This variant of the t5-small model, is fine-tuned specifically for the task of text summarization. This model aims to generate concise, coherent, and informative summaries from extensive text documents, leveraging the power of the T5's text-to-text approach.

This model is intended for use in summarizing long-form texts into concise, informative abstracts. It's particularly useful for professionals and researchers who need to quickly grasp the essence of detailed reports, research papers, or articles without reading the entire text.

Install with pip:

Use in python:

The model was trained using the Big Patent Dataset, comprising 1.3 million US patent documents and their corresponding human-written summaries. This dataset was chosen for its rich language and complex structure, representative of the challenging nature of document summarization tasks.

Training involved multiple subsets of the dataset to ensure broad coverage and robust model performance across varied document types.

Training was conducted over three rounds, with initial settings including a learning rate of 0.00002, batch size of 8, and 4 epochs. Subsequent rounds adjusted these parameters to refine model performance further, for respectively 0.0003, 8 and 12. As well, a linear decay learning rate schedule was applied to enhance model learning efficiency over time.

Model performance was evaluated using the ROUGE metric, highlighting its capability to generate summaries closely aligned with human-written abstracts.

This model card was written by Fernanda Kipper