Natural Language Processing 2

My seconde iteration of works for "Natural Language Processing" Coursera course.

Week1

Predict tag of Stackoverflow with linear model. (multi-tag) Used GridSearch CV for optimisation on tfidf model.

Accuracy: 0.3631 (low because multi-tag)
F1-score micro: : 0.6752548016404758
F1-score macro: : 0.5062362622222868
F1-score weighted: : 0.6540253209151559
Precision micro: : 0.4810782509911088
Precision macro: : 0.3396351761816072
Precision weighted: : 0.5102553014775187

Week2

Named entity recognition with LSTMs.

TODO

Week3

Find duplicate questions by their embedding in word2vec. TODO

Week4

Seq2Seq model, encoder-decoder to learn to addition and substraction. Model could be used for other tasks. TODO

Week5

TODO

Natural Language Processing course resources

https://www.coursera.org/learn/language-processing

Running on Google Colab

Google has released its own flavour of Jupyter called Colab, which has free GPUs!

Here's how you can use it:

1. Open https://colab.research.google.com, click Sign in in the upper right corner, use your Google credentials to sign in.
2. Click GITHUB tab, paste https://github.com/hse-aml/natural-language-processing and press Enter
3. Choose the notebook you want to open, e.g. week1/week1-MultilabelClassification.ipynb
4. Click File -> Save a copy in Drive... to save your progress in Google Drive
5. If you need a GPU, click Runtime -> Change runtime type and select GPU in Hardware accelerator box
6. Execute the following code in the first cell that downloads dependencies (change for your week number):

! wget https://raw.githubusercontent.com/hse-aml/natural-language-processing/master/setup_google_colab.py -O setup_google_colab.py
import setup_google_colab
# please, uncomment the week you're working on
# setup_google_colab.setup_week1()  
# setup_google_colab.setup_week2()
# setup_google_colab.setup_week3()
# setup_google_colab.setup_week4()
# setup_google_colab.setup_project()
# setup_google_colab.setup_honor()

7. If you run many notebooks on Colab, they can continue to eat up memory, you can kill them with ! pkill -9 python3 and check with ! nvidia-smi that GPU memory is freed.

Known issues:

• No support for ipywidgets, so we cannot use fancy tqdm progress bars. For now, we use a simplified version of a progress bar suitable for Colab.
• Blinking animation with IPython.display.clear_output(). It's usable, but still looking for a workaround.

Running elsewhere

AWS

Docker