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fastNLP/reproduction/text_classification
yh_cc e361b32c3a 在@linzehui 的帮助下seq2seq终于有了第一个版本; 目前实现了Seq2Seq的Transformer和LSTM版本，但metric和loss还没update; 4 years ago
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data delete the alias in files. 5 years ago
model 在@linzehui 的帮助下seq2seq终于有了第一个版本; 目前实现了Seq2Seq的Transformer和LSTM版本，但metric和loss还没update; 4 years ago
test add TC/MTL16Loader 5 years ago
utils -update DPCNN & train script 5 years ago
README.md [verify]charcnn use pipe,remove dataloader 5 years ago
train_HAN.py 增加fastNLP.embeddings模块并修改对应的现有代码以适配fastNLP.embeddings 5 years ago
train_awdlstm.py datasetloader改成pipe 5 years ago
train_bert.py Merge branch 'dev0.5.0' of https://github.com/fastnlp/fastNLP into dev0.5.0 5 years ago
train_char_cnn.py [bugfix]修复了一些文档错误 4 years ago
train_dpcnn.py [update] change data-loader to pipe 5 years ago
train_lstm.py datasetloader改成pipe 5 years ago
train_lstm_att.py datasetloader改成pipe 5 years ago

README.md

text_classification任务模型复现

这里使用fastNLP复现以下模型：

char_cnn :论文链接Character-level Convolutional Networks for Text Classification

dpcnn:论文链接Deep Pyramid Convolutional Neural Networks for TextCategorization

HAN:论文链接Hierarchical Attention Networks for Document Classification

LSTM+self_attention:论文链接A Structured Self-attentive Sentence Embedding

AWD-LSTM:论文链接Regularizing and Optimizing LSTM Language Models

#数据集来源
IMDB：http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
SST-2：https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8
SST：https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip
yelp_full:https://drive.google.com/drive/folders/0Bz8a_Dbh9Qhbfll6bVpmNUtUcFdjYmF2SEpmZUZUcVNiMUw1TWN6RDV3a0JHT3kxLVhVR2M
yelp_polarity:https://drive.google.com/drive/folders/0Bz8a_Dbh9Qhbfll6bVpmNUtUcFdjYmF2SEpmZUZUcVNiMUw1TWN6RDV3a0JHT3kxLVhVR2M

dataset classes train samples dev samples test samples refer
yelp_polarity 2 560k - 38k char_cnn
yelp_full 5 650k - 50k char_cnn
IMDB 2 25k - 25k IMDB
sst-2 2 67k 872 1.8k GLUE

数据集及复现结果汇总

使用fastNLP复现的结果vs论文汇报结果(/前为fastNLP实现，后面为论文报道,-表示论文没有在该数据集上列出结果)

model name yelp_p yelp_f sst-2 IMDB
char_cnn 93.80/95.12 - - -
dpcnn 95.50/97.36 - - -
HAN - - - -
LSTM 95.74/- 64.16/- - 88.52/-
AWD-LSTM 95.96/- 64.74/- - 88.91/-
LSTM+self_attention 96.34/- 65.78/- - 89.53/-
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一款轻量级的自然语言处理（NLP）工具包，目标是减少用户项目中的工程型代码，例如数据处理循环、训练循环、多卡运行等

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