Sukuna404/distilbert-emotion-fp32

DistilBERT Emotion Detection — FP32

Fine-tuned version of DistilBERT for 6-class emotion classification using the 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)

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