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---
license: mit
language:
- en
tags:
- emotion-classification
- emotion
- mental-health
- bert
- text-classification
pipeline_tag: text-classification
base_model:
- bert-base-uncased
datasets:
- google-research-datasets/go_emotions
---
# 😄 Emotion Classification with BERT
This model is a fine-tuned version of `bert-base-uncased` for **multi-label emotion classification**.
It predicts **eight basic emotions** from a given piece of text using sigmoid-based multi-label classification.
---
## 🧠 Model Details
- **Base model**: `bert-base-uncased`
- **Fine-tuned for**: Multi-label emotion classification
- **Emotion labels**:
- `anger`
- `fear`
- `disgust`
- `sadness`
- `surprise`
- `joy`
- `anticipation`
- `trust`
- **Intended use**: Emotion detection in messages, sentiment analysis, chatbot tuning, mental health signal recognition, etc.
---
## 📦 Usage
```python
import torch
from transformers import BertTokenizer, BertForSequenceClassification
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load model and tokenizer
model_path = "sajeewa/emotion-classification-bert"
emotion_labels = ["anger", "fear", "disgust", "sadness", "surprise", "joy", "anticipation", "trust"]
tokenizer = BertTokenizer.from_pretrained(model_path)
model = BertForSequenceClassification.from_pretrained(model_path, num_labels=len(emotion_labels)).to(device)
# Emotion prediction function
def predict_emotions(text: str):
model.eval()
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=50).to(device)
inputs.pop("token_type_ids", None)
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.sigmoid(logits).cpu().numpy()[0]
return {label: round(float(score), 4) for label, score in zip(emotion_labels, probs)}
# Example usage
example_text = "I'm feeling lonely today."
predictions = predict_emotions(example_text)
dominant_emotion = max(predictions, key=predictions.get)
print({dominant_emotion: predictions[dominant_emotion]}) |