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| ## **Model Card** |
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| ### **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] |
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| --- |
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| ### **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 |
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| - **Limitations**: |
| - Performance may degrade for out-of-distribution data. |
| - Requires task-specific tokenization for different use cases. |
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| --- |
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| ### **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 |
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| --- |
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| ### **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 |
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| #### **Known Challenges** |
| - BLEU score evaluation yielded lower-than-expected results. |
| - Additional evaluation methodologies (ROUGE, Exact Match) were applied to validate results. |
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| --- |
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| ### **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. |
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| --- |
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| ### **Model Usage** |
| - **Loading the Model**: |
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| ```python |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
| |
| model = AutoModelForSeq2SeqLM.from_pretrained("path_to_finetuned_model") |
| tokenizer = AutoTokenizer.from_pretrained("path_to_finetuned_model") |
| ``` |
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| - **Example Inference Code**: |
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| ```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) |
| ``` |
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| --- |
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| ### **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. |
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| --- |
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| ### **License** |
| - The model is shared under [Insert License Name, e.g., MIT License]. |
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| --- |
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| ### **Acknowledgments** |
| - Hugging Face for the `transformers` library. |
| - The fine-tuning dataset contributors. |
| - GPU resources provided by [Cloud Provider/University Lab]. |
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| --- |
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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! |
