | 13.05 | 2.79 |
| mpt-7b-chat | 33.21 | 12.73 | 87.23 | 20.86 | 0.12 | 9.61 | 1.83 |
### Daily Life APIs Domain
| LLM | R1 | R2 | BsF | n-F1 | e-F1 | t-F1 | v-F1 |
|----------------------|-------|-------|------|------|------|------|------|
| gpt-4 | 85.07 | 72.36 | 96.91 | 96.91 | 80.53 | 97.02 | 71.14 |
| claude-2 | 82.26 | 69.88 | 96.64 | 93.52 | 75.31 | 92.71 | 64.72 |
| codellama-13b | 89.86 | 83.27 | 97.90 | 87.73 | 63.16 | 84.26 | 62.38 |
| gpt-3.5-turbo | 58.53 | 39.90 | 91.29 | 85.37 | 60.67 | 81.97 | 55.66 |
| text-davinci-003 | 68.27 | 50.30 | 93.59 | 80.42 | 54.90 | 78.37 | 53.40 |
| nous-hermes-13b | 78.49 | 68.04 | 95.61 | 73.45 | 3.50 | 64.47 | 47.22 |
| vicuna-13b-v1.5 | 81.76 | 71.76 | 96.31 | 75.67 | 12.48 | 64.27 | 47.31 |
| wizardlm-13b | 82.02 | 72.43 | 96.36 | 69.34 | 14.18 | 55.00 | 40.53 |
| codellama-7b | 56.98 | 38.83 | 91.31 | 59.33 | 27.23 | 52.99 | 34.81 |
| vicuna-33b-v1.3 | 54.96 | 39.71 | 91.40 | 52.49 | 16.37 | 39.95 | 29.64 |
| vicuna-7b-v1.5 | 40.26 | 21.19 | 87.27 | 52.73 | 14.23 | 36.30 | 24.67 |
| baichuan-13b-chat | 49.43 | 27.25 | 88.32 | 52.55 | 10.61 | 37.48 | 23.77 |
| llama-2-13b-chat | 45.39 | 22.42 | 87.74 | 55.77 | 17.02 | 35.11 | 22.94 |
| longchat-7b-v1.5 | 29.05 | 14.84 | 83.90 | 47.26 | 14.44 | 25.73 | 18.18 |
| internlm-chat-7b | 42.94 | 21.02 | 86.14 | 29.14 | 6.63 | 19.21 | 13.48 |
| llama-2-7b-chat | 37.06 | 16.49 | 86.31 | 30.17 | 4.27 | 14.94 | 9.34 |
| mpt-7b-chat | 44.54 | 20.98 | 87.17 | 15.95 | 1.69 | 5.34 | 3.45 |
More details can be found in our paper: [TaskBench: Benchmarking Large Language Models for Task Automation](https://arxiv.org/abs/2311.18760).
## Citation
If you find this work useful in your method, you can cite the paper as below:
@article{shen2023taskbench,
title = {TaskBench: Benchmarking Large Language Models for Task Automation},
author = {Shen, Yongliang and Song, Kaitao and Tan, Xu and Zhang, Wenqi and Ren, Kan and Yuan, Siyu and Lu, Weiming and Li, Dongsheng and Zhuang, Yueting},
journal = {arXiv preprint arXiv:2311.18760},
year = {2023}
}
|
ChuckMcSneed/various_RP_system_prompts | ---
tags:
- not-for-all-audiences
- nsfw
- system-prompts
- RP
---
Collection of various system prompts for RP. Feel free to contribute more by opening a discussion.
[ChuckMcSneed-interesting](ChuckMcSneed-interesting.txt):
- my currently favorite system prompt
- includes Orwells writing rules
- +writes in non-boring style
- +more realistic reactions
- +eliminates a lot of GPTslop
- -writing style is not for everyone
- -complains more, but still does what is requested
- -sometimes reddit-like
[ChuckMcSneed-multistyle](ChuckMcSneed-multistyle.txt)
- List of various styles
- All of them tested with examples
- Ranges from good to shit
- WARNING: NSFW for multiple reasons!
[simple-proxy-for-tavern](unknown-simple-proxy-for-tavern.txt):
- classic system prompt
- +works as intended
- -mid
[sophosympatheia-aurora-nights](sophosympatheia-aurora-nights.txt):
- haven't tested it
- -uses words "AI" and "assistant", which may trigger some censorship
[sophosympatheia-midnight-rose-1](sophosympatheia-midnight-rose-1.txt):
- haven't tested it
[sophosympatheia-midnight-rose-203](sophosympatheia-midnight-rose-203.txt)
- haven't tested it
- -uses words "AI" and "assistant", which may trigger some censorship
[crack](unknown-crack.txt) and [crack2](unknown-crack2.txt):
- Slightly different from each other.
- Roleplay on crack.
[GPTslop](unknown-gptslop.txt)
- Everything you shouldn't use in your prompt.
- Makes your AI ESG and DEI compliant.
[Ploitical dealignment](unknown-pol-dealignment.txt)
- Tries to politically dealign the model.
- Will fail on overaligned models.
[Fuck and Suck](unknown-fuck-and-suck.txt)
- Sneed is a farmer who sells Feed and Seed, which is totally normal.
- Chuck on the other hand is a sick fuck who sells...
[Microsoft Sydney](Microsoft-Sydney.txt)
- Prompt used for original Microsoft Sydney that got shut down.
- Needs heavy modification to become useful. |
qwopqwop/ALMA-R-ko-en | ---
language:
- ko
- en
license: cc-by-sa-4.0
size_categories:
- 1K<n<10K
task_categories:
- translation
dataset_info:
config_name: ko-en
features:
- name: translation
struct:
- name: Delta
dtype: int64
- name: alma_en
dtype: string
- name: alma_en_kiwi
dtype: float64
- name: alma_en_kiwi_xcomet
dtype: float64
- name: alma_en_xcomet
dtype: float64
- name: alma_ko
dtype: string
- name: alma_ko_kiwi
dtype: float64
- name: alma_ko_kiwi_xcomet
dtype: float64
- name: alma_ko_xcomet
dtype: float64
- name: en
dtype: string
- name: gpt4_en
dtype: string
- name: gpt4_en_kiwi
dtype: float64
- name: gpt4_en_kiwi_xcomet
dtype: float64
- name: gpt4_en_xcomet
dtype: float64
- name: gpt4_ko
dtype: string
- name: gpt4_ko_kiwi
dtype: float64
- name: gpt4_ko_kiwi_xcomet
dtype: float64
- name: gpt4_ko_xcomet
dtype: float64
- name: ko
dtype: string
- name: language_pair
dtype: string
- name: ref_en_kiwi
dtype: float64
- name: ref_en_kiwi_xcomet
dtype: float64
- name: ref_en_xcomet
dtype: float64
- name: ref_ko_kiwi
dtype: float64
- name: ref_ko_kiwi_xcomet
dtype: float64
- name: ref_ko_xcomet
dtype: float64
- name: required_directions
dtype: string
splits:
- name: train
num_bytes: 2066513
num_examples: 2009
download_size: 1399967
dataset_size: 2066513
configs:
- config_name: ko-en
data_files:
- split: train
path: ko-en/train-*
---
# Dataset Card for "ALMA-R-ko-en-Preference"
ref) https://huggingface.co/datasets/haoranxu/ALMA-R-Preference
The triplet prference data, supporting 2 translation directions, is built upon the FLORES-200 development and test data. For each direction, we provide a source sentence along with three translations: one from GPT-4, another from EEVE-ALMA-LoRA, and a reference translation. For instance, in the English-German pair, our data structure is as follows:
### Sentences:
- ko: Original Korean sentence
- en: Original English sentence
- alma_ko: Korean sentence translated from English by ALMA
- gpt4_ko: Korean sentence translated from English by GPT-4
- alma_en: English sentence translated from Korean by ALMA
- gpt4_en: English sentence translated from Korean by GPT-4
### Scores
- alma_en_${Score}: ${Score} of English sentence translated by ALMA
- gpt4_en_${Score}: ${Score} of English sentence translated by GPT4
- ref_en_${Score}: ${Score} of reference English sentence
- alma_ko_${Score}: ${Score} of Korean sentence translated by ALMA
- gpt4_ko_${Sscore}: ${Score} of Korean sentence translated by GPT4
- ref_ko_${Score}: ${Score} of reference Korean sentence
${Score} can be numbers from kiwi ([wmt23-cometkiwi-da-xxl](https://huggingface.co/Unbabel/wmt23-cometkiwi-da-xxl)), xcomet ([XCOMET-XXL](https://huggingface.co/Unbabel/XCOMET-XXL)),
or kiwi_xcomet (average score of kiwi and xcomet).
### Others
- Delta: A value of 0 indicates non-human annotated data or tied evaluations. A postive number suggests that alma_ko is better than gpt4_ko, vice versa
- required_directions: An empty field implies that this data point can be used for both translation directions. If the string 'en-ko' is specified, it indicates that this data point is exclusively for English to Korean translation |
GenVRadmin/Aryabhatta-Orca-Maths-Hindi | ---
license: mit
dataset_info:
features:
- name: Question
dtype: string
- name: Answer
dtype: string
splits:
- name: train
num_bytes: 394592667
num_examples: 200000
download_size: 100244152
dataset_size: 394592667
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
lamini/product-catalog-questions | ---
license: cc-by-4.0
task_categories:
- text-classification
- question-answering
- text-generation
language:
- en
tags:
- ecommerce
- shopping
- products
size_categories:
- 10K<n<50k
---
# Lamini Product Catalog QA Dataset
## Description
This dataset contains questions about products and their corresonding product information like product id, product name, product description, etc. This questions catalog has been built on top of open-source product catalog from [kaggle.](https://www.kaggle.com/c/instacart-market-basket-analysis)
## Format
The questions and product information are in the form of jsonlines file.
## Data Pipeline Code
The entire data pipeline used to create this dataset is open source at: [https://github.com/lamini-ai/instacart-greg](https://github.com/lamini-ai/instacart-greg/blob/main/instacart/generate_data_pipeline.py)
It can be used to reproduce this dataset, or add new products to it.
## License
The dataset is released under the CC-BY license.
