AventIQ-AI/bart-content-generation

1# Model Card: BART-Based Content Generation Model2 3## Model Overview4 5This 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.6 7## Model Details8 9- **Model Architecture:** BART  10- **Base Model:** `facebook/bart-base`  11- **Task:** Content Generation  12- **Dataset:** cnn_dailymail  13- **Framework:** Hugging Face Transformers  14- **Training Hardware:** CUDA15 16## Installation17 18To use the model, install the necessary dependencies:19 20```sh21pip install transformers torch datasets evaluate22```23 24## Usage25 26### Load the Model and Tokenizer27```python28from transformers import AutoTokenizer, AutoModelForSeq2SeqLM29import torch30 31# Load fine-tuned model32model_path = "fine_tuned_model"33device = "cuda" if torch.cuda.is_available() else "cpu"34model = AutoModelForSeq2SeqLM.from_pretrained(model_path).to(device)35tokenizer = AutoTokenizer.from_pretrained(model_path)36 37# Define test text38input_text = "Technology"39inputs = tokenizer(input_text, return_tensors="pt").to(device)40 41# Generate output42with torch.no_grad():43    output_ids = model.generate(**inputs)44    output_text = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]45 46print(f"Generated Content: {output_text}")47```48 49## Training Details50 51### Data Preprocessing52The dataset was split into:53- **Train:** 80%  54- **Validation:** 10%  55- **Test:** 10%  56 57Tokenization was applied using the `facebook/bart-base` tokenizer with truncation and padding.58 59### Fine-Tuning60- **Epochs:** 3  61- **Batch Size:** 4  62- **Learning Rate:** 2e-5  63- **Weight Decay:** 0.01  64- **Evaluation Strategy:** Epoch-wise  65 66## Evaluation Metrics67The model was evaluated using the ROUGE metric:68```python69import evaluate70rouge = evaluate.load("rouge")71 72# Example evaluation73references = ["The generated story was highly creative and engaging."]74predictions = ["The output was imaginative and captivating."]75results = rouge.compute(predictions=predictions, references=references)76print("Evaluation Metrics (ROUGE):", results)77```78 79## Performance80- **ROUGE Score:** Achieved competitive scores for content generation quality  81- **Inference Speed:** Optimized for efficient text generation  82- **Generalization:** Works well on diverse text generation tasks but may require domain-specific fine-tuning.83 84## Limitations85- May generate slightly verbose or overly detailed content in some cases.86- Requires GPU for optimal performance.87 88## Future Improvements89- Experiment with larger models like `bart-large` for enhanced generation quality.90- Fine-tune on domain-specific datasets for better adaptation to specific content types.91 92