use_cuda=torch.cuda.is_available()
) # You can set class weights by using the optional weight argument
train_df, eval_df = train_test_split(train,
test_size=0.1,
random_state=SEED * i)
model.train_model(train_df,
eval_df=eval_df,
macro_f1=macro_f1,
weighted_f1=weighted_f1,
accuracy=sklearn.metrics.accuracy_score)
model = ClassificationModel(MODEL_TYPE,
args["best_model_dir"],
args=args,
use_cuda=torch.cuda.is_available())
dev_predictions, dev_raw_outputs = model.predict(dev_sentences)
dev_preds[:, i] = dev_predictions
print("Completed Fold {}".format(i))
# select majority class of each instance (row)
final_dev_predictions = []
for row in dev_preds:
row = row.tolist()
final_dev_predictions.append(int(max(set(row), key=row.count)))
dev['predictions'] = final_dev_predictions
final_test_predictions = []
else:
model.train_model(train,
macro_f1=macro_f1,
use_cuda=torch.cuda.is_available()
) # You can set class weights by using the optional weight argument
train_df, eval_df = train_test_split(train,
test_size=0.1,
random_state=SEED * i)
model.train_model(train_df,
eval_df=eval_df,
macro_f1=macro_f1,
weighted_f1=weighted_f1,
accuracy=sklearn.metrics.accuracy_score)
model = ClassificationModel(MODEL_TYPE,
args["best_model_dir"],
args=args,
use_cuda=torch.cuda.is_available())
predictions, raw_outputs = model.predict(test_sentences)
test_preds[:, i] = predictions
print("Completed Fold {}".format(i))
# select majority class of each instance (row)
final_predictions = []
for row in test_preds:
row = row.tolist()
final_predictions.append(int(max(set(row), key=row.count)))
test['predictions'] = final_predictions
else:
model.train_model(train,
macro_f1=macro_f1,
weighted_f1=weighted_f1,
accuracy=sklearn.metrics.accuracy_score)
predictions, raw_outputs = model.predict(test_sentences)
test['predictions'] = predictions
use_cuda=torch.cuda.is_available()
) # You can set class weights by using the optional weight argument
train_df, eval_df = train_test_split(train,
test_size=0.1,
random_state=SEED * i)
model.train_model(train_df,
eval_df=eval_df,
macro_f1=macro_f1,
weighted_f1=weighted_f1,
accuracy=sklearn.metrics.accuracy_score)
model = ClassificationModel(MODEL_TYPE,
args["best_model_dir"],
args=args,
use_cuda=torch.cuda.is_available())
dev_predictions, dev_raw_outputs = model.predict(dev_sentences)
dev_preds[:, i] = dev_predictions
test_predictions, test_raw_outputs = model.predict(test_sentences)
test_preds[:, i] = test_predictions
print("Completed Fold {}".format(i))
# select majority class of each instance (row)
final_dev_predictions = []
for row in dev_preds:
row = row.tolist()
final_dev_predictions.append(int(max(set(row), key=row.count)))
dev['predictions'] = final_dev_predictions
final_test_predictions = []
for row in test_preds:
row = row.tolist()