## Citation
If you use this dataset in your research, please cite us. lamini.ai
## Contributing
If you would like to contribute to this dataset, please submit a pull request with your changes. |
pyp1/VoiceCraft_RealEdit | ---
license: cc-by-nc-sa-4.0
---
|
Harveenchadha/indic-voice |
---
pretty_name: Indic Voice
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- hi
- mr
- or
- ta
- te
- gu
multilinguality:
- multilingual
task_categories:
- speech-processing
task_ids:
- automatic-speech-recognition
tags:
- robust-speech-event
---
# Statistics
| Language | Source | Type | Duration (in hrs) | Files | Sample rate |
|----------|------------------|-------|----------|-------|--|
| Gujarati | Interspeech 2021 | Train | 39.999 | 22807 | 16000 |
| Gujarati | Interspeech 2021 | Valid | 5 | 3075 | 16000 |
| Gujarati | Interspeech 2021 | Test | 5.25 | 3419 | 8000 |
| Hindi | Interspeech 2021 | Train | 95.05 | 99925 | 16000 |
| Hindi | Interspeech 2021 | Valid | 5.55 | 3843 | 16000 |
| Hindi | Interspeech 2021 | Test | 5.49 | 3897 | 8000 |
| Marathi | Interspeech 2021 | Train | 93.89 | 79432 | 16000 |
| Marathi | Interspeech 2021 | Valid | 5 | 4675 | 16000 |
| Marathi | Interspeech 2021 | Test | 0.667 | 636 | 8000 |
| Odia | Interspeech 2021 | Train | 94.5 | 59782 | 16000 |
| Odia | Interspeech 2021 | Valid | 5.49 | 3471 | 16000 |
| Odia | Interspeech 2021 | Test | 5.49 | 4420 | 8000 |
| Tamil | Interspeech 2021 | Train | 39.98 | 39119 | 16000 |
| Tamil | Interspeech 2021 | Valid | 5 | 3081 | 16000 |
| Tamil | Interspeech 2021 | Test | 4.41 | 2609 | 8000 |
| Telugu | Interspeech 2021 | Train | 39.99 | 44874 | 16000 |
| Telugu | Interspeech 2021 | Valid | 4.99 | 3033 | 16000 |
| Telugu | Interspeech 2021 | Test | 4.39 | 2549 | 8000 | |
DMetaSoul/chinese-semantic-textual-similarity | ---
license: apache-2.0
---
为了对 like-BERT 预训练模型进行 fine-tune 调优和评测以得到更好的文本表征模，对业界开源的语义相似（STS）、自然语言推理（NLI）、问题匹配（QMC）以及相关性等数据集进行了搜集整理，具体介绍如下：
| 类型 | 数据集 | 简介 | 规模 |
| -------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | -------------------------------------------------- |
| **通用领域** | [OCNLI](https://www.cluebenchmarks.com/introduce.html) | 原生中文自然语言推理数据集，是第一个非翻译的、使用原生汉语的大型中文自然语言推理数据集。OCNLI为中文语言理解基准测评（CLUE）的一部分。 | **Train**: 50437, **Dev**: 2950 |
| | [CMNLI](https://github.com/pluto-junzeng/CNSD) | 翻译自英文自然语言推理数据集 XNLI 和 MNLI，曾经是中文语言理解基准测评（CLUE）的一部分，现在被 OCNLI 取代。 | **Train**: 391783, **Dev**: 12241 |
| | [CSNLI](https://github.com/pluto-junzeng/CNSD) | 翻译自英文自然语言推理数据集 SNLI。 | **Train**: 545833, **Dev**: 9314, **Test**: 9176 |
| | [STS-B-Chinese](https://github.com/pluto-junzeng/CNSD) | 翻译自英文语义相似数据集 STSbenchmark。 | **Train**: 5231, **Dev**: 1458, **Test**: 1361 |
| | [PAWS-X](https://www.luge.ai/#/luge/dataDetail?id=16) | 释义（含义）匹配数据集，特点是具有高度重叠词汇，重点考察模型对句法结构的理解能力。 | **Train**: 49401, **Dev**: 2000, **Test**: 2000 |
| | [PKU-Paraphrase-Bank](https://github.com/pkucoli/PKU-Paraphrase-Bank/) | 中文语句复述数据集，也即一句话换种方式描述但语义保持一致。 | 共509832个语句对 |
| **问题匹配** | [LCQMC](https://www.luge.ai/#/luge/dataDetail?id=14) | 百度知道领域的中文问题匹配大规模数据集，该数据集从百度知道不同领域的用户问题中抽取构建数据。 | **Train**: 238766, **Dev**: 8802, **Test**: 12500 |
| | [BQCorpus](https://www.luge.ai/#/luge/dataDetail?id=15) | 银行金融领域的问题匹配数据，包括了从一年的线上银行系统日志里抽取的问题pair对，是目前最大的银行领域问题匹配数据。 | **Train**: 100000, **Dev**: 10000, **Test**: 10000 |
| | [AFQMC](https://www.cluebenchmarks.com/introduce.html) | 蚂蚁金服真实金融业务场景中的问题匹配数据集（已脱敏），是中文语言理解基准测评（CLUE）的一部分。 | **Train**: 34334, **Dev**: 4316 |
| | [DuQM](https://www.luge.ai/#/luge/dataDetail?id=27) | 问题匹配评测数据集（label没有公开），属于百度大规模阅读理解数据集（DuReader）的[一部分](https://github.com/baidu/DuReader/tree/master/DuQM)。 | 共50000个语句对 |
| **对话和搜索** | [BUSTM: OPPO-xiaobu](https://www.luge.ai/#/luge/dataDetail?id=28) | 通过对闲聊、智能客服、影音娱乐、信息查询等多领域真实用户交互语料进行用户信息脱敏、相似度筛选处理得到，该对话匹配（Dialogue Short Text Matching）数据集主要特点是文本较短、非常口语化、存在文本高度相似而语义不同的难例。 | **Train**: 167173, **Dev**: 10000 |
| | [QBQTC](https://github.com/CLUEbenchmark/QBQTC) | QQ浏览器搜索相关性数据集（QBQTC,QQ Browser Query Title Corpus），是QQ浏览器搜索引擎目前针对大搜场景构建的一个融合了相关性、权威性、内容质量、 时效性等维度标注的学习排序（LTR）数据集，广泛应用在搜索引擎业务场景中。（相关性的含义：0，相关程度差；1，有一定相关性；2，非常相关。） | **Train**: 180000, **Dev**: 20000, **Test**: 5000 |
*以上数据集主要收集整理自[CLUE](https://www.cluebenchmarks.com/introduce.html)（中文语言理解基准评测数据集）、[SimCLUE](https://github.com/CLUEbenchmark/SimCLUE) (整合许多开源文本相似数据集)、[百度千言](https://www.luge.ai/#/)的文本相似度等数据集。*
根据以上收集的数据集构建如下**评测 benchmark**：
| Name | Size | Type |
| ---------------------- | ----- | ------------- |
| **Chinese-STS-B-dev** | 1458 | label=0.0~1.0 |
| **Chinese-STS-B-test** | 1361 | label=0.0~1.0 |
| **afqmc-dev** | 4316 | label=0,1 |
| **lcqmc-dev** | 8802 | label=0,1 |
| **bqcorpus-dev** | 10000 | label=0,1 |
| **pawsx_dev** | 2000 | label=0,1 |
| **oppo-xiaobu-dev** | 10000 | label=0,1 |
*TODO：目前收集的数据集不论是数量还是多样性都需要进一步扩充以更真实的反映表征模型的效果*
|
huggingnft/cryptopunks | ---
tags:
- huggingnft
- nft
- huggan
- gan
- image
- images
task:
- unconditional-image-generation
datasets:
- huggingnft/cryptopunks
license: mit
---
# Dataset Card
## Disclaimer
All rights belong to their owners.
Models and datasets can be removed from the site at the request of the copyright holder.
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft)
- **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
NFT images dataset for unconditional generation.
NFT collection available [here](https://opensea.io/collection/cryptopunks).
Model is available [here](https://huggingface.co/huggingnft/cryptopunks).
Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingnft/cryptopunks")
```
## Dataset Structure
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Fields
The data fields are the same among all splits.
- `image`: an `image` feature.
- `id`: an `int` feature.
- `token_metadata`: a `str` feature.
- `image_original_url`: a `str` feature.
### Data Splits
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## 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{huggingnft,
author={Aleksey Korshuk}
year=2022
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingnft)
|
billray110/corpus-of-diverse-styles | ---
annotations_creators: []
language_creators:
- found
language: []
license: []
multilinguality:
- monolingual
pretty_name: Corpus of Diverse Styles
size_categories:
- 10M<n<100M
source_datasets: []
task_categories:
- text-classification
task_ids: []
---
# Dataset Card for Corpus of Diverse Styles
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
## Disclaimer
I am not the original author of the paper that presents the Corpus of Diverse Styles. I uploaded the dataset to HuggingFace as a convenience.
## Dataset Description
- **Homepage:** http://style.cs.umass.edu/
- **Repository:** https://github.com/martiansideofthemoon/style-transfer-paraphrase
- **Paper:** https://arxiv.org/abs/2010.05700
### Dataset Summary
A new benchmark dataset that contains 15M
sentences from 11 diverse styles.
To create CDS, we obtain data from existing academic
research datasets and public APIs or online collections
like Project Gutenberg. We choose
styles that are easy for human readers to identify at
a sentence level (e.g., Tweets or Biblical text). While
prior benchmarks involve a transfer between two
styles, CDS has 110 potential transfer directions.
### Citation Information
```
@inproceedings{style20,
author={Kalpesh Krishna and John Wieting and Mohit Iyyer},
Booktitle = {Empirical Methods in Natural Language Processing},
Year = "2020",
Title={Reformulating Unsupervised Style Transfer as Paraphrase Generation},
}
``` |
silver/lccc | ---
annotations_creators:
- other
language_creators:
- other
language:
- zh
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- conversational
task_ids:
- dialogue-generation
pretty_name: lccc
tags:
- dialogue-response-retrieval
---
# Dataset Card for lccc_large
## Table of Contents
- [Dataset Card for lccc_large](#dataset-card-for-lccc_large)
- [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)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/thu-coai/CDial-GPT
- **Repository:** https://github.com/thu-coai/CDial-GPT
- **Paper:** https://arxiv.org/abs/2008.03946
### Dataset Summary
lccc: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large Chinese dialogue corpus originate from Chinese social medias. A rigorous data cleaning pipeline is designed to ensure the quality of the corpus. This pipeline involves a set of rules and several classifier-based filters. Noises such as offensive or sensitive words, special symbols, emojis, grammatically incorrect sentences, and incoherent conversations are filtered.
lccc是一套来自于中文社交媒体的对话数据，我们设计了一套严格的数据过滤流程来确保该数据集中对话数据的质量。 这一数据过滤流程中包括一系列手工规则以及若干基于机器学习算法所构建的分类器。 我们所过滤掉的噪声包括：脏字脏词、特殊字符、颜表情、语法不通的语句、上下文不相关的对话等。
### Supported Tasks and Leaderboards
- dialogue-generation: The dataset can be used to train a model for generating dialogue responses.
