def cross_validation(lang):
print(lang)
model_name = 'EMBEDDIA/crosloengual-bert'
logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger('transformers')
transformers_logger.setLevel(logging.WARNING)
# Train and Evaluation data needs to be in a Pandas Dataframe containing at least two columns.
# If the Dataframe has a header, it should contain a 'text' and a 'labels' column.
# If no header is present, the Dataframe should contain at least two columns,
# with the first column is the text with type str, and the second column in the label with type int.
accs = []
f1s = []
df = load_single_lang(lang)
kf = KFold(n_splits=10)
for train_index, test_index in kf.split(df.index):
df_train = df.iloc[train_index]
df_test = df.iloc[test_index]
# hyperparameters
model_args = ClassificationArgs()
model_args.logging_steps = 1000000
model_args.save_eval_checkpoints = False
model_args.save_steps = 1000000
model_args.no_cache = True
model_args.save_model_every_epoch = False
model_args.num_train_epochs = 1
model_args.learning_rate = 2e-4
model_args.train_batch_size = 32
model_args.overwrite_output_dir = True
'''
model_args.train_custom_parameters_only = True
model_args.custom_parameter_groups = [
{
"params": ["classifier.weight"],
"lr": 2e-4,
},
{
"params": ["classifier.bias"],
"lr": 2e-4,
"weight_decay": 0.0,
},
]
'''
# Create a ClassificationModel
model = ClassificationModel('bert',
model_name,
num_labels=3,
args=model_args)
print(model.get_named_parameters())
# Train the model
print('Training ...')
model.train_model(df_train)
# Evaluate the model
print('Evaluating ...')
predictions, raw_outputs = model.predict(df_test['text'].values)
out = eval(df_test['labels'].values, predictions)
accs.append(out['acc'])
f1s.append(out['avg_f1'])
del model
# write results to file
with open('results_csebert.txt', 'a+') as f:
f.write("{} {} {}\n".format(lang, statistics.mean(accs),
statistics.mean(f1s)))
def fit_and_evaluate(train_langs, test_lang):
print(train_langs, test_lang)
model_name = 'EMBEDDIA/crosloengual-bert'
logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger('transformers')
transformers_logger.setLevel(logging.WARNING)
# Train and Evaluation data needs to be in a Pandas Dataframe containing at least two columns.
# If the Dataframe has a header, it should contain a 'text' and a 'labels' column.
# If no header is present, the Dataframe should contain at least two columns,
# with the first column is the text with type str, and the second column in the label with type int.
df_train, df_test = load_dataset(train_langs,
test_lang,
train_on_test_lang=True)
# hyperparameters
model_args = ClassificationArgs()
model_args.logging_steps = 1000000
model_args.save_eval_checkpoints = False
model_args.save_steps = 1000000
model_args.no_cache = True
model_args.save_model_every_epoch = False
model_args.num_train_epochs = 1
model_args.learning_rate = 2e-4
model_args.train_batch_size = 32
model_args.overwrite_output_dir = True
'''
model_args.train_custom_parameters_only = True
model_args.custom_parameter_groups = [
{
"params": ["classifier.weight"],
"lr": 2e-4,
},
{
"params": ["classifier.bias"],
"lr": 2e-4,
"weight_decay": 0.0,
},
]
'''
# Create a ClassificationModel
model = ClassificationModel('bert',
model_name,
num_labels=3,
args=model_args)
print(model.get_named_parameters())
# Train the model
print('Training ...')
model.train_model(df_train)
# Evaluate the model
print('Evaluating ...')
predictions, raw_outputs = model.predict(df_test['text'].values)
out = eval(df_test['labels'].values, predictions)
# write results to file
with open('results_csebert.txt', 'a+') as f:
f.write("{} {} {}\n".format(train_langs, test_lang, out))
del model
