AngusMonroe/text-classification

Text classification based on RNN using pytorch

text-classification

• This is for multi-class short text classification.
• Model is built with Word Embedding, LSTM ( or GRU), and Fully-connected layer by Pytorch.
• A mini-batch is created by 0 padding and processed by using torch.nn.utils.rnn.PackedSequence.
• Cross-entropy Loss + Adam optimizer.
• Support pretrained word embedding (GloVe).

Reference

paper:

Learning to Classify Short and Sparse Text & Web with Hidden Topics from Large-scale Data Collections

code:

https://github.com/keishinkickback/Pytorch-RNN-text-classification

Model

• Embedding --> Dropout --> LSTM(GRU) --> Dropout --> FC.

Preprocessing

• download GloVe embeddings.

python preprocess.py

Data Format

train data at ./data/aminer_train.tsv

label	sentence
<lable>	<sentence>

Training

• The following command starts training. Run it with -h for optional arguments.

python main.py

Query

1. run the server

   python server.py
   

2. GET Method:

   http://166.111.5.228:5012/query/<query>
   

3. Return in json, Example:

   http://166.111.5.228:5012/query/John
   {"tag": "0"}
   

   0-学者，1-文章，2-会议，3-chitchat

File orgnization

|- main.py 
|- classify.py 
|- [dir] glove (word library)
|- [dir] data (dataset)
|- [dir] gen (well-trained models)