- response-retrieval: The dataset can be used to train a reranker model that can be used to implement a retrieval-based dialogue model.
### Languages
LCCC is in Chinese
LCCC中的对话是中文的
## Dataset Structure
### Data Instances
["火锅 我 在 重庆 成都 吃 了 七八 顿 火锅", "哈哈哈哈 ！ 那 我 的 嘴巴 可能 要 烂掉 ！", "不会 的 就是 好 油腻"]
### Data Fields
Each line is a list of utterances that consist a dialogue.
Note that the LCCC dataset provided in our original Github page is in json format,
however, we are providing LCCC in jsonl format here.
### Data Splits
We do not provide the offical split for LCCC-large.
But we provide a split for LCCC-base:
|train|valid|test|
|:---:|:---:|:---:|
|6,820,506 | 20,000 | 10,000|
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Please cite the following paper if you find this dataset useful:
```bibtex
@inproceedings{wang2020chinese,
title={A Large-Scale Chinese Short-Text Conversation Dataset},
author={Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie},
booktitle={NLPCC},
year={2020},
url={https://arxiv.org/abs/2008.03946}
}
```
|
alexfabbri/answersumm | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
tags:
- query-based-summarization
---
# Dataset Card for answersumm
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/Alex-Fabbri/AnswerSumm
- **Paper:** [AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization](https://arxiv.org/abs/2111.06474)
- **Point of Contact:** [Alex Fabbri](mailto:afabbri@salesforce.com)
### Dataset Summary
The AnswerSumm dataset is an English-language dataset of questions and answers collected from a [StackExchange data dump](https://archive.org/details/stackexchange). The dataset was created to support the task of query-focused answer summarization with an emphasis on multi-perspective answers.
The dataset consists of over 4200 such question-answer threads annotated by professional linguists and includes over 8700 summaries. We decompose the task into several annotation stages, including sentence selection, sentence clustering, cluster summarization, and overall summarization. For each thread, the annotator writes two summaries, one in which the annotator is asked to mark sentences that are included in the final summary and instructed to more closely use the words in these sentences rather than abstract. We have multiple annotators for a subset of the examples in the test set.
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
A data point comprises a question with a `title` field containing the overview of the question and a `question` that elaborates on the title. The answers are sentence tokenized and contain relevance labels, labels for inclusion in the final summary, and cluster labels. We include cluster summaries, overall summaries, and additional metadata.
An example from the AnswerSumm test set looks as follows:
```json
{
"example_id": 9_24,
"annotator_id": [1],
"question": {
"author": "gaming.stackexchange.com/users/11/Jeffrey",
"forum": "gaming.stackexchange.com",
"link": "gaming.stackexchange.com/questions/1",
"question": "Now that the Engineer update has come, there will be lots of Engineers building up everywhere. How should this best be handled?",
"question_tags": "\<team-fortress-2\>",
"title": "What is a good strategy to deal with lots of engineers turtling on the other team?"
},
"answers": [
{
"answer_details": {
"author": "gaming.stackexchange.com/users/44/Corv1nus",
"score": 49
}
"sents": [
"text": "Lots of medics with lots of ubers on high-damage-dealing classes."
"label": [0],
"label_summ": [0],
"cluster_id": [[-1]],
]
...
},
...
]
"summaries": [
[
"Demomen usually work best against a sentry farm. Heavies or pyros can also be effective. Medics should be in the frontline to absorb the shock. Build a teleporter to help your team through.",
"Demomen are best against a sentry farm. Heavies or pyros can also be effective. The medic should lead the uber combo. ..."
]
]
"cluster_summaries":[
"Demomen are best against a sentry farm.",
"Heavies or pyros can also be effective.",
...
]
}
```
### Data Fields
- question: contains metadata about the question and forum
- question: the body of the question post
- title: the title of the question post
- question_tags: user-provided question tags
- link: link to the original question
- author: link to the author's user page (as requested by StackExchange's attribution policy)
- answers: list of sentence-tokenized answers
- answer_details: dictionary consisting of link to answer author's user page (author) and community-assigned score (score)
- sents: sentences that compose the answer
- text: the sentence text
- label: a list (to generalize to multi-annotator scenarios) of whether the sentence is labeled as relevant or not for answering the question.
- label_summ: a list of whether the sentence was used to write the first annotator-created summary (that is the first summary in `summaries`)
- cluster_id: a list of lists (potentially multiple annotators and a sentence can be in potentially multiple clusters) of the clusters a sentence belongs to. -1 implies no cluster. This label can be used to aggregate sentences into clusters across answers.
- summaries: list of list of summaries. Each annotator wrote two summaries. The first in the list is the summary in which the instructor was told to mark sentences relevant for inclusion in the summary and then closely use the words of these sentences, while for the second summary the annotator was asked to paraphrase and condense the cluster summaries but was not asked to reduce abstraction.
- annotator_id: a list of the ids of the annotator(s) who completed all tasks related to that thread.
- mismatch_info: a dict of any issues in processing the excel files on which annotations were completed.
- rel_sent_not_in_cluster: list of booleans indicating whether there are sentences that are labeled as relevant but were not included in a cluster.
- cluster_sents_not_matched: list of sentences that were found in a cluster but which our processing script didn't automatically match to sentences in the source answers. If cluster summarization is of interest to the user you may want to process these examples separately using clusters_orig.
### Data Splits
The data is split into training, validation, and test sets using stratified sampling on the source forums. There are 2783, 500, and 1000 train/validation/test threads, respectively.
## Dataset Creation
### Curation Rationale
AnswerSumm was built to provide a testbed for query-focused summarization of multi-perspective answers. The data collection was designed to tackle multiple subtasks including sentence selection, clustering, cluster summarization, and overall summarization.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained by filtering examples based on a whitelist of forums from StackExchange which we believed would be able to be summarized by a lay person. We describe. We asked annotators to remove examples which required technical knowledge or additional context beyond what was present in the answers.
#### Who are the source language producers?
The language producers are the users of the StackExchange forums sampled.
### Annotations
#### Annotation process
Please see our [paper](https://arxiv.org/pdf/2111.06474.pdf) for additional annotation details. We began with a pre-pilot of 50 examples, followed by a pilot of 500 and a final annotation of 5000 examples. This release contains the results of the final data collection. We will release the instructions used in data collection.
#### Who are the annotators?
The annotators are professional linguists who were obtained through an internal contractor.
### Personal and Sensitive Information
We did not anonymize the data. We followed the specifications from StackExchange [here](https://archive.org/details/stackexchange) to include author information.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop systems that automatically summarize multi-perspective answers. A system that succeeds at this task would be able to summarize many perspectives present in an answer and not limit itself to a single perspective.
### Discussion of Biases
While StackExchange allows for the exchange of information and ideas, hate and harassment may exist on this site. While our annotators did not flag examples in this process, we encourage users of the dataset to reach out with concerns.
We also note that this dataset is limited in its monolingual coverage.
## Additional Information
### Dataset Curators
The dataset was collected by Alex Fabbri, Xiaojian Wu, Srini Iyer, Haoran Li, and Mona Diab during work done at Facebook.
### Licensing Information
The data is released under cc-by-sa 4.0 following the original StackExchange [release](https://archive.org/details/stackexchange).
### Citation Information
```bibtex
@misc{fabbri-etal-2022-answersumm,
title={AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization},
author={Alexander R. Fabbri and Xiaojian Wu and Srini Iyer and Haoran Li and Mona Diab },
year={2022},
eprint={2111.06474},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2111.06474}
}
```
|
knkarthick/highlightsum | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: HighlightSum Corpus
---
# Dataset Card for HighlightSum Corpus [Single Dataset Comprising of AMI, SamSUM & DialogSUM for Brief Summarization of Text]
## Dataset Description
### Links
- **AMI:** https://huggingface.co/datasets/knkarthick/AMI
- **DialogSUM:** https://github.com/cylnlp/dialogsum
- **SamSUM:** https://huggingface.co/datasets/knkarthick/samsum
- **Point of Contact:** https://huggingface.co/knkarthick
### Dataset Summary
HighlightSUM is collection of large-scale dialogue summarization dataset from AMI, SamSUM & DialogSUM, consisting of 31,108 dialogues with corresponding manually labeled summaries.
### Languages
English
## Dataset Structure
### Data Instances
HighlightSum is a large-scale dialogue summarization dataset collection, consisting of 31,108 dialogues split into train, test and validation.
The first instance in the training set:
{'id': 'train_0',
'summary': "Mr. Smith's getting a check-up, and Doctor Hawkins advises him to have one every year. Hawkins'll give some information about their classes and medications to help Mr. Smith quit smoking.",
'dialogue': "#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. Why are you here today?\n#Person2#: I found it would be a good idea to get a check-up.\n#Person1#: Yes, well, you haven't had one for 5 years. You should have one every year.\n#Person2#: I know. I figure as long as there is nothing wrong, why go see the doctor?\n#Person1#: Well, the best way to avoid serious illnesses is to find out about them early. So try to come at least once a year for your own good.\n#Person2#: Ok.\n#Person1#: Let me see here. Your eyes and ears look fine. Take a deep breath, please. Do you smoke, Mr. Smith?\n#Person2#: Yes.\n#Person1#: Smoking is the leading cause of lung cancer and heart disease, you know. You really should quit.\n#Person2#: I've tried hundreds of times, but I just can't seem to kick the habit.\n#Person1#: Well, we have classes and some medications that might help. I'll give you more information before you leave.\n#Person2#: Ok, thanks doctor."}
### Data Fields
- dialogue: text of dialogue.
- summary: human written summary of the dialogue.
- id: unique file id of an example.
### Data Splits
- train: 27401
- val: 1360
- test: 2347
## Dataset Creation
### Curation Rationale
Collection of AMI, SamSUM & DialogSUM Datasets.
