from bert_sklearn import BertClassifier
from bert_sklearn import BertRegressor
from bert_sklearn import load_model

# define model
model = BertClassifier()   # for classification 
# model = BertRegressor()  # for regression 
 
# fit model
model.fit(X_train, y_train)

# make predictions
y_pred = model.predict(X_test)

# make probabilty predictions
y_pred = model.predict_proba(X_test)

# score model on test data
model.score(X_test, y_test)

# save model to disk
savefile='/data/mymodel.bin'
model.save(savefile)

# load model from disk
new_model = load_model(savefile)

# do stuff with new model
new_model.score(X_test, y_test)

# try different options...
model.bert_model = 'bert-large-uncased'
model.num_mlp_layers = 3
model.max_seq_length = 196
model.epochs = 4
model.learning_rate = 4e-5
model.gradient_accumulation_steps = 4

# fit model
model.fit(X_train, y_train)

# do stuff...
model.score(X_test, y_test)

from sklearn.model_selection import GridSearchCV

params = {'epochs':[3, 4], 'learning_rate':[2e-5, 3e-5, 5e-5]}

# wrap classifier in GridSearchCV
clf = GridSearchCV(BertClassifier(validation_fraction=0), 
                    params,
                    scoring='accuracy',
                    verbose=True)

# fit gridsearch 
clf.fit(X_train ,y_train)