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333b1a308b
* Draft layer for BILUO actions * Fixes to biluo layer * WIP on BILUO layer * Add tests for BILUO layer * Format * Fix transitions * Update test * Link in the simple_ner * Update BILUO tagger * Update __init__ * Import simple_ner * Update test * Import * Add files * Add config * Fix label passing for BILUO and tagger * Fix label handling for simple_ner component * Update simple NER test * Update config * Hack train script * Update BILUO layer * Fix SimpleNER component * Update train_from_config * Add biluo_to_iob helper * Add IOB layer * Add IOBTagger model * Update biluo layer * Update SimpleNER tagger * Update BILUO * Read random seed in train-from-config * Update use of normal_init * Fix normalization of gradient in SimpleNER * Update IOBTagger * Remove print * Tweak masking in BILUO * Add dropout in SimpleNER * Update thinc * Tidy up simple_ner * Fix biluo model * Unhack train-from-config * Update setup.cfg and requirements * Add tb_framework.py for parser model * Try to avoid memory leak in BILUO * Move ParserModel into spacy.ml, avoid need for subclass. * Use updated parser model * Remove incorrect call to model.initializre in PrecomputableAffine * Update parser model * Avoid divide by zero in tagger * Add extra dropout layer in tagger * Refine minibatch_by_words function to avoid oom * Fix parser model after refactor * Try to avoid div-by-zero in SimpleNER * Fix infinite loop in minibatch_by_words * Use SequenceCategoricalCrossentropy in Tagger * Fix parser model when hidden layer * Remove extra dropout from tagger * Add extra nan check in tagger * Fix thinc version * Update tests and imports * Fix test * Update test * Update tests * Fix tests * Fix test Co-authored-by: Ines Montani <ines@ines.io>
113 lines
4.0 KiB
Python
113 lines
4.0 KiB
Python
#!/usr/bin/env python
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# coding: utf8
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"""Example of training spaCy's named entity recognizer, starting off with an
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existing model or a blank model.
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For more details, see the documentation:
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* Training: https://spacy.io/usage/training
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* NER: https://spacy.io/usage/linguistic-features#named-entities
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Compatible with: spaCy v2.0.0+
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Last tested with: v2.1.0
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"""
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from __future__ import unicode_literals, print_function
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import plac
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import random
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from pathlib import Path
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import spacy
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from spacy.util import minibatch, compounding
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# training data
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TRAIN_DATA = [
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("Who is Shaka Khan?", {"entities": [(7, 17, "PERSON")]}),
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("I like London and Berlin.", {"entities": [(7, 13, "LOC"), (18, 24, "LOC")]}),
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]
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@plac.annotations(
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model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
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output_dir=("Optional output directory", "option", "o", Path),
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n_iter=("Number of training iterations", "option", "n", int),
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)
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def main(model=None, output_dir=None, n_iter=100):
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"""Load the model, set up the pipeline and train the entity recognizer."""
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if model is not None:
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nlp = spacy.load(model) # load existing spaCy model
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print("Loaded model '%s'" % model)
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else:
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nlp = spacy.blank("en") # create blank Language class
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print("Created blank 'en' model")
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# create the built-in pipeline components and add them to the pipeline
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# nlp.create_pipe works for built-ins that are registered with spaCy
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if "simple_ner" not in nlp.pipe_names:
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ner = nlp.create_pipe("simple_ner")
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nlp.add_pipe(ner, last=True)
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# otherwise, get it so we can add labels
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else:
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ner = nlp.get_pipe("simple_ner")
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# add labels
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for _, annotations in TRAIN_DATA:
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for ent in annotations.get("entities"):
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print("Add label", ent[2])
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ner.add_label(ent[2])
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# get names of other pipes to disable them during training
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pipe_exceptions = ["simple_ner"]
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
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with nlp.disable_pipes(*other_pipes): # only train NER
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# reset and initialize the weights randomly – but only if we're
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# training a new model
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if model is None:
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nlp.begin_training()
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print("Transitions", list(enumerate(nlp.get_pipe("simple_ner").get_tag_names())))
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for itn in range(n_iter):
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random.shuffle(TRAIN_DATA)
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losses = {}
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# batch up the examples using spaCy's minibatch
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batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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nlp.update(
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batch,
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drop=0.0, # dropout - make it harder to memorise data
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losses=losses,
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)
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print("Losses", losses)
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# test the trained model
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for text, _ in TRAIN_DATA:
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doc = nlp(text)
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print("Entities", [(ent.text, ent.label_) for ent in doc.ents])
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print("Tokens", [(t.text, t.ent_type_, t.ent_iob) for t in doc])
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# save model to output directory
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if output_dir is not None:
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output_dir = Path(output_dir)
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if not output_dir.exists():
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output_dir.mkdir()
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nlp.to_disk(output_dir)
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print("Saved model to", output_dir)
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# test the saved model
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print("Loading from", output_dir)
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nlp2 = spacy.load(output_dir)
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for text, _ in TRAIN_DATA:
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doc = nlp2(text)
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print("Entities", [(ent.text, ent.label_) for ent in doc.ents])
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print("Tokens", [(t.text, t.ent_type_, t.ent_iob) for t in doc])
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if __name__ == "__main__":
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plac.call(main)
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# Expected output:
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# Entities [('Shaka Khan', 'PERSON')]
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# Tokens [('Who', '', 2), ('is', '', 2), ('Shaka', 'PERSON', 3),
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# ('Khan', 'PERSON', 1), ('?', '', 2)]
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# Entities [('London', 'LOC'), ('Berlin', 'LOC')]
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# Tokens [('I', '', 2), ('like', '', 2), ('London', 'LOC', 3),
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# ('and', '', 2), ('Berlin', 'LOC', 3), ('.', '', 2)]
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