---
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
```python
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:
```bibtex
@misc{go_emotions_dataset,
  author = {Google AI},
  title = {GoEmotions Dataset},
  year = {2021},
  url = {https://huggingface.co/datasets/go_emotions}
}
```