license: mit dataset_info: features:
- name: input_ids sequence: int32
- name: attention_mask sequence: int8
- name: labels sequence: int64 splits:
- name: train num_bytes: 121374360 num_examples: 43410
- name: validation num_bytes: 15171096 num_examples: 5426
- name: test num_bytes: 15173892 num_examples: 5427 download_size: 2670120 dataset_size: 151719348 configs:
- config_name: default
data_files:
- split: train path: data/train-*
- split: validation path: data/validation-*
- split: test path: data/test-*
📚 GoEmotions Dataset (Processed for Multi-Label Classification)
📖 Dataset Overview
This dataset is a preprocessed version of the GoEmotions dataset, containing multi-label emotion annotations for text inputs. It consists of train, validation, and test splits.
🔢 Dataset Statistics
| Split | Samples |
|---|---|
| Train | XX,XXX |
| Validation | X,XXX |
| Test | X,XXX |
📌 Features
| Feature | Type | Description |
|---|---|---|
input_ids |
list[int] |
Tokenized input text |
attention_mask |
list[int] |
Attention mask for tokens |
labels |
list[int] |
Multi-label emotion encoding |
📂 How to Load
from datasets import load_dataset
dataset = load_dataset("codewithdark/go-emotions-processed")
print(dataset["train"][0])
🏋️♂️ Preprocessing Steps
- Tokenization:
bert-base-uncased - Multi-label encoding: Binary encoding of emotions
- Train/Validation/Test split:
80/10/10
🎯 Labels (Emotions)
The dataset contains 27 emotion categories, including:
- Admiration, Joy, Sadness, Anger, Optimism, Disgust, Love, etc.
🛠️ Citation
If you use this dataset, please cite:
@misc{go_emotions_dataset,
author = {Google AI},
title = {GoEmotions Dataset},
year = {2021},
url = {https://huggingface.co./datasets/go_emotions}
}