datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ukr-emotions-binary
EmoBench-UA: Emotions Detection Dataset in Ukrainian Texts
EmoBench-UA: the first of its kind emotions detection dataset in Ukrainian texts. This dataset covers the detection of basic emotions: Joy, Anger, Fear, Disgust, Surprise, Sadness, or None.
Any text can contain any amount of emotion -- only one, several, or none at all. The texts with None emotions are the ones where the labels per emotions classes are 0.
Binary: specifically this dataset contains binary labels… See the full description on the dataset page: https://huggingface.co/datasets/ukr-detect/ukr-emotions-binary.ukr-emotions-intensity
EmoBench-UA: Emotions Detection Dataset in Ukrainian Texts
EmoBench-UA: the first of its kind emotions detection dataset in Ukrainian texts. This dataset covers the detection of basic emotions: Joy, Anger, Fear, Disgust, Surprise, Sadness, or None.
Any text can contain any amount of emotion -- only one, several, or none at all. The texts with None emotions are the ones where the labels per emotions classes are 0.
Intensity: specifically this dataset contains intensity labels… See the full description on the dataset page: https://huggingface.co/datasets/ukr-detect/ukr-emotions-intensity.ukr-emotions-per-annotator
EmoBench-UA: Emotions Detection Dataset in Ukrainian Texts
EmoBench-UA: the first of its kind emotions detection dataset in Ukrainian texts. This dataset covers the detection of basic emotions: Joy, Anger, Fear, Disgust, Surprise, Sadness, or None.
Any text can contain any amount of emotion -- only one, several, or none at all. The texts with None emotions are the ones where the labels per emotions classes are 0.
Per annotator: specifically this dataset contains concatenated… See the full description on the dataset page: https://huggingface.co/datasets/ukr-detect/ukr-emotions-per-annotator.adcumen-viewer-emotions
AdCumen Viewer Emotions Dataset
Dataset for the paper "Decoding Viewer Emotions in Video Ads" by Alexey Antonov, Shravan Sampath Kumar, Jiefei Wei, William Headley, Orlando Wood, and Giovanni Montana, published in Nature Scientific Reports.
Code: github.com/gmontana/DecodingViewerEmotions
Model weights: dnamodel/tsam-viewer-emotions
Dataset Description
The dataset consists of 26,637 five-second video clips extracted from video advertisements, annotated for seven… See the full description on the dataset page: https://huggingface.co/datasets/dnamodel/adcumen-viewer-emotions.ru-izard-emotions
Dataset Card for RuIzardEmotions
Dataset Summary
The RuIzardEmotions dataset is a high-quality translation of the go-emotions dataset and the other emotion-detection dataset. It contains 30k Reddit comments labeled for 10 emotion categories (joy, sadness, anger, enthusiasm, surprise, disgust, fear, guilt, shame and neutral).
The datasets were translated using the accurate translator DeepL and additional processing. The idea for the dataset was inspired by the Izard's… See the full description on the dataset page: https://huggingface.co/datasets/Djacon/ru-izard-emotions.Unified_Dataset_with_EmotionsGo-Emotions-Processedgoogle_go_emotions_hindi_translated113-go-emotions-mergego_emotionsExplainableAI-emotions-DPO-ORPO-RLHF
Preference Dataset for Explainable Multi-Label Emotion Classification
This repository contains a preference dataset compiled to compare two model-generated responses for explaining multi-label emotion classifications on Tweets. The dataset is accompanied by human annotations indicating which response was preferred, based on a set of defined dimensions (clarity, correctness, helpfulness, and verbosity). The annotation guidelines are included to describe how these preference judgments… See the full description on the dataset page: https://huggingface.co/datasets/imhmdf/ExplainableAI-emotions-DPO-ORPO-RLHF.Go_Emotionstherapy_emotions_ruSynthetic dataset of therapeutic data on russian language. Created by Claude Sonnet 4.6. Suitable for fine tuning multilabel classification models.
Emotion_SinlLama