def run_experiment(dataset_id, dataset_dict, task, embeddings, mappings, data):
# set network hyperparameters and mappings/datasets
model = BiLSTM(network_params)
model.setMappings(mappings, embeddings)
model.setDataset(dataset_dict, data)
# path to store the trained model and model results
experiment_name = f'{dataset_id}_{task.lower()}'
model.modelSavePath = models_dir / f'{experiment_name}.h5'
model.storeResults(results_dir / f'{experiment_name}.csv')
# build and train the model
model.buildModel()
model.fit(
epochs=500) # do not limit training by epochs - use early stopping
def run_experiment(datasets_dict, lang, task, embeddings, mappings, data):
# set network hyperparameters and mappings/datasets
model = BiLSTM(network_params)
model.setMappings(mappings, embeddings)
model.setDataset(datasets_dict, data)
# define the experiment name
lang_prefix = f'{lang.lower()}_' if lang is not None else ''
task_suffix = f'_{task.lower()}' if task is not None else ''
experiment_name = lang_prefix + 'datasets' + task_suffix
# path to store the trained model and model results
model.modelSavePath = models_dir / f'{experiment_name}.h5'
model.storeResults(results_dir / f'{experiment_name}.csv')
# build and train the model
model.buildModel()
model.fit(
epochs=500) # do not limit training by epochs - use early stopping
