mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-24 17:06:29 +03:00
Get data flowing through pipeline. Needs redesign
This commit is contained in:
Matthew Honnibal
parent
1d7c18e58a
commit
5211645af3
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@ -135,7 +135,7 @@ def Tok2Vec(width, embed_size, preprocess=None):
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>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
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>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
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)
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if preprocess is not None:
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if preprocess not in (False, None):
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tok2vec = preprocess >> tok2vec
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# Work around thinc API limitations :(. TODO: Revise in Thinc 7
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tok2vec.nO = width
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@ -41,8 +41,7 @@ def train(language, output_dir, train_data, dev_data, n_iter, tagger, parser, ne
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gold_train = list(read_gold_json(train_path))
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gold_dev = list(read_gold_json(dev_path)) if dev_path else None
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train_model(lang, gold_train, gold_dev, output_path, tagger_cfg, parser_cfg,
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entity_cfg, n_iter)
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train_model(lang, gold_train, gold_dev, output_path, n_iter)
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if gold_dev:
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scorer = evaluate(lang, gold_dev, output_path)
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print_results(scorer)
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@ -58,24 +57,30 @@ def train_config(config):
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prints("%s not found in config file." % setting, title="Missing setting")
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def train_model(Language, train_data, dev_data, output_path, tagger_cfg, parser_cfg,
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entity_cfg, n_iter):
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def train_model(Language, train_data, dev_data, output_path, n_iter, **cfg):
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print("Itn.\tN weight\tN feats\tUAS\tNER F.\tTag %\tToken %")
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with Language.train(output_path, train_data,
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pos=tagger_cfg, deps=parser_cfg, ner=entity_cfg) as trainer:
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nlp = Language(pipeline=['tensor', 'dependencies', 'entities'])
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for itn, epoch in enumerate(trainer.epochs(n_iter, augment_data=None)):
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for docs, golds in partition_all(12, epoch):
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trainer.update(docs, golds)
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# TODO: Get spaCy using Thinc's trainer and optimizer
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with nlp.begin_training(train_data, **cfg) as (trainer, optimizer):
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for itn, epoch in enumerate(trainer.epochs(n_iter)):
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losses = defaultdict(float)
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for docs, golds in epoch:
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grads = {}
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def get_grads(W, dW, key=None):
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grads[key] = (W, dW)
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for proc in nlp.pipeline:
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loss = proc.update(docs, golds, drop=0.0, sgd=get_grads)
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losses[proc.name] += loss
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for key, (W, dW) in grads.items():
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optimizer(W, dW, key=key)
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if dev_data:
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dev_scores = trainer.evaluate(dev_data).scores
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else:
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defaultdict(float)
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print_progress(itn, trainer.nlp.parser.model.nr_weight,
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trainer.nlp.parser.model.nr_active_feat,
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**dev_scores)
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print_progress(itn, losses['dep'], **dev_scores)
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def evaluate(Language, gold_tuples, output_path):
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@ -11,7 +11,8 @@ from .lemmatizer import Lemmatizer
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from .train import Trainer
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from .syntax.parser import get_templates
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from .syntax.nonproj import PseudoProjectivity
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from .pipeline import DependencyParser, EntityRecognizer
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from .pipeline import DependencyParser, NeuralDependencyParser, EntityRecognizer
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from .pipeline import TokenVectorEncoder, NeuralEntityRecognizer
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from .syntax.arc_eager import ArcEager
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from .syntax.ner import BiluoPushDown
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from .compat import json_dumps
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@ -31,111 +32,49 @@ class BaseDefaults(object):
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@classmethod
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def create_vocab(cls, nlp=None):
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lemmatizer = cls.create_lemmatizer(nlp)
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if nlp is None or nlp.path is None:
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lex_attr_getters = dict(cls.lex_attr_getters)
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# This is very messy, but it's the minimal working fix to Issue #639.
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# This defaults stuff needs to be refactored (again)
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lex_attr_getters[IS_STOP] = lambda string: string.lower() in cls.stop_words
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vocab = Vocab(lex_attr_getters=lex_attr_getters, tag_map=cls.tag_map,
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lemmatizer=lemmatizer)
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else:
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vocab = Vocab.load(nlp.path, lex_attr_getters=cls.lex_attr_getters,
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tag_map=cls.tag_map, lemmatizer=lemmatizer)
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lex_attr_getters = dict(cls.lex_attr_getters)
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# This is messy, but it's the minimal working fix to Issue #639.
