sajeewa/emotion-classification-bert

😄 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.

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🧠 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.

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📦 Usage

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]})