from __future__ import unicode_literals, print_function
import os
import random
from pathlib import Path
import spacy
# training data
# Note: If you're using an existing model, make sure to mix in examples of
# other entity types that spaCy correctly recognized before. Otherwise, your
# model might learn the new type, but "forget" what it previously knew.
# https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting
[docs]def pseudo_rehersal(model):
#TODO add the rehersal
return 0
[docs]def train(dataset=[], model=None, new_model_name=None, output_dir=os.getcwd()+"/models", n_iter=10, Label="random_ent", verbose=False):
"""Set up the pipeline and entity recognizer, and train the new entity."""
if not dataset:
return None
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
if verbose:
print("Loaded model '%s'" % model)
else:
nlp = spacy.blank('en') # create blank Language class
if verbose:
print("Created blank 'en' model")
# Add entity recognizer to model if it's not in the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
if 'ner' not in nlp.pipe_names:
ner = nlp.create_pipe('ner')
nlp.add_pipe(ner)
# otherwise, get it, so we can add labels to it
else:
ner = nlp.get_pipe('ner')
ner.add_label(Label) # add new entity label to entity recognizer
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
with nlp.disable_pipes(*other_pipes): # only train NER
optimizer = nlp.begin_training()
for itn in range(n_iter):
random.shuffle(dataset)
losses = {}
for text, annotations in dataset:
nlp.update([text], [annotations], sgd = optimizer, drop=0.35,losses=losses)
print(losses)
'''
# test the trained model
test_text = 'Do you like MIT?'
doc = nlp(test_text)
print("Entities in '%s'" % test_text)
for ent in doc.ents:
print(ent.label_, ent.text)
'''
# save model to output directory
if output_dir is not None:
output_dir = Path(output_dir)
if not output_dir.exists():
output_dir.mkdir()
nlp.meta['name'] = new_model_name # rename model
nlp.to_disk(output_dir)
print("Saved model to", output_dir)
'''
# test the saved model
print("Loading from", output_dir)
nlp2 = spacy.load(output_dir)
doc2 = nlp2(test_text)
for ent in doc2.ents:
print(ent.label_, ent.text)
'''
return nlp