Intent classification with BERT

main.py

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = 'Falconsai/intent_classification'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

input_text = "My order hasn't been delivered."
inputs = tokenizer(input_text, return_tensors='pt', padding=True, truncation=True, max_length=64)

with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits

predicted_class_id = torch.argmax(logits, dim=1).item()
predicted_label = model.config.id2label[predicted_class_id]
print(f"Predicted intent: {predicted_label}")