Follow the [data/train_and_test]

1. For name in {train, test}, create files {name}.words.txt and {name}.tags.txt that contain one sentence per line, each word / tag separated by space using IOBES tagging scheme.

2. Create files vocab.words.txt, vocab.tags.txt and vocab.chars.txt that contain one token per line. This can be automized using the corresponding function in [src/preprocessing.py] and altering the fields DATASET_DIR to point to the locations of the file in Step 1 where DATA_DIR pointing to the output directory.

3. Create a glove.*.npz file containing one array embeddings of shape (size_vocab_words, 300) using GloVe 840B vectors. This can be built by using the corresponding function in [src/preprocessing.py] after completing Step 2 and altering the field VECTOR_DIR to point desired output directory.

Tensorflow 1.15 should be used, other versions are untested. Remaining packages should be operational wihout a specified version.

Once produced all the required data files, use the main.py with specifying correct parameters. These are:

1. model (-m): Three base architectures; lcfor LSTM-CRF, llc for LSTM-LSTM-CRF and lcc for LSTM-CRF-CRF.
2. embeddings (-e): Three embeddings to pair with a base architecture; glove for GloVe, m2v for Morph2Vec and hybrid for their combination. Make sure that correct .npz files are present in the data folder and call preprocessing each time a different embedding is used.
3. preprocessing (-p): Flag to use preprocessing scripts, non-mandatory. Must be called in between new embedding selections.
4. mode (-a): Four modes, directories are used as stated in [src/data.json]; train to train a model from scratch, k_fold to perform cross-validation (default=5), test for testing and validating a specific input (default=None) and use for generating an output from a specified file using a trained model (default=None).

main.py -m <model> -e <embed> -p (preprocess flag) -a <mode>

If multiple tests are aimed to be performed with slight changes on the parameters, check out [src/multiple_run.sh] script.