datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
test-sync-commentshackernews-comments
Hackernews Comments Dataset
A dataset of all HN API items from id=0 till id=41422887 (so from 2006 till 02 Sep 2024). The dataset is build by scraping the HN API according to its official schema and docs. Scraper code is also available on github: nixiesearch/hnscrape
Dataset contents
No cleaning, validation or filtering was performed. The resulting data files are raw JSON API response dumps in zstd-compressed JSONL files. An example payload:
{
"by": "goldfish"… See the full description on the dataset page: https://huggingface.co/datasets/nixiesearch/hackernews-comments.Toxic_Russian_Commentshttps://www.kaggle.com/datasets/alexandersemiletov/toxic-russian-comments
0 - neutral user comments
1 - toxic user comments
Toxic Russian Comments Dataset
This dataset contains labelled comments from the popular Russian social network ok.ru.
The data was used in a competition where participants had to automatically label each comment with at least one of the four predefined classes. The classes represent different levels of toxicity. The competition was held on the All Cups platform.
Each… See the full description on the dataset page: https://huggingface.co/datasets/AlexSham/Toxic_Russian_Comments.reddit_news_articles_commentsdouban_movie_commentsru-comments-classificationtruexa-comments
Truha audience comments
45,918 real audience comments from the public satirical news channel
«Труха⚡️Україна» (June 30 – July 22, 2026), collected from posts and
cleaned (ads, links, mentions, ultra-short fragments, duplicates and
phone numbers dropped — 50,000 raw → 45,918 clean).
The audience writes in both Ukrainian and Russian (a mix, not a
clean split — a share of comments code-switch between the two), so the
corpus carries language: [ru, uk].
These are the "taste anchor"… See the full description on the dataset page: https://huggingface.co/datasets/hausmer/truexa-comments.cs_facebook-comments
Dataset Card for Czech Facebook comments
Dataset Description
The dataset contains user comments from Facebook. Each comment contains text, sentiment (positive/negative/neutral).
The dataset has in total (train+validation+test) 6,600 reviews. The data is balanced.
Dataset Features
Each sample contains:
comment_id: unique string identifier of the comment.
sentiment_str: string representation of the rating - "pozitivní" / "neutrální" / "negativní"
sentiment_int:… See the full description on the dataset page: https://huggingface.co/datasets/fewshot-goes-multilingual/cs_facebook-comments.hf-issues-dataset-with-comments
Dataset Summary
GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets repository. It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond.
Supported Tasks and Leaderboards
For each of the tasks tagged for this dataset, give a brief description… See the full description on the dataset page: https://huggingface.co/datasets/selfishark/hf-issues-dataset-with-comments.50-YouTube-Commentsgenerated-news-commentsThis dataset has been generated by an LLM.
Convert to a DuckDB
# 1) Download the JSONL file:
wget "https://huggingface.co/datasets/speech-uk/generated-news-comments/resolve/main/generated_comments.jsonl"
# 2) Open a DuckDB session in the terminal, then import the JSONL file into a table:
duckdb
$ CREATE TABLE generated_comments AS SELECT * FROM read_json_auto('generated_comments.jsonl');
# 3) Export the data from memory to a file:
$ ATTACH 'my_database.db';
$ COPY FROM… See the full description on the dataset page: https://huggingface.co/datasets/speech-uk/generated-news-comments.bilibili_comments_sharegpt
林亦LYi B站留言 sharegpt 格式
把 train-test-validation 全合并了，因为使用上是混合其他对话资料训练，没有 overfitting 问题。如果你只是单训练这一份资料，小心overfitting
资料清理也把 B站表情符号去掉了，本来想保留但是无法都放到 system prompt 里，所以还是下次吧
Youtube_shorts_comments
Fine-tuned distilgpt2 on this dataset
The average amount of emojis in a YouTube short comment is 4.59 (Based on this dataset)
12 millions view 😂😂😂 good god 🤦♂️
trendyol-electronics-products-features-and-commentsTupac-hit-em-up-comments
Hit 'Em Up by 2Pac (Tupac Shakur) stands as one of the most influential and iconic diss tracks in the history of rap. Written and performed by the legendary Tupac Shakur, the song embodies his raw, unfiltered artistry and fearless commentary. Known not only as a rapper but as a poet, Tupac consistently tackled social issues, justice, and the realities of life through his music. His unparalleled lyrical genius has inspired generations of artists, including Eminem, Kendrick Lamar, and J.… See the full description on the dataset page: https://huggingface.co/datasets/sleeping-ai/Tupac-hit-em-up-comments.lemmy-world-commentsThis is a data set of lemmy.world comments.
rstp_comments_2UX-comments
Cross-Domain Polarity Models to Evaluate User eXperience in E-learning
Abstract
Virtual learning environments are growing in importance as fast as e-learning, which is becoming highly demanded by universities and students worldwide.
