datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
emotion
Dataset Card for "emotion"
Dataset Summary
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
An example looks as follows.
{
"text": "im feeling quite sad and sorry for myself but… See the full description on the dataset page: https://huggingface.co/datasets/dair-ai/emotion.pg19
Dataset Card for "pg19"
Paraquet version of pg19
Statistics (in # of characters): total_len: 11425076324, average_len: 399450.2595622684
emotion** Attention: There appears an overlap in train / test. I trained a model on the train set and achieved 100% acc on test set. With the original emotion dataset this is not the case (92.4% acc)**
go_emotions
Dataset Card for GoEmotions
Dataset Summary
The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral.
The raw data is included as well as the smaller, simplified version of the dataset with predefined train/val/test
splits.
Supported Tasks and Leaderboards
This dataset is intended for multi-class, multi-label emotion classification.
Languages
The data is in English.
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/go_emotions.reachy-mini-emotions-library
Reachy Mini Emotions Library
Curated emotion recordings for the Reachy Mini robot, maintained by
Pollen Robotics. Each move is a JSON trajectory (head pose, antennas,
body yaw, sampled over time) paired with an Opus audio track.
Motion is sampled at 50 Hz; audio is mono Ogg/Opus (decoded natively by
the robot). Requires reachy_mini ≥ v1.8.4 (its move loader resolves
non-.wav audio sidecars).
File layout
Files live at the root of the dataset, named <emotion>.json +… See the full description on the dataset page: https://huggingface.co/datasets/pollen-robotics/reachy-mini-emotions-library.Emotional_SpeechThis dataset contains audio-text pairs in the webdataset format.
The audio files are short speech segments from publicly available videos & the texts are descriptions of emotions the speakers seems to be feeling. Some captions also describe the speakers gender and age.
All files with the substring "part1" in the name contain unique audio files with unique captions.
All files with the substring "part2" , "part3", ... in the name contain the same audio files as in "part1", but with different… See the full description on the dataset page: https://huggingface.co/datasets/EQ4You/Emotional_Speech.emotion-vectors-gemma-4-31b-it-postfix
Emotion vectors, google/gemma-4-31b-it (corrected extraction)
Residual-stream activations for google/gemma-4-31b-it, pooled per story and averaged per
emotion. Each emotion ends up as one direction in the model's activation space.
Read LINEAGE.md before using this. This set supersedes
abotresol/emotion-vectors-gemma-4-31b-it. The earlier extraction ran
while the tokenizer padded on the left, so the step that skips a story's first
50 tokens skipped padding instead. This set… See the full description on the dataset page: https://huggingface.co/datasets/abotresol/emotion-vectors-gemma-4-31b-it-postfix.emotion
EmotionClassification
An MTEB dataset
Massive Text Embedding Benchmark
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.
Task category
t2c
Domains
Social, Written
Reference
https://www.aclweb.org/anthology/D18-1404
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["EmotionClassification"])… See the full description on the dataset page: https://huggingface.co/datasets/mteb/emotion.Emotion_new_collected_datasetemolia-thinking
Emolia-Thinking — a VoiceNet-annotated, balanced subset of Emolia
Emolia-Thinking is a richly annotated speech dataset created for the VoiceNet project. It takes a balanced subset of the Emolia corpus — balanced across speaker-embedding clusters and emotion-embedding clusters so that speakers, voices and emotional states are evenly represented rather than dominated by the most common cases — and annotates every clip along the full VoiceNet Extended voice-performance taxonomy… See the full description on the dataset page: https://huggingface.co/datasets/VoiceNet/emolia-thinking.emotionsquality
Dataset Card for "quality"
More Information needed
emova-alignment-7m
EMOVA-Alignment-7M
🤗 EMOVA-Models | 🤗 EMOVA-Datasets | 🤗 EMOVA-Demo
📄 Paper | 🌐 Project-Page | 💻 Github | 💻 EMOVA-Speech-Tokenizer-Github
Overview
EMOVA-Alignment-7M is a comprehensive dataset curated for omni-modal pre-training, including vision-language and speech-language alignment.
This dataset is created using open-sourced image-text pre-training datasets, OCR datasets, and 2,000 hours of ASR and TTS data.
This dataset is part of the EMOVA-Datasets… See the full description on the dataset page: https://huggingface.co/datasets/Emova-ollm/emova-alignment-7m.yarn-train-tokenized-16k-mistral
Dataset Card for "yarn-train-tokenized-16k-mistral"
More Information needed
emova-sft-4m
EMOVA-SFT-4M
🤗 EMOVA-Models | 🤗 EMOVA-Datasets | 🤗 EMOVA-Demo
📄 Paper | 🌐 Project-Page | 💻 Github | 💻 EMOVA-Speech-Tokenizer-Github
Overview
EMOVA-SFT-4M is a comprehensive dataset curated for omni-modal instruction tuning, including textual, visual, and audio interactions. This dataset is created by gathering open-sourced multi-modal instruction datasets and synthesizing high-quality omni-modal conversation data to enhance user experience. This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/Emova-ollm/emova-sft-4m.laions_got_talent_with_voice_emotion_speed_tags_for_orpheus_tuningLAION's Got Talent: Generated Voice Acting Dataset
Overview
"LAION's Got Talent" is a synthetic voice acting dataset designed to offer a broad range of emotional expressions, vocal bursts, and multi-language utterances. This dataset is a component of the BUD-E project, led by LAION with support from Intel, and aims to drive forward research in context-aware and empathetic AI voice assistants.
Updated Composition
Voices and Languages
English: 11 OpenAI voices, each… See the full description on the dataset page: https://huggingface.co/datasets/laion/laions_got_talent_with_voice_emotion_speed_tags_for_orpheus_tuning.Robot-EQ
RobotEQ-Data
Official dataset release for RobotEQ.
