README.md

---
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.

- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Paper [optional]:** [More Information Needed]
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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact


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- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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

## Model Card Authors [optional]

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## Model Card Contact

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