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