text_summarization

Introduction

The Fine-Tuned T5 Small model is a variant of the T5 transformer model, engineered specifically for text summarization tasks. It is designed to produce concise and coherent summaries from input text by leveraging a pre-trained model that captures essential information efficiently.

Architecture

The model is based on the T5 architecture and is fine-tuned for summarization tasks. The fine-tuning process involves adjusting hyperparameters like batch size and learning rate to optimize performance. The model utilizes a batch size of 8 and a learning rate of 2e-5 to ensure swift convergence and robust optimization.

Training

The model is trained on a diverse dataset comprising documents and their human-generated summaries. This dataset variety equips the model to generate high-quality summaries by capturing critical information while maintaining coherence and fluency. The training process is geared to allow the model to perform effectively across various document summarization applications.

Guide: Running Locally

To use the model for text summarization locally, follow these steps:

1. Install the Transformers library:

   pip install transformers
   

2. Load the summarization pipeline:

   from transformers import pipeline
   summarizer = pipeline("summarization", model="Falconsai/text_summarization")
   

3. Input your text for summarization:

   ARTICLE = "Your text here"
   print(summarizer(ARTICLE, max_length=1000, min_length=30, do_sample=False))
   

For improved performance, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure to handle large texts efficiently.

License

This model is released under the Apache 2.0 license, allowing for broad use and modification, provided that proper attribution is maintained.