| --- |
| 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") |
