Models for cross-lingual complex word identification.
You are advised to use python environments. For reporting results you should only use the docker image that can be built as follows:
- build it by running
docker build -t cwi - < Dockerfile - get an interactive terminal on the image with
docker run -i -t cwi bash - run commands as you normally would (remember this is a very minimal linux installation)
If you want to run the image with a new version of the code, add the option --no-cache to the build.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train` (we won't use use for now)
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources (e.g., dictionaries, word lists, etc.).
│ ├── interim <- Intermediate data that has been transformed (e.g., data after processing).
│ ├── processed <- The final, canonical data sets for modeling (the final datasets that would not suffer further processing).
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details (don't worry about this for now)
│
├── models <- Trained and serialized models, model predictions, or model summaries
│ (if the trained models are going to be stored, this is the place to save them)
│
├── 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`.
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt` (don't worry about this for now)
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download, generate or preprocess data
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │
│ ├── models <- Scripts to train models and then use trained models to make predictions
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
The following results need to be tested for the crosslingual model.
Below are the command line options for testing for various combinations of Language with French as test data:
| Language Choice Combination | Command Line Command |
|---|---|
| English Only | python src/models/run_crosslingual.py -s E |
| Spanish Only | python src/models/run_crosslingual.py -s S |
| German Only | python src/models/run_crosslingual.py -s G |
| English and Spanish | python src/models/run_crosslingual.py -s ES |
| English and German | python src/models/run_crosslingual.py -s EG |
| Spanish and German | python src/models/run_crosslingual.py -s SG |
| English, Spanish and German | python src/models/run_crosslingual.py -s ESG |
| Language Choice Combination | Command Line Command |
|---|---|
| Spanish Only | python src/models/run_crosslingual.py -s S -l english |
| German Only | python src/models/run_crosslingual.py -s G -l english |
| Spanish and German | python src/models/run_crosslingual.py -s SG -l english |
| Language Choice Combination | Command Line Command |
|---|---|
| English Only | python src/models/run_crosslingual.py -s E -l spanish |
| German Only | python src/models/run_crosslingual.py -s G -l spanish |
| English and German | python src/models/run_crosslingual.py -s EG -l spanish |
| Language Choice Combination | Command Line Command |
|---|---|
| English Only | python src/models/run_crosslingual.py -s E -l german |
| Spanish Only | python src/models/run_crosslingual.py -s S -l german |
| English and Spanish | python src/models/run_crosslingual.py -s ES -l german |
Project based on the cookiecutter data science project template. #cookiecutterdatascience