hummingbird
/
fastNLP

# 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](https://arxiv.org/pdf/1509.01626v3.pdf)

dpcnn:论文链接[Deep Pyramid Convolutional Neural Networks for TextCategorization](https://ai.tencent.com/ailab/media/publications/ACL3-Brady.pdf)

HAN:论文链接[Hierarchical Attention Networks for Document Classiﬁcation](https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf)

LSTM+self_attention:论文链接[A Structured Self-attentive Sentence Embedding](https://arxiv.org/pdf/1703.03130.pdf)

AWD-LSTM:论文链接[Regularizing and Optimizing LSTM Language Models](https://arxiv.org/pdf/1708.02182.pdf)

#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](https://arxiv.org/pdf/1509.01626v3.pdf)|
yelp_full | 5|650k | - |50k|[char_cnn](https://arxiv.org/pdf/1509.01626v3.pdf)|
IMDB | 2 |25k | - |25k|[IMDB](https://ai.stanford.edu/~ang/papers/acl11-WordVectorsSentimentAnalysis.pdf)|
sst-2 | 2 |67k | 872 |1.8k|[GLUE](https://arxiv.org/pdf/1804.07461.pdf)|

# 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/-