README.md

---
language: en
license: mit
pipeline_tag: text-classification
tags:
- text-classification
- transformers
- pytorch
- multi-label-classification
- multi-class-classification
- emotion
- bert
- go_emotions
- emotion-classification
datasets:
- google-research-datasets/go_emotions
metrics:
- f1
- precision
- recall
widget:
- text: I’m just chilling today.
  example_title: Neutral Example
- text: Thank you for saving my life!
  example_title: Gratitude Example
- text: I’m nervous about my exam tomorrow.
  example_title: Nervousness Example
base_model:
- google-bert/bert-base-uncased
---

# GoEmotions BERT Classifier

Fine-tuned [BERT-base-uncased](https://huggingface.co/bert-base-uncased) on [go_emotions](https://huggingface.co/datasets/go_emotions) for multi-label classification (28 emotions).

## Model Details

- **Architecture**: BERT-base-uncased (110M parameters)
- **Training Data**: [GoEmotions](https://huggingface.co/datasets/google-research-datasets/go_emotions) (58k Reddit comments, 28 emotions)
- **Loss Function**: Focal Loss (gamma=2)
- **Optimizer**: AdamW (lr=2e-5, weight_decay=0.01)
- **Epochs**: 5
- **Hardware**: Kaggle T4 x2 GPUs

## Try It Out
For accurate predictions with optimized thresholds, use the [Gradio demo](https://logasanjeev-goemotions-bert-demo.hf.space).

## Performance

- **Micro F1**: 0.6025 (optimized thresholds)
- **Macro F1**: 0.5266
- **Precision**: 0.5425
- **Recall**: 0.6775
- **Hamming Loss**: 0.0372
- **Avg Positive Predictions**: 1.4564

### Class-Wise Performance
The following table shows per-class metrics on the test set using optimized thresholds (see `thresholds.json`):

| Emotion        | F1 Score | Precision | Recall | Support |
|----------------|----------|-----------|--------|---------|
| admiration     | 0.7022   | 0.6980    | 0.7063 | 504     |
| amusement      | 0.8171   | 0.7692    | 0.8712 | 264     |
| anger          | 0.5123   | 0.5000    | 0.5253 | 198     |
| annoyance      | 0.3820   | 0.2908    | 0.5563 | 320     |
| approval       | 0.4112   | 0.3485    | 0.5014 | 351     |
| caring         | 0.4601   | 0.4045    | 0.5333 | 135     |
| confusion      | 0.4488   | 0.4533    | 0.4444 | 153     |
| curiosity      | 0.5721   | 0.4402    | 0.8169 | 284     |
| desire         | 0.4068   | 0.6857    | 0.2892 | 83      |
| disappointment | 0.3476   | 0.3220    | 0.3775 | 151     |
| disapproval    | 0.4126   | 0.3433    | 0.5169 | 267     |
| disgust        | 0.4950   | 0.6329    | 0.4065 | 123     |
| embarrassment  | 0.5000   | 0.7368    | 0.3784 | 37      |
| excitement     | 0.4084   | 0.4432    | 0.3786 | 103     |
| fear           | 0.6311   | 0.5078    | 0.8333 | 78      |
| gratitude      | 0.9173   | 0.9744    | 0.8665 | 352     |
| grief          | 0.2500   | 0.5000    | 0.1667 | 6       |
| joy            | 0.6246   | 0.5798    | 0.6770 | 161     |
| love           | 0.8110   | 0.7630    | 0.8655 | 238     |
| nervousness    | 0.3830   | 0.3750    | 0.3913 | 23      |
| optimism       | 0.5777   | 0.5856    | 0.5699 | 186     |
| pride          | 0.4138   | 0.4615    | 0.3750 | 16      |
| realization    | 0.2421   | 0.5111    | 0.1586 | 145     |
| relief         | 0.5385   | 0.4667    | 0.6364 | 11      |
| remorse        | 0.6797   | 0.5361    | 0.9286 | 56      |
| sadness        | 0.5391   | 0.6900    | 0.4423 | 156     |
| surprise       | 0.5724   | 0.5570    | 0.5887 | 141     |
| neutral        | 0.6895   | 0.5826    | 0.8444 | 1787    |

## Usage

The model uses optimized thresholds stored in `thresholds.json` for predictions. Example in Python:

```python
from transformers import BertForSequenceClassification, BertTokenizer
import torch
import json
import requests

# Load model and tokenizer
repo_id = "logasanjeev/goemotions-bert"
model = BertForSequenceClassification.from_pretrained(repo_id)
tokenizer = BertTokenizer.from_pretrained(repo_id)

# Load thresholds
thresholds_url = f"https://huggingface.co/{repo_id}/raw/main/thresholds.json"
thresholds_data = json.loads(requests.get(thresholds_url).text)
emotion_labels = thresholds_data["emotion_labels"]
thresholds = thresholds_data["thresholds"]

# Predict
text = "I’m just chilling today."
encodings = tokenizer(text, padding='max_length', truncation=True, max_length=128, return_tensors='pt')
with torch.no_grad():
    logits = torch.sigmoid(model(**encodings).logits).numpy()[0]
predictions = [(emotion_labels[i], logit) for i, (logit, thresh) in enumerate(zip(logits, thresholds)) if logit >= thresh]
print(sorted(predictions, key=lambda x: x[1], reverse=True))
# Output: [('neutral', 0.8147)]