Create a Document Summarization App with LLM on CPU

Table of Contents

1. Introduction
2. Lamini Plan T5: An Open Source Language Model
3. Lamini Plan T5 Parameters and Capabilities
4. Working with Lamini Plan T5 in a Streamlit Application
5. Setting Up Lamini Plan T5 on a Local CPU Machine
6. Using the Summarization Pipeline of Lamini Plan T5
7. Preprocessing the Document with LangChain
8. Tokenization and Model Loading with T5 Tokenizer
9. Implementing the Summarization Pipeline in Streamlit
10. Uploading and Displaying PDF Files
11. Extracting the Summary with Lamini Plan T5
12. Conclusion

Introduction

In this article, we will explore how to Create a Streamlit application to summarize documents using the Lamini Plan T5 language model. Lamini Plan T5 is an open source model with 248 million parameters that can be fine-tuned for various natural language processing tasks. We will learn how to leverage this language model in a Streamlit application, focusing on the summarization pipeline. The application will allow users to upload PDF files and generate a concise summary of the document.

Lamini Plan T5: An Open Source Language Model

Lamini Plan T5 is a highly underrated language model released by Google a few years ago. With 248 million parameters, it may not be as large as some of the more popular models like GPT-3, but it still delivers impressive results. It is fine-tuned on the Lamini LM series, which helps it excel in tasks like summarization and text generation. Despite its smaller size, Lamini Plan T5 proves to be a powerful language model for various applications.

Lamini Plan T5 Parameters and Capabilities

Lamini Plan T5 contains 248 million parameters, which makes it a mid-sized model in the Current landscape of large language models. Despite its smaller size, it is capable of performing well in tasks like summarization and text generation. It has been fine-tuned on the Lamini LM series and is particularly effective in tasks requiring less than 500 million parameters. With the right setup, Lamini Plan T5 can be easily deployed on a local CPU machine, making it accessible to developers without the need for expensive hardware or reliance on cloud-Based APIs.

Working with Lamini Plan T5 in a Streamlit Application

In this article, we will explore how to work with Lamini Plan T5 in a Streamlit application. Streamlit is a web framework that simplifies the process of creating data science applications. We will build an application that allows users to upload PDF files and generate summaries using the Lamini Plan T5 language model. The application will leverage the summarization pipeline provided by Lamini Plan T5 to extract concise summaries from the uploaded documents.

Setting Up Lamini Plan T5 on a Local CPU Machine

Before we begin building the Streamlit application, we need to set up Lamini Plan T5 on our local CPU machine. We will download the model files and store them locally for offline use. This approach eliminates the need for API keys or internet connectivity during the application's runtime. We will load the model and the tokenizer, which are essential components for utilizing Lamini Plan T5's capabilities.

Using the Summarization Pipeline of Lamini Plan T5

The summarization pipeline is one of the key features of Lamini Plan T5. It provides an easy-to-use interface for extracting summaries from large documents. In this article, we will focus on leveraging the summarization pipeline in our Streamlit application. We will pass the preprocessed document to the pipeline, which will generate a summary using the underlying language model. The summarization pipeline handles the heavy lifting, allowing us to create a user-friendly application without worrying about the intricacies of NLP.

Preprocessing the Document with LangChain

To prepare the document for summarization, we will utilize LangChain, a powerful library for text processing tasks. With LangChain, we can split the text into smaller chunks, allowing for more efficient processing. The library provides various functionalities for working with text, including document loading, chunking, and text splitting. By using LangChain, we can streamline the preprocessing step and ensure that our application can handle large documents effectively.

Tokenization and Model Loading with T5 Tokenizer

In order to work with Lamini Plan T5, we need to tokenize the input text and load the model. The T5 tokenizer is specifically designed to work with the T5 models, including Lamini Plan T5. We will use the tokenizer to convert the text into tokens, which can be understood by the language model. Additionally, we will load the base model, which serves as the backbone of Lamini Plan T5. These steps are crucial for utilizing Lamini Plan T5's capabilities and generating accurate summaries.

Implementing the Summarization Pipeline in Streamlit

In this section, we will implement the summarization pipeline in our Streamlit application. We will define a function that takes the file path as input and returns the summary using the Lamini Plan T5 language model. The function will utilize the pipeline provided by Lamini Plan T5 to process the document and generate a concise summary. We will integrate this function into our Streamlit application, allowing users to upload PDF files and retrieve summarizations with a single click.

Uploading and Displaying PDF Files

To provide a seamless user experience, our Streamlit application will allow users to upload PDF files directly from their local machine. We will utilize Streamlit's file uploader widget to enable file uploads and handle the file processing in the backend. Additionally, we will incorporate a PDF viewer to display the uploaded PDF file to the user. This feature enhances the usability of the application and allows users to review the document before generating the summary.

Extracting the Summary with Lamini Plan T5

Once the file is uploaded and the user clicks on the "Summarize" button, we will use the Lamini Plan T5 model to extract the summary from the uploaded document. The summarization pipeline will process the document and return the summary text. We will display the generated summary to the user as part of the Streamlit application. The summarization pipeline leverages the power of Lamini Plan T5 to provide accurate and concise summaries for a wide range of documents.

Conclusion

In this article, we have explored how to create a Streamlit application to summarize documents using the Lamini Plan T5 language model. We have learned about the capabilities and parameters of Lamini Plan T5 and how to set it up on a local CPU machine. By leveraging the summarization pipeline provided by Lamini Plan T5, we have built an application that enables users to upload PDF files and generate concise summaries. With the power of language models like Lamini Plan T5, we can automate the summarization process and save valuable time and effort.