This paper investigates how to automatically evaluate User eXperience in this domain using sentiment analysis techniques.
For this purpose, a corpus has been built with the opinions of 583 users (107 English speakers and 476 Spanish… See the full description on the dataset page: https://huggingface.co/datasets/ELiRF/UX-comments.4chan_archive_ShareGPT_with_rating_and_commentsI took adamo1139/4chan_archive_ShareGPT_fixed_newlines_unfiltered and removed broken samples, like those having no two-sided conversation or containing empty responses (most likely someone just posted an image there and that wasn't scraped).
Then I used Hermes 3 8B (mostly W8A8) to add comments to each sample and add a final score, from 0 to 5. The result of this is this dataset. I had to process a few billions tokens to create this.
I now plan to further filter down the dataset and most… See the full description on the dataset page: https://huggingface.co/datasets/adamo1139/4chan_archive_ShareGPT_with_rating_and_comments.comments-lvGTA-VI-Comments
Grand Theft Auto VI (GTA VI), one of the most anticipated games of this century, recently released its first trailer, breaking numerous YouTube records and generating nearly a million comments worldwide. To facilitate research into online discourse surrounding this cultural phenomenon, I am releasing a dataset of over 200,000 comments from the trailer, including associated metadata. This dataset is intended for responsible use in artificial intelligence research.eval-corm_black_comments
Ranking Evaluation Dataset
sivabharani_commentsreddit-comments
Dataset Card for Reddit-EL
The Reddit Entity Linking is created from the social media website, Reddit.
This dataset contains the Forum comments subcorpus of the Reddit-EL.
Citation Information
@article{10.1016/j.ipm.2020.102479,
author = {Botzer, Nicholas and Ding, Yifan and Weninger, Tim},
title = {Reddit entity linking dataset},
year = {2021},
issue_date = {May 2021},
publisher = {Pergamon Press, Inc.},
address = {USA},
volume = {58}… See the full description on the dataset page: https://huggingface.co/datasets/naist-nlp/reddit-comments.social_interaction_quality_of_comments让我们说中文：
微博（Micro-blog）是指一种基于用户关系信息分享、传播以及获取的通过关注机制分享简短实时信息的广播式的社交媒体，比如twitter、新浪微博。
人们在微博上主要进行的活动就是社交活动，在社交活动当中，人们找到与自己有相同爱好的人，并且参加到感兴趣的话题中的讨论；或者关注自己在现实生活中认识的人，通过其发布的内容来获取最新的动态。
在社交平台上，社交的属性是不可忽略的，而作为微博上最广泛的交互方式，“评论”是人们进行社交活动的最主要方式。评论天然带有社交属性，因此从社交质量的角度对评论进行评价是一件很有意义的事情。
特别地，大语言模型（LLM）尽管已经做了良好对齐，但是其输出仍与人类有较为明显的风格差异，在社交媒体的场景中表现的尤为明显。因此，我们制定了“社交质量”评价标准，同时发布一个使用我们的标准对微博上评论的打分，希望可以推动相关研究的进一步开展。
Let's speak English:
Micro-blog refers to a broadcast-style social media that shares short, real-time… See the full description on the dataset page: https://huggingface.co/datasets/rjhuang/social_interaction_quality_of_comments.replit-comments-categorized
Dataset Card for [Dataset Name]
Dataset Summary
Comments from Replit's Community, sourced via moderator GraphQL queries and personally labeled :). For use in Replit + Weights and Biases Hackathon.
Supported Tasks and Leaderboards
Text Classification
Languages
English
Dataset Structure
Data Instances
{"label":3,"text":"@KENDALPETERSON\nShut up you dont have a permit to brag."}
Labels
0: General
1: Spam
2: NSFW
3: Harassment… See the full description on the dataset page: https://huggingface.co/datasets/RayhanADev/replit-comments-categorized.max-messenger-commentscyberbullying_socialmedia_commentsgithub-issues-commentsreddit_comments_all