Warning: The dataset contains a lot of dirty words, insults, etc. Please, consider it before checking the notebooks.
These notebooks were created in order to make a presentation about NLP and Keras, to show "how easy" it could be to create some neural networks for NLP problems with Keras. You can find slides here.
Notebooks to understand Natural Language Processing (NLP) with Python. We use data from a Kaggle Challenge to find toxic comments in the Wikipedia forum.
Notebooks are splitted into 5 parts:
- 1_exploration.ipynb: understanding data with wordcloud, NMF and TSNE.
- 2_logistic_regression.ipynb: playing with logistic regression in NLP context.
- 3_logistic_regression_pipeline.ipynb: playing with logistic regression, a step further. We add scikit-learn pipeline to automate our logistic regression and try to find the best logistic regression (ever ?).
- 4_deep_learning_for_nlp_intro_with_keras.ipynb: playing from simple neural network to stacked bi-directionnal LSTM in order to compare performance with this different neural network architecture.
- 5_deep_learning_for_nlp_submission.ipynb: final neural network. A bi-directionnal LSTM with pooling and regularization.
Have fun!
Do not hesitate to clone this repository to play with the notebooks.
You can use the conda environment provided with the environment.yml file. Use command conda env create -f environment.yml.
To avoid heavy computation on NMF and TSNE, we include in data/work some pickle with pre-computed NMF and TSNE.