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https://github.com/LeeSureman/Flat-Lattice-Transformer
code for ACL 2020 paper: FLAT: Chinese NER Using Flat-Lattice Transformer
https://github.com/LeeSureman/Flat-Lattice-Transformer
Last synced: about 1 month ago
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code for ACL 2020 paper: FLAT: Chinese NER Using Flat-Lattice Transformer
- Host: GitHub
- URL: https://github.com/LeeSureman/Flat-Lattice-Transformer
- Owner: LeeSureman
- Created: 2020-04-29T04:44:42.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-05-10T05:18:21.000Z (over 2 years ago)
- Last Synced: 2024-08-02T16:55:37.563Z (5 months ago)
- Language: Python
- Size: 102 KB
- Stars: 997
- Watchers: 13
- Forks: 177
- Open Issues: 92
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- StarryDivineSky - LeeSureman/Flat-Lattice-Transformer
README
[English](#Requirement)
[中文](#运行环境)
# 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](https://arxiv.org/pdf/2004.11795.pdf).
# Requirement:
```
Python: 3.7.3
PyTorch: 1.2.0
FastNLP: 0.5.0
Numpy: 1.16.4
```
you can go [here](https://fastnlp.readthedocs.io/zh/latest/) 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](https://drive.google.com/file/d/1_Zlf0OAZKVdydk7loUpkzD2KPEotUE8u/view?usp=sharing) or [Baidu Pan](https://pan.baidu.com/s/1pLO6T9D)
Word(Lattice) embeddings:
yj, (ctb.50d.vec) : [Google Drive](https://drive.google.com/file/d/1K_lG3FlXTgOOf8aQ4brR9g3R40qi1Chv/view?usp=sharing) or [Baidu Pan](https://pan.baidu.com/s/1pLO6T9D)
ls, (sgns.merge.word.bz2) : [Baidu Pan](https://pan.baidu.com/s/1luy-GlTdqqvJ3j-A4FcIOw)
2. Modify the `paths.py` to add the pretrained embedding and the dataset
3. 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 (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](https://fitlog.readthedocs.io/zh/latest/) to know more about Fitlog.
Cite:
========
[bibtex](https://www.aclweb.org/anthology/2020.acl-main.611.bib)
---
# 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](https://fastnlp.readthedocs.io/zh/latest/) 深入了解 FastNLP 这个库.
如何运行？
====
1. 请下载预训练的embedding
从[Google Drive](https://drive.google.com/file/d/1_Zlf0OAZKVdydk7loUpkzD2KPEotUE8u/view?usp=sharing) 或 [Baidu Pan](https://pan.baidu.com/s/1pLO6T9D) 下载字和 Bigram 的 embedding (gigaword_chn.all.a2b.{'uni' or 'bi'}.ite50.vec)
从[Google Drive](https://drive.google.com/file/d/1K_lG3FlXTgOOf8aQ4brR9g3R40qi1Chv/view?usp=sharing) 或 [Baidu Pan](https://pan.baidu.com/s/1pLO6T9D) 下载词的 embedding (ctb.50d.vec)(yj)
从[Baidu Pan](https://pan.baidu.com/s/1luy-GlTdqqvJ3j-A4FcIOw) 下载词的embedding (sgns.merge.bigram.bz2)(ls)
2. 修改 `paths.py` 来添加预训练的 embedding 和你的数据集
3. 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 (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](https://fitlog.readthedocs.io/zh/latest/) 深入了解 Fitlog 这个工具
引用:
========
[bibtex](https://www.aclweb.org/anthology/2020.acl-main.611.bib)
更新说明：
========
5.7共提交两个版本，其中V2使用tensor.unique()用于去除相对位置中重复组合（记为Flat_unique），V3使用标量替代了FLAt中的相对位置编码(记为Flat_scalar).详见[FLAT瘦身日记](https://zhuanlan.zhihu.com/p/509248057)
使用这两种方法的显存占用如下表所示
batch_size=10
|seq_len| 50 | 100 | 150 | 200 | 250 | 300 |
|:-------|----:|-----:|-----:|-----:|-----:|-----:|
|Flat|1096MB | 1668MB |2734MB|4118MB|5938MB|8374MB|
|Flat_unique|964MB|1204MB|1610MB|2166MB|2922MB|3940MB|
|Flat_scalar|878MB|916MB|1028MB|1062MB|1148MB|1322MB|
|Bert+Flat|1605MB|2237MB|3333MB|4725MB|6571MB|9039MB|
|Bert+Flat_unique|1495MB|1685MB|2129MB|2697MB|3453MB|4585MB|
|Bert+Flat_scalar|1409MB|1481MB|1565MB|1617MB|1705MB|2051MB|