After fine tuning model with model.train it gives different predictions for same text
This is how I fine tuned the model:input_ids=tokenizer(str(parseddata), padding=True, truncation=True, max_length=500, 
    return_tensors="pt")
    labels = torch.tensor([0])

    lr_scheduler = get_scheduler(
        name="linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=2
    )

    # del banmodel

    model.train(mode=True)
    for i in range(2):
        outputs = model(input_ids, labels=labels)
        loss = outputs[0]
        loss.backward()
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()This is how I predicted it:def predict_label(text):
    # input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)
    input_ids=tokenizer(text, padding=True, truncation=True, max_length=500, return_tensors="pt")
    logits = model(**input_ids)[0]
    probs = torch.nn.functional.softmax(logits, dim=1)
    
    return probsOnly after training it model gives different answers for the same text input. However, when I close the entire process and turn it on again it works and gives me the same prediction. Any help would be extremely appreciated thanks.
2023-03-05T10:25:13Z
what do you mean by closing the entire process?