yh646492956/bert-punctuation-restoration

Punctuation Restoration with BERT

The aim of this experiment is to see how BERT performs on punctuation restoration. The purpose of this mini-experiment is to see how well the pre-trained (on English Wiki and BookCorpus with MLM and NSP as pre-training tasks) BERT performs on conversational data hence why I will be using movie transcripts.

Dataset

We will be using Download the dataset @ Cornell Movie Corpus

Setup & Installation

Clone the repo:

git clone https://github.com/matthiaslmz/BERT-Punctuation-Restoration.git

Next, create a new conda environment:

conda env create -f enviroment.yml
conda activate whatisbert

Make sure to download bert-base-uncased model, vocab and config file @:

1. pytorch_model
2. vocab
3. config

CUDA

1. If an error occurs, first make sure that you have a GPU that is available.
2. If error persists, it could be that the defined training batch size is too large for the GPU, decrease if necessary
3. By default in pipeline.py, if a GPU is available, torch.cuda.is_available() defaults to the first GPU, indexed 0. If you need to use the X^{th} GPU, make sure to edit like the following: DEVICE = "cuda:X-1" if torch.cuda.is_available() else cpu

IMPORTANT!

When deciding the number of punctuations you want to keep using the kept_punctuation parameter in create_moveis_dataset(), you MUST also make sure that the number of heads in the final layer of the model is same. This can be specified with the num_labels in BERTPuncResto