| # Model Card: BART-Based Content Generation 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:** BART | |
| - **Base Model:** `facebook/bart-base` | |
| - **Task:** Content Generation | |
| - **Dataset:** cnn_dailymail | |
| - **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 = "Technology" | |
| 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:** 4 | |
| - **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. | |