model = ParserModel(embeddings=embedding_matrix,
n_features=n_features,
n_pos=n_pos,
n_tags=n_tags,
tag_size=tag_size,
n_actions=n_actions,
hidden_size=hidden_size)
# Compile the model
from tensorflow import keras
model.compile(
# Optimizer
optimizer=keras.optimizers.Adam(),
# Loss function to minimize
loss=keras.losses.SparseCategoricalCrossentropy(name='train_loss'),
# List of metrics to monitor
metrics=[keras.metrics.SparseCategoricalAccuracy(name='train acc')],
)
# Train the model
EPOCHS = 3
history = model.fit(ds_train, epochs=EPOCHS, validation_data=ds_dev)
# Parse test
UAS, LAS = parser.parse(test_sents[:3], model, conllu=True)
print('UAS', UAS, 'LAS', LAS)
