Prompt Data Extraction

IMPORTANT NOTE: The code and data shared here is available for academic non-commercial use only

Python module and scripts to run automated data extraction pipelines built using MaterialsBERT, GPT-3.5 and LlaMa 2 models.

Developed for the data extraction methods described in:

Data Extraction from Polymer Literature using Large Language Models.
S. Gupta, A. Mahmood, P. Shetty, A. Adeboye and R. Ramprasad,
2024 (Submitted)

The extracted data can be visualized freely at https://polymerscholar.org.

Installation

1. Make sure you have conda installed.
2. Clone the git repository: git clone https://github.com/Ramprasad-Group/PromptDataExtraction && cd PromptDataExtraction.
3. Source the env.sh script from a bash terminal. It will setup a new conda environment and install the required python packages, compilers and CUDA libraries.
4. A settings.yaml file will be generated with default configuration options.
5. If the environment was already installed before, source the env.sh script to activate it.

This package requires a PostgreSQL server to store and manage literature extracted data.

The MaterialsBERT model can be downloaded from the Huggingface Hub and the path to the model should be set in the settings.

Usage

Edit the newly created settings.yaml file to update required paths, usernames, passwords, database connection details, API keys, etc.

The following scripts are available to process multiple properties, models and articles:

• parse_papers.py: Parse and extract paragraphs from a corpus directory containing full text HTML or XML articles.

• filter_polymer_papers.py: Identify the polymer papers using the Title and/or the Abstract of the articles.

• run_heuristic_filters.sh: Filter the polymer-related paragraphs using property specific heuristic filters.

• run_ner_filters.sh: Filter the heuristically filtered paragraphs using NER filters and MaterialsBERT.

• run_methods.sh: Add new extraction method/configuration to the database.

• run_ner_pipeline.sh: Perform data extraction on the NER-filtered paragraphs using NER-based MaterialsBERT pipeline.

• run_gpt_pipeline.sh: Perform data extraction on the NER-filtered paragraphs using LLM pipeline.

• run_post_process_ner.sh: Run post-processing validatation and filtering on the NER pipeline extracted data.

• run_post_process_llm.sh: Run post-processing validatation and filtering on the LLM pipeline extracted data.

These scripts interface with the backend module. More fine-grained tasks can be performed by the module. To list the available commands, run python backend -h.

About

Developed by:
Ramprasad Research Group,
MSE, Georgia Institute of Technology.

Copyright 2024 Georgia Tech Research Corporation.
All Rights Reserved. See the LICENSE file for details.