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BIDS-Xu-Lab/section_specific_annotation_of_PICO
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This file briefly describes the steps to train/predict PICO entities. 1. Create virtual environment using conda. $ conda create -n PICO python=3.7 $ conda activate PICO 2. Install PyTorch. Using Cuda, please choose compatible cuda version with torch (reference: https://pytorch.org/get-started/previous-versions/): $ conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.6 -c pytorch -c conda-forge Without using Cuda $ conda install pytorch cpuonly -c pytorch 3. Install transformers. $ pip install transformers==4.6.0 4. Install seqeval $ pip install seqeval 5. Install tensorboardX $ pip install tensorboardX 6. config PICO_ner.py for input data dir, model dir, and output data dir. 7. Run PICO_ner.py to train the model and predict on the test dataset.
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Towards precise PICO extraction from ab-stracts of randomized controlled trials using a section-specific learning approach
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