Comments (3)
Use the TfidfTransformer. Serialize the trained transformer as part of the model.
from mycroft.
Actually does this even make sense? Keras' weighting mechanism only applies to training samples, not at test time. Wouldn't scaling a word vector just turn it into another word? Have to see if there's precedent in the literature for this.
It is troubling that in the experimental results in #20 a bag-of-words SVM does as well as a neural LSTM, because that indicates that I'm not able to effectively make use of sequential data. It's possible that the issue is that I'm treating all words as equally informative in the neural network models. TF-IDF weighting is one way to address this. Would max pooling in a convent model (#17) be another?
from mycroft.
Nah. Not going to do this. Too many other avenues to explore.
from mycroft.
Related Issues (20)
- Support command-line interface for custom models HOT 2
- Specify a validation set instead of a random validation portion
- Documentation pass
- Crossvalidation HOT 1
- Add a unit test for the demo
- Release this as a Pypi package HOT 5
- Models are subcommands of a training command HOT 1
- Add regularization to built-in models HOT 1
- Get vocabulary from data HOT 1
- Reload a trained model for additional training
- Better optimization options
- Error when specifying a non-default sequence length HOT 1
- Explain the metrics
- Support for Very Large Vocabularies HOT 4
- Create a website for this project
- mask_zero for ConvolutionNetClassifier? HOT 1
- Predict needs --label-name and --omit-labels options HOT 1
- Change meaning of maximum vocabulary in training
- Train function takes a --test option HOT 1
- Support multiple text inputs
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from mycroft.