dair-ai/emotion
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Fine-tuned and quantized version of DeBERTa for 6-class emotion classification.
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
repo_id = "Sukuna404/deberta-emotion-quantized-int8"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id).to("cpu")
model.eval()
def predict(text):
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
Base model
microsoft/deberta-v3-base