metadata
language: en
license: apache-2.0
base_model: microsoft/deberta-v3-base
tags:
- text-classification
- emotion-detection
- quantization
- int8
- torchao
datasets:
- dair-ai/emotion
metrics:
- accuracy
- f1
DeBERTa Emotion Detection — INT8 Quantized
Fine-tuned and quantized version of DeBERTa for 6-class emotion classification.
Usage
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