BERT PyTorch Implementation (work in progress)

Introduction

This is an implementation of the BERT architecture (Bidirectional Encoder Representations from Transformers paper). This repository provides a PyTorch implementation of BERT from scratch, including the model architecture, data preprocessing and training.

Installation

Setup environment

conda env create -f ./env/env.yaml
conda activate bert
python -m ipykernel install --user --name=bert

Remove environment

conda deactivate
conda remove -n bert --all -y
jupyter kernelspec uninstall bert -y

Usage

python scripts/prepare_dataset.py --num_samples=10000

python scripts/train_model.py --d_model=64 --n_layers=4 --n_heads=4 --batch_size=8 --training_steps=1000

Implementation

Datasets

Unlike in the original BERT paper, this implementation doesn't yet use the bookcorpus dataset for the pretraining. In the Huggingface version of the bookcorpus most samples are short, independent sentences from books without any connection to other sentences.

Preprocessing

The preprocessing is split into three distinct parts:

1. Splitting by context length: Each sample from original dataset is split into multiple samples of length context_length. Each sample must contain at least two sentences.
2. Preparing next sentence prediction (NSP): For NSP, half of the samples will contain two consecutive sequences of sentences (IsNext) and the other half will contain two non-consecutive sequences of sentences (NotNext). For the IsNext-case a sample will randomly be split into two segments at a sentence delimiter. For the NotNext-case two samples will be randomly split into two segments each that will be combined with a segment form the other sample.
3. Preparing masked language modeling (MLM): For MLM, (by default) 15% of tokens will be masked. Of these, 80% will be replaced by the [MASK] token, 10% will be replaced by a random token and the remaining 10% will not be replaced.