June 30, 2019

507 words 3 mins read



Google AI 2018 BERT pytorch implementation

repo name codertimo/BERT-pytorch
repo link https://github.com/codertimo/BERT-pytorch
language Python
size (curr.) 84 kB
stars (curr.) 3307
created 2018-10-15
license Apache License 2.0


LICENSE GitHub issues GitHub stars CircleCI PyPI PyPI - Status Documentation Status

Pytorch implementation of Google AI’s 2018 BERT, with simple annotation

BERT 2018 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Paper URL : https://arxiv.org/abs/1810.04805


Google AI’s BERT paper shows the amazing result on various NLP task (new 17 NLP tasks SOTA), including outperform the human F1 score on SQuAD v1.1 QA task. This paper proved that Transformer(self-attention) based encoder can be powerfully used as alternative of previous language model with proper language model training method. And more importantly, they showed us that this pre-trained language model can be transfer into any NLP task without making task specific model architecture.

This amazing result would be record in NLP history, and I expect many further papers about BERT will be published very soon.

This repo is implementation of BERT. Code is very simple and easy to understand fastly. Some of these codes are based on The Annotated Transformer

Currently this project is working on progress. And the code is not verified yet.


pip install bert-pytorch


NOTICE : Your corpus should be prepared with two sentences in one line with tab(\t) separator

0. Prepare your corpus

Welcome to the \t the jungle\n
I can stay \t here all night\n

or tokenized corpus (tokenization is not in package)

Wel_ _come _to _the \t _the _jungle\n
_I _can _stay \t _here _all _night\n

1. Building vocab based on your corpus

bert-vocab -c data/corpus.small -o data/vocab.small

2. Train your own BERT model

bert -c data/corpus.small -v data/vocab.small -o output/bert.model

Language Model Pre-training

In the paper, authors shows the new language model training methods, which are “masked language model” and “predict next sentence”.

Masked Language Model

Original Paper : 3.3.1 Task #1: Masked LM

Input Sequence  : The man went to [MASK] store with [MASK] dog
Target Sequence :                  the                his


Randomly 15% of input token will be changed into something, based on under sub-rules

  1. Randomly 80% of tokens, gonna be a [MASK] token
  2. Randomly 10% of tokens, gonna be a [RANDOM] token(another word)
  3. Randomly 10% of tokens, will be remain as same. But need to be predicted.

Predict Next Sentence

Original Paper : 3.3.2 Task #2: Next Sentence Prediction

Input : [CLS] the man went to the store [SEP] he bought a gallon of milk [SEP]
Label : Is Next

Input = [CLS] the man heading to the store [SEP] penguin [MASK] are flight ##less birds [SEP]
Label = NotNext

“Is this sentence can be continuously connected?”

understanding the relationship, between two text sentences, which is not directly captured by language modeling


  1. Randomly 50% of next sentence, gonna be continuous sentence.
  2. Randomly 50% of next sentence, gonna be unrelated sentence.


Junseong Kim, Scatter Lab (codertimo@gmail.com / junseong.kim@scatterlab.co.kr)


This project following Apache 2.0 License as written in LICENSE file

Copyright 2018 Junseong Kim, Scatter Lab, respective BERT contributors

Copyright (c) 2018 Alexander Rush : The Annotated Trasnformer

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