| --- |
| language: en |
| license: apache-2.0 |
| base_model: distilbert-base-uncased |
| tags: |
| - text-classification |
| - emotion-detection |
| - distilbert |
| - pytorch |
| - safetensors |
| datasets: |
| - dair-ai/emotion |
| metrics: |
| - accuracy |
| - f1 |
| --- |
| |
| # DistilBERT Emotion Detection — FP32 |
|
|
| Fine-tuned version of DistilBERT for 6-class emotion classification using the [dair-ai/emotion](https://huggingface.co/datasets/dair-ai/emotion) dataset. |
|
|
| ## Model Performance |
| | Format | Accuracy | F1 Macro | |
| |----------|----------|----------| |
| | FP32 | 92.50% | 0.8799 | |
| | INT8* | 91.95% | 0.8659 | |
|
|
| *INT8 is applied at runtime via `quantize_dynamic` — see usage below. |
| |
| ## Labels |
| | ID | Emotion | |
| |----|----------| |
| | 0 | sadness | |
| | 1 | joy | |
| | 2 | love | |
| | 3 | anger | |
| | 4 | fear | |
| | 5 | surprise | |
| |
| ## Usage (CPU — recommended) |
| ```python |
| import torch |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| |
| repo_id = "Sukuna404/distilbert-emotion-fp32" |
| |
| tokenizer = AutoTokenizer.from_pretrained(repo_id) |
| model = AutoModelForSequenceClassification.from_pretrained(repo_id) |
| |
| # Optional: apply INT8 quantization at runtime for faster CPU inference |
| model = torch.quantization.quantize_dynamic( |
| model, |
| {torch.nn.Linear}, |
| dtype=torch.qint8 |
| ) |
| |
| model.eval() |
| model.cpu() |
| |
| def predict(text: str) -> str: |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="longest") |
| with torch.no_grad(): |
| outputs = model(**inputs) |
| pred_id = outputs.logits.argmax().item() |
| return model.config.id2label[pred_id] |
| |
| print(predict("I am so happy today!")) # joy |
| print(predict("I am really angry!")) # anger |
| ``` |
| |
| ## Limitations |
| - English only |
| - 6 emotion classes only |
| |
