llama-argument-classification

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Model Details

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("armaniii/llama-argument-classification")
tokenizer = AutoTokenizer.from_pretrained("armaniii/lllama-argument-classification")

model.to(device)
model.eval()

for batch in tqdm.tqdm(data):
    with torch.no_grad():
        input_text = tokenizer(batch, padding=True, truncation=True,max_length=2048,return_tensors="pt").to(device)
        output = model(**input_text)
        logits = output.logits
        predicted_class = torch.argmax(logits, dim=1)
        # Convert logits to a list of predicted labels
        predictions.extend(predicted_class.cpu().tolist())

    # Get the ground truth labels
df["predictions"] = predictions

num2label = {
0:"NoArgument",
1:"Argument"
}

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(https://arxiv.org/abs/2405.00828)

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