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lex_attr_getters[IS_STOP] = lambda string: string.lower() in cls.stop_words
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vocab = Vocab(lex_attr_getters=lex_attr_getters, tag_map=cls.tag_map,
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lemmatizer=lemmatizer)
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for tag_str, exc in cls.morph_rules.items():
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for orth_str, attrs in exc.items():
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vocab.morphology.add_special_case(tag_str, orth_str, attrs)
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return vocab
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@classmethod
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def add_vectors(cls, nlp=None):
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if nlp is None or nlp.path is None:
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return False
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else:
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vec_path = nlp.path / 'vocab' / 'vec.bin'
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if vec_path.exists():
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return lambda vocab: vocab.load_vectors_from_bin_loc(vec_path)
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@classmethod
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def create_tokenizer(cls, nlp=None):
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rules = cls.tokenizer_exceptions
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if cls.token_match:
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token_match = cls.token_match
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if cls.prefixes:
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prefix_search = util.compile_prefix_regex(cls.prefixes).search
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else:
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prefix_search = None
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if cls.suffixes:
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suffix_search = util.compile_suffix_regex(cls.suffixes).search
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else:
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suffix_search = None
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if cls.infixes:
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infix_finditer = util.compile_infix_regex(cls.infixes).finditer
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else:
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infix_finditer = None
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token_match = cls.token_match
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prefix_search = util.compile_prefix_regex(cls.prefixes).search \
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if cls.prefixes else None
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suffix_search = util.compile_suffix_regex(cls.suffixes).search \
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if cls.suffixes else None
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infix_finditer = util.compile_infix_regex(cls.infixes).finditer \
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if cls.infixes else None
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vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp)
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return Tokenizer(vocab, rules=rules,
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prefix_search=prefix_search, suffix_search=suffix_search,
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infix_finditer=infix_finditer, token_match=token_match)
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@classmethod
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def create_tagger(cls, nlp=None):
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if nlp is None:
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return Tagger(cls.create_vocab(), features=cls.tagger_features)
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elif nlp.path is False:
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return Tagger(nlp.vocab, features=cls.tagger_features)
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elif nlp.path is None or not (nlp.path / 'pos').exists():
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return None
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else:
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return Tagger.load(nlp.path / 'pos', nlp.vocab)
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@classmethod
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def create_parser(cls, nlp=None, **cfg):
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if nlp is None:
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return DependencyParser(cls.create_vocab(), features=cls.parser_features,
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**cfg)
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elif nlp.path is False:
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return DependencyParser(nlp.vocab, features=cls.parser_features, **cfg)
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elif nlp.path is None or not (nlp.path / 'deps').exists():
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return None
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else:
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return DependencyParser.load(nlp.path / 'deps', nlp.vocab, **cfg)
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@classmethod
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def create_entity(cls, nlp=None, **cfg):
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if nlp is None:
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return EntityRecognizer(cls.create_vocab(), features=cls.entity_features, **cfg)
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elif nlp.path is False:
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return EntityRecognizer(nlp.vocab, features=cls.entity_features, **cfg)
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elif nlp.path is None or not (nlp.path / 'ner').exists():
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return None
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else:
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return EntityRecognizer.load(nlp.path / 'ner', nlp.vocab, **cfg)
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@classmethod
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def create_matcher(cls, nlp=None):
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if nlp is None:
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return Matcher(cls.create_vocab())
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elif nlp.path is False:
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return Matcher(nlp.vocab)
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elif nlp.path is None or not (nlp.path / 'vocab').exists():
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return None
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else:
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return Matcher.load(nlp.path / 'vocab', nlp.vocab)
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@classmethod
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def create_pipeline(self, nlp=None):
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def create_pipeline(cls, nlp=None):
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meta = nlp.meta if nlp is not None else {}
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# Resolve strings, like "cnn", "lstm", etc
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pipeline = []
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if nlp is None:
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return []
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if nlp.tagger:
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pipeline.append(nlp.tagger)
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if nlp.parser:
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pipeline.append(nlp.parser)
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pipeline.append(PseudoProjectivity.deprojectivize)
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if nlp.entity:
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pipeline.append(nlp.entity)
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for entry in cls.pipeline:
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factory = cls.Defaults.factories[entry]
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pipeline.append(factory(self, **meta.get(entry, {})))
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return pipeline
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factories = {
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'make_doc': create_tokenizer,
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'tensor': lambda nlp, **cfg: TokenVectorEncoder(nlp.vocab, **cfg),
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'tags': lambda nlp, **cfg: Tagger(nlp.vocab, **cfg),
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'dependencies': lambda nlp, **cfg: NeuralDependencyParser(nlp.vocab, **cfg),
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'entities': lambda nlp, **cfg: NeuralEntityRecognizer(nlp.vocab, **cfg),
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}
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token_match = TOKEN_MATCH
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prefixes = tuple(TOKENIZER_PREFIXES)
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suffixes = tuple(TOKENIZER_SUFFIXES)
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@ -161,120 +100,30 @@ class Language(object):
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Defaults = BaseDefaults
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lang = None
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@classmethod
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def setup_directory(cls, path, **configs):
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"""
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Initialise a model directory.
