def parser(vocab): vocab.strings.add("ROOT") config = { "learn_tokens": False, "min_action_freq": 30, "update_with_oracle_cut_size": 100, } cfg = {"model": DEFAULT_PARSER_MODEL} model = registry.resolve(cfg, validate=True)["model"] parser = DependencyParser(vocab, model, **config) parser.cfg["token_vector_width"] = 4 parser.cfg["hidden_width"] = 32 # parser.add_label('right') parser.add_label("left") parser.initialize(lambda: [_parser_example(parser)]) sgd = Adam(0.001) for i in range(10): losses = {} doc = Doc(vocab, words=["a", "b", "c", "d"]) example = Example.from_dict(doc, { "heads": [1, 1, 3, 3], "deps": ["left", "ROOT", "left", "ROOT"] }) parser.update([example], sgd=sgd, losses=losses) return parser
def main(train_loc, dev_loc, model_dir, tag_map_loc=None): if tag_map_loc: with open(tag_map_loc) as file_: tag_map = json.loads(file_.read()) else: tag_map = DEFAULT_TAG_MAP train_sents = list(read_conllx(train_loc)) train_sents = PseudoProjectivity.preprocess_training_data(train_sents) actions = ArcEager.get_actions(gold_parses=train_sents) features = get_templates('basic') model_dir = pathlib.Path(model_dir) if not (model_dir / 'deps').exists(): (model_dir / 'deps').mkdir() with (model_dir / 'deps' / 'config.json').open('wb') as file_: file_.write( json.dumps( {'pseudoprojective': True, 'labels': actions, 'features': features}).encode('utf8')) vocab = Vocab(lex_attr_getters=Language.Defaults.lex_attr_getters, tag_map=tag_map) # Populate vocab for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: for word in words: _ = vocab[word] for dep in deps: _ = vocab[dep] for tag in tags: _ = vocab[tag] if tag_map: for tag in tags: assert tag in tag_map, repr(tag) tagger = Tagger(vocab, tag_map=tag_map) parser = DependencyParser(vocab, actions=actions, features=features) for itn in range(15): for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: doc = Doc(vocab, words=words) gold = GoldParse(doc, tags=tags, heads=heads, deps=deps) tagger(doc) parser.update(doc, gold) doc = Doc(vocab, words=words) tagger.update(doc, gold) random.shuffle(train_sents) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f' % (itn, scorer.uas, scorer.tags_acc)) nlp = Language(vocab=vocab, tagger=tagger, parser=parser) nlp.end_training(model_dir) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
def parser(vocab): parser = DependencyParser(vocab) parser.cfg["token_vector_width"] = 4 parser.cfg["hidden_width"] = 32 # parser.add_label('right') parser.add_label("left") parser.begin_training([], **parser.cfg) sgd = Adam(NumpyOps(), 0.001) for i in range(10): losses = {} doc = Doc(vocab, words=["a", "b", "c", "d"]) gold = GoldParse(doc, heads=[1, 1, 3, 3], deps=["left", "ROOT", "left", "ROOT"]) parser.update([doc], [gold], sgd=sgd, losses=losses) return parser
def parser(vocab): parser = DependencyParser(vocab) parser.cfg["token_vector_width"] = 4 parser.cfg["hidden_width"] = 32 # parser.add_label('right') parser.add_label("left") parser.begin_training([], **parser.cfg) sgd = Adam(NumpyOps(), 0.001) for i in range(10): losses = {} doc = Doc(vocab, words=["a", "b", "c", "d"]) gold = GoldParse(doc, heads=[1, 1, 3, 3], deps=["left", "ROOT", "left", "ROOT"]) parser.update([doc], [gold], sgd=sgd, losses=losses) return parser
def main(train_loc, dev_loc, model_dir, tag_map_loc): with open(tag_map_loc) as file_: tag_map = json.loads(file_.read()) train_sents = list(read_conllx(train_loc)) train_sents = PseudoProjectivity.preprocess_training_data(train_sents) actions = ArcEager.get_actions(gold_parses=train_sents) features = get_templates('basic') model_dir = pathlib.Path(model_dir) with (model_dir / 'deps' / 'config.json').open('w') as file_: json.dump({'pseudoprojective': True, 'labels': actions, 'features': features}, file_) vocab = Vocab(lex_attr_getters=Language.Defaults.lex_attr_getters, tag_map=tag_map) # Populate vocab for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: for word in words: _ = vocab[word] for dep in deps: _ = vocab[dep] for tag in tags: _ = vocab[tag] for tag in tags: assert tag in tag_map, repr(tag) tagger = Tagger(vocab, tag_map=tag_map) parser = DependencyParser(vocab, actions=actions, features=features) for itn in range(15): for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: doc = Doc(vocab, words=words) gold = GoldParse(doc, tags=tags, heads=heads, deps=deps) tagger(doc) parser.update(doc, gold) doc = Doc(vocab, words=words) tagger.update(doc, gold) random.shuffle(train_sents) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f' % (itn, scorer.uas, scorer.tags_acc)) nlp = Language(vocab=vocab, tagger=tagger, parser=parser) nlp.end_training(model_dir) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
def train_parser(nlp, train_data, left_labels, right_labels): parser = DependencyParser(nlp.vocab, left_labels=left_labels, right_labels=right_labels) for itn in range(1000): random.shuffle(train_data) loss = 0 for words, heads, deps in train_data: doc = Doc(nlp.vocab, words=words) gold = GoldParse(doc, heads=heads, deps=deps) loss += parser.update(doc, gold) parser.model.end_training() return parser
