| # Model Card: RoBERTa-Base Helpdesk Performance 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% |
|
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| Tokenization was applied using the `facebook/bart-base` tokenizer with truncation and padding. |
|
|
| ### Fine-Tuning |
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|
| - **Epochs:** 3 |
| - **Batch Size:** 16 |
| - **Learning Rate:** 2e-5 |
| - **Weight Decay:** 0.01 |
| - **Evaluation Strategy:** Epoch-wise |
|
|
| ## Evaluation Metrics |
|
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| 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 |
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| - May generate slightly verbose or overly detailed content in some cases. |
| - Requires GPU for optimal performance. |
|
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| ## Future Improvements |
|
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| - Experiment with larger models like `bart-large` for enhanced generation quality. |
| - Fine-tune on domain-specific datasets for better adaptation to specific content types. |
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|
