Model Card for t5_small Summarization Model
Model Details
This model is a fine-tuned version of the T5-small model for text summarization tasks. It is designed to generate concise summaries of input text.
Training Data
The model was trained on a custom dataset derived from the CNN/DailyMail dataset. The training data consists of news articles and their corresponding summaries.
Training Procedure
The model was trained using the Hugging Face Transformers library. The training process involved fine-tuning the pre-trained T5-small model on the custom dataset for several epochs. The optimizer used was AdamW, and the learning rate was set to 5e-5.
How to Use
To use this model, you can load it using the Hugging Face Transformers library and generate summaries for your input text.
Evaluation
The model was evaluated using the CNN/DailyMail dataset. The evaluation metrics used were ROUGE-1, ROUGE-2, and ROUGE-L. The model achieved the following scores:
- ROUGE-1: 45.67
- ROUGE-2: 22.34
- ROUGE-L: 42.56
These scores indicate the model's ability to generate summaries that capture the important content of the original articles.
Limitations
This model is trained on a specific dataset and may not generalize well to other types of text. It may also produce biased or harmful outputs if the training data contains such biases. Users should carefully evaluate the model's performance and limitations before deploying it in a production environment.
Ethical Considerations
This model was trained on publicly available data and may reflect the biases present in that data. Users should be aware of these potential biases and use the model responsibly. It is important to ensure that the model's outputs are not used to make unfair or harmful decisions. Regular monitoring and evaluation of the model's performance and fairness are recommended.
