A little library for text analysis with RNNs.
Warning: very alpha, work in progress.
via Github (version under active development)
git clone http://github.com/IndicoDataSolutions/passage.git
python setup.py develop
or via pip
sudo pip install passage
Using Passage to do binary classification of text, this example:
- Tokenizes some training text, converting it to a format Passage can use.
- Defines the model's structure as a list of layers.
- Creates the model with that structure and a cost to be optimized.
- Trains the model for one iteration over the training text.
- Uses the model and tokenizer to predict on new text.
- Saves and loads the model.
from passage.preprocessing import Tokenizer
from passage.layers import Embedding, GatedRecurrent, Dense
from passage.models import RNN
from passage.utils import save, load
tokenizer = Tokenizer()
train_tokens = tokenizer.fit_transform(train_text)
layers = [
Embedding(size=128, n_features=tokenizer.n_features),
GatedRecurrent(size=128),
Dense(size=1, activation='sigmoid')
]
model = RNN(layers=layers, cost='BinaryCrossEntropy')
model.fit(train_tokens, train_labels)
model.predict(tokenizer.transform(test_text))
save(model, 'save_test.pkl')
model = load('save_test.pkl')
Where:
- train_text is a list of strings ['hello world', 'foo bar']
- train_labels is a list of labels [0, 1]
- test_text is another list of strings
Without sizeable datasets RNNs have difficulty achieving results better than traditional sparse linear models. Below are a few datasets that are appropriately sized, useful for experimentation. Hopefully this list will grow over time, please feel free to propose new datasets for inclusion through either an issue or a pull request.
Note: None of these datasets were created by indico, nor should their inclusion here indicate any kind of endorsement
Blogger Dataset: http://www.cs.biu.ac.il/~koppel/blogs/blogs.zip (Age and gender data)