README.md

573 Group Project

Collaborators: Connor Boyle, Martin Horst, Nikitas Tampakis

This code can be run most conveniently through our Google Colab notebook.

NOTE: Cached Models

The cached models for D4 will remain available for a short period on the UW Google Drive. To download the full 700+ MB trained models, you can use this link.

Training

Using Development Set

You can train a model using the following command (after activating the correct environment):

$ python src/train.py --train-file <TRAIN_FILE> --model-directory <MODEL_DIRECTORY>

replacing <TRAIN_FILE> with the path to the training data and <MODEL_DIRECTORY> with the path to where the model checkpoints will be saved.

The above command will train using the default hyperparameters for our training loop. It will also use a random 10% of the training data in <TRAIN_FILE> file as a per-epoch validation dataset.

Using Final Test Set

You can train a model using the following command (after activating the correct environment):

$ python src/train.py --train-file <TRAIN_FILE> --dev-file <DEV_FILE> --model-directory <MODEL_DIRECTORY>

replacing <TRAIN_FILE> with the path to the training data, <DEV_FILE> with the path to the dev dataset file, and <MODEL_DIRECTORY> with the path to where the model checkpoints will be saved.

The above command will train using the default hyperparameters for our training loop. It will also use <DEV_FILE> as a per-epoch validation dataset.

Data

These represent the maximum tokenized tweet lengths from the BERT tokenizer for our train, dev, and test files for Spanglish and Hinglish. E.G.: r, ##t, @, fra, ##lal, ##icio, ##ux, ##xe, t, ##bh, i, have, bad, sides, too, ., when, i, say, bad, it, ', s, ho, ##rri, ##bly, bad, .

• Spanglish_train.conll: 82
• Spanglish_dev.conll: 78
• Spanglish_test_conll_unlabeled.txt: 76
• Hinglish_train_14k_split_conll.txt: 85
• Hinglish_dev_3k_split_conll.txt: 70
• Hinglish_test_unlabeled_conll_updated.txt: 77

Classifier

The classifier can be run from the shell with the following command:

$ python src/classify.py --test-file <TEST_FILE> --model-directory <MODEL_DIRECTORY>/<MODEL_INSTANCE> --output-file <OUTPUT_FILE>

replacing <TEST_FILE> with the path to a testing data file ( e.g. data/Semeval_2020_task9_data/Spanglish/Spanglish_test_conll_unlabeled.txt) and <OUTPUT_FILE> with the path to an output file (e.g. output.txt) <MODEL_DIRECTORY> with the path to the directory where trained model instances have been saved, and <MODEL_INSTANCE> with any of the (0-indexed) model instances saved before each epoch, or the FINAL model instance saved after the last epoch (NOTE: in our submitted, trained models, only the FINAL model instance has been saved).

Environment

We load and save our base Python environment using Conda. You can load the environment for the first time by running the following command from the root of the repository:

$ conda env create -f=src/environment.yml

You can then activate the base environment with the following command:

$ conda activate 573

To update your current base environment with a new or changed environment.yml file, run the following command:

$ conda env update -f=src/environment.yml

Dependencies

On top of the base environment, you will need to install package dependencies from requirements.txt (make sure you have activated the base environment you want to use):

$ pip install -r src/requirements.txt