/insight

Repository for Project Insight: NLP as a Service

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

Project Insight

NLP as a Service

Project Insight

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Contents

  1. Introduction
  2. Installation
  3. Project Details
  4. License

Introduction

Project Insight is designed to create NLP as a service with code base for both front end GUI (streamlit) and backend server (FastApi) the usage of transformers models on various downstream NLP task.

The downstream NLP tasks covered:

  • News Classification

  • Entity Recognition

  • Sentiment Analysis

  • Summarization

  • Information Extraction To Do

The user can select different models from the drop down to run the inference.

The users can also directly use the backend fastapi server to have a command line inference.

Features of the solution

  • Python Code Base: Built using Fastapi and Streamlit making the complete code base in Python.
  • Expandable: The backend is desinged in a way that it can be expanded with more Transformer based models and it will be available in the front end app automatically.
  • Micro-Services: The backend is designed with a microservices architecture, with dockerfile for each service and leveraging on Nginx as a reverse proxy to each independently running service.
    • This makes it easy to update, manitain, start, stop individual NLP services.

Installation

  • Clone the Repo.
  • Run the Docker Compose to spin up the Fastapi based backend service.
  • Run the Streamlit app with the streamlit run command.

Setup and Documentation

  1. Download the models

    • Download the models from here
    • Save them in the specific model folders inside the src_fastapi folder.
  2. Running the backend service.

    • Go to the src_fastapi folder
    • Run the Docker Compose comnand
    $ cd src_fastapi
    src_fastapi:~$ sudo docker-compose up -d
  3. Running the frontend app.

    • Go to the src_streamlit folder
    • Run the app with the streamlit run command
    $ cd src_streamlit
    src_streamlit:~$ streamlit run NLPfily.py
  4. Access to Fastapi Documentation: Since this is a microservice based design, every NLP task has its own seperate documentation

Project Details

Demonstration

Project Insight Demo

Directory Details

  • Front End: Front end code is in the src_streamlit folder. Along with the Dockerfile and requirements.txt

  • Back End: Back End code is in the src_fastapi folder.

    • This folder contains directory for each task: Classification, ner, summary...etc
    • Each NLP task has been implemented as a microservice, with its own fastapi server and requirements and Dockerfile so that they can be independently mantained and managed.
    • Each NLP task has its own folder and within each folder each trained model has 1 folder each. For example:
    - sentiment
        > app
            > api
                > distilbert
                    - model.bin
                    - network.py
                    - tokeniser files
                >roberta
                    - model.bin
                    - network.py
                    - tokeniser files
    
    • For each new model under each service a new folder will have to be added.

    • Each folder model will need the following files:

      • Model bin file.
      • Tokenizer files
      • network.py Defining the class of the model if customised model used.
    • config.json: This file contains the details of the models in the backend and the dataset they are trained on.

How to Add a new Model

  1. Fine Tune a transformer model for specific task. You can leverage the transformers-tutorials

  2. Save the model files, tokenizer files and also create a network.py script if using a customized training network.

  3. Create a directory within the NLP task with directory_name as the model name and save all the files in this directory.

  4. Update the config.json with the model details and dataset details.

  5. Update the <service>pro.py with the correct imports and conditions where the model is imported. For example for a new Bert model in Classification Task, do the following:

    • Create a new directory in classification/app/api/. Directory name bert.

    • Update config.json with following:

      "classification": {
      "model-1": {
          "name": "DistilBERT",
          "info": "This model is trained on News Aggregator Dataset from UC Irvin Machine Learning Repository. The news headlines are classified into 4 categories: **Business**, **Science and Technology**, **Entertainment**, **Health**. [New Dataset](https://archive.ics.uci.edu/ml/datasets/News+Aggregator)"
      },
      "model-2": {
          "name": "BERT",
          "info": "Model Info"
      }
      }
    • Update classificationpro.py with the following snippets:

      Only if customized class used

      from classification.bert import BertClass

      Section where the model is selected

      if model == "bert":
          self.model = BertClass()
          self.tokenizer = BertTokenizerFast.from_pretrained(self.path)

License

This project is licensed under the GPL-3.0 License - see the LICENSE.md file for details