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config.yaml
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config.yaml
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lang: en #system language
train:
data: pate #model train/test data (More on dataset_mapping.py)
simplify: False #Simplified model data for training
embedding: en #Recognizer model embedding
max_len: 30 #Max. length neural model can accept (50, or 30 for simplified)
batch_size: 32 #Batch size for the neural model
epochs: 30 #Training epochs
BERT_dim: 3072 #DistillBERT dimensions
lstm_units: 256 # LSTM units
dense_unit: 50 #Dense layer units
optimizer: adam #Model default optimizer
activation: relu #Dense layer activation
patience: 5 #Early stopping patience
dropout: 0.5 #Dropout for RNN
recurrent_dropout: 0.5 #Recurrent dropout for RNN-LSTM
validation_split: 0.1 #Split ratio for train/valid set
run: 8 #Number of run for the current config
amount: 10 #Amount of fine-tuning data (1 ~ 10) -> (10 ~ 100%)
fine_tune:
epochs: 20 #Fine-tuning epochs
lr: 0.0001 #Learning rate for fine-tuning each layer
method: chain #fine-tuning default method
predict:
final_model_en: model/predict/en/fine_tuned_model.h5 #Final fine-tuned saved model for English
directories:
data: data/ #Default data directory
source_model: model/best_model #Source model prefix directories
fine_tuned_model: model/fine_tuned_model #fine-tuned model prefix directories
tag_index: model/idx.pickle #label index directories
unit_index: model/new_unit.pickle #unit index directories
saved_output: output/predicted_output #neural model output directories
saved_score_en: output/results_en.csv #Score directories English
input_modified_for_simplify: data/TBAQ_Simplified/input_modified_train.pickle #Modified English path for simplification
input_simplified: data/TBAQ_Simplified/input_simplified.pickle #Simplified english input path
timex_for_simplify: data/TBAQ_Simplified/output_train.pickle #Simplified english output path
timex_predict_output: output/timex_output.json #Timex output for model prediction
external:
corenlp_ap: http://localhost:9000 #Corenlp endpoint
normalizer:
domain_identifier: 1 #Domain specifier value 1 for VA, 0 for news
debugging:
log_level: logging.WARNING #Logging level for debugging