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"""
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for name, config in configs.items():
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directory = path / name
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if directory.exists():
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shutil.rmtree(str(directory))
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directory.mkdir()
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with (directory / 'config.json').open('w') as file_:
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data = json_dumps(config)
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file_.write(data)
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if not (path / 'vocab').exists():
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(path / 'vocab').mkdir()
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def __init__(self, vocab=True, make_doc=True, pipeline=None, meta={}):
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self.meta = dict(meta)
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@classmethod
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@contextmanager
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def train(cls, path, gold_tuples, **configs):
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parser_cfg = configs.get('deps', {})
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if parser_cfg.get('pseudoprojective'):
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# preprocess training data here before ArcEager.get_labels() is called
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gold_tuples = PseudoProjectivity.preprocess_training_data(gold_tuples)
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for subdir in ('deps', 'ner', 'pos'):
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if subdir not in configs:
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configs[subdir] = {}
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if parser_cfg:
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configs['deps']['actions'] = ArcEager.get_actions(gold_parses=gold_tuples)
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if 'ner' in configs:
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configs['ner']['actions'] = BiluoPushDown.get_actions(gold_parses=gold_tuples)
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cls.setup_directory(path, **configs)
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self = cls(
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path=path,
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vocab=False,
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tokenizer=False,
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tagger=False,
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parser=False,
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entity=False,
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matcher=False,
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vectors=False,
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pipeline=False)
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self.vocab = self.Defaults.create_vocab(self)
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self.tokenizer = self.Defaults.create_tokenizer(self)
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self.tagger = self.Defaults.create_tagger(self)
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self.parser = self.Defaults.create_parser(self)
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self.entity = self.Defaults.create_entity(self)
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self.pipeline = self.Defaults.create_pipeline(self)
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yield Trainer(self, gold_tuples)
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self.end_training()
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self.save_to_directory(path)
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def __init__(self, **overrides):
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"""
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Create or load the pipeline.
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Arguments:
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**overrides: Keyword arguments indicating which defaults to override.
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Returns:
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Language: The newly constructed object.
