An ERLab Project
Please view this article in that regard.
Clone this github repository and run:
python3 setup.py installPlease find sample experiment commands below:
To generate the mathematical vectors for our text segments, the following command can be used:
coronai_segment2vector --gpu=2 --input_csv=.../text_segment_dataset.csv \
--output_pkl=.../output.pkl \
--path_to_bert_weights=.../NCBI_BERT_pubmed_mimic_uncased_L-24_H-1024_A-16 \
--batch_size=25This batch size works fine on a 2080Ti GPU. Note that part of this code is responsible for preprocessing
and storing AllenNLP compatible instances to be fed to the BERT-based pipeline.
There is no need to create them more than once, so if the input_text_sequence_instances.pkl file exists
in the folder of your output_pkl file, it will be used directly. You can also
get it from our shared drive, along with some representations and other files.
To do this, you need the mathematical representations of course. When you have those, the following command should be used:
coronai_unsupervised_clustering --method_name=kmeans \
--method_params=[random_state:int:2019] --method_searchspace=n_clusters:5:100 \
--output_bundle=...path_to_output_folder/kmeans.pkl \
--input_files=...path_to/full_representations_dataset.pkl
coronai_unsupervised_clustering --method_name=birch \
--method_params=[branching_factor:int:50,threshold:float:0.5] \
--method_searchspace=n_clusters:3:100 \
--output_bundle=...path_to_output_folder/birch.pkl \
--input_files=...path_to/full_representations_dataset.pklThe method searchspace allows you to choose one parameter and do a search over it. In our case, we use number of clusters. In every step, the output file will be updated (so you do not need to wait forever to be able to check things). Also, the format for inputting method parameters are obvious.
The documentation for this project is available in here
username: user
password: mlisLifePlease view here