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Update training examples to use "simple style"
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@ -14,55 +14,49 @@ following types of relations: ROOT, PLACE, QUALITY, ATTRIBUTE, TIME, LOCATION.
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('best', 'QUALITY', 'hotel') --> hotel with QUALITY best
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('hotel', 'PLACE', 'show') --> show PLACE hotel
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('berlin', 'LOCATION', 'hotel') --> hotel with LOCATION berlin
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Developed for: spaCy 2.0.0a18
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Last updated for: spaCy 2.0.0a19
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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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import spacy
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from spacy.gold import GoldParse
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from spacy.tokens import Doc
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from pathlib import Path
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# training data: words, head and dependency labels
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# training data: texts, heads and dependency labels
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# for no relation, we simply chose an arbitrary dependency label, e.g. '-'
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TRAIN_DATA = [
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(
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['find', 'a', 'cafe', 'with', 'great', 'wifi'],
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[0, 2, 0, 5, 5, 2], # index of token head
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['ROOT', '-', 'PLACE', '-', 'QUALITY', 'ATTRIBUTE']
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),
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(
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['find', 'a', 'hotel', 'near', 'the', 'beach'],
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[0, 2, 0, 5, 5, 2],
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['ROOT', '-', 'PLACE', 'QUALITY', '-', 'ATTRIBUTE']
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),
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(
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['find', 'me', 'the', 'closest', 'gym', 'that', "'s", 'open', 'late'],
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[0, 0, 4, 4, 0, 6, 4, 6, 6],
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['ROOT', '-', '-', 'QUALITY', 'PLACE', '-', '-', 'ATTRIBUTE', 'TIME']
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),
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(
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['show', 'me', 'the', 'cheapest', 'store', 'that', 'sells', 'flowers'],
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[0, 0, 4, 4, 0, 4, 4, 4], # attach "flowers" to store!
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['ROOT', '-', '-', 'QUALITY', 'PLACE', '-', '-', 'PRODUCT']
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),
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(
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['find', 'a', 'nice', 'restaurant', 'in', 'london'],
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[0, 3, 3, 0, 3, 3],
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['ROOT', '-', 'QUALITY', 'PLACE', '-', 'LOCATION']
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),
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(
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['show', 'me', 'the', 'coolest', 'hostel', 'in', 'berlin'],
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[0, 0, 4, 4, 0, 4, 4],
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['ROOT', '-', '-', 'QUALITY', 'PLACE', '-', 'LOCATION']
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),
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(
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['find', 'a', 'good', 'italian', 'restaurant', 'near', 'work'],
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[0, 4, 4, 4, 0, 4, 5],
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['ROOT', '-', 'QUALITY', 'ATTRIBUTE', 'PLACE', 'ATTRIBUTE', 'LOCATION']
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)
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("find a cafe with great wifi", {
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'heads': [0, 2, 0, 5, 5, 2], # index of token head
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'deps': ['ROOT', '-', 'PLACE', '-', 'QUALITY', 'ATTRIBUTE']
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}),
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("find a hotel near the beach", {
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'heads': [0, 2, 0, 5, 5, 2],
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'deps': ['ROOT', '-', 'PLACE', 'QUALITY', '-', 'ATTRIBUTE']
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}),
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("find me the closest gym that's open late", {
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'heads': [0, 0, 4, 4, 0, 6, 4, 6, 6],
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'deps': ['ROOT', '-', '-', 'QUALITY', 'PLACE', '-', '-', 'ATTRIBUTE', 'TIME']
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}),
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("show me the cheapest store that sells flowers", {
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'heads': [0, 0, 4, 4, 0, 4, 4, 4], # attach "flowers" to store!
