mirror of https://github.com/explosion/spaCy.git
Add script to do conllu training
This commit is contained in:
Matthew Honnibal
parent
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'''Train for CONLL 2017 UD treebank evaluation. Takes .conllu files, writes
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.conllu format for development data, allowing the official scorer to be used.
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'''
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from __future__ import unicode_literals
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import plac
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import tqdm
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import re
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import spacy
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import spacy.util
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from spacy.gold import GoldParse, minibatch
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from spacy.syntax.nonproj import projectivize
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from collections import Counter
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from timeit import default_timer as timer
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from spacy._align import align
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def prevent_bad_sentences(doc):
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'''This is an example pipeline component for fixing sentence segmentation
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mistakes. The component sets is_sent_start to False, which means the
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parser will be prevented from making a sentence boundary there. The
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rules here aren't necessarily a good idea.'''
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for token in doc[1:]:
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if token.nbor(-1).text == ',':
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token.is_sent_start = False
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elif not token.nbor(-1).whitespace_:
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token.is_sent_start = False
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elif not token.nbor(-1).is_punct:
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token.is_sent_start = False
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return doc
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def load_model(lang):
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'''This shows how to adjust the tokenization rules, to special-case
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for ways the CoNLLU tokenization differs. We need to get the tokenizer
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accuracy high on the various treebanks in order to do well. If we don't
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align on a content word, all dependencies to and from that word will
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be marked as incorrect.
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'''
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English = spacy.util.get_lang_class(lang)
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English.Defaults.infixes += ('(?<=[^-\d])[+\-\*^](?=[^-\d])',)
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English.Defaults.infixes += ('(?<=[^-])[+\-\*^](?=[^-\d])',)
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English.Defaults.infixes += ('(?<=[^-\d])[+\-\*^](?=[^-])',)
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English.Defaults.token_match = re.compile(r'=+').match
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nlp = English()
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nlp.tokenizer.add_special_case('***', [{'ORTH': '***'}])
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nlp.tokenizer.add_special_case("):", [{'ORTH': ")"}, {"ORTH": ":"}])
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nlp.tokenizer.add_special_case("and/or", [{'ORTH': "and"}, {"ORTH": "/"}, {"ORTH": "or"}])
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nlp.tokenizer.add_special_case("non-Microsoft", [{'ORTH': "non-Microsoft"}])
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nlp.tokenizer.add_special_case("mis-matches", [{'ORTH': "mis-matches"}])
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nlp.tokenizer.add_special_case("X.", [{'ORTH': "X"}, {"ORTH": "."}])
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nlp.tokenizer.add_special_case("b/c", [{'ORTH': "b/c"}])
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return nlp
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def get_token_acc(docs, golds):
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'''Quick function to evaluate tokenization accuracy.'''
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miss = 0
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hit = 0
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for doc, gold in zip(docs, golds):
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for i in range(len(doc)):
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token = doc[i]
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align = gold.words[i]
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if align == None:
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miss += 1
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else:
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hit += 1
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return miss, hit
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def golds_to_gold_tuples(docs, golds):
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'''Get out the annoying 'tuples' format used by begin_training, given the
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GoldParse objects.'''
