BERT-toxicity-classification

This repo show how to train bert model on Jigsaw Unintended Bias in Toxicity Classification
star me and i will keep update the code
this repo is modified from google open source code for bert , thank Jon Mischo advice here

LB Score

1. 2019-04-06: 0.91216
2. 2019-04-07: 0.91455(add text clean method reference here)

How to output the prediction on test data by finetuning bert model

prepare

1. download the pretrain model
2. download the data and unzip to input folder
3. split the train and dev data(for convenience, i just tyde this command and not recommanded)

cat train.csv | tail -n 1000 > dev_1000.csv

train model

1. run run_classifier.py

python run_classifier.py \
  --data_dir=input/ --vocab_file=uncased_L-12_H-768_A-12/vocab.txt \
  --bert_config_file=uncased_L-12_H-768_A-12/bert_config.json \
  --init_checkpoint=uncased_L-12_H-768_A-12/bert_model.ckpt \
  --task_name=toxic \
  --do_train=True \
  --do_eval=True \
  --do_predict=True \
  --output_dir=model_output/

2. the model will train 10 epochs, but you can stop it depend on your time
3. the checkpoint will be saved on the model_output, also the prediton on the test data(see model_output/test_result.tsv)

generate the submission

1. run encode.py
2. upload the output/sub.csv to kaggle

What is the different with official code**

1. add csv handler(line 243 in run_classifier.py)
2. add ToxicProcessor(line 264 in run_classifier.py)

To do

1. text clean and OOV
2. CV
3. average different checkpoint prediction

like this repo? you can buy me a cup of coffee