Text Summarizer

This is an end to end project build using nlp, docker and fastapi that can Summarize any text dialouge given to it. You can train it on different data also by just giving link of data folder.

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Inspiration

I was inspired to create this project by the growing interest in AI and its potential to solve real-world problems. I also wanted to learn more about Docker and how it could be used to simplify the deployment of AI applications.

What it does

This project is a text summarizer using natural language processing (NLP) and the Hugging Face Transformers library. It is deployed using a Docker container and FastAPI. This project can be used to summarize text of any length, from a short paragraph to a long article. It can be used to summarize news articles, blog posts, research papers, and other types of text content. If we just give data folder link to config file just a change can make whole different application.

How we built it

Here is a brief overview of how I built the text summarization project:

  1. Data collection I had taken a data from github whick include dialouge of different person which was free to use.
  2. Model training Next, I trained a text summarization model using the Hugging Face Transformers library. I used the google/pegasus model, which is a transformer-based model that has been shown to be effective for text summarization.
  3. Model deployment Once my model was trained, I deployed it using Docker and FastAPI. I created a Docker image that contains my model and all of its dependencies. I also developed a FastAPI API that exposes my model to the outside world.
  4. Testing Finally, I tested my model to make sure that it was working as expected. I used a variety of test cases, including both seen and unseen data. I also used a human evaluation to assess the quality of the summaries generated by my model. I am pleased to report that my model performed well on both the automatic and human evaluations. I am confident that my model can be used to generate accurate and informative summaries of text of varying lengths and complexity.

Challenges we ran into

One of the biggest challenges I faced was finding a good dataset to train my NLP model on. I wanted to use a dataset that was large enough to train a robust model, but also diverse enough to capture the different ways that people write and speak.Another challenge was deploying my model in a way that was scalable and reliable. I wanted to make sure that my model could handle a large number of requests without crashing. Also Another challenge was optimizing my Docker image for size and performance. I wanted to make sure that my Docker image was as small as possible so that it would be quick to deploy. I also wanted to make sure that my image was optimized for performance so that my model could handle a large number of requests without slowing down.

Accomplishments that we're proud of

Here are some specific accomplishments that I am proud of:

I was able to successfully train a text summarization model that is able to accurately summarize text of varying lengths and complexity. I was able to deploy my model using Docker, which makes it easy to deploy and manage my application in any environment that supports Docker. I was able to develop a web API using FastAPI that exposes my model to the outside world, making it easy for others to use my model to summarize text.

What we learned

I learned a lot while working on this project, including:

  • How to use natural language processing (NLP) to summarize text.
  • How to use the Hugging Face Transformers library to train and deploy NLP models.
  • How to use Docker to build and deploy containerized applications.
  • How to use FastAPI to develop and deploy web APIs.

One of the most important things I learned while working on this project was how to use Docker to build and deploy containerized applications. This makes it much easier to deploy and manage AI applications, which can often be complex and require a lot of dependencies.

What's next for Text Summarizer

A journalist can use this project to quickly summarize a news article so that they can write a short summary for their website or social media. A student can use this project to summarize a research paper so that they can better understand the key findings. A businessperson can use this project to summarize a long report so that they can quickly get the most important information. Overall, this project is a versatile tool that can be used to summarize text for a variety of purposes.

Overall, I learned a lot from working on this project. I am particularly proud of my ability to use Docker to build and deploy a scalable and reliable AI application.

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