Game Of Papers - A recommendation system for research papers.

Use the model using the transformer library

Easiest way to get started with the model is using it with the transformers library

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("allenai/scibert_scivocab_uncased")

model = AutoModel.from_pretrained("adit-negi/recommendor-bert")

Demo https://www.gameofpapers.com

Training the model

Installation

pip install -r requirement.txt

Usage

Train

python train_single.py --epochs 50 --batch_size 128 --clip 1.0 --lr 1e-3 --embed_dim 300 \
 --freeze False --space_joiner True --dropout 0.2 \
 --loss_fn triplet --max_len 30 \
 --PRE_TRAINED_MODEL_NAME 'bert-base-uncased' \
 --model_path './ckpt/best_model_v6_triplet' \
 --train_dir './data/14k_data.csv' \
 --use_aux True --use_aug_data True

TrainScibert

python3 train_single.py --epochs 50 --batch_size 128 --clip 1.0 --lr 1e-3 --embed_dim 300 \
 --freeze False --space_joiner True --dropout 0.2 \
 --loss_fn triplet --max_len 30 \
 --PRE_TRAINED_MODEL_NAME 'allenai/scibert_scivocab_uncased' \
 --model_path './ckpt/scibert_model_v6_triplet_2' \
 --train_dir './data/paper_triplet_data.csv' \
 --use_aux True --use_aug_data True

TestDB

python evaluate_single.py --embed_dim 300 \
 --freeze False --space_joiner True --dropout 0.2 \
 --loss_fn triplet --max_len 30 \
 --PRE_TRAINED_MODEL_NAME 'emilyalsentzer/Bio_ClinicalBERT' \
 --model_path './ckpt/best_model_v6_triplet' \
 --train_dir './data/100k_data.csv'

Inference

python inference.py --embed_dim 300 \
 --PRE_TRAINED_MODEL_NAME 'emilyalsentzer/Bio_ClinicalBERT' \
 --model_path './ckpt/best_model_v6_triplet' \

Testing the model against baselines

Easy way to test is to run python3 baseline_model_experiments/calculate_metrics.py This already runs a bunch of baseline models for you and gives you the result.

Compare our model against any pretrained model on huggingface

1. Run any baseline model by navigating to baseline_model_experiments/test_any_huggingface_model/huggingface_pretrained.py

   1.1 Add the name of the model you want to test in AutoModel function

2. Run the file, it will create a sample.json, with embeddings and sorted reccomendations for papers.

3. Read the sample.json, append it to the baseline_results list in calculate_metrics.py file and run it.

Running the webserver

Dockerizing Flask with Postgres, Gunicorn, and Nginx and run this locally or in production

Want to learn how to build this?

Check out the post.

Want to use this project?

Development

Uses the default Flask development server.

1. Rename .env.dev-sample to .env.dev.

2. Update the environment variables in the docker-compose.yml and .env.dev files.

   • (M1 chip only) Remove -slim-buster from the Python dependency in services/web/Dockerfile to suppress an issue with installing psycopg2

3. Build the images and run the containers:

   $ docker-compose up -d --build

   Test it out at http://localhost:5000. The "web" folder is mounted into the container and your code changes apply automatically.

4. To seed data into the database download the sql dump from - http://drive.google.com/sqldata and run pg_dump -U hello_flask -h 127.0.0.1 hello_flask_dev > gameofpapers.sql. When prompted enter the password provided in the docker-compose file.

Production

Uses gunicorn + nginx.

1. Rename .env.prod-sample to .env.prod and .env.prod.db-sample to .env.prod.db. Update the environment variables.

2. Build the images and run the containers:

   $ docker-compose -f docker-compose.prod.yml up -d --build

   Test it out at http://localhost:1337. No mounted folders. To apply changes, the image must be re-built.