Flat-Lattice-Transformer

code for ACL 2020 paper: FLAT: Chinese NER Using Flat-Lattice Transformer.

Models and results can be found at our ACL 2020 paper FLAT: Chinese NER Using Flat-Lattice Transformer.

Requirement:

Python: 3.7.3
PyTorch: 1.2.0
FastNLP: 0.5.0
Numpy: 1.16.4

you can go here to know more about FastNLP.

How to run the code?

1. Download the character embeddings and word embeddings.

   Character and Bigram embeddings (gigaword_chn.all.a2b.{'uni' or 'bi'}.ite50.vec) : Google Drive or Baidu Pan

   Word(Lattice) embeddings (ctb.50d.vec): Google Drive or Baidu Pan

2. Modify the paths.py to add the pretrained embedding and the dataset

3. Run following commands

cd V0
python preprocess.py (add '--clip_msra' if you need to train FLAT on MSRA NER dataset)
python flat_main.py --dataset <dataset_name> (ontonotes, msra, weibo or resume)

If you want to run the code on other datasets, you can write a function whose output format is like '' If you want to record experiment result, you can use fitlog:

pip install fitlog
fitlog init V0
cd V0
fitlog log logs

then set use_fitlog = True in flat_main.py.

you can go here to know more about Fitlog.

Cite:

bibtex