AquilaX-AI/classification

Inference

from transformers import AutoModelForSequenceClassification, DistilBertTokenizer
import time
import torch
import re

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = AutoModelForSequenceClassification.from_pretrained("AquilaX-AI/classification").to(device)
tokenizer = DistilBertTokenizer.from_pretrained("AquilaX-AI/classification")


start = time.time()

question = "give me a scan result"
question = re.sub(r"[,?.'\"']", '', question)
inputs = tokenizer(question, return_tensors="pt").to(device)
with torch.no_grad():
    logits = model(**inputs).logits
predicted_class_id = logits.argmax().item()
predicted_class = model.config.id2label[predicted_class_id]

print(predicted_class)
print(time.time() - start)