This is an elegant implementation of HAN for Sentiment Classification Task.
The original paper is Hierarchical Attention Networks for Document Classification
The core codes are from this repo: textClassifierHATT, I refactor the original code to make it easier to understand.
First, check your python version and related libraries:
Python >= 3.6
numpy
pandas
re
bs4
pickle
sklearn
gensim
nltk
keras
tensorflow
Then, make sure that you alreay have the pre-trained word-vector files: glove.6B.zip or GoogleNews-vectors-negative300.bin
Next, get your training data. In this case, I use IMDB movie reviews(known as SST-2) for training. One thing you should notice is that we should pass a tsv file, the format of this file must look like this:
id sentiment review
"5814_8" 1 "With all this stuff going down at the moment with MJ i've started listening to his music,
watching the odd documentary here and there, watched The Wiz and watched Moonwalker again.
Maybe i just want to get a certain insight into this guy who i thought was really cool in the eighties
just to maybe make up my mind whether he is guilty or innocent."
which means that the head of this file is "id\tsentiment\treview", and the data starts from second line.
Here are some examples:
python HAN.py --help
and you will get an output like picture below(if everything goes well):
"""
This means:
The path of your training data is ./train_data.tsv
The path of your embedding path is ./*.bin
The epoch is 20
"""
python HAN.py --full_data_path=train_data.tsv --embedding_path=GoogleNews-vectors-negative300.bin --epoch=20
python HAN.py -d=train_data.tsv -s=GoogleNews-vectors-negative300.bin --epoch=20 # Same as command above
"""
This means:
You have alreay trained your data, and you will get your training data from pickle file(processed_data.pickle in this case)
Moreover, you have saved the model you trained, and you will load your model from a *h5 file(model.h5 in this case)
"""
python HAN.py --training_data_ready --model_ready
