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?

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:

yj, (ctb.50d.vec) : Google Drive or Baidu Pan

ls, (sgns.merge.word.bz2) : Baidu Pan
Modify the paths.py to add the pretrained embedding and the dataset
Run following commands

python preprocess.py (add '--clip_msra' if you need to train FLAT on MSRA NER dataset)
cd V0 (without Bert) / V1 (with Bert)
python flat_main.py --dataset <dataset_name> (ontonotes, msra, weibo or resume)

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

运行环境:

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

你可以在这里深入了解 FastNLP 这个库.

如何运行？

请下载预训练的embedding

从Google Drive 或 Baidu Pan 下载字和 Bigram 的 embedding (gigaword_chn.all.a2b.{'uni' or 'bi'}.ite50.vec)

从Google Drive 或 Baidu Pan 下载词的 embedding (ctb.50d.vec)(yj)

从Baidu Pan 下载词的embedding (sgns.merge.bigram.bz2)(ls)
修改 paths.py 来添加预训练的 embedding 和你的数据集
运行下面的代码

python preprocess.py (add '--clip_msra' if you need to train FLAT on MSRA NER dataset)
cd V0 (without Bert) / V1 (with Bert)
python flat_main.py --dataset <dataset_name> (ontonotes, msra, weibo or resume)

如果你想方便地记录和观察实验结果, 你可以使用fitlog:

pip install fitlog
fitlog init V0
cd V0
fitlog log logs

然后把flat_main.py里的 use_fitlog 设置为 True 就行你可以在这里深入了解 Fitlog 这个工具

引用: