Create README.md

README.md

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+# 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.
+