Awesome

scikit-learn wrapper to finetune BERT

A scikit-learn wrapper to finetune Google's BERT model for text and token sequence tasks based on the huggingface pytorch port.

Includes configurable MLP as final classifier/regressor for text and text pair tasks
Includes token sequence classifier for NER, PoS, and chunking tasks
Includes SciBERT and BioBERT pretrained models for scientific and biomedical domains.

Try in Google Colab!

installation

requires python >= 3.5 and pytorch >= 0.4.1

git clone -b master https://github.com/charles9n/bert-sklearn
cd bert-sklearn
pip install .

basic operation

model.fit(X,y) i.e finetune BERT

X: list, pandas dataframe, or numpy array of text, text pairs, or token lists
y : list, pandas dataframe, or numpy array of labels/targets

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

# define model
model = BertClassifier()         # text/text pair classification
# model = BertRegressor()        # text/text pair regression
# model = BertTokenClassifier()  # token sequence classification

# finetune 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)

See demo notebook.

model options

# 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

# finetune
model.fit(X_train, y_train)

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

See options

hyperparameter tuning

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)

See demo_tuning_hyperparameters notebook.

GLUE datasets

The train and dev data sets from the GLUE(Generalized Language Understanding Evaluation) benchmarks were used with bert-base-uncased model and compared againt the reported results in the Google paper and GLUE leaderboard.

	MNLI(m/mm)	QQP	QNLI	SST-2	CoLA	STS-B	MRPC	RTE
BERT base(leaderboard)	84.6/83.4	89.2	90.1	93.5	52.1	87.1	84.8	66.4
bert-sklearn	83.7/83.9	90.2	88.6	92.32	58.1	89.7	86.8	64.6

Individual runs can be found can be found here.

CoNLL-2003 Named Entity Recognition(NER)

NER results for CoNLL-2003 shared task

	dev f1	test f1
BERT paper	96.4	92.4
bert-sklearn	96.04	91.97

Span level stats on test:

processed 46666 tokens with 5648 phrases; found: 5740 phrases; correct: 5173.
accuracy:  98.15%; precision:  90.12%; recall:  91.59%; FB1:  90.85
              LOC: precision:  92.24%; recall:  92.69%; FB1:  92.46  1676
             MISC: precision:  78.07%; recall:  81.62%; FB1:  79.81  734
              ORG: precision:  87.64%; recall:  90.07%; FB1:  88.84  1707
              PER: precision:  96.00%; recall:  96.35%; FB1:  96.17  1623

See ner_english notebook for a demo using 'bert-base-cased' model.

NCBI Biomedical NER

NER results using bert-sklearn with SciBERT and BioBERT on the the NCBI disease Corpus name recognition task.

Previous SOTA for this task is 87.34 for f1 on the test set.

	test f1 (bert-sklearn)	test f1 (from papers)
BERT base cased	85.09	85.49
SciBERT basevocab cased	88.29	86.91
SciBERT scivocab cased	87.73	86.45
BioBERT pubmed_v1.0	87.86	87.38
BioBERT pubmed_pmc_v1.0	88.26	89.36
BioBERT pubmed_v1.1	87.26	NA

See ner_NCBI_disease_BioBERT_SciBERT notebook for a demo using SciBERT and BioBERT models.

See SciBERT paper and BioBERT paper for more info on the respective models.

Other examples

See IMDb notebook for a text classification demo on the Internet Movie Database review sentiment task.
See chunking_english notebook for a demo on syntactic chunking using the CoNLL-2000 chunking task data.
See ner_chinese notebook for a demo using 'bert-base-chinese' for Chinese NER.

tests

Run tests with pytest :

python -m pytest -sv tests/