Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+  - english
+  - emotion-classification
+  - text-classification
+  - fine-tuned
+  - roberta
+base_model: j-hartmann/emotion-english-distilroberta-base
+pipeline_tag: text-classification
+---
+
+# English Text Emotion Recognition Model
+
+Fine-tuned RoBERTa-style model for **multi-class emotion classification in English text**.
+
+This model was trained for 6 epochs on an English emotion dataset and achieves modest validation performance (~90% accuracy).  
+It is suitable as a starting point for English emotion detection tasks, but would benefit from longer training, more data, or a better-suited base model.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Bimsara Serasinghe
+- **Shared by:** Bimsara Serasinghe
+- **Model type:** Text Classification (fine-tuned transformer for multi-class emotion detection)
+- **Language(s) (NLP):** English
+- **License:** Apache-2.0
+- **Finetuned from model:** j-hartmann/emotion-english-distilroberta-base
+### Model Sources
+
+- **Repository:** https://huggingface.co/ShanukaB/English_Text_Emotion_Recogniton_Model
+
+## Uses
+
+### Direct Use
+
+Use the model directly with Hugging Face `pipeline` to classify English sentences into emotion categories.
+
+### Downstream Use
+
+- Building emotion-aware English chatbots
+- Social media emotion/sentiment monitoring (Twitter/X, Reddit, comments)
+- Mental health & wellbeing tools with emotion detection
+- Customer support & feedback analysis
+- Academic/research experiments in English affective computing
+
+### Out-of-Scope Use
+
+- High-stakes automated decisions (mental health diagnosis, hiring, legal)
+- Safety-critical real-time systems without thorough validation
+- Non-English languages (poor generalization expected)
+
+### Recommendations
+
+- Use model outputs only as a signal — combine with human judgment in sensitive contexts
+- Fine-tune further (more epochs, larger/cleaner dataset, or emotion-specialized base like roberta-base-go_emotions)
+- Evaluate on your specific use-case domain before production
+
+## How to Get Started with the Model
+
+```python
+from transformers import pipeline
+import joblib
+
+# Load the fine-tuned model
+classifier = pipeline(
+    "text-classification",
+    model="YOUR_USERNAME/YOUR_MODEL_NAME",
+    tokenizer="YOUR_USERNAME/YOUR_MODEL_NAME"
+)
+
+# Optional: load saved label encoder (if uploaded to repo)
+# label_encoder = joblib.load("label_encoder.pkl")
+
+texts = [
+    "I'm so happy today! 🎉",
+    "This is really making me angry...",
+    "I feel so scared right now 😨",
+    "This is disgusting, I can't believe it."
+]
+
+for text in texts:
+    result = classifier(text)[0]
+    # If labels are saved as "LABEL_0", "LABEL_1", etc.
+    # num_label = int(result["label"].split("_")[-1])
+    # emotion = label_encoder.inverse_transform([num_label])[0] if label_encoder else result["label"]
+    print(f"Text: {text}")
+    print(f"→ {result['label']} (confidence: {result['score']:.3f})\n")