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'deps': ['ROOT', '-', '-', 'QUALITY', 'PLACE', '-', '-', 'PRODUCT']
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}),
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("find a nice restaurant in london", {
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'heads': [0, 3, 3, 0, 3, 3],
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'deps': ['ROOT', '-', 'QUALITY', 'PLACE', '-', 'LOCATION']
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}),
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("show me the coolest hostel in berlin", {
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'heads': [0, 0, 4, 4, 0, 4, 4],
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'deps': ['ROOT', '-', '-', 'QUALITY', 'PLACE', '-', 'LOCATION']
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}),
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("find a good italian restaurant near work", {
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'heads': [0, 4, 4, 4, 0, 4, 5],
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'deps': ['ROOT', '-', 'QUALITY', 'ATTRIBUTE', 'PLACE', 'ATTRIBUTE', 'LOCATION']
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})
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]
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@ -88,8 +82,8 @@ def main(model=None, output_dir=None, n_iter=100):
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else:
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parser = nlp.get_pipe('parser')
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for _, _, deps in TRAIN_DATA:
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for dep in deps:
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for text, annotations in TRAIN_DATA:
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for dep in annotations.get('deps', []):
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parser.add_label(dep)
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'parser']
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@ -98,10 +92,8 @@ def main(model=None, output_dir=None, n_iter=100):
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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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for words, heads, deps in TRAIN_DATA:
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doc = Doc(nlp.vocab, words=words)
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gold = GoldParse(doc, heads=heads, deps=deps)
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nlp.update([doc], [gold], sgd=optimizer, losses=losses)
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for text, annotations in TRAIN_DATA:
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nlp.update([text], [annotations], sgd=optimizer, losses=losses)
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print(losses)
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# test the trained model
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@ -147,6 +139,7 @@ if __name__ == '__main__':
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# ('find', 'ROOT', 'find'),
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# ('cheapest', 'QUALITY', 'gym'),
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# ('gym', 'PLACE', 'find')
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# ('work', 'LOCATION', 'near')
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# ]
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# show me the best hotel in berlin
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# [
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@ -8,22 +8,24 @@ For more details, see the documentation:
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* NER: https://alpha.spacy.io/usage/linguistic-features#named-entities
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Developed for: spaCy 2.0.0a18
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Last updated for: spaCy 2.0.0a18
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Last updated for: spaCy 2.0.0a19
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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.gold import GoldParse, biluo_tags_from_offsets
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# training data
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TRAIN_DATA = [
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('Who is Shaka Khan?', [(7, 17, 'PERSON')]),
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('I like London and Berlin.', [(7, 13, 'LOC'), (18, 24, 'LOC')])
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('Who is Shaka Khan?', {
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'entities': [(7, 17, 'PERSON')]
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}),
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('I like London and Berlin.', {
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'entities': [(7, 13, 'LOC'), (18, 24, 'LOC')]
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})
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]
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@ -45,25 +47,28 @@ def main(model=None, output_dir=None, n_iter=100):
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if 'ner' not in nlp.pipe_names:
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ner = nlp.create_pipe('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('ner')
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# function that allows begin_training to get the training data
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get_data = lambda: reformat_train_data(nlp.tokenizer, TRAIN_DATA)
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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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ner.add_label(ent[2])
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# get names of other pipes to disable them during training
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
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with nlp.disable_pipes(*other_pipes): # only train NER
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optimizer = nlp.begin_training(get_data)
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optimizer = nlp.begin_training()
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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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for raw_text, entity_offsets in TRAIN_DATA:
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doc = nlp.make_doc(raw_text)
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gold = GoldParse(doc, entities=entity_offsets)
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for text, annotations in TRAIN_DATA:
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nlp.update(
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[doc], # Batch of Doc objects
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[gold], # Batch of GoldParse objects
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drop=0.5, # Dropout -- make it harder to memorise data
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sgd=optimizer, # Callable to update weights
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[text], # batch of texts
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[annotations], # batch of annotations
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drop=0.5, # dropout - make it harder to memorise data
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sgd=optimizer, # callable to update weights
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losses=losses)
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print(losses)
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@ -90,25 +95,13 @@ def main(model=None, output_dir=None, n_iter=100):
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print('Tokens', [(t.text, t.ent_type_, t.ent_iob) for t in doc])
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def reformat_train_data(tokenizer, examples):
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"""Reformat data to match JSON format.
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https://alpha.spacy.io/api/annotation#json-input
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tokenizer (Tokenizer): Tokenizer to process the raw text.
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examples (list): The trainig data.
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RETURNS (list): The reformatted training data."""
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output = []
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for i, (text, entity_offsets) in enumerate(examples):
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doc = tokenizer(text)
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ner_tags = biluo_tags_from_offsets(tokenizer(text), entity_offsets)
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words = [w.text for w in doc]
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tags = ['-'] * len(doc)
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heads = [0] * len(doc)
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deps = [''] * len(doc)
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sentence = (range(len(doc)), words, tags, heads, deps, ner_tags)
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output.append((text, [(sentence, [])]))
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return output
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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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@ -24,16 +24,14 @@ For more details, see the documentation:
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* NER: https://alpha.spacy.io/usage/linguistic-features#named-entities
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Developed for: spaCy 2.0.0a18
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Last updated for: spaCy 2.0.0a18
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Last updated for: spaCy 2.0.0a19
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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.gold import GoldParse, minibatch
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# new entity label
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@ -45,20 +43,29 @@ LABEL = 'ANIMAL'
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# model might learn the new type, but "forget" what it previously knew.
