# Model Card: RoBERTa-Base Customer Support Analysis Model

## Model Overview

This model is a fine-tuned version of `facebook/bart-base` trained for content generation tasks. It has been optimized for high-quality text generation while maintaining efficiency.

## Model Details

- **Model Architecture:** Roberta-base
- **Base Model:** `facebook/bart-base`
- **Task:** Content Generation
- **Dataset:** cardiffnlp/tweet_eval
- **Framework:** Hugging Face Transformers
- **Training Hardware:** CUDA
- 
## Installation

To use the model, install the necessary dependencies:

```sh
pip install transformers torch datasets evaluate
```

## Usage

### Load the Model and Tokenizer

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

# Load fine-tuned model
model_path = "fine_tuned_model"
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForSeq2SeqLM.from_pretrained(model_path).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Define test text
input_text = "Generate a creative story about space exploration."
inputs = tokenizer(input_text, return_tensors="pt").to(device)

# Generate output
with torch.no_grad():
    output_ids = model.generate(**inputs)
    output_text = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]

print(f"Generated Content: {output_text}")
```

## Training Details

### Data Preprocessing

The dataset was split into:

- **Train:** 80%
- **Validation:** 10%
- **Test:** 10%

Tokenization was applied using the `facebook/bart-base` tokenizer with truncation and padding.

### Fine-Tuning

- **Epochs:** 3
- **Batch Size:** 16
- **Learning Rate:** 2e-5
- **Weight Decay:** 0.01
- **Evaluation Strategy:** Epoch-wise

## Evaluation Metrics

The model was evaluated using the ROUGE metric:

```python
import evaluate
rouge = evaluate.load("rouge")

# Example evaluation
references = ["The generated story was highly creative and engaging."]
predictions = ["The output was imaginative and captivating."]
results = rouge.compute(predictions=predictions, references=references)
print("Evaluation Metrics (ROUGE):", results)
```

## Performance

- **ROUGE Score:** Achieved competitive scores for content generation quality
- **Inference Speed:** Optimized for efficient text generation
- **Generalization:** Works well on diverse text generation tasks but may require domain-specific fine-tuning.

## Limitations

- May generate slightly verbose or overly detailed content in some cases.
- Requires GPU for optimal performance.

## Future Improvements

- Experiment with larger models like `bart-large` for enhanced generation quality.
- Fine-tune on domain-specific datasets for better adaptation to specific content types.