lczd / portuguese_wsc

Solver for Winograd Schema Challenge like questions in Portuguese.

portuguese_wsc

Currently under development

Solver for Winograd Schema Challenge in Portuguese. Portuguese translations for original Winograd Schema Challenge are also being proposed here.

• Code for Language Model based on Pytorch's Word-level language modeling RNN example
• Code for parallelization of PyTorch model based on PyTorch-Encoding package with help from this medium post.
• Idea of using language model for solving Winograd Schema Challenge based on paper "A Simple Method for Commonsense Reasoning":

@article{DBLP:journals/corr/abs-1806-02847,
  author    = {Trieu H. Trinh and
               Quoc V. Le},
  title     = {A Simple Method for Commonsense Reasoning},
  journal   = {CoRR},
  volume    = {abs/1806.02847},
  year      = {2018},
  url       = {http://arxiv.org/abs/1806.02847},
  archivePrefix = {arXiv},
  eprint    = {1806.02847},
  timestamp = {Mon, 13 Aug 2018 16:46:22 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1806-02847},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

---

Project Setup

• This project has not been tested in machines without CUDA GPUs available.

• A Dockerfile is available, and may be used with docker build -t wsc_port . followed by nvidia-docker run -it -v $PWD/models:/code/models wsc_port.

• The Dockerfile contains a few different options for running, which can be selected by commenting and uncommenting the final sections of it.

• For running outside of Docker container, Conda is required

• To create the conda environment: conda env create -f environment.yml

• Makefile contains some of the commands used to run the code. These commands must be run from inside the environment.

  • to setup the environment for running the project: make dev_init. This command also makes sure make processed_data is run, which prepares data needed to train model
  • running make corpus will speed up first run of code (but is not necessary)
  • make train trains a model
  • make winograd_test runs evaluation of Winograd Schema Challenge
  • make generate runs language model for generation of text

• Code runs for both English and Portuguese cases, and this setting is controlled by the variable PORTUGUESE in src.consts

• Run tests with make tests, which is equivalent to pytest --cov=src tests/. Use pytest --cov=src --cov-report=html tests/ for generation of HTML test report. Needs pytest and pytest-cov packages. If there are import errors, should run pip install -e . to locally install package from source code.

---

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`.
├── README.md          <- The top-level README for developers using this project.
├── environment.yml    <- Contains project's requirements, generated from Anaconda environment.
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported.
│
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── githooks           <- Contains githooks scripts being used for development. Git hook directory for repo needs to be set to this folder.
│
├── models             <- Trained and serialized models, model predictions, or model summaries.
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting.
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module.
│   │
│   └── scripts           
└── tests              <- Tests module, using Pytest.

Project based on the cookiecutter data science project template. #cookiecutterdatascience