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Add a LSTM-CRF model at Conlll2003 Dataset #122

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Description

Add the LSTM-CRF model for Conll2003 dataset at reproduction dir based on fastNLP lib, inspired by the paper https://arxiv.org/pdf/1508.01991.pdf

Main reason

Provide a new demo for how fastNLP can facilitate the development of the deep learning model. FYI:
https://github.com/hazelnutsgz/fastNLP/tree/hazelnutsgz-crf-lstm/reproduction/LSTM-CRF

Checklist 检查下面各项是否完成

Please feel free to remove inapplicable items for your PR.

  • The PR title starts with [$CATEGORY] (例如[bugfix]修复bug,[new]添加新功能,[test]修改测试,[rm]删除旧代码)
  • Changes are complete (i.e. I finished coding on this PR) 修改完成才提PR
  • All changes have test coverage 修改的部分顺利通过测试。对于fastnlp/fastnlp/的修改,测试代码必须提供在fastnlp/test/
  • Code is well-documented 注释写好,API文档会从注释中抽取
  • To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change 修改导致例子或tutorial有变化,请找核心开发人员

Changes

  • Interactive jupyter notebook
  • Well-structured codebase for training & testing
  • A README file for the instruction

Mention:

@yhcc @xpqiu @FengZiYjun @2017alan

@hazelnutsgz hazelnutsgz changed the title Hazelnutsgz crf lstm Add a LSTM-CRF model at Conlll2003 Dataset Jan 10, 2019
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codecov-io commented Jan 10, 2019

Codecov Report

Merging #122 into master will decrease coverage by 0.11%.
The diff coverage is n/a.

Impacted file tree graph

@@            Coverage Diff            @@
##           master    #122      +/-   ##
=========================================
- Coverage   70.31%   70.2%   -0.12%     
=========================================
  Files          82      82              
  Lines        5407    5407              
=========================================
- Hits         3802    3796       -6     
- Misses       1605    1611       +6
Impacted Files Coverage Δ
fastNLP/models/biaffine_parser.py 94.44% <0%> (-2.23%) ⬇️

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@xpqiu
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xpqiu commented Jan 10, 2019

Great!
the logs and binary files are unnecessary to be committed.

Update README
@hazelnutsgz
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OK, I have updated my commit just now, thanks for your careful review.

@xuyige
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xuyige commented Jan 11, 2019

i think the data as well as the training code may not necessary in reproduction
the reproduction should contain a trained model that can be directly used

@hazelnutsgz
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i think the data as well as the training code may not necessary in reproduction
the reproduction should contain a trained model that can be directly used

Thanks for your comments @xuyige , the followings are my replies and proposals:

Reply

  1. Could you please elaborate the meaning of "the training code"? I am a little confused.
  2. I think the dataset, say, the Conll2003, is necessary for fastNLP users to reproduce the work, the reasons are as follows:
    1. Acquiring the Conll2003 dataset with fastNLP is not as easy as acquiring mnist dataset by network api provided by the framework(tf&torch), so the preloaded dataset is necessary for the NLP novice to ramp up with the project.
    2. Some other projects under the reproduction directory, say, Char-aware_NLM , also introduced the train.txt, test.txt

Proposal

Based on the design of how tf&pytorch loaded the mnist dataset(by network), I think the fastNLP may consider the data downloading APIs for some widely acknowledged NLP datasets, eg, SQUAD.

@xuyige
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xuyige commented Jan 12, 2019

i think the data as well as the training code may not necessary in reproduction
the reproduction should contain a trained model that can be directly used

Thanks for your comments @xuyige , the followings are my replies and proposals:

Reply

  1. Could you please elaborate the meaning of "the training code"? I am a little confused.
  2. I think the dataset, say, the Conll2003, is necessary for fastNLP users to reproduce the work, the reasons are as follows:
    1. Acquiring the Conll2003 dataset with fastNLP is not as easy as acquiring mnist dataset by network api provided by the framework(tf&torch), so the preloaded dataset is necessary for the NLP novice to ramp up with the project.
    2. Some other projects under the reproduction directory, say, Char-aware_NLM , also introduced the train.txt, test.txt

Proposal

Based on the design of how tf&pytorch loaded the mnist dataset(by network), I think the fastNLP may consider the data downloading APIs for some widely acknowledged NLP datasets, eg, SQUAD.

i am so regret to point out that the char-aware-nlm were borrowed from other projects.
it is an outdated version. codes haven't been updated for months.
the data downloading has been considered, but a server is need for downloading, so we put it into todo list
the preload dataset is a good suggestion, we will discuss it soon

@xpqiu xpqiu requested a review from LinyangLee January 17, 2019 07:03
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should be working, codes seem to be fine
but things still don't add up
I am currently working on this one

@hazelnutsgz
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should be working, codes seem to be fine
but things still don't add up
I am currently working on this one

Thanks for your review~

@Hou-jing
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Is the util file missing here? When I run it, I am prompted that there is no load_data function.
image

@yhcc
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yhcc commented Jun 27, 2022

Yes, we don't have load_data function. You may use an old version.

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7 participants