criterion = nn.NLLLoss()
optimizer = radam.RAdam(model.parameters(), lr=learning_rate)
print(model)
total_trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print('The number of trainable parameters is: %d' % (total_trainable_params))
# train the model
if skip_training == False:
train(model, word_num_layers, char_num_layers, morph_num_layers, num_epochs, pairs_batch_train, pairs_batch_dev, word_hidden_size,
char_hidden_size, morph_hidden_size, batch_size, criterion, optimizer, patience, device)
model.load_state_dict(torch.load('weights/model_lower.pt'))
else:
model.load_state_dict(torch.load('weights/model_lower.pt'))
model.eval()
batch_size = 1
print('\nTEST DATA \n')
all_predicted, all_true = evaluate.get_predictions(data_test, model, word_num_layers, char_num_layers, morph_num_layers, word_hidden_size,
char_hidden_size, morph_hidden_size, batch_size, device)
evaluate.print_scores(all_predicted, all_true, tag2idx)
print('\nWIKI DATA \n')
indexed_morph = morph_to_idx(whole_data, morph2idx, word2morph)
indexed_whole_data = combine_data(indexed_data, indexed_char,
indexed_morph, MAX_SEQ_LENGTH)
# initialize the model
model = NERModel(word_embedding_dim, char_embedding_dim,
morph_embedding_dim, word_hidden_size,
char_hidden_size, morph_hidden_size, len(char2idx),
len(morph2idx), num_tags, word_num_layers,
char_num_layers, morph_num_layers,
dropout_prob).to(device)
# load the model
if lowercase_model == False:
model.load_state_dict(
torch.load('weights/model_upper.pt',
map_location=torch.device('cpu')))
else:
model.load_state_dict(
torch.load('weights/model_lower.pt',
map_location=torch.device('cpu')))
model.eval()
batch_size = 1
print('Processing the document')
evaluate_document(output_path, word_num_layers, char_num_layers,
morph_num_layers, word_hidden_size, char_hidden_size,
morph_hidden_size, batch_size, indexed_whole_data,
whole_data, model, device)
print('Done')
if arr[i][j] < 3:
arr[i][j] = enc_tags.inverse_transform([arr[i][j]])[0]
elif arr[i][j] == 3:
arr[i][j] = 'X'
else:
raise KeyError(str(arr[i][j])+' as key not found in Label Encoder ')
return arr
if __name__ == "__main__":
my_parser = argparse.ArgumentParser()
my_parser.version = '1.0'
my_parser.add_argument('-g','--grouped_entities', action='store_true',help='if used, evaluate all metrics on exact entity-level matching, instead of just wordpiece-level tokens ')
args = my_parser.parse_args()
grouped_entities = args.grouped_entities
meta_data = joblib.load(config.METADATA_PATH)
enc_tags = meta_data['enc_tags']
num_tags = len(list(enc_tags.classes_))
sentences, tags = preprocess_data(enc_tags)
test_dataloader = get_dataloader(sentences, tags)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = NERModel(num_tags)
model.load_state_dict(torch.load(config.MODEL_PATH,map_location=device))
tags_ypred, tags_ytrue = evaluate(test_dataloader, model, device, num_tags, grouped_entities=grouped_entities)
# tags_ypred = enc_tags.inverse_transform(tags_ypred)
# tags_ytrue = enc_tags.inverse_transform(tags_ytrue)
tags_ypred = decode_transform(tags_ypred, enc_tags)
tags_ytrue = decode_transform(tags_ytrue, enc_tags)
# print(tags_ytrue,tags_ypred)
print(seqeval_classification_report(tags_ytrue, tags_ypred))