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# https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting
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TRAIN_DATA = [
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("Horses are too tall and they pretend to care about your feelings",
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[(0, 6, 'ANIMAL')]),
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("Horses are too tall and they pretend to care about your feelings", {
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'entities': [(0, 6, 'ANIMAL')]
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}),
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("Do they bite?", []),
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("Do they bite?", {
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'entities': []
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}),
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("horses are too tall and they pretend to care about your feelings",
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[(0, 6, 'ANIMAL')]),
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("horses are too tall and they pretend to care about your feelings", {
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'entities': [(0, 6, 'ANIMAL')]
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}),
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("horses pretend to care about your feelings", [(0, 6, 'ANIMAL')]),
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("horses pretend to care about your feelings", {
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'entities': [(0, 6, 'ANIMAL')]
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}),
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("they pretend to care about your feelings, those horses",
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[(48, 54, 'ANIMAL')]),
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("they pretend to care about your feelings, those horses", {
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'entities': [(48, 54, 'ANIMAL')]
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}),
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("horses?", [(0, 6, 'ANIMAL')])
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("horses?", {
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'entities': [(0, 6, 'ANIMAL')]
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})
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]
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@ -90,15 +97,13 @@ def main(model=None, new_model_name='animal', output_dir=None, n_iter=50):
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# get names of other pipes to disable them during training
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
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with nlp.disable_pipes(*other_pipes): # only train NER
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random.seed(0)
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optimizer = nlp.begin_training()
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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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gold_parses = get_gold_parses(nlp.make_doc, TRAIN_DATA)
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for batch in minibatch(gold_parses, size=3):
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docs, golds = zip(*batch)
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nlp.update(docs, golds, losses=losses, sgd=optimizer,
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drop=0.35)
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for text, annotations in TRAIN_DATA:
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nlp.update([text], [annotations], sgd=optimizer, drop=0.35,
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losses=losses)
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print(losses)
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# test the trained model
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@ -125,19 +130,5 @@ def main(model=None, new_model_name='animal', output_dir=None, n_iter=50):
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print(ent.label_, ent.text)
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def get_gold_parses(tokenizer, train_data):
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"""Shuffle and create GoldParse objects.
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tokenizer (Tokenizer): Tokenizer to processs the raw text.
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train_data (list): The training data.
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YIELDS (tuple): (doc, gold) tuples.
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"""
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random.shuffle(train_data)
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for raw_text, entity_offsets in train_data:
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doc = tokenizer(raw_text)
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gold = GoldParse(doc, entities=entity_offsets)
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yield doc, gold
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if __name__ == '__main__':
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plac.call(main)
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@ -13,24 +13,19 @@ 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.gold import GoldParse
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from spacy.tokens import Doc
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# training data
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TRAIN_DATA = [
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(
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['They', 'trade', 'mortgage', '-', 'backed', 'securities', '.'],
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[1, 1, 4, 4, 5, 1, 1],
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['nsubj', 'ROOT', 'compound', 'punct', 'nmod', 'dobj', 'punct']
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),
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(
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['I', 'like', 'London', 'and', 'Berlin', '.'],
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[1, 1, 1, 2, 2, 1],
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['nsubj', 'ROOT', 'dobj', 'cc', 'conj', 'punct']
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)
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("They trade mortgage-backed securities.", {
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'heads': [1, 1, 4, 4, 5, 1, 1],
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'deps': ['nsubj', 'ROOT', 'compound', 'punct', 'nmod', 'dobj', 'punct']
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}),
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("I like London and Berlin", {
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'heads': [1, 1, 1, 2, 2, 1],
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'deps': ['nsubj', 'ROOT', 'dobj', 'cc', 'conj', 'punct']
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})
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]
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@ -38,7 +33,7 @@ TRAIN_DATA = [
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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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def main(model=None, output_dir=None, n_iter=1000):
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def main(model=None, output_dir=None, n_iter=10):
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"""Load the model, set up the pipeline and train the parser."""
