BAT

BORN FOR AUTO-TAGGING: FASTER AND BETTER WITH NEW OBJECTIVE FUNCTIONS

To extract keywords in e-commerce documents, we need to get high macrof1 and macrof2. Others loss can not achieve good enough metrics, so we create PBP and CECLA loss to achieve this purpose. In addition, we create BAT model to attain better performance than others SOTA model.

paper link

The data we used can not release, so we implement on CONLL2003 English dataset and get the great f1 on it.

Table of Contents

• Introduction
• EnvironmentSetting
• Installation
• QuickStart
• Performance
• Reference
• Citation

Introduction

放論文模型跟loss function 放CONLL2003資料表現

Environment Setting

• python: 3.7
• recommend to use pipenv to build the develop environment

Installation

pip install -r requirements.txt

pip install -e .

Quick Start

CONLL2003-English mission

After checking requirements and finishing installation, you can follow this step:

(1) Get CONLL2003 dataset from here and detail from here. Then move train.txt, dev.txt, and test.txt to /BAT/data.

(2) Tune the configure file. (or you can use default)

cd /BAT/config

vim conll2003.yaml

(3) Train model.

We connect xlm-roberta-large(freeze) model and bat model, and only train on bat model.

Enter to python environment.

$ python
>>> import nltk
>>> nltk.download('punkt')

cd /BAT

CUDA_VISIBLE_DEVICES=0 python sample_conll.py --config-name conll2003.yaml

or (use tee to save training log)

CUDA_VISIBLE_DEVICES=0  python -u sample_conll.py --config-name conll2003.yaml 2>&1 | tee -a conll2003.log 

Performance

Our sample code get f1:93 in CONLL2003-English NER mission.

Reference

we follow https://github.com/wzhouad/NLL-IE preprocess for CONLL2003-English data.

Citation

If this repository is helpful to you, please cite this paper.

@inproceedings{Liu-bat-2022,
  author    = {Chiung-Ju Liu, Huang-Ting Shieh},
  title     = {BAT: BORN FOR AUTO-TAGGING: FASTER AND BETTER WITH NEW OBJECTIVE FUNCTIONS},
  journal   = {arXiv preprint arXiv:2206.07264},
  year      = {2022}
}