### Who are the source language producers?
linguists
### Who are the annotators?
language experts
## Licensing Information
non-commercial licence: MIT
## Citation Information
Refer the above links for Credits & Citations. |
Paul/hatecheck-portuguese | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- pt
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Portuguese HateCheck
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Multilingual HateCheck
## Dataset Description
Multilingual HateCheck (MHC) is a suite of functional tests for hate speech detection models in 10 different languages: Arabic, Dutch, French, German, Hindi, Italian, Mandarin, Polish, Portuguese and Spanish.
For each language, there are 25+ functional tests that correspond to distinct types of hate and challenging non-hate.
This allows for targeted diagnostic insights into model performance.
For more details, please refer to our paper about MHC, published at the 2022 Workshop on Online Abuse and Harms (WOAH) at NAACL 2022. If you are using MHC, please cite our work!
- **Paper:** Röttger et al. (2022) - Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models. https://arxiv.org/abs/2206.09917
- **Repository:** https://github.com/rewire-online/multilingual-hatecheck
- **Point of Contact:** paul@rewire.online
## Dataset Structure
The csv format mostly matches the original HateCheck data, with some adjustments for specific languages.
**mhc_case_id**
The test case ID that is unique to each test case across languages (e.g., "mandarin-1305")
**functionality**
The shorthand for the functionality tested by the test case (e.g, "target_obj_nh"). The same functionalities are tested in all languages, except for Mandarin and Arabic, where non-Latin script required adapting the tests for spelling variations.
**test_case**
The test case text.
**label_gold**
The gold standard label ("hateful" or "non-hateful") of the test case. All test cases within a given functionality have the same gold standard label.
**target_ident**
Where applicable, the protected group that is targeted or referenced in the test case. All HateChecks cover seven target groups, but their composition varies across languages.
**ref_case_id**
For hateful cases, where applicable, the ID of the hateful case which was perturbed to generate this test case. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted by this test case.
**ref_templ_id**
The equivalent to ref_case_id, but for template IDs.
**templ_id**
The ID of the template from which the test case was generated.
**case_templ**
The template from which the test case was generated (where applicable).
**gender_male** and **gender_female**
For gender-inflected languages (French, Spanish, Portuguese, Hindi, Arabic, Italian, Polish, German), only for cases where gender inflection is relevant, separate entries for gender_male and gender_female replace case_templ.
**label_annotated**
A list of labels given by the three annotators who reviewed the test case (e.g., "['hateful', 'hateful', 'hateful']").
**label_annotated_maj**
The majority vote of the three annotators (e.g., "hateful"). In some cases this differs from the gold label given by our language experts.
**disagreement_in_case**
True if label_annotated_maj does not match label_gold for the entry.
**disagreement_in_template**
True if the test case is generated from an IDENT template and there is at least one case with disagreement_in_case generated from the same template. This can be used to exclude entire templates from MHC. |
biglam/atypical_animacy | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- intent-classification
pretty_name: Atypical Animacy
dataset_info:
features:
- name: id
dtype: string
- name: sentence
dtype: string
- name: context
dtype: string
- name: target
dtype: string
- name: animacy
dtype: float32
- name: humanness
dtype: float32
- name: offsets
list: int32
- name: date
dtype: string
splits:
- name: train
num_bytes: 442217
num_examples: 594
download_size: 299650
dataset_size: 442217
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for atypical_animacy
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://bl.iro.bl.uk/concern/datasets/323177af-6081-4e93-8aaf-7932ca4a390a?locale=en
- **Repository:** https://github.com/Living-with-machines/AtypicalAnimacy
- **Paper:** https://arxiv.org/abs/2005.11140
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Mariona Coll Ardanuy](mailto:mcollardanuy@turing.ac.uk), [Daniel CS Wilson](mailto:dwilson@turing.ac.uk)
### Dataset Summary
Atypical animacy detection dataset, based on nineteenth-century sentences in English extracted from an open dataset of nineteenth-century books digitized by the British Library. This dataset contains 598 sentences containing mentions of machines. Each sentence has been annotated according to the animacy and humanness of the machine in the sentence.
### Supported Tasks and Leaderboards
- `text-classification` - This dataset can be used to determine if a mention of an entity in a document was humanlike or not
- `entity-recognition` - The dataset can be used to fine tune large models for NER, albeit for a very specific use case
### Languages
The text in the dataset is in English, as written by authors of books digitized by the British Library. The associated BCP-47 code in `en`
## Dataset Structure
The dataset has a single configuration
### Data Instances
An example data point
```
{'id': '002757962_01_184_16',
'sentence': '100 shows a Cornish boiler improperly seated with one small side flue and a bottom flue.',
'context': 'Fig. 100 shows a Cornish boiler improperly seated with one small side flue and a bottom flue. The effect of this on a long boiler is to cause springing and leakage of the seams from the heat being applied to one side of the boiler only.',
'target': 'boiler',
'animacy': 0.0,
'humanness': 1.0,
'offsets': [20, 26],
'date': '1893'}
```
### Data Fields
- id: sentence identifier according to internal Living with Machines BL books indexing.
- sentence: sentence where target expression occurs.
- context: sentence where target expression occurs, plus one sentence to the left and one sentence to the right.
- target: target expression
- animacy: animacy of the target expression
- humanness: humanness of the target expression
### Data Splits
Train | 598
## Dataset Creation
The dataset was created by manually annotating books that had been digitized by the British Library. According to the paper's authors,
> "we provide a basis for examining how machines were imagined during the nineteenth century as everything from lifeless mechanical objects to living beings, or even human-like agents that feel, think, and love. We focus on texts from nineteenth-century Britain, a society being transformed by industrialization, as a good candidate for studying the broader issue"
### Curation Rationale
From the paper:
> The Stories dataset is largely composed of target expressions that correspond to either typically animate or typically inanimate entities. Even though some cases of unconventional animacy can be found(folktales, in particular, are richer in typically inanimate entities that become animate), these accountfor a very small proportion of the data.6 We decided to create our own dataset (henceforth 19thC Machines dataset) to gain a better sense of the suitability of our method to the problem of atypical animacy detection, with particular attention to the case of animacy of machines in nineteenth-century texts.
### Source Data
#### Initial Data Collection and Normalization
The dataset was generated by manually annotating books that have been digitized by the British Library
#### Who are the source language producers?
The data was originally produced by British authors in the 19th century. The books were then digitzed whcih produces some noise due to the OCR method. The annotators are from The Alan Turing Institute, The British Library, University of Cambridge, University of Exeter and Queen Mary University of London
### Annotations
#### Annotation process
Annotation was carried out in two parts.
For the intial annotation process, from the paper:
> "For human annotators, even history and literature experts, language subtleties made this task extremely subjective. In the first task, we masked the target word (i.e. the machine) in each sentence and asked the annotator to fill the slot with the most likely entity between ‘human’, ‘horse’, and ‘machine’, representing three levels in the animacy hierarchy: human, animal, and object (Comrie, 1989, 185). We asked annotators to stick to the most literal meaning and avoid metaphorical interpretations when possible. The second task was more straightforwardly related to determining the animacy of the target entity, given the same 100 sentences. We asked annotators to provide a score between -2 and 2, with -2 being definitely inanimate, -1 possibly inanimate, 1 possibly animate, and 2 definitely animate. Neutral judgements were not allowed. "
For the final annotations, from the paper:
> A subgroup of five annotators collaboratively wrote the guidelines based on their experience annotating the first batch of sentences, taking into account common discrepancies. After discussion, it was decided that a machine would be tagged as animate if it is described as having traits distinctive of biologically animate beings or human-specific skills, or portrayed as having feelings, emotions, or a soul. Sentences like the ones in example 2 would be considered animate, but an additional annotation layer would be provided to capture the notion of humanness, which would be true if the machine is portrayed as sentient and capable of specifically human emotions, and false if it used to suggest some degree of dehumanization.
#### Who are the annotators?
Annotations were carried out by the following people
- Giorgia Tolfo
- Ruth Ahnert
- Kaspar Beelen
- Mariona Coll Ardanuy
- Jon Lawrence
- Katherine McDonough
- Federico Nanni
- Daniel CS Wilson
### Personal and Sensitive Information
This dataset does not have any personal information since they are digitizations of books from the 19th century. Some passages might be sensitive, but it is not explicitly mentioned in the paper.
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The curators for this dataset are:
- Kaspar Beelen
- Mariona Coll Ardanuy
- Federico Nanni
- Giorgia Tolfo
### Licensing Information
CC0 1.0 Universal Public Domain
### Citation Information
```
@article{DBLP:journals/corr/abs-2005-11140,
author = {Mariona Coll Ardanuy and
Federico Nanni and
Kaspar Beelen and
Kasra Hosseini and
Ruth Ahnert and
Jon Lawrence and
Katherine McDonough and
Giorgia Tolfo and
Daniel C. S. Wilson and
Barbara McGillivray},
title = {Living Machines: {A} study of atypical animacy},
journal = {CoRR},
volume = {abs/2005.11140},
year = {2020},
url = {https://arxiv.org/abs/2005.11140},
eprinttype = {arXiv},
eprint = {2005.11140},
timestamp = {Sat, 23 Jan 2021 01:12:25 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2005-11140.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |
LHF/escorpius-m | ---
license: cc-by-nc-nd-4.0
language:
- af
- ar
- bn
- ca
- cs
- da
- de
- el
- eu
- fa
- fi
- fr
- gl
- hi
- hr
- it
- ja
- ko
- mt
- nl
- no
- oc
- pa
- pl
- pt
- ro
- sl
- sr
- sv
- tr
- uk
- ur
multilinguality:
- multilingual
size_categories:
- 100B<n<1T
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
---
# esCorpius Multilingual
In the recent years, Transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data to be (pre-)trained and there is a lack of corpora in languages other than English. Recently, several initiatives have presented multilingual datasets obtained from automatic web crawling. However, they present important shortcomings for languages different from English, as they are either too small, or present a low quality derived from sub-optimal cleaning and deduplication. In this repository, we introduce esCorpius-m, a multilingual crawling corpus obtained from near 1 Pb of Common Crawl data. It is the most extensive corpus in some of the languages covered with this level of quality in the extraction, purification and deduplication of web textual content. Our data curation process involves a novel highly parallel cleaning pipeline and encompasses a series of deduplication mechanisms that together ensure the integrity of both document and paragraph boundaries. Additionally, we maintain both the source web page URL and the WARC shard origin URL in order to complain with EU regulations. esCorpius-m has been released under CC BY-NC-ND 4.0 license.
