Description
Dependencies Here is the list of packages required for running the Python code: pandas
torch
keras
numpy
tqdm
gensim
sklearn
nltk
spacy
networkx
en_core_web_sm
How to Run Unzip sequences_labels.zip, word2vec_100_10_5.zip, propheno_scoms.zip, and propheno_masks.zip files located in the data directory. Run the main.py file using the following command: python main.py \
--path path_to_data_folder \
--epochs 20 \
--bert_epochs 4 \
--seeds 15
Input Parameters path -> this parameter is the path to the data folder where the training and test sets are located. epochs -> this parameter is used as the number of epochs that the CNN and RNN models are trained. bert_epochs - this parameter is used as the number of epochs for fine-tuning the BERT model. seeds -> this parameter shows the number of times to repeat the training and averaging results. Jupyter Notebook The notebook for the code is also available in the root folder which can be used as an alternative to the main.py.
torch
keras
numpy
tqdm
gensim
sklearn
nltk
spacy
networkx
en_core_web_sm
How to Run Unzip sequences_labels.zip, word2vec_100_10_5.zip, propheno_scoms.zip, and propheno_masks.zip files located in the data directory. Run the main.py file using the following command: python main.py \
--path path_to_data_folder \
--epochs 20 \
--bert_epochs 4 \
--seeds 15
Input Parameters path -> this parameter is the path to the data folder where the training and test sets are located. epochs -> this parameter is used as the number of epochs that the CNN and RNN models are trained. bert_epochs - this parameter is used as the number of epochs for fine-tuning the BERT model. seeds -> this parameter shows the number of times to repeat the training and averaging results. Jupyter Notebook The notebook for the code is also available in the root folder which can be used as an alternative to the main.py.
Datos disponibles | feb 27 2021 |
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Editor | ZENODO |