Project Overview

This repository contains a comprehensive pipeline for processing PDF documents, extracting features, and classifying them into different categories. Access Streamlit dashboard for quick inference here : https://clascify-doraemon.streamlit.app/ . Access detailed project report here : https://drive.google.com/file/d/1Ni8SkQJm62X6zkBp52kb3Qx-SMwoqsJd/view?usp=sharing

Repository Structure

├── Dataset/
│   ├── pdfs/
│   │   ├── publishable/
│   │   └── nonpublishable/
│   ├── texts/
│   │   ├── publishable/
│   │   └── nonpublishable/
│   ├── keywords/
│   └── vectors/
├── KDSH_2025_Dataset/
├── Sample/
│   ├── pdfs/
│   ├── texts/
│   ├── keywords/
│   └── vectors/
├── sci-pdf-parser/
│   ├── vila/
│   └── main.py
├── Binary_classification.py
├── Conference_classification.py
├── Corruption.py
├── Dashboard.py
├── FULL_CODE.ipynb
├── Inference.py
├── Mistral7b_Instruct_1.py
├── Mistral7b_Instruct_2.py
├── PDFparserFITZ.py
├── Pathway_inference.py
├── Scibert_embeddings.py
├── credentials.json
├── doraemon_binary_classifier.pt
├── doraemon_conference_classifier.pt
├── requirements.txt
└── results.csv

Step-by-Step Guide

1. Setup and Installation

1. Clone the repository:

   git clone https://github.com/who-else-but-arjun/claSCIfy.git

2. Navigate to the project directory:

   cd claSCIfy

3. Install dependencies:

   pip install -r requirements.txt

2. Preprocessing

1. Place PDFs in the appropriate directory:
   • Save publishable PDFs in Dataset/pdfs/publishable/.
   • Save non-publishable PDFs in Dataset/pdfs/nonpublishable/.
2. Run PDF Parser:
   • Convert PDFs to JSON format:

     python PDFparserFITZ.py

3. Corrupt Text Data:
   • Create non-publishable datasets:

     python Corruption.py

4. Extract Features:
   • Generate feature vectors and keywords:

     python Scibert_embeddings.py

   • Feature vectors are saved in Dataset/vectors/.
   • Keywords are saved in Dataset/keywords/.

3. Training Models

1. Binary Classification:
   • Train the binary classification model:

     python Binary_classification.py

   • This script uses doraemon_binary_classifier.pt for training.
2. Conference Classification:
   • Train the conference classification model:

     python Conference_classification.py

   • This script uses doraemon_conference_classifier.pt and classifies documents into conferences (EMNLP, KDD, TMLR, CVPR, NEURIPS).

4. Inference

1. Place test PDFs in the Sample/pdfs/ directory.
2. Run inference:

   python Inference.py

3. Results:
   • The results are saved in results.csv.

5. Additional Features

1. Justification Generation:
   • Use the following scripts for generating justifications:

     python Mistral7b_Instruct_1.py
     python Mistral7b_Instruct_2.py

2. Pathway Inference:
   • Implement pathway connector and vector store service:

     python Pathway_inference.py

6. Streamlit Dashboard

1. Deploy the dashboard:

   streamlit run Dashboard.py

2. Use the dashboard for quick PDF inferences and assessments.

Functionality of Each Code

• Binary_classification.py: Trains and runs the binary classification model for publishability and non publishability.
• Conference_classification.py: Trains and runs the conference classification model for conference prediction.
• Corruption.py: Corrupts text data to create non-publishable datasets.
• Dashboard.py: Deploys a Streamlit dashboard for quick PDF inference.
• FULL_CODE.ipynb: Contains the full pipeline code in a Jupyter notebook format.
• Inference.py: Runs inference on the sample data and saves the results.
• Mistral7b_Instruct_1.py & Mistral7b_Instruct_2.py: Different approaches for generating justifications using Mistral.
• PDFparserFITZ.py: Parses PDFs into JSON format.
• Pathway_inference.py: Integrates pathway features like gdrive connector and vector store server to fetch PDFs, and processes them.
• Scibert_embeddings.py: Creates feature vectors using SciBERT embeddings.
• main.py (in sci-pdf-parser): Parses PDFs to JSON format using the VILA model.

Troubleshooting

• Ensure all dependencies are installed by running:

  pip install -r requirements.txt

• Verify that all necessary files are placed in their respective directories.
• Check log outputs for specific errors during execution.