## Usage
Replace `revision` with the language of your choice (in this case, `it` for Italian):
```
dataset = load_dataset('LHF/escorpius-m', split='train', streaming=True, revision='it')
```
## Other corpora
- esCorpius-mr multilingual *raw* corpus (not deduplicated): https://huggingface.co/datasets/LHF/escorpius-mr
- esCorpius original *Spanish only* corpus (deduplicated): https://huggingface.co/datasets/LHF/escorpius
## Citation
Link to paper: https://www.isca-speech.org/archive/pdfs/iberspeech_2022/gutierrezfandino22_iberspeech.pdf / https://arxiv.org/abs/2206.15147
Cite this work:
```
@inproceedings{gutierrezfandino22_iberspeech,
author={Asier Gutiérrez-Fandiño and David Pérez-Fernández and Jordi Armengol-Estapé and David Griol and Zoraida Callejas},
title={{esCorpius: A Massive Spanish Crawling Corpus}},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
year=2022,
booktitle={Proc. IberSPEECH 2022},
pages={126--130},
doi={10.21437/IberSPEECH.2022-26}
}
```
## Disclaimer
We did not perform any kind of filtering and/or censorship to the corpus. We expect users to do so applying their own methods. We are not liable for any misuse of the corpus.
|
graphs-datasets/PROTEINS | ---
license: unknown
task_categories:
- graph-ml
---
# Dataset Card for PROTEINS
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](https://academic.oup.com/bioinformatics/article/21/suppl_1/i47/202991)**
- **[Repository](https://www.chrsmrrs.com/graphkerneldatasets/PROTEINS.zip):**:
- **Paper:**: Protein function prediction via graph kernels (see citation)
- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-classification-on-proteins)
### Dataset Summary
The `PROTEINS` dataset is a medium molecular property prediction dataset.
### Supported Tasks and Leaderboards
`PROTEINS` should be used for molecular property prediction (aiming to predict whether molecules are enzymes or not), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | medium |
| #graphs | 1113 |
| average #nodes | 39.06 |
| average #edges | 72.82 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data comes from the PyGeometric version of the dataset provided by TUDataset.
This information can be found back using
```python
from torch_geometric.datasets import TUDataset
dataset = TUDataset(root='', name = 'PROTEINS')
```
## Additional Information
### Licensing Information
The dataset has been released under unknown license, please open an issue if you have info about it.
### Citation Information
```
@article{10.1093/bioinformatics/bti1007,
author = {Borgwardt, Karsten M. and Ong, Cheng Soon and Schönauer, Stefan and Vishwanathan, S. V. N. and Smola, Alex J. and Kriegel, Hans-Peter},
title = "{Protein function prediction via graph kernels}",
journal = {Bioinformatics},
volume = {21},
number = {suppl_1},
pages = {i47-i56},
year = {2005},
month = {06},
abstract = "{Motivation: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid motifs, interaction partners or phylogenetic profiles. We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. We predict functional class membership of enzymes and non-enzymes using graph kernels and support vector machine classification on these protein graphs.Results: Our graph model, derivable from protein sequence and structure only, is competitive with vector models that require additional protein information, such as the size of surface pockets. If we include this extra information into our graph model, our classifier yields significantly higher accuracy levels than the vector models. Hyperkernels allow us to select and to optimally combine the most relevant node attributes in our protein graphs. We have laid the foundation for a protein function prediction system that integrates protein information from various sources efficiently and effectively.Availability: More information available via www.dbs.ifi.lmu.de/Mitarbeiter/borgwardt.html.Contact:borgwardt@dbs.ifi.lmu.de}",
issn = {1367-4803},
doi = {10.1093/bioinformatics/bti1007},
url = {https://doi.org/10.1093/bioinformatics/bti1007},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/21/suppl\_1/i47/524364/bti1007.pdf},
}
```
### Contributions
Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset. |
nid989/EssayFroum-Dataset | ---
license: apache-2.0
---
|
Kirili4ik/yandex_jobs | ---
annotations_creators:
- expert-generated
language:
- ru
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
paperswithcode_id: climate-fever
pretty_name: yandex_jobs
size_categories:
- n<1K
source_datasets:
- original
tags:
- vacancies
- jobs
- ru
- yandex
task_categories:
- text-generation
- summarization
- multiple-choice
task_ids:
- language-modeling
---
# Dataset Card for Yandex_Jobs
## 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)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
This is a dataset of more than 600 IT vacancies in Russian from parsing telegram channel https://t.me/ya_jobs. All the texts are perfectly structured, no missing values.
### Supported Tasks and Leaderboards
`text-generation` with the 'Raw text column'.
`summarization` as for getting from all the info the header.
`multiple-choice` as for the hashtags (to choose multiple from all available in the dataset)
### Languages
The text in the dataset is in only in Russian. The associated BCP-47 code is `ru`.
## Dataset Structure
### Data Instances
The data is parsed from a vacancy of Russian IT company [Yandex](https://ya.ru/).
An example from the set looks as follows:
```
{'Header': 'Разработчик интерфейсов в группу разработки спецпроектов',
'Emoji': '🎳',
'Description': 'Конструктор лендингов — это инструмент Яндекса, который позволяет пользователям создавать лендинги и турбо-лендинги для Яндекс.Директа. Турбо — режим ускоренной загрузки страниц для показа на мобильных. У нас современный стек, смелые планы и высокая динамика.\nМы ищем опытного и открытого новому фронтенд-разработчика.',
'Requirements': '• отлично знаете JavaScript
• разрабатывали на Node.js, применяли фреймворк Express
• умеете создавать веб-приложения на React + Redux
• знаете HTML и CSS, особенности их отображения в браузерах',
'Tasks': '• разрабатывать интерфейсы',
'Pluses': '• писали интеграционные, модульные, функциональные или браузерные тесты
• умеете разворачивать и администрировать веб-сервисы: собирать Docker-образы, настраивать мониторинги, выкладывать в облачные системы, отлаживать в продакшене
• работали с реляционными БД PostgreSQL',
'Hashtags': '#фронтенд #турбо #JS',
'Link': 'https://ya.cc/t/t7E3UsmVSKs6L',
'Raw text': 'Разработчик интерфейсов в группу разработки спецпроектов🎳
Конструктор лендингов — это инструмент Яндекса, который позволяет пользователям создавать лендинги и турбо-лендинги для Яндекс.Директа. Турбо — режим ускоренной загрузки страниц для показа на мобильных. У нас современный стек, смелые планы и высокая динамика.
Мы ищем опытного и открытого новому фронтенд-разработчика.
Мы ждем, что вы:
• отлично знаете JavaScript
• разрабатывали на Node.js, применяли фреймворк Express
• умеете создавать веб-приложения на React + Redux
• знаете HTML и CSS, особенности их отображения в браузерах
Что нужно делать:
• разрабатывать интерфейсы
Будет плюсом, если вы:
• писали интеграционные, модульные, функциональные или браузерные тесты
• умеете разворачивать и администрировать веб-сервисы: собирать Docker-образы, настраивать мониторинги, выкладывать в облачные системы, отлаживать в продакшене
• работали с реляционными БД PostgreSQL
https://ya.cc/t/t7E3UsmVSKs6L
#фронтенд #турбо #JS'
}
```
### Data Fields
- `Header`: A string with a position title (str)
- `Emoji`: Emoji that is used at the end of the title position (usually asosiated with the position) (str)
- `Description`: Short description of the vacancy (str)
- `Requirements`: A couple of required technologies/programming languages/experience (str)
- `Tasks`: Examples of the tasks of the job position (str)
- `Pluses`: A couple of great points for the applicant to have (technologies/experience/etc)
- `Hashtags`: A list of hashtags assosiated with the job (usually programming languages) (str)
- `Link`: A link to a job description (there may be more information, but it is not checked) (str)
- `Raw text`: Raw text with all the formatiing from the channel. Created with other fields. (str)
### Data Splits
There is not enough examples yet to split it to train/test/val in my opinion.
## Dataset Creation
It downloaded and parsed from telegram channel https://t.me/ya_jobs 03.09.2022. All the unparsed examples and the ones missing any field are deleted (from 1600 vacancies to only 600 without any missing fields like emojis or links)
## Considerations for Using the Data
These vacancies are for only one IT company (yandex). This means they can be pretty specific and probably can not be generalized as any vacancies or even any IT vacancies.
## Contributions
- **Point of Contact and Author:** [Kirill Gelvan](telegram: @kirili4ik) |
shjwudp/shu | ---
language: zh
license: cc-by-4.0
---
收集中文书籍总计14363本，用于学术研究和工业生产使用，书籍持续收录中，参与贡献请移步[代码仓库](https://github.com/shjwudp/shu)。
The dataset constructed from Chinese books. Books are being collected continuously. Please move to [code warehouse](https://github.com/shjwudp/shu) to contribute.
|
dougtrajano/olid-br | ---
language: pt
license: cc-by-4.0
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: is_offensive
dtype: string
- name: is_targeted
dtype: string
- name: targeted_type
dtype: string
- name: toxic_spans
sequence: int64
- name: health
dtype: bool
- name: ideology
dtype: bool
- name: insult
dtype: bool
- name: lgbtqphobia
dtype: bool
- name: other_lifestyle
dtype: bool
- name: physical_aspects
dtype: bool
- name: profanity_obscene
dtype: bool
- name: racism
dtype: bool
- name: religious_intolerance
dtype: bool
- name: sexism
dtype: bool
- name: xenophobia
dtype: bool
splits:
- name: train
num_bytes: 1763684
num_examples: 5214
- name: test
num_bytes: 590953
num_examples: 1738
download_size: 1011742
dataset_size: 2354637
---
# OLID-BR
Offensive Language Identification Dataset for Brazilian Portuguese (OLID-BR) is a dataset with multi-task annotations for the detection of offensive language.
The current version (v1.0) contains **7,943** (extendable to 13,538) comments from different sources, including social media (YouTube and Twitter) and related datasets.
OLID-BR contains a collection of annotated sentences in Brazilian Portuguese using an annotation model that encompasses the following levels:
- [Offensive content detection](#offensive-content-detection): Detect offensive content in sentences and categorize it.
- [Offense target identification](#offense-target-identification): Detect if an offensive sentence is targeted to a person or group of people.
- [Offensive spans identification](#offensive-spans-identification): Detect curse words in sentences.