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"""
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if 'data_dir' in overrides and 'path' not in overrides:
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raise ValueError("The argument 'data_dir' has been renamed to 'path'")
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path = util.ensure_path(overrides.get('path', True))
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if path is True:
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path = util.get_data_path() / self.lang
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if not path.exists() and 'path' not in overrides:
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path = None
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self.meta = overrides.get('meta', {})
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self.path = path
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self.vocab = self.Defaults.create_vocab(self) \
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if 'vocab' not in overrides \
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else overrides['vocab']
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add_vectors = self.Defaults.add_vectors(self) \
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if 'add_vectors' not in overrides \
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else overrides['add_vectors']
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if self.vocab and add_vectors:
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add_vectors(self.vocab)
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self.tokenizer = self.Defaults.create_tokenizer(self) \
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if 'tokenizer' not in overrides \
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else overrides['tokenizer']
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self.tagger = self.Defaults.create_tagger(self) \
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if 'tagger' not in overrides \
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else overrides['tagger']
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self.parser = self.Defaults.create_parser(self) \
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if 'parser' not in overrides \
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else overrides['parser']
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self.entity = self.Defaults.create_entity(self) \
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if 'entity' not in overrides \
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else overrides['entity']
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self.matcher = self.Defaults.create_matcher(self) \
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if 'matcher' not in overrides \
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else overrides['matcher']
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if 'make_doc' in overrides:
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self.make_doc = overrides['make_doc']
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elif 'create_make_doc' in overrides:
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self.make_doc = overrides['create_make_doc'](self)
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elif not hasattr(self, 'make_doc'):
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self.make_doc = lambda text: self.tokenizer(text)
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if 'pipeline' in overrides:
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self.pipeline = overrides['pipeline']
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elif 'create_pipeline' in overrides:
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self.pipeline = overrides['create_pipeline'](self)
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if vocab is True:
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factory = self.Defaults.create_vocab
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vocab = factory(self, **meta.get('vocab', {}))
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self.vocab = vocab
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if make_doc is True:
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factory = self.Defaults.create_tokenizer
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make_doc = factory(self, **meta.get('tokenizer', {}))
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self.make_doc = make_doc
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if pipeline is True:
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self.pipeline = self.Defaults.create_pipeline(self)
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elif pipeline:
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self.pipeline = list(pipeline)
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# Resolve strings, like "cnn", "lstm", etc
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for i, entry in enumerate(self.pipeline):
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if entry in self.Defaults.factories:
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factory = self.Defaults.factories[entry]
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self.pipeline[i] = factory(self, **meta.get(entry, {}))
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else:
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self.pipeline = [self.tagger, self.parser, self.matcher, self.entity]
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self.pipeline = []
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def __call__(self, text, tag=True, parse=True, entity=True):
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def __call__(self, text, **disabled):
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"""
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Apply the pipeline to some text. The text can span multiple sentences,
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and can contain arbtrary whitespace. Alignment into the original string
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@ -294,18 +143,24 @@ class Language(object):
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('An', 'NN')
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"""
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doc = self.make_doc(text)
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if self.entity and entity:
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# Add any of the entity labels already set, in case we don't have them.
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for token in doc:
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if token.ent_type != 0:
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self.entity.add_label(token.ent_type)
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skip = {self.tagger: not tag, self.parser: not parse, self.entity: not entity}
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for proc in self.pipeline:
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if proc and not skip.get(proc):
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proc(doc)
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name = getattr(proc, 'name', None)
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if name in disabled and not disabled[named]:
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continue
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proc(doc)
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return doc
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def pipe(self, texts, tag=True, parse=True, entity=True, n_threads=2, batch_size=1000):
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@contextmanager
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def begin_training(self, gold_tuples, **cfg):
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contexts = []
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for proc in self.pipeline:
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if hasattr(proc, 'begin_training'):
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context = proc.begin_training(gold_tuples, pipeline=self.pipeline)
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contexts.append(context)
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trainer = Trainer(self, gold_tuples, **cfg)
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yield trainer, trainer.optimizer
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def pipe(self, texts, n_threads=2, batch_size=1000, **disabled):
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"""
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Process texts as a stream, and yield Doc objects in order.
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@ -317,55 +172,28 @@ class Language(object):
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parse (bool)
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entity (bool)
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"""
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skip = {self.tagger: not tag, self.parser: not parse, self.entity: not entity}
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stream = (self.make_doc(text) for text in texts)
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for proc in self.pipeline:
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if proc and not skip.get(proc):
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if hasattr(proc, 'pipe'):
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stream = proc.pipe(stream, n_threads=n_threads, batch_size=batch_size)
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else:
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stream = (proc(item) for item in stream)
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name = getattr(proc, 'name', None)
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if name in disabled and not disabled[named]:
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continue
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if hasattr(proc, 'pipe'):
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stream = proc.pipe(stream, n_threads=n_threads, batch_size=batch_size)
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else:
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stream = (proc(item) for item in stream)
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for doc in stream:
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yield doc
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def save_to_directory(self, path):
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"""
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Save the Vocab, StringStore and pipeline to a directory.
