training.md

Training

Command:

combo --mode train \
      --training_data_path your_training_path \
      --validation_data_path your_validation_path

Options:

combo --helpfull

Examples (for clarity without training/validation data paths):

• train on gpu 0

  combo --mode train --cuda_device 0

• use pretrained embeddings:

  combo --mode train --pretrained_tokens your_pretrained_embeddings_path --embedding_dim your_embeddings_dim

• use pretrained transformer embeddings:

  combo --mode train --pretrained_transformer_name your_choosen_pretrained_transformer

• predict only dependency tree:

  combo --mode train --targets head,deprel

• use part-of-speech tags for predicting only dependency tree

  combo --mode train --targets head,deprel --features token,char,upostag

Configuration

Advanced

Config template config.template.jsonnet is formed in allennlp format so you can freely modify it. There is configuration for all the training/model parameters (learning rates, epochs number etc.). Some of them use jsonnet syntax to get values from configuration flags, however most of them can be modified directly there.