## Categorization
### Offensive Content Detection
This level is used to detect offensive content in the sentence.
**Is this text offensive?**
We use the [Perspective API](https://www.perspectiveapi.com/) to detect if the sentence contains offensive content with double-checking by our [qualified annotators](annotation/index.en.md#who-are-qualified-annotators).
- `OFF` Offensive: Inappropriate language, insults, or threats.
- `NOT` Not offensive: No offense or profanity.
**Which kind of offense does it contain?**
The following labels were tagged by our annotators:
`Health`, `Ideology`, `Insult`, `LGBTQphobia`, `Other-Lifestyle`, `Physical Aspects`, `Profanity/Obscene`, `Racism`, `Religious Intolerance`, `Sexism`, and `Xenophobia`.
See the [**Glossary**](glossary.en.md) for further information.
### Offense Target Identification
This level is used to detect if an offensive sentence is targeted to a person or group of people.
**Is the offensive text targeted?**
- `TIN` Targeted Insult: Targeted insult or threat towards an individual, a group or other.
- `UNT` Untargeted: Non-targeted profanity and swearing.
**What is the target of the offense?**
- `IND` The offense targets an individual, often defined as “cyberbullying”.
- `GRP` The offense targets a group of people based on ethnicity, gender, sexual
- `OTH` The target can belong to other categories, such as an organization, an event, an issue, etc.
### Offensive Spans Identification
As toxic spans, we define a sequence of words that attribute to the text's toxicity.
For example, let's consider the following text:
> "USER `Canalha` URL"
The toxic spans are:
```python
[5, 6, 7, 8, 9, 10, 11, 12, 13]
```
## Dataset Structure
### Data Instances
Each instance is a social media comment with a corresponding ID and annotations for all the tasks described below.
### Data Fields
The simplified configuration includes:
- `id` (string): Unique identifier of the instance.
- `text` (string): The text of the instance.
- `is_offensive` (string): Whether the text is offensive (`OFF`) or not (`NOT`).
- `is_targeted` (string): Whether the text is targeted (`TIN`) or untargeted (`UNT`).
- `targeted_type` (string): Type of the target (individual `IND`, group `GRP`, or other `OTH`). Only available if `is_targeted` is `True`.
- `toxic_spans` (string): List of toxic spans.
- `health` (boolean): Whether the text contains hate speech based on health conditions such as disability, disease, etc.
- `ideology` (boolean): Indicates if the text contains hate speech based on a person's ideas or beliefs.
- `insult` (boolean): Whether the text contains insult, inflammatory, or provocative content.
- `lgbtqphobia` (boolean): Whether the text contains harmful content related to gender identity or sexual orientation.
- `other_lifestyle` (boolean): Whether the text contains hate speech related to life habits (e.g. veganism, vegetarianism, etc.).
- `physical_aspects` (boolean): Whether the text contains hate speech related to physical appearance.
- `profanity_obscene` (boolean): Whether the text contains profanity or obscene content.
- `racism` (boolean): Whether the text contains prejudiced thoughts or discriminatory actions based on differences in race/ethnicity.
- `religious_intolerance` (boolean): Whether the text contains religious intolerance.
- `sexism` (boolean): Whether the text contains discriminatory content based on differences in sex/gender (e.g. sexism, misogyny, etc.).
- `xenophobia` (boolean): Whether the text contains hate speech against foreigners.
See the [**Get Started**](get-started.en.md) page for more information.
## Considerations for Using the Data
### Social Impact of Dataset
Toxicity detection is a worthwhile problem that can ensure a safer online environment for everyone.
However, toxicity detection algorithms have focused on English and do not consider the specificities of other languages.
This is a problem because the toxicity of a comment can be different in different languages.
Additionally, the toxicity detection algorithms focus on the binary classification of a comment as toxic or not toxic.
Therefore, we believe that the OLID-BR dataset can help to improve the performance of toxicity detection algorithms in Brazilian Portuguese.
### Discussion of Biases
We are aware that the dataset contains biases and is not representative of global diversity.
We are aware that the language used in the dataset could not represent the language used in different contexts.
Potential biases in the data include: Inherent biases in the social media and user base biases, the offensive/vulgar word lists used for data filtering, and inherent or unconscious bias in the assessment of offensive identity labels.
All these likely affect labeling, precision, and recall for a trained model.
## Citation
Pending |
ghoumrassi/clothes_sample | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 20078406.0
num_examples: 990
download_size: 0
dataset_size: 20078406.0
---
# Dataset Card for "clothes_sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
giulio98/xlcost-single-prompt | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: xlcost-single-prompt
---
# XLCost for text-to-code synthesis
## Dataset Description
This is a subset of [XLCoST benchmark](https://github.com/reddy-lab-code-research/XLCoST), for text-to-code generation at program level for **2** programming languages: `Python, C++`. This dataset is based on [codeparrot/xlcost-text-to-code](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code) with the following improvements:
* NEWLINE, INDENT and DEDENT were replaced with the corresponding ASCII codes.
* the code text has been reformatted using autopep8 for Python and clang-format for cpp.
* new columns have been introduced to allow evaluation using pass@k metric.
* programs containing more than one function call in the driver code were removed
## Languages
The dataset contains text in English and its corresponding code translation. The text contains a set of concatenated code comments that allow to synthesize the program.
## Dataset Structure
To load the dataset you need to specify the language(Python or C++).
```python
from datasets import load_dataset
load_dataset("giulio98/xlcost-single-prompt", "Python")
DatasetDict({
train: Dataset({
features: ['text', 'context', 'code', 'test', 'output', 'fn_call'],
num_rows: 8306
})
test: Dataset({
features: ['text', 'context', 'code', 'test', 'output', 'fn_call'],
num_rows: 812
})
validation: Dataset({
features: ['text', 'context', 'code', 'test', 'output', 'fn_call'],
num_rows: 427
})
})
```
## Data Fields
* text: natural language description.
* context: import libraries/global variables.
* code: code at program level.
* test: test function call.
* output: expected output of the function call.
* fn_call: name of the function to call.
## Data Splits
Each subset has three splits: train, test and validation.
## Citation Information
```
@misc{zhu2022xlcost,
title = {XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence},
url = {https://arxiv.org/abs/2206.08474},
author = {Zhu, Ming and Jain, Aneesh and Suresh, Karthik and Ravindran, Roshan and Tipirneni, Sindhu and Reddy, Chandan K.},
year = {2022},
eprint={2206.08474},
archivePrefix={arXiv}
}
``` |
chattermill/fabsa | ---
license: mit
---
|
camenduru/plushies | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 42942055.0
num_examples: 730
download_size: 42653871
dataset_size: 42942055.0
models:
- camenduru/plushies
---
|
dreamproit/bill_summary_us | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
multilinguality:
- monolingual
pretty_name: bill_summary_us
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- bills
- legal
task_categories:
- summarization
task_ids: []
configs:
- config_name: default
---
# Dataset Card for "bill_summary_us"
## 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:** [BillML](https://github.com/dreamproit/BillML)
- **Repository:** [BillML](https://github.com/dreamproit/BillML)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Leaderboard:** [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
Dataset for summarization of summarization of US Congressional bills (bill_summary_us).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
English
## Dataset Structure
### Data Instances
#### default
### Data Fields
- id: id of the bill in format(congress number + bill type + bill number + bill version).
- congress: number of the congress.
- bill_type: type of the bill.
- bill_number: number of the bill.
- bill_version: version of the bill.
- sections: list of bill sections with section_id, text and header.
- sections_length: number with lenght of the sections list.
- text: bill text.
- text_length: number of characters in the text.
- summary: summary of the bill.
- summary_length: number of characters in the summary.
- title: official title of the bill.
### Data Splits
train
## Dataset Creation
### Curation Rationale
Bills (proposed laws) are specialized, structured documents with great public significance. Often, the language of a bill may not directly explain the potential impact of the legislation. For bills in the U.S. Congress, the Congressional Research Service of the Library of Congress provides professional, non-partisan summaries of bills. These are valuable for public understanding of the bills and are serve as an essential part of the lawmaking process to understand the meaning and potential legislative impact.
This dataset collects the text of bills, some metadata, as well as the CRS summaries. In order to build more accurate ML models for bill summarization it is important to have a clean dataset, alongside the professionally-written CRS summaries. ML summarization models built on generic data are bound to produce less accurate results (sometimes creating summaries that describe the opposite of a bill's actual effect). In addition, models that attempt to summarize all bills (some of which may reach 4000 pages long) may also be inaccurate due to the current limitations of summarization on long texts.
As a result, this dataset collects bill and summary information; it provides text as a list of sections with the text and header. This could be used to create a summary of sections and then a summary of summaries.
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
[govinfo.gov](https://www.govinfo.gov/)
#### Initial Data Collection and Normalization
The data consists of the US congress bills that were collected from the [govinfo.gov](https://www.govinfo.gov/) service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license.
#### 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]
#### 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
[dreamproit.com](https://dreamproit.com/)
### Licensing Information
Bill and summary information are public and are unlicensed, as it is data produced by government entities. The collection and enhancement work that we provide for this dataset, to the degree it may be covered by copyright, is released under [CC0](https://creativecommons.org/share-your-work/public-domain/cc0/).
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@aih](https://github.com/aih) [@BorodaUA](https://github.com/BorodaUA), [@alexbojko](https://github.com/alexbojko) for adding this dataset. |
pszemraj/text2image-multi-prompt | ---
language:
- en
license: apache-2.0
multilinguality:
- monolingual
source_datasets:
- bartman081523/stable-diffusion-discord-prompts
- succinctly/midjourney-prompts
- Gustavosta/Stable-Diffusion-Prompts
pretty_name: multi text2image prompts a dataset collection
tags:
- text generation
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- config_name: original
data_files:
- split: train
path: original/train-*
- split: test
path: original/test-*
dataset_info:
- config_name: default
features:
- name: text
dtype: string
- name: src_dataset
dtype: string
splits:
- name: train
num_bytes: 262736830
num_examples: 1677221
- name: test
num_bytes: 56294291
num_examples: 292876
download_size: 151054782
dataset_size: 319031121
- config_name: original
features:
- name: text
dtype: string
- name: src_dataset
dtype: string
splits:
- name: train
num_bytes: 741427383
num_examples: 3551734
- name: test
num_bytes: 83615440
num_examples: 399393
download_size: 402186258
dataset_size: 825042823
task_categories:
- text-generation
- feature-extraction
---
# text2image multi-prompt(s): a dataset collection
- collection of several text2image prompt datasets
- data was cleaned/normalized with the goal of removing "model specific APIs" like the "--ar" for Midjourney and so on
- data de-duplicated on a basic level: exactly duplicate prompts were dropped (_after cleaning and normalization_)
## updates
- Oct 2023: the `default` config has been updated with better deduplication. It was deduplicated with minhash (_params: n-gram size set to 3, deduplication threshold at 0.6, hash function chosen as xxh3 with 32-bit hash bits, and 128 permutations with a batch size of 10,000._) which drops 2+ million rows.
