Project Insight

NLP as a Service

Introduction
- Features
Installation
Project Details
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.

Installation

Clone the Repo.
Run the Docker file to create the Docker images.
Run the Docker images to start the front end and back end. service

Project Details

Demonstration

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 script for each task: classificationpro.py, nerpro.py, sentimentpro.py...
- app_fastapi.py is the main application file that calls each servie based on the api call made by the front end.
- Each NLP task has its own folder and within each folder each trained model has 1 folder each. For example:
```
- sentiment
    - 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

Fine Tune a transformer model for specific task. You can leverage the transformers-tutorials
Savve the model files, tokenizer files and also create a network.py script if using a customized training network.
Create a directory within the NLP task with directory_name as the model name and save all the files in this directory.
Update the config.json with the model details and dataset details.

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. 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

skkeyan / insight