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A toolkit to download, augment, and benchmark OpenPMC-VL; a large dataset of image-text pairs extracted from open-access scientific articles on PubMedCentral.

## Table of Contents

1. [Hugging Face Dataset and Checkpoint](#hugging-face-dataset-and-checkpoint)
2. [Installing Dependencies](#installing-dependencies)
3. [Download and Parse Image-Caption Pairs](#download-and-parse-image-caption-pairs-from-pubmed-articles)
4. [Run Benchmarking Experiments](#run-benchmarking-experiments)
5. [References](#references)

## Hugging Face Dataset and Checkpoint

- **Dataset:** [Open_PMC Dataset on Hugging Face](https://huggingface.co/datasets/vector-institute/open_pmc)
- **Checkpoint:** [Open_PMC_CLIP Model Checkpoint on Hugging Face](https://huggingface.co/vector-institute/open_pmc_clip)

## Installing dependencies

We use
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**Note:** Since these submodules (`mmlearn` and `open_clip`) are only part of the main branch in a single repository, if you change your branch to a branch where these submodules don't exist, your python interpretor won't be able to find these packages and you will face errors.


## Download and parse image-caption pairs from Pubmed Articles
The codebase used to download Pubmed articles and parse image-text pairs from them is stored in `openpmcvl/foundation`.
This codebase heavily relies on [Build PMC-OA](https://github.com/WeixiongLin/Build-PMC-OA) codebase[[1]](#1).
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python -u src/fetch_oa.py --num-retries 5 --extraction-dir path/to/download/directory/other --license-type other --volumes 0 1 2 3 4 5 6 7 8 9 10 11
```


## Run Benchmarking Experiments
We use `mmlearn` to run benchmarking experiments.
Many experiments can be run with our dataset and `mmlearn`.
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For more comprehensive examples of shell scripts that run various experiments with OpenPMC-VL, refer to `openpmcvl/experiment/scripts`.
For more information about `mmlearn`, please refer to the package's [official codebase](https://github.com/VectorInstitute/mmlearn).



## References
<a id="1">[1]</a> PMC-OA paper:
```latex
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