## **Model Card**

### **Model Details**
- **Model Name**: Fine-tuned mBART for Sequence-to-Sequence Translation  
- **Model Architecture**: mBART  
- **Checkpoint**: `checkpoint-3375`  
- **Dataset**: Custom tokenized dataset  
- **Fine-tuned on**: Hugging Face `transformers` library  
- **Languages Supported**: [Include source and target languages, e.g., English -> Spanish]

---

### **Intended Use**
- **Primary Use Case**: 
  - Sequence-to-sequence text translation tasks
  - Adaptable to other NLP tasks like summarization with slight modification  
- **Intended Users**:  
  - Researchers and developers working on translation tasks  
  - AI practitioners requiring fine-tuned LLMs for sequence-to-sequence tasks  

- **Limitations**:  
  - Performance may degrade for out-of-distribution data.
  - Requires task-specific tokenization for different use cases.

---

### **Training Details**
- **Framework**: Hugging Face `transformers`  
- **Hardware**: NVIDIA GPU with mixed precision (fp16)  
- **Hyperparameters**:  
  - Epochs: 3  
  - Batch size: 2  
  - Warmup steps: 250  
  - Weight decay: 0.01  
  - Evaluation steps: 500  

---

### **Metrics**
#### **Evaluation Results**
- **BLEU Score**: *0 (under review)*  
- **ROUGE Metrics**: ROUGE-1, ROUGE-2, ROUGE-L (tested on evaluation set)  
- **Other Custom Metrics**: Exact match, token-level accuracy  

#### **Known Challenges**
- BLEU score evaluation yielded lower-than-expected results.  
- Additional evaluation methodologies (ROUGE, Exact Match) were applied to validate results.

---

### **Limitations and Bias**
- **Data Bias**: The model's performance is tied to the quality of the fine-tuning dataset. Any bias in the dataset may affect outputs.  
- **Generalization**: Performance on unseen domains or low-resource languages may vary significantly.  

---

### **Model Usage**
- **Loading the Model**:  

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("path_to_finetuned_model")
tokenizer = AutoTokenizer.from_pretrained("path_to_finetuned_model")
```

- **Example Inference Code**:

```python
input_text = "Your input text here."
inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)

# Generate translation
outputs = model.generate(**inputs)
decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded_output)
```

---

### **Future Improvements**
- Optimize training data quality and quantity for better BLEU scores.  
- Evaluate with a larger range of benchmarks (e.g., multilingual datasets).  
- Fine-tune the hyperparameters to improve generalization.  

---

### **License**
- The model is shared under [Insert License Name, e.g., MIT License].  

---

### **Acknowledgments**
- Hugging Face for the `transformers` library.  
- The fine-tuning dataset contributors.  
- GPU resources provided by [Cloud Provider/University Lab].  

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This structure provides a comprehensive overview of your fine-tuned model while addressing details for end-users and researchers. Let me know if you want further customization!