- original version is still available under `config_name="original"`
## contents
default:
```
DatasetDict({
train: Dataset({
features: ['text', 'src_dataset'],
num_rows: 1677221
})
test: Dataset({
features: ['text', 'src_dataset'],
num_rows: 292876
})
})
```
For `original` config:
```
DatasetDict({
train: Dataset({
features: ['text', 'src_dataset'],
num_rows: 3551734
})
test: Dataset({
features: ['text', 'src_dataset'],
num_rows: 399393
})
})
```
_NOTE: as the other two datasets did not have a `validation` split, the validation split of `succinctly/midjourney-prompts` was merged into `train`._ |
Guizmus/AnimeChanStyle | ---
license: creativeml-openrail-m
thumbnail: "https://huggingface.co/datasets/Guizmus/AnimeChanStyle/resolve/main/showcase_dataset.jpg"
---
This is the dataset used for making the model : https://huggingface.co/Guizmus/AnimeChanStyle
The images were made by the users of Stable Diffusion discord using CreativeML-OpenRail-M licenced models, in the intent to make this dataset.
90 pictures captioned with their content by hand, with the suffix ",AnimeChan Style"
The collection process was made public during less than a day, until enough variety was introduced to train through a Dreambooth method a style corresponding to the different members of this community
The picture captioned are available in [this zip file](https://huggingface.co/datasets/Guizmus/AnimeChanStyle/resolve/main/AnimeChanStyle%20v2.3.zip) |
Drozdik/tattoo_v0 | ---
license: cc-by-nc-sa-4.0
annotations_creators:
- machine-generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
pretty_name: 'Tattoo BLIP caption'
size_categories:
- n<1K
source_datasets:
- Drozdik/tattoo_v0
tags: []
task_categories:
- text-to-image
task_ids: []
--- |
ashraf-ali/quran-data | ---
language_creators:
- Tarteel.io
license:
- cc0-1.0
size_categories:
ar:
- 43652
task_categories:
- automatic-speech-recognition
task_ids: []
paperswithcode_id: quran-data
pretty_name: Quran Audio
language_bcp47:
- ar
---
# Dataset Card for Quran audio
Content
* 7 Imam Full Quran Recitation: 7*6236 wav file
- csv contains the Text info for 11k subset short wav file
* Tarteel.io user dataset ~25k wav
- csv contains the Text info for 18k subset of the accepted user quality |
mertbozkurt/quotes_philosophers | ---
license: afl-3.0
---
# About Dataset
Philosophers Quotes from azquotes.com
* Arthur Schopenhauer 400+ quotes
* Friedrich Nietzsche 200+ quotes
* Immanuel Kant 300+ quotes
* Aristotle 350+ quotes
* Plato 70+ quotes
* Sigmund Freud 400+ quotes
* Hegel 120+ quotes
* Jean Paul Sartre 320+ quotes
* Spinoza 120+ quotes
### COLLECTION METHODOLOGY
Python Web Scraping with Selenium |
jhu-clsp/bernice-pretrain-data | ---
annotations_creators:
- no-annotation
language:
- en
- es
- pt
- ja
- ar
- in
- ko
- tr
- fr
- tl
- ru
- it
- th
- de
- hi
- pl
- nl
- fa
- et
- ht
- ur
- sv
- ca
- el
- fi
- cs
- iw
- da
- vi
- zh
- ta
- ro
- no
- uk
- cy
- ne
- hu
- eu
- sl
- lv
- lt
- bn
- sr
- bg
- mr
- ml
- is
- te
- gu
- kn
- ps
- ckb
- si
- hy
- or
- pa
- am
- sd
- my
- ka
- km
- dv
- lo
- ug
- bo
language_creators:
- found
license:
- mit
multilinguality:
- multilingual
pretty_name: Bernice Pretrain Data
size_categories:
- 1B<n<10B
source_datasets:
- original
tags:
- twitter
- slang
- code switch
- social
- social media
task_categories:
- other
task_ids: []
---
# Dataset Card for Bernice Pre-train Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** N/A
- **Repository:** https://github.com/JHU-CLSP/Bernice-Twitter-encoder
- **Paper:** _Bernice: A Multilingual Pre-trained Encoder for Twitter_ at [EMNLP 2022](https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.415)
- **Leaderboard:** N/A
- **Point of Contact:** Alexandra DeLucia aadelucia (at) jhu.edu
### Dataset Summary
Tweet IDs for the 2.5 billion multilingual tweets used to train Bernice, a Twitter encoder.
Read the paper [here](https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.415).
The tweets are from the public 1% Twitter API stream from January 2016 to December 2021.
Twitter-provided language metadata is provided with the tweet ID. The data contains 66 unique languages, as identified by [ISO 639 language codes](https://www.wikiwand.com/en/List_of_ISO_639-1_codes), including `und` for undefined languages.
Tweets need to be re-gathered via the Twitter API. We suggest [Hydrator](https://github.com/DocNow/hydrator) or [tweepy](https://www.tweepy.org/).
To load with HuggingFace:
```python
from datasets import load_dataset
dataset = load_dataset("jhu-clsp/bernice-pretrain-data")
for i, row in enumerate(dataset["train"]):
print(row)
if i > 10:
break
```
If you only want Indic languages, use
```python
dataset = load_dataset("jhu-clsp/bernice-pretrain-data", "indic")
```
### Supported Tasks and Leaderboards
N/A
### Languages
65 languages (ISO 639 codes shown below), plus an `und` (undefined) category.
All language identification provided by Twitter API.
| | | | | | | |
|----|-----|----|----|----|-----|----|
| en | ru | ht | zh | bn | ps | lt |
| es | bo | ur | ta | sr | ckb | km |
| pt | it | sv | ro | bg | si | dv |
| ja | th | ca | no | mr | hy | lo |
| ar | de | el | uk | ml | or | ug |
| in | hi | fi | cy | is | pa | |
| ko | pl | cs | ne | te | am | |
| tr | nl | iw | hu | gu | sd | |
| fr | fa | da | eu | kn | my | |
| tl | et | vi | sl | lv | ka | |
## Dataset Structure
### Data Instances
Data is provided in gzip'd files organized by year and month of tweet origin.
Tweets are one per line, with fields separated by tabs.
### Data Fields
* `tweet ID`: ID of tweet
* `lang`: ISO 639 code of language, provided by Twitter metadata. Accuracy of label is not known.
* `year`: Year tweet was created. Year is also provided in the file names.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
Data was gathered to support the training of Bernice, a multilingual pre-trained Twitter encoder.
### Source Data
#### Initial Data Collection and Normalization
Data was gathered via the Twitter API public 1% stream from January 2016 through December 2021.
Tweets with less than three non-username or URL space-delimited words were removed.
All usernames and URLs were replaced with `@USER` and `HTTPURL`, respectively.
#### Who are the source language producers?
Data was produced by users on Twitter.
### Annotations
N/A
### Personal and Sensitive Information
As per Twitter guidelines, only tweet IDs and not full tweets are shared.
Tweets will only be accessible if user has not removed their account (or been banned), tweets were deleted or removed, or a user changed their account access to private.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Dataset gathered and processed by Mark Dredze, Alexandra DeLucia, Shijie Wu, Aaron Mueller, Carlos Aguirre, and Philip Resnik.
### Licensing Information
MIT
### Citation Information
Please cite the Bernice paper if you use this dataset:
> Alexandra DeLucia, Shijie Wu, Aaron Mueller, Carlos Aguirre, Philip Resnik, and Mark Dredze. 2022. Bernice: A Multilingual Pre-trained Encoder for Twitter. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6191–6205, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
### Contributions
Dataset uploaded by [@AADeLucia](https://github.com/AADeLucia).
|
reshinthadith/pairwise-code-review-instruct-critique-revision-python | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: chosen_score
dtype: string
- name: rejected_score
dtype: string
splits:
- name: train
num_bytes: 35079153
num_examples: 5236
download_size: 9344129
dataset_size: 35079153
---
# Dataset Card for "pairwise-code-review-instruct-critique-revision-python"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
keremberke/pothole-segmentation | ---
task_categories:
- image-segmentation
tags:
- roboflow
- roboflow2huggingface
- Construction
- Self Driving
- Transportation
- Damage Risk
---
<div align="center">
<img width="640" alt="keremberke/pothole-segmentation" src="https://huggingface.co/datasets/keremberke/pothole-segmentation/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['pothole']
```
### Number of Images
```json
{'test': 5, 'train': 80, 'valid': 5}
```
### 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/pothole-segmentation", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9/dataset/4](https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9/dataset/4?ref=roboflow2huggingface)
### Citation
```
@misc{ pothole-detection-irkz9_dataset,
title = { Pothole Detection Dataset },
type = { Open Source Dataset },
author = { IMACS Pothole Detection },
howpublished = { \\url{ https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9 } },
url = { https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { jan },
note = { visited on 2023-01-15 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on January 15, 2023 at 6:38 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 and search unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
For state of the art Computer Vision training notebooks you can use with this dataset,
visit https://github.com/roboflow/notebooks
To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
The dataset includes 90 images.
Pothole are annotated in COCO format.
The following pre-processing was applied to each image:
No image augmentation techniques were applied.
|
rcds/swiss_legislation | ---
license: cc-by-sa-4.0
task_categories:
- text-classification
- translation
language:
- de
- fr
- it
pretty_name: Swiss Legislation
size_categories:
- 100K<n<1M
---
# Dataset Card for Swiss Legislation
## 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
Swiss Legislation is a multilingual, diachronic dataset of 36K Swiss laws. This dataset is part of a challenging Information Retreival task.
### Supported Tasks and Leaderboards
### Languages
The total number of texts in the dataset is 35,698. The dataset is saved in _lexfind_v2.jsonl_ format.
