BERT-Sequence-Labeling

This repostiory integrates HuggingFaces‘s models in an end-to-end pipeline for sequence labeling. Here
is a complete list of the available models.

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Install

git clone https://github.com/avramandrei/BERT-Sequence-Labeling.git
cd BERT-Sequence-Labeling
pip3 install -r requirements.txt

Input Format

The files used for training, validation and testing must be in a format similar to the CoNLL:

# sent_id = email-enronsent20_01-0048
# text = Please let us know if you have additional questions.
1    Please    please    INTJ    UH    _    2    discourse    2:discourse    _
2    let    let    VERB    VB    Mood=Imp|VerbForm=Fin    0    root    0:root    _
3    us    we    PRON    PRP    Case=Acc|Number=Plur|Person=1|PronType=Prs    2    obj    2:obj|4:nsubj:xsubj    _
4    know    know    VERB    VB    VerbForm=Inf    2    xcomp    2:xcomp    _
5    if    if    SCONJ    IN    _    7    mark    7:mark    _
6    you    you    PRON    PRP    Case=Nom|Person=2|PronType=Prs    7    nsubj    7:nsubj    _
7    have    have    VERB    VBP    Mood=Ind|Tense=Pres|VerbForm=Fin    4    advcl    4:advcl:if    _
8    additional    additional    ADJ    JJ    Degree=Pos    9    amod    9:amod    _
9    questions    question    NOUN    NNS    Number=Plur    7    obj    7:obj    SpaceAfter=No
10    .    .    PUNCT    .    _    2    punct    2:punct    _

Training

To train a model, use the train.py script. This will start training a model that will predict the labels of the column specified by the [predict_column] argument.

python3 train.py [path_train_file] [path_dev_file] [tokens_column] [predict_column] [lang_model_name]

Inference

To predict new values, use the predict.py script. This will create a new file by replacing the predicted column of the test file with the predicted values.

python3 predict.py [path_test_file] [model_path] [tokens_column] [predict_column] [lang_model_name]

Results

English EWT

Cite

Please consider citing the following paper as a thank you to the authors:
```
@article{avram2020upb,
title={UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction},
author={Avram, Andrei-Marius and Cercel, Dumitru-Clementin and Chiru, Costin-Gabriel},
journal={arXiv e-prints},
pages={arXiv—2009},
year={2020}
}