.. | ||
data | ||
model | ||
test | ||
utils | ||
README.md | ||
train_awdlstm.py | ||
train_bert.py | ||
train_char_cnn.py | ||
train_dpcnn.py | ||
train_HAN.py | ||
train_lstm_att.py | ||
train_lstm.py |
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
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/- |