Evaluation & Scripts
For inference scripts, evaluation scripts, and data production tooling, see the RobotEQ code repository.
Dataset Statistics
Item
Count
Behavior judgment scenarios (synthetic)
1,812
Behavior judgment scenarios (real POV)
223
Behavior judgment scenarios (total)
2,035
Behavior judgment behavior annotations
3,171
Spatial grounding questions
825… See the full description on the dataset page: https://huggingface.co/datasets/Tongji-Emotion/Robot-EQ.pg19-test
Dataset Card for "pg19-test"
More Information needed
dialogs-ru-emotional-conversations
Dialogs: A Studio-Quality Expressive Conversational Russian Speech Corpus
Dialogs is a 20.6-hour studio-quality corpus of expressive, conversational
Russian speech, designed for dialog-oriented and emotional text-to-speech.
Unlike existing Russian corpora — mostly single-speaker read speech or large but
low-quality web-mined audio — Dialogs was recorded by professional theatre actors
performing scripted dialogs face-to-face, capturing natural turn-taking,
timing, and expressive… See the full description on the dataset page: https://huggingface.co/datasets/langswap/dialogs-ru-emotional-conversations.Emolia
Dataset Card for Emolia
Dataset Description
This dataset is an enhanced version of the Emilia dataset, enriched with detailed emotion annotations. The annotations were generated using models from the EmoNet suite to provide deeper insight into the emotional content of speech. This work is based on the research and models described in the blog post "Do They See What We See?".
The annotations include 54 scores for each sample, covering a wide range of emotional and… See the full description on the dataset page: https://huggingface.co/datasets/laion/Emolia.Emilia-with-Emotion-Annotations
Dataset Card for Emilia with Emotion Annotations
Dataset Description
This dataset is an enhanced version of the Emilia dataset, enriched with detailed emotion annotations. The annotations were generated using models from the EmoNet suite to provide deeper insight into the emotional content of speech. This work is based on the research and models described in the blog post "Do They See What We See?".
The annotations include 54 scores for each sample, covering a wide range… See the full description on the dataset page: https://huggingface.co/datasets/laion/Emilia-with-Emotion-Annotations.emotion-vectors-gemma-4-31b-it
Emotion vectors — gemma-4-31b-it (instruct) probed on the external gemma-4-4B story corpus
Data provenance (what made these activations)
Probed model: google/gemma-4-31b-it (instruct)
Input corpus: snae/emotion_stories_gemma_4_4B — stories written by gemma-4-4B, a smaller EXTERNAL model (generator is NOT the probed model)
Per-story pooled residual-stream activations and per-emotion mean vectors,
extracted with gemma4-emotion-vectors… See the full description on the dataset page: https://huggingface.co/datasets/abotresol/emotion-vectors-gemma-4-31b-it.emotion-vectors-gemma-4-31b
Emotion vectors — gemma-4-31b (base) probed on the external gemma-4-4B story corpus
Data provenance (what made these activations)
Probed model (whose activations these are): google/gemma-4-31b (base)
Input corpus: snae/emotion_stories_gemma_4_4B — third-person emotion stories written by gemma-4-4B, a smaller EXTERNAL model (the open replication's published corpus; generator is NOT the probed model)
Per-story pooled residual-stream activations and per-emotion… See the full description on the dataset page: https://huggingface.co/datasets/abotresol/emotion-vectors-gemma-4-31b.emotion-vectors-gemma-4-31b-postfix
Emotion vectors, google/gemma-4-31b (corrected extraction)
Residual-stream activations for google/gemma-4-31b, pooled per story and averaged per
emotion. Each emotion ends up as one direction in the model's activation space.
Read LINEAGE.md before using this. This set supersedes
abotresol/emotion-vectors-gemma-4-31b. The earlier extraction ran
while the tokenizer padded on the left, so the step that skips a story's first
50 tokens skipped padding instead. This set re-extracts… See the full description on the dataset page: https://huggingface.co/datasets/abotresol/emotion-vectors-gemma-4-31b-postfix.Long-Data-Collections-Pretrain-Without-Books
Dataset Card for "Long-Data-Collections-Pretrain-Without-Books"
Paraquet version of the pretrain split of togethercomputer/Long-Data-Collections WITHOUT books
Statistics (in # of characters): total_len: 236088622215, average_len: 25159.041601590307
BRIGHTER-emotion-categories
BRIGHTER Emotion Categories Dataset
This dataset contains the emotion categories data from the BRIGHTER paper: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages.
Dataset Description
The BRIGHTER Emotion Categories dataset is a comprehensive multi-language, multi-label emotion classification dataset with separate configurations for each language. It represents one of the largest human-annotated emotion datasets across multiple… See the full description on the dataset page: https://huggingface.co/datasets/brighter-dataset/BRIGHTER-emotion-categories.emo_webds_2EmoVerse
EmoVerse
EmoVerse is a visual emotion dataset for affective image understanding. The dataset is organized around eight emotion categories:
Amusement, Anger, Awe, Contentment, Disgust, Excitement, Fear, and Sadness.
The released package contains annotation files and Parquet shards for the image records and annotations. The Parquet rows store file-level data: each row describes one packed file and includes both metadata and the file content as a binary column.… See the full description on the dataset page: https://huggingface.co/datasets/alkalol/EmoVerse.emo_parlerpg_books-tokenized-bos-eos-chunked-65536
Dataset Card for "pg_books-tokenized-bos-eos-chunked-65536"
The pg19 dataset tokenized under LLaMA into 64k chunks, bookended with BOS and EOS