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https://github.com/charles9n/bert-sklearn
a sklearn wrapper for Google's BERT model
https://github.com/charles9n/bert-sklearn
bert conll-2003 language-model named-entity-recognition natural-language-processing ner nlp pytorch scikit-learn transfer-learning
Last synced: 5 days ago
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a sklearn wrapper for Google's BERT model
- Host: GitHub
- URL: https://github.com/charles9n/bert-sklearn
- Owner: charles9n
- License: apache-2.0
- Created: 2019-02-18T03:48:10.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-10-26T10:35:20.000Z (about 2 years ago)
- Last Synced: 2024-12-31T16:10:07.731Z (12 days ago)
- Topics: bert, conll-2003, language-model, named-entity-recognition, natural-language-processing, ner, nlp, pytorch, scikit-learn, transfer-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 510 KB
- Stars: 298
- Watchers: 10
- Forks: 71
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-bert - charles9n/bert-sklearn
README
# scikit-learn wrapper to finetune BERT
A scikit-learn wrapper to finetune [Google's BERT](https://github.com/google-research/bert) model for text and token sequence tasks based on the [huggingface pytorch](https://github.com/huggingface/pytorch-pretrained-BERT) 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`**](https://github.com/allenai/scibert) and [**`BioBERT`**](https://github.com/dmis-lab/biobert) pretrained models for scientific and biomedical domains.
Try in [Google Colab](https://colab.research.google.com/drive/1-wTNA-qYmOBdSYG7sRhIdOrxcgPpcl6L)!
## installation
requires python >= 3.5 and pytorch >= 0.4.1
```bash
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
```python3
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](https://github.com/charles9n/bert-sklearn/blob/master/demo.ipynb) notebook.
## model options
```python3
# 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](https://github.com/charles9n/bert-sklearn/blob/master/Options.md)
## hyperparameter tuning
```python3
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](https://github.com/charles9n/bert-sklearn/blob/master/demo_tuning_hyperparams.ipynb) notebook.
## GLUE datasets
The train and dev data sets from the [GLUE(Generalized Language Understanding Evaluation) ](https://github.com/nyu-mll/GLUE-baselines) benchmarks were used with `bert-base-uncased` model and compared againt the reported results in the Google paper and [GLUE leaderboard](https://gluebenchmark.com/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](https://github.com/charles9n/bert-sklearn/tree/master/glue_examples).
## CoNLL-2003 Named Entity Recognition(NER)
NER results for [**`CoNLL-2003`**](https://www.clips.uantwerpen.be/conll2003/ner/) shared task
| | dev f1 | test f1 |
| - | - | - |
| BERT paper| 96.4 | 92.4|
| bert-sklearn | 96.04 | 91.97|
Span level stats on test:
```bash
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](https://github.com/charles9n/bert-sklearn/blob/master/other_examples/ner_english.ipynb) 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`**](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655/) name recognition task.
Previous [SOTA](https://arxiv.org/pdf/1711.07908.pdf) 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](https://github.com/charles9n/bert-sklearn/blob/master/other_examples/ner_NCBI_disease_BioBERT_SciBERT.ipynb) for a demo using **`SciBERT`** and **`BioBERT`** models.
See [SciBERT paper](https://arxiv.org/pdf/1903.10676.pdf) and [BioBERT paper](https://arxiv.org/pdf/1901.08746.pdf) for more info on the respective models.
## Other examples
* See [IMDb notebook](https://github.com/charles9n/bert-sklearn/blob/master/other_examples/IMDb.ipynb) for a text classification demo on the Internet Movie Database review sentiment task.
* See [chunking_english notebook](https://github.com/charles9n/bert-sklearn/blob/master/other_examples/chunker_english.ipynb) for a demo on syntactic chunking using the [**`CoNLL-2000`**](https://www.clips.uantwerpen.be/conll2003/ner/) chunking task data.
* See [ner_chinese notebook](https://github.com/charles9n/bert-sklearn/blob/master/other_examples/ner_chinese.ipynb) for a demo using `'bert-base-chinese'` for Chinese NER.
## tests
Run tests with pytest :
```bash
python -m pytest -sv tests/
```
## references
* [Google `BERT` github](https://github.com/google-research/bert) and [paper: "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" (10/2018) by
J. Devlin, M. Chang, K. Lee, and K. Toutanova](https://arxiv.org/abs/1810.04805)
* [huggingface `pytorch-pretrained-BERT` github](https://github.com/huggingface/pytorch-pretrained-BERT)
* [`SciBERT` github](https://github.com/allenai/scibert) and [paper: "SCIBERT: Pretrained Contextualized Embeddings for Scientific Text" (3/2019) by I. Beltagy, A. Cohan, and K. Lo](https://arxiv.org/pdf/1903.10676.pdf)
* [`BioBERT` github](https://github.com/dmis-lab/biobert) and [paper: "BioBERT: a pre-trained biomedical language representation model for biomedical text mining" (2/2019) by J. Lee, W. Yoon, S. Kim , D. Kim, S. Kim , C.H. So, and J. Kang
](https://arxiv.org/pdf/1901.08746.pdf)