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tuples = []
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for doc, gold in zip(docs, golds):
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text = doc.text
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ids, words, tags, heads, labels, iob = zip(*gold.orig_annot)
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sents = [((ids, words, tags, heads, labels, iob), [])]
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tuples.append((text, sents))
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return tuples
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def split_text(text):
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paragraphs = text.split('\n\n')
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paragraphs = [par.strip().replace('\n', ' ') for par in paragraphs]
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return paragraphs
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def read_conllu(file_):
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docs = []
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doc = []
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sent = []
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for line in file_:
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if line.startswith('# newdoc'):
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if doc:
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docs.append(doc)
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doc = []
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elif line.startswith('#'):
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continue
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elif not line.strip():
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if sent:
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doc.append(sent)
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sent = []
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else:
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sent.append(line.strip().split())
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if sent:
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doc.append(sent)
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if doc:
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docs.append(doc)
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return docs
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def get_docs(nlp, text):
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paragraphs = split_text(text)
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docs = [nlp.make_doc(par) for par in paragraphs]
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return docs
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def get_golds(docs, conllu):
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# sd is spacy doc; cd is conllu doc
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# cs is conllu sent, ct is conllu token
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golds = []
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for sd, cd in zip(docs, conllu):
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words = []
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tags = []
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heads = []
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deps = []
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for cs in cd:
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for id_, word, lemma, pos, tag, morph, head, dep, _1, _2 in cs:
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if '.' in id_:
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continue
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i = len(words)
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id_ = int(id_)-1
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head = int(head)-1 if head != '0' else id_
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head_dist = head - id_
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words.append(word)
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tags.append(tag)
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heads.append(i+head_dist)
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deps.append('ROOT' if dep == 'root' else dep)
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heads, deps = projectivize(heads, deps)
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entities = ['-'] * len(words)
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gold = GoldParse(sd, words=words, tags=tags, heads=heads, deps=deps,
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entities=entities)
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golds.append(gold)
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return golds
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def parse_dev_data(nlp, text_loc, conllu_loc):
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with open(text_loc) as file_:
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docs = get_docs(nlp, file_.read())
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with open(conllu_loc) as file_:
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conllu_dev = read_conllu(file_)
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golds = list(get_golds(docs, conllu_dev))
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scorer = nlp.evaluate(zip(docs, golds))
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return docs, scorer
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def print_progress(itn, losses, scores):
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scores = {}
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for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc',
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'ents_p', 'ents_r', 'ents_f', 'cpu_wps', 'gpu_wps']:
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scores[col] = 0.0
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scores['dep_loss'] = losses.get('parser', 0.0)
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scores['ner_loss'] = losses.get('ner', 0.0)
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scores['tag_loss'] = losses.get('tagger', 0.0)
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scores.update(scorer.scores)
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tpl = '\t'.join((
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'{:d}',
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'{dep_loss:.3f}',
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'{ner_loss:.3f}',
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'{uas:.3f}',
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'{ents_p:.3f}',
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'{ents_r:.3f}',
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'{ents_f:.3f}',
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'{tags_acc:.3f}',
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'{token_acc:.3f}',
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))
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print(tpl.format(itn, **scores))
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def print_conllu(docs, file_):
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for i, doc in enumerate(docs):
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file_.write("# newdoc id = {i}\n".format(i=i))
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for j, sent in enumerate(doc.sents):
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file_.write("# sent_id = {i}.{j}\n".format(i=i, j=j))
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file_.write("# text = {text}\n".format(text=sent.text))
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for k, t in enumerate(sent):
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if t.head.i == t.i:
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head = 0
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else:
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head = k + (t.head.i - t.i) + 1
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fields = [str(k+1), t.text, t.lemma_, t.pos_, t.tag_, '_', str(head), t.dep_, '_', '_']
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file_.write('\t'.join(fields) + '\n')
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file_.write('\n')
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def main(spacy_model, conllu_train_loc, text_train_loc, conllu_dev_loc, text_dev_loc,
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output_loc):
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with open(conllu_train_loc) as file_:
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conllu_train = read_conllu(file_)
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nlp = load_model(spacy_model)
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print("Get docs")
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with open(text_train_loc) as file_:
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docs = get_docs(nlp, file_.read())
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golds = list(get_golds(docs, conllu_train))
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print("Create parser")
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nlp.add_pipe(nlp.create_pipe('parser'))
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nlp.add_pipe(nlp.create_pipe('tagger'))
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for gold in golds:
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for tag in gold.tags:
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if tag is not None:
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nlp.tagger.add_label(tag)
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optimizer = nlp.begin_training(lambda: golds_to_gold_tuples(docs, golds))
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n_train_words = sum(len(doc) for doc in docs)
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print(n_train_words)
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print("Begin training")
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for i in range(10):
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with open(text_train_loc) as file_:
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docs = get_docs(nlp, file_.read())
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docs = docs[:len(golds)]
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with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
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losses = {}
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for batch in minibatch(list(zip(docs, golds)), size=1):
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if not batch:
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continue
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batch_docs, batch_gold = zip(*batch)
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nlp.update(batch_docs, batch_gold, sgd=optimizer,
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drop=0.2, losses=losses)
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pbar.update(sum(len(doc) for doc in batch_docs))
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with nlp.use_params(optimizer.averages):
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dev_docs, scorer = parse_dev_data(nlp, text_dev_loc, conllu_dev_loc)
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print_progress(i, losses, scorer.scores)
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with open(output_loc, 'w') as file_:
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print_conllu(dev_docs, file_)
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if __name__ == '__main__':
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plac.call(main)
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