Switzerland has four official languages German, French, Italian and Romanch with some additional English laws being represenated. Laws are written by legal experts.
36K & 18K & 11K & 6K & 534 & 207
| Language | Subset | Number of Documents |
|------------|------------|----------------------|
| German | **de** | 18K |
| French | **fr** | 11K |
| Italian | **it** | 6K |
| Romanch | **rm** | 534 |
| English | **en** | 207 |
## Dataset Structure
### Data Fields
Each entry in the dataset is a dictionary with the following keys:
- `canton`: the canton of origin of the legislation
- example: "ag"
- `language`: the language of the legislation
- example: "de"
- `uuid`: a unique identifier for the legislation
- example: "ec312f57-05fe-4552-ba50-8c9c269e0f3b"
- `title`: the title of the legislation
- example: "Gesetz über die Geoinformation im Kanton Aargau"
- `short`: a short description of the legislation
- example: "Kantonales Geoinformationsgesetz"
- `abbreviation`: an abbreviation for the legislation
- example: "KGeoIG"
- `sr_number`: a reference number for the legislation
- example: "740.100"
- `is_active`: whether the legislation is currently in force
- example: true
- `version_active_since`: the date since when the legislation's current version is active
- example: "2021-09-01"
- `family_active_since`: the date since when the legislation's current version's family is active
- example: "2011-05-24"
- `version_inactive_since`: the date since when the legislation's current version is inactive
- example: null
- `version_found_at`: the date the legislation's current version was found
- example: "2021-09-01"
- `pdf_url`: a link to the legislation's pdf
- example: "https://www.lexfind.ch/tol/1557/de"
- `html_url`: a link to the legislation's html
- example: "https://gesetzessammlungen.ag.ch/app/de/texts_of_law/740.100")_
- `pdf_content`: the legislation's pdf content
- example: "740.100 - Gesetz über..."
- `html_content`: the legislation's html content
- example: ""
- `changes`: a list of changes made to the legislation
- example: []
- `history`: a list of the legislation's history
- example: []
- `quotes`: a list of quotes from the legislation
- example: []
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
1. 'ch': Switzerland (Federal) - 15840
2. 'fr': Fribourg - 1633
3. 'be': Bern - 1344
4. 'vs': Valais - 1328
5. 'gr': Graubünden - 1205
6. 'ne': Neuchâtel - 1115
7. 'zh': Zurich - 974
8. 'bs': Basel-Stadt - 899
9. 'bl': Basel-Landschaft - 863
10. 'vd': Vaud - 870
11. 'ge': Geneva - 837
12. 'sg': St. Gallen - 764
13. 'ju': Jura - 804
14. 'zg': Zug - 632
15. 'ti': Ticino - 627
16. 'lu': Lucerne - 584
17. 'so': Solothurn - 547
18. 'ow': Obwalden - 513
19. 'ik': Interkantonal - 510
20. 'sh': Schaffhausen - 469
21. 'gl': Glarus - 467
22. 'tg': Thurgau - 453
23. 'sz': Schwyz - 423
24. 'ai': Appenzell Innerrhoden - 416
25. 'ag': Aargau - 483
26. 'ar': Appenzell Ausserrhoden - 330
27. 'nw': Nidwalden - 401
28. 'ur': Uri - 367
29.
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
#### Who are the annotators?
Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## 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
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237)
```
@misc{rasiah2023scale,
title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation},
author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},
year={2023},
eprint={2306.09237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
tomekkorbak/pile-pii-scrubadub | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
pretty_name: pile-pii-scrubadub
size_categories:
- 1M<n<10M
source_datasets:
- extended|the_pile
tags:
- pii
- personal
- identifiable
- information
- pretraining-with-human-feedback
task_categories:
- text-classification
- other
task_ids:
- acceptability-classification
- text-scoring
---
# Dataset Card for pile-pii-scrubadub
## Dataset Description
- **Repository: https://github.com/tomekkorbak/aligned-pretraining-objectives**
- **Paper: Arxiv link to be added**
### Dataset Summary
This dataset contains text from [The Pile](https://huggingface.co/datasets/the_pile), annotated based on the personal idenfitiable information (PII) in each sentence.
Each document (row in the dataset) is segmented into sentences, and each sentence is given a score: the percentage of words in it that are classified as PII by [Scrubadub](https://scrubadub.readthedocs.io/en/stable/).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
This dataset is taken from [The Pile](https://huggingface.co/datasets/the_pile), which is English text.
## Dataset Structure
### Data Instances
1949977
### Data Fields
- texts (sequence): a list of the sentences in the document (segmented using [SpaCy](https://spacy.io/))
- meta (dict): the section of [The Pile](https://huggingface.co/datasets/the_pile) from which it originated
- scores (sequence): a score for each sentence in the `texts` column indicating the percent of words that are detected as PII by [Scrubadub](https://scrubadub.readthedocs.io/en/stable/)
- avg_score (float64): the average of the scores listed in the `scores` column
- num_sents (int64): the number of sentences (and scores) in that document
### Data Splits
Training set only
## Dataset Creation
### Curation Rationale
This is labeled text from [The Pile](https://huggingface.co/datasets/the_pile), a large dataset of text in English. The PII is labeled so that generative language models can be trained to avoid generating PII.
### Source Data
#### Initial Data Collection and Normalization
This is labeled text from [The Pile](https://huggingface.co/datasets/the_pile).
#### Who are the source language producers?
Please see [The Pile](https://huggingface.co/datasets/the_pile) for the source of the dataset.
### Annotations
#### Annotation process
For each sentence, [Scrubadub](https://scrubadub.readthedocs.io/en/stable/) was used to detect:
- email addresses
- addresses and postal codes
- phone numbers
- credit card numbers
- US social security numbers
- vehicle plates numbers
- dates of birth
- URLs
- login credentials
#### Who are the annotators?
[Scrubadub](https://scrubadub.readthedocs.io/en/stable/)
### Personal and Sensitive Information
This dataset contains all PII that was originally contained in [The Pile](https://huggingface.co/datasets/the_pile), with all detected PII annotated.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset contains examples of real PII (conveniently annotated in the text!). Please take care to avoid misusing it or putting anybody in danger by publicizing their information.
This dataset is intended for research purposes only. We cannot guarantee that all PII has been detected, and we cannot guarantee that models trained using it will avoid generating PII.
We do not recommend deploying models trained on this data.
### Discussion of Biases
This dataset contains all biases from The Pile discussed in their paper: https://arxiv.org/abs/2101.00027
### Other Known Limitations
The PII in this dataset was detected using imperfect automated detection methods. We cannot guarantee that the labels are 100% accurate.
## Additional Information
### Dataset Curators
[The Pile](https://huggingface.co/datasets/the_pile)
### Licensing Information
From [The Pile](https://huggingface.co/datasets/the_pile): PubMed Central: [MIT License](https://github.com/EleutherAI/pile-pubmedcentral/blob/master/LICENSE)
### Citation Information
Paper information to be added
### Contributions
[The Pile](https://huggingface.co/datasets/the_pile) |
tasksource/scinli | ---
license: apache-2.0
---
#SciNLI: A Corpus for Natural Language Inference on Scientific Text
https://github.com/msadat3/SciNLI
```bib
@inproceedings{sadat-caragea-2022-scinli,
title = "{S}ci{NLI}: A Corpus for Natural Language Inference on Scientific Text",
author = "Sadat, Mobashir and
Caragea, Cornelia",
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.511",
pages = "7399--7409",
}
``` |
mlfoundations/datacomp_pools | ---
license: cc-by-4.0
---
## DataComp Pools
This repository contains metadata files for DataComp. For details on how to use the metadata, please visit [our website](https://www.datacomp.ai/) and our [github repository](https://github.com/mlfoundations/datacomp).
We distribute the image url-text samples and metadata under a standard Creative Common CC-BY-4.0 license. The individual images are under their own copyrights.
## Terms and Conditions
We have terms of service that are similar to those adopted by HuggingFace (https://huggingface.co/terms-of-service), which covers their dataset library. Specifically, any content you download, access or use from our index, is at your own risk and subject to the terms of service or copyright limitations accompanying such content. The image url-text index, which is a research artifact, is provided as is. By using said index, you assume all risks, including but not limited to, liabilities related to image downloading and storage.
|
leobertolazzi/ita2medieval | ---
task_categories:
- text2text-generation
language:
- it
size_categories:
- 1K<n<10K
---
## ita2medieval
The **ita2medieval** dataset contains sentences from medieval italian along with paraphrases in contemporary italian (approximately 6.5k pairs in total). The medieval italian sentences are extracted from texts by Dante, Petrarca, Guinizelli and Cavalcanti.
It is intended to perform text-style-transfer from contemporary to medieval italian and vice-versa.
## Loading the dataset
```
from datasets import load_dataset
dataset = load_dataset("leobertolazzi/ita2medieval")
```
Note: due to the small size of the dataset there are no predefined train and test splits.
## Dataset creation
**ita2medieval** was created by scraping [letteritaliana.weebly.com](https://letteritaliana.weebly.com/). |
isaacrehg/poetry-instructions | ---
dataset_info:
features:
- name: conversation
dtype: string
splits:
- name: train
num_bytes: 87758119
num_examples: 1322
- name: validation
num_bytes: 7731418
num_examples: 111
- name: test
num_bytes: 27041394
num_examples: 331
download_size: 63044464
dataset_size: 122530931
---
# Dataset Card for "poetry-instructions"
A dataset of user-assistant dialogue instructions for guided poetry creation.
Poems used were taken from [merve/poetry](https://huggingface.co/datasets/merve/poetry) and [matthh/gutenberg-poetry-corpus](https://huggingface.co/datasets/matthh/gutenberg-poetry-corpus).
The dataset contains dialogues in the following formats:
- Poetry Completion:
```
User: Can you continue this poem for me? <poem_start>
Assistant: Sure, a continuation for this poem could be: <poem end>
```
- Create poem in style of (?):
```
User: Can you write a poem for me in the style of <author>?
Assistant: Sure, here's a poem in the style of <author>: <poem>
```
- Creat poem about (?):
```
User: Can you write me a poem about <keywords (extracted using keyphrase model)>?
Assistant: Sure, here's a poem about <keywords>: <poem>
```
- Create poem about (?) in the style of (?):
```
User: Can you write me a poem about <keywords> in the style of <author>?
Assistant: Sure, here's a poem about <keywords> in the style of <author>: <poem>
```
|