BERT_NER_CLI Step by Step Guide

Before started, would like to appreciate for Google Research Team and @Kaiyinzhou's previous work at here.

• Environment

• Folder structure

• Fine-Tune model

• Training with GCP GPU/TPU

• Evaluating

• Predicting

Environment

• Python 3.5+

• Tensorflow 1.11+

Folder structure

Fine-Tune model

Training with GCP GPU/TPU

I found this pretty detailed instructions of how to deploy code, mount folders and execute .py files with Google Colab and utilizing their FREE TPU/GPU capabilities.

BERT-Base, Uncased or BERT-Large, Uncased need to be unzipped and upload to your Google Drive folder and be mounted.

I used Colab GPU (K80) fine-tuning the model, took me around 30 mins.

Evaluating

An evaluation script can be found here. A quick evaluation with Uncased 12-layer result in 93.26 f1 score. 24-layer result will be tried and provided here later.

Predicting

A simple command line program was provided here for testing purpose. Simply run

python predict_cli.py

The program will firstly load the model and waiting for inputs.