Update README.md

README.md

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----
-license: mit
----
+---
+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]})