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def to_disk(self, path):
|
||||
raise NotImplemented
|
||||
|
||||
Arguments:
|
||||
path (string or pathlib path): Path to save the model.
|
||||
"""
|
||||
configs = {
|
||||
'pos': self.tagger.cfg if self.tagger else {},
|
||||
'deps': self.parser.cfg if self.parser else {},
|
||||
'ner': self.entity.cfg if self.entity else {},
|
||||
}
|
||||
def from_disk(self, path):
|
||||
raise NotImplemented
|
||||
|
||||
path = util.ensure_path(path)
|
||||
if not path.exists():
|
||||
path.mkdir()
|
||||
self.setup_directory(path, **configs)
|
||||
def to_bytes(self, path):
|
||||
raise NotImplemented
|
||||
|
||||
strings_loc = path / 'vocab' / 'strings.json'
|
||||
with strings_loc.open('w', encoding='utf8') as file_:
|
||||
self.vocab.strings.dump(file_)
|
||||
self.vocab.dump(path / 'vocab' / 'lexemes.bin')
|
||||
# TODO: Word vectors?
|
||||
if self.tagger:
|
||||
self.tagger.model.dump(str(path / 'pos' / 'model'))
|
||||
if self.parser:
|
||||
self.parser.model.dump(str(path / 'deps' / 'model'))
|
||||
if self.entity:
|
||||
self.entity.model.dump(str(path / 'ner' / 'model'))
|
||||
def from_bytes(self, path):
|
||||
raise NotImplemented
|
||||
|
||||
def end_training(self, path=None):
|
||||
if self.tagger:
|
||||
self.tagger.model.end_training()
|
||||
if self.parser:
|
||||
self.parser.model.end_training()
|
||||
if self.entity:
|
||||
self.entity.model.end_training()
|
||||
# NB: This is slightly different from before --- we no longer default
|
||||
# to taking nlp.path
|
||||
if path is not None:
|
||||
self.save_to_directory(path)
|
||||
|
|
|
|||
|
|
@ -9,7 +9,8 @@ import numpy
|
|||
cimport numpy as np
|
||||
|
||||
from .tokens.doc cimport Doc
|
||||
from .syntax.parser cimport Parser
|
||||
from .syntax.parser cimport Parser as LinearParser
|
||||
from .syntax.nn_parser cimport Parser as NeuralParser
|
||||
from .syntax.parser import get_templates as get_feature_templates
|
||||
from .syntax.beam_parser cimport BeamParser
|
||||
from .syntax.ner cimport BiluoPushDown
|
||||
|
|
@ -30,13 +31,13 @@ from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP
|
|||
from ._ml import Tok2Vec, flatten, get_col, doc2feats
|
||||
|
||||
|
||||
|
||||
class TokenVectorEncoder(object):
|
||||
'''Assign position-sensitive vectors to tokens, using a CNN or RNN.'''
|
||||
name = 'tok2vec'
|
||||
|
||||
@classmethod
|
||||
def Model(cls, width=128, embed_size=5000, **cfg):
|
||||
return Tok2Vec(width, embed_size, preprocess=False)
|
||||
return Tok2Vec(width, embed_size, preprocess=doc2feats())
|
||||
|
||||
def __init__(self, vocab, model=True, **cfg):
|
||||
self.vocab = vocab
|
||||
|
|
@ -76,10 +77,11 @@ class TokenVectorEncoder(object):
|
|||
doc.vocab.morphology.assign_tag_id(&doc.c[j], tag_id)
|
||||
idx += 1
|
||||
|
||||
def update(self, docs_feats, golds, drop=0., sgd=None):
|
||||
def update(self, docs, golds, drop=0., sgd=None):
|
||||
return 0.0
|
||||
cdef int i, j, idx
|
||||
cdef GoldParse gold
|
||||
docs, feats = docs_feats
|
||||
feats = self.doc2feats(docs)
|
||||
scores, finish_update = self.tagger.begin_update(feats, drop=drop)
|
||||
|
||||
tag_index = {tag: i for i, tag in enumerate(docs[0].vocab.morphology.tag_names)}
|
||||
|
|
@ -95,7 +97,7 @@ class TokenVectorEncoder(object):
|
|||
finish_update(d_scores, sgd)
|
||||
|
||||
|
||||
cdef class EntityRecognizer(Parser):
|
||||
cdef class EntityRecognizer(LinearParser):
|
||||
"""
|
||||
Annotate named entities on Doc objects.