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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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@ -57,8 +52,8 @@ def main(model=None, output_dir=None, n_iter=1000):
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parser = nlp.get_pipe('parser')
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# add labels to the parser
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for _, _, deps in TRAIN_DATA:
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for dep in deps:
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for _, annotations in TRAIN_DATA:
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for dep in annotations.get('deps', []):
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parser.add_label(dep)
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# get names of other pipes to disable them during training
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@ -68,10 +63,8 @@ def main(model=None, output_dir=None, n_iter=1000):
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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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for words, heads, deps in TRAIN_DATA:
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doc = Doc(nlp.vocab, words=words)
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gold = GoldParse(doc, heads=heads, deps=deps)
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nlp.update([doc], [gold], sgd=optimizer, losses=losses)
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for text, annotations in TRAIN_DATA:
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nlp.update([text], [annotations], sgd=optimizer, losses=losses)
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print(losses)
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# test the trained model
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@ -9,17 +9,14 @@ the documentation:
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* POS Tagging: https://alpha.spacy.io/usage/linguistic-features#pos-tagging
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Developed for: spaCy 2.0.0a18
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Last updated for: spaCy 2.0.0a18
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Last updated for: spaCy 2.0.0a19
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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.tokens import Doc
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from spacy.gold import GoldParse
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# You need to define a mapping from your data's part-of-speech tag names to the
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@ -29,16 +26,16 @@ from spacy.gold import GoldParse
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# You may also specify morphological features for your tags, from the universal
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# scheme.
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TAG_MAP = {
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'N': {"pos": "NOUN"},
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'V': {"pos": "VERB"},
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'J': {"pos": "ADJ"}
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'N': {'pos': 'NOUN'},
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'V': {'pos': 'VERB'},
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'J': {'pos': 'ADJ'}
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}
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# Usually you'll read this in, of course. Data formats vary.
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# Ensure your strings are unicode.
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TRAIN_DATA = [
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(["I", "like", "green", "eggs"], ["N", "V", "J", "N"]),
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(["Eat", "blue", "ham"], ["V", "J", "N"])
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("I like green eggs", {'tags': ['N', 'V', 'J', 'N']}),
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("Eat blue ham", {'tags': ['V', 'J', 'N']})
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]
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|
@ -64,10 +61,8 @@ def main(lang='en', output_dir=None, n_iter=25):
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for i in range(n_iter):
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random.shuffle(TRAIN_DATA)
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losses = {}
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for words, tags in TRAIN_DATA:
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doc = Doc(nlp.vocab, words=words)
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gold = GoldParse(doc, tags=tags)
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nlp.update([doc], [gold], sgd=optimizer, losses=losses)
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for text, annotations in TRAIN_DATA:
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nlp.update([text], [annotations], sgd=optimizer, losses=losses)
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print(losses)
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# test the trained model
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|
|
|
@ -9,7 +9,7 @@ see the documentation:
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* Text classification: https://alpha.spacy.io/usage/text-classification
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||||
|
||||
Developed for: spaCy 2.0.0a18
|
||||
Last updated for: spaCy 2.0.0a18
|
||||
Last updated for: spaCy 2.0.0a19
|
||||
"""
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||||
from __future__ import unicode_literals, print_function
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import plac
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||||
|
@ -18,9 +18,8 @@ from pathlib import Path
|
|||
import thinc.extra.datasets
|
||||
|
||||
import spacy
|
||||
from spacy.gold import GoldParse, minibatch
|
||||
from spacy.gold import minibatch
|
||||
from spacy.util import compounding
|
||||
from spacy.pipeline import TextCategorizer
|
||||
|
||||
|
||||
@plac.annotations(
|
||||
|
@ -52,10 +51,8 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000):
|
|||
print("Loading IMDB data...")
|
||||
(train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=n_texts)
|
||||
print("Using %d training examples" % n_texts)
|
||||
train_docs = [nlp.tokenizer(text) for text in train_texts]
|
||||
train_gold = [GoldParse(doc, cats=cats) for doc, cats in
|
||||
zip(train_docs, train_cats)]
|
||||
train_data = list(zip(train_docs, train_gold))
|
||||
train_data = list(zip(train_texts,
|
||||
[{'cats': cats} for cats in train_cats]))
|
||||
|
||||
# get names of other pipes to disable them during training
|
||||
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'textcat']
|
||||
|
@ -68,8 +65,9 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000):
|
|||
# batch up the examples using spaCy's minibatch
|
||||
batches = minibatch(train_data, size=compounding(4., 32., 1.001))
|
||||
for batch in batches:
|
||||
docs, golds = zip(*batch)
|
||||
nlp.update(docs, golds, sgd=optimizer, drop=0.2, losses=losses)
|
||||
texts, annotations = zip(*batch)
|
||||
nlp.update(texts, annotations, sgd=optimizer, drop=0.2,
|
||||
losses=losses)
|
||||
with textcat.model.use_params(optimizer.averages):
|
||||
# evaluate on the dev data split off in load_data()
|
||||
scores = evaluate(nlp.tokenizer, textcat, dev_texts, dev_cats)
|
||||
|
|
Loading…
Reference in New Issue
Block a user