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--- |
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library_name: transformers |
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tags: |
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- emotion-classification |
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- text-classification |
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- roberta |
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- goemotions |
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- sentiment-analysis |
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license: mit |
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datasets: |
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- google-research-datasets/go_emotions |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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base_model: roberta-base |
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--- |
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# RoBERTa Emotion Classifier (7-class) |
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Fine-tuned RoBERTa model for emotion classification on 7 emotions: **happy, sad, angry, fear, disgust, surprise, neutral**. |
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## Model Details |
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- **Developed by:** VanshajR |
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- **Base Model:** `roberta-base` (125M parameters) |
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- **Task:** Multi-class emotion classification |
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- **Dataset:** GoEmotions (27 emotions mapped to 7) |
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- **Training Samples:** ~58,000 |
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- **Language:** English |
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- **License:** MIT |
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## Performance |
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Evaluated on GoEmotions test set: |
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| Metric | Score | |
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|--------|-------| |
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| **Accuracy** | **57.77%** | |
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| **Macro F1** | **0.4787** | |
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| Precision | 0.5289 | |
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| Recall | 0.4958 | |
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### Per-Class Performance |
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| Emotion | Precision | Recall | F1-Score | Support | |
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|---------|-----------|--------|----------|---------| |
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| Happy | 0.62 | 0.67 | 0.64 | 2,362 | |
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| Sad | 0.54 | 0.51 | 0.52 | 1,210 | |
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| Angry | 0.58 | 0.43 | 0.49 | 1,145 | |
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| Fear | 0.42 | 0.31 | 0.36 | 428 | |
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| Disgust | 0.48 | 0.26 | 0.34 | 361 | |
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| Surprise | 0.43 | 0.43 | 0.43 | 623 | |
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| Neutral | 0.64 | 0.86 | 0.73 | 8,711 | |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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# Load model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("VanshajR/roberta-emotion-7class") |
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model = AutoModelForSequenceClassification.from_pretrained("VanshajR/roberta-emotion-7class") |
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# Classify emotion |
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text = "I'm so excited about this project!" |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) |
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predicted_class = torch.argmax(predictions, dim=-1).item() |
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# Emotion labels |
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emotions = ["happy", "sad", "angry", "fear", "disgust", "surprise", "neutral"] |
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print(f"Predicted emotion: {emotions[predicted_class]}") |
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print(f"Confidence: {predictions[0][predicted_class].item():.2%}") |
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``` |
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## Training Details |
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### Training Data |
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- **Dataset:** GoEmotions (Google Research) |
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- **Emotion Mapping:** 27 fine-grained emotions → 7 basic emotions |
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- **Training Samples:** ~58,000 Reddit comments |
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- **Preprocessing:** Truncation to 128 tokens, lowercase normalization |
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### Training Procedure |
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- **Optimizer:** AdamW (lr=2e-5, weight_decay=0.01) |
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- **Batch Size:** 16 (train), 32 (eval) |
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- **Epochs:** 3 |
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- **Max Length:** 128 tokens |
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- **Training Regime:** fp32 |
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### Compute Infrastructure |
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- **Hardware:** NVIDIA RTX 3070 (8GB VRAM) |
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- **Training Time:** ~2 hours |
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- **Framework:** PyTorch 2.1.0, Transformers 4.35.0 |
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## Limitations and Bias |
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- **Language:** English only |
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- **Domain:** Primarily trained on Reddit comments (may not generalize to formal text) |
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- **Class Imbalance:** Better performance on frequent emotions (happy, neutral) vs rare emotions (fear, disgust) |
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- **Subjective Task:** Human annotators often disagree on emotions (~25-30% disagreement rate) |
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## Intended Use |
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✅ **Recommended:** |
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- Emotion detection in conversational text |
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- Evaluating emotion-controlled text generation |
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- Research on emotion understanding in dialogue |
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- Sentiment analysis applications |
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❌ **Not Recommended:** |
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- Clinical diagnosis or mental health assessment |
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- High-stakes decision making |
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- Non-English languages |
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## Citation |
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```bibtex |
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@misc{vanshajr2024roberta, |
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author = {Vanshaj R}, |
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title = {RoBERTa Emotion Classifier for 7-Class Emotion Detection}, |
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year = {2024}, |
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publisher = {HuggingFace}, |
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url = {https://huggingface.co/VanshajR/roberta-emotion-7class} |
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} |
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``` |
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## Related Work |
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Part of the **Emotion-Controlled Response Generation** project: |
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- 🔗 [GitHub Repository](https://github.com/VanshajR/emotion-controlled-generation) |
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- 🔗 [GPT-2 Emotion-Conditioned Model](https://huggingface.co/VanshajR/gpt2-emotion-prefix) |
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- 📄 [Full Project Report](https://github.com/VanshajR/emotion-controlled-generation/blob/main/PROJECT_REPORT.md) |
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