def train_parser(nlp, train_data, left_labels, right_labels): parser = DependencyParser( nlp.vocab, left_labels=left_labels, right_labels=right_labels) for itn in range(1000): random.shuffle(train_data) loss = 0 for words, heads, deps in train_data: doc = Doc(nlp.vocab, words=words) gold = GoldParse(doc, heads=heads, deps=deps) loss += parser.update(doc, gold) parser.model.end_training() return parser
def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None): LangClass = spacy.util.get_lang_class(lang_name) train_sents = list(read_conllx(train_loc)) train_sents = PseudoProjectivity.preprocess_training_data(train_sents) actions = ArcEager.get_actions(gold_parses=train_sents) features = get_templates('basic') model_dir = pathlib.Path(model_dir) if not model_dir.exists(): model_dir.mkdir() if not (model_dir / 'deps').exists(): (model_dir / 'deps').mkdir() if not (model_dir / 'pos').exists(): (model_dir / 'pos').mkdir() with (model_dir / 'deps' / 'config.json').open('wb') as file_: file_.write( json.dumps( {'pseudoprojective': True, 'labels': actions, 'features': features}).encode('utf8')) vocab = LangClass.Defaults.create_vocab() if not (model_dir / 'vocab').exists(): (model_dir / 'vocab').mkdir() else: if (model_dir / 'vocab' / 'strings.json').exists(): with (model_dir / 'vocab' / 'strings.json').open() as file_: vocab.strings.load(file_) if (model_dir / 'vocab' / 'lexemes.bin').exists(): vocab.load_lexemes(model_dir / 'vocab' / 'lexemes.bin') if clusters_loc is not None: clusters_loc = pathlib.Path(clusters_loc) with clusters_loc.open() as file_: for line in file_: try: cluster, word, freq = line.split() except ValueError: continue lex = vocab[word] lex.cluster = int(cluster[::-1], 2) # Populate vocab for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: for word in words: _ = vocab[word] for dep in deps: _ = vocab[dep] for tag in tags: _ = vocab[tag] if vocab.morphology.tag_map: for tag in tags: assert tag in vocab.morphology.tag_map, repr(tag) tagger = Tagger(vocab) parser = DependencyParser(vocab, actions=actions, features=features, L1=0.0) for itn in range(30): loss = 0. for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: doc = Doc(vocab, words=words) gold = GoldParse(doc, tags=tags, heads=heads, deps=deps) tagger(doc) loss += parser.update(doc, gold, itn=itn) doc = Doc(vocab, words=words) tagger.update(doc, gold) random.shuffle(train_sents) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.tags_acc)) nlp = LangClass(vocab=vocab, tagger=tagger, parser=parser) nlp.end_training(model_dir) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None): LangClass = spacy.util.get_lang_class(lang_name) train_sents = list(read_conllx(train_loc)) train_sents = PseudoProjectivity.preprocess_training_data(train_sents) actions = ArcEager.get_actions(gold_parses=train_sents) features = get_templates('basic') model_dir = pathlib.Path(model_dir) if not model_dir.exists(): model_dir.mkdir() if not (model_dir / 'deps').exists(): (model_dir / 'deps').mkdir() if not (model_dir / 'pos').exists(): (model_dir / 'pos').mkdir() with (model_dir / 'deps' / 'config.json').open('wb') as file_: file_.write( json.dumps( {'pseudoprojective': True, 'labels': actions, 'features': features}).encode('utf8')) vocab = LangClass.Defaults.create_vocab() if not (model_dir / 'vocab').exists(): (model_dir / 'vocab').mkdir() else: if (model_dir / 'vocab' / 'strings.json').exists(): with (model_dir / 'vocab' / 'strings.json').open() as file_: vocab.strings.load(file_) if (model_dir / 'vocab' / 'lexemes.bin').exists(): vocab.load_lexemes(model_dir / 'vocab' / 'lexemes.bin') if clusters_loc is not None: clusters_loc = pathlib.Path(clusters_loc) with clusters_loc.open() as file_: for line in file_: try: cluster, word, freq = line.split() except ValueError: continue lex = vocab[word] lex.cluster = int(cluster[::-1], 2) # Populate vocab for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: for word in words: _ = vocab[word] for dep in deps: _ = vocab[dep] for tag in tags: _ = vocab[tag] if vocab.morphology.tag_map: for tag in tags: assert tag in vocab.morphology.tag_map, repr(tag) tagger = Tagger(vocab) parser = DependencyParser(vocab, actions=actions, features=features, L1=0.0) for itn in range(30): loss = 0. for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: doc = Doc(vocab, words=words) gold = GoldParse(doc, tags=tags, heads=heads, deps=deps) tagger(doc) loss += parser.update(doc, gold, itn=itn) doc = Doc(vocab, words=words) tagger.update(doc, gold) random.shuffle(train_sents) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.tags_acc)) nlp = Language(vocab=vocab, tagger=tagger, parser=parser) nlp.end_training(model_dir) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))