added qa lab

homeworks_advanced/extra_Lab_QA/LICENSE

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+The MIT License
+
+Copyright (c) 2019 Christopher Chute http://chrischute.com
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

homeworks_advanced/extra_Lab_QA/README.md

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+#### Extra Lab: QA system
+
+In this homework your goal is to build the QA system for Russian language using the [SberQuAD dataset](https://arxiv.org/pdf/1912.09723.pdf). The preprocessing code and baseline solution (BiDAF) are the slightly adapted version of the [Stanford CS224n Starter code](https://github.com/chrischute/squad).
+
+The starting point of this assighnment is the `SberQuAD_preprocessing_and_problem_statement.ipynb` notebook.
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/girafe-ai/ml-mipt/blob/advanced_f20/homeworks_advanced/extra_Lab_QA/SberQuAD_preprocessing_and_problem_statement.ipynb)
+
+
+Next comes the original instructions from the https://github.com/chrischute/squad repository.
+
+P.s. Downgrading PyTorch is not required, starter code works fine on PyTorch 1.4
+P.p.s. If you are running in Colab, mount your Google Drive and store the checkpoints/word vectors there. [Official instruction](https://colab.research.google.com/notebooks/io.ipynb), [Habr post](https://habr.com/ru/post/348058/). Restarting the kernel after you finished the preprocessing (and saved the data to your disk) might be a good idea to release the memory.
+
+#### Setup
+
+1. Make sure you have [Miniconda](https://docs.conda.io/en/latest/miniconda.html) installed
+    1. Conda is a package manager that sandboxes your project’s dependencies in a virtual environment
+    2. Miniconda contains Conda and its dependencies with no extra packages by default (as opposed to Anaconda, which installs some extra packages)
+
+2. cd into src, run `conda env create -f environment.yml`
+    1. This creates a Conda environment called `squad`
+
+3. Run `source activate squad`
+    1. This activates the `squad` environment
+    2. Do this each time you want to write/test your code
+
+4. Run `python setup.py`
+    1. This downloads SQuAD 2.0 training and dev sets, as well as the GloVe 300-dimensional word vectors (840B)
+    2. This also pre-processes the dataset for efficient data loading
+    3. For a MacBook Pro on the Stanford network, `setup.py` takes around 30 minutes total  
+
+5. Browse the code in `train.py`
+    1. The `train.py` script is the entry point for training a model. It reads command-line arguments, loads the SQuAD dataset, and trains a model.
+    2. You may find it helpful to browse the arguments provided by the starter code. Either look directly at the `parser.add_argument` lines in the source code, or run `python train.py -h`.