---
library_name: transformers
pipeline_tag: text-classification
---

# Model Card for Model ID

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## Model Details

```python
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"
}
```
### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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## Uses

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### Direct Use

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

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## Training Details

### Training Data

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#### Summary



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## Environmental Impact


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## Technical Specifications [optional]

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## More Information [optional]
(https://arxiv.org/abs/2405.00828)

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