File size: 2,080 Bytes
1e57902 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
---
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}
}
```
|