|
||||
"""
|
||||
|
|
@ -104,7 +106,7 @@ cdef class EntityRecognizer(Parser):
|
|||
feature_templates = get_feature_templates('ner')
|
||||
|
||||
def add_label(self, label):
|
||||
Parser.add_label(self, label)
|
||||
LinearParser.add_label(self, label)
|
||||
if isinstance(label, basestring):
|
||||
label = self.vocab.strings[label]
|
||||
|
||||
|
|
@ -118,21 +120,31 @@ cdef class BeamEntityRecognizer(BeamParser):
|
|||
feature_templates = get_feature_templates('ner')
|
||||
|
||||
def add_label(self, label):
|
||||
Parser.add_label(self, label)
|
||||
LinearParser.add_label(self, label)
|
||||
if isinstance(label, basestring):
|
||||
label = self.vocab.strings[label]
|
||||
|
||||
|
||||
cdef class DependencyParser(Parser):
|
||||
cdef class DependencyParser(LinearParser):
|
||||
TransitionSystem = ArcEager
|
||||
feature_templates = get_feature_templates('basic')
|
||||
|
||||
def add_label(self, label):
|
||||
Parser.add_label(self, label)
|
||||
LinearParser.add_label(self, label)
|
||||
if isinstance(label, basestring):
|
||||
label = self.vocab.strings[label]
|
||||
|
||||
|
||||
cdef class NeuralDependencyParser(NeuralParser):
|
||||
name = 'parser'
|
||||
TransitionSystem = ArcEager
|
||||
|
||||
|
||||
cdef class NeuralEntityRecognizer(NeuralParser):
|
||||
name = 'entity'
|
||||
TransitionSystem = BiluoPushDown
|
||||
|
||||
|
||||
cdef class BeamDependencyParser(BeamParser):
|
||||
TransitionSystem = ArcEager
|
||||
|
||||
|
|
|
|||
|
|
@ -238,11 +238,7 @@ cdef class Parser:
|
|||
upper.begin_training(upper.ops.allocate((500, hidden_width)))
|
||||
return tok2vec, lower, upper
|
||||
|
||||
@classmethod
|
||||
def Moves(cls):
|
||||
return TransitionSystem()
|
||||
|
||||
def __init__(self, Vocab vocab, moves=True, model=True, **cfg):
|
||||
def __init__(self, Vocab vocab, model=True, **cfg):
|
||||
"""
|
||||
Create a Parser.
|
||||
|
||||
|
|
@ -262,9 +258,13 @@ cdef class Parser:
|
|||
Arbitrary configuration parameters. Set to the .cfg attribute
|
||||
"""
|
||||
self.vocab = vocab
|
||||
self.moves = self.Moves(self.vocab) if moves is True else moves
|
||||
self.model = self.Model(self.moves.n_moves) if model is True else model
|
||||
self.moves = self.TransitionSystem(self.vocab.strings, {})
|
||||
self.cfg = cfg
|
||||
if 'actions' in self.cfg:
|
||||
for action, labels in self.cfg.get('actions', {}).items():
|
||||
for label in labels:
|
||||
self.moves.add_action(action, label)
|
||||
self.model = model
|
||||
|
||||
def __reduce__(self):
|
||||
return (Parser, (self.vocab, self.moves, self.model, self.cfg), None, None)
|
||||
|
|
@ -440,6 +440,17 @@ cdef class Parser:
|
|||
# order, or the model goes out of synch
|
||||
self.cfg.setdefault('extra_labels', []).append(label)
|
||||
|
||||
def begin_training(self, gold_tuples, **cfg):
|
||||
if 'model' in cfg:
|
||||
self.model = cfg['model']
|
||||
actions = self.moves.get_actions(gold_parses=gold_tuples)
|
||||
for action, labels in actions.items():
|
||||
for label in labels:
|
||||
self.moves.add_action(action, label)
|
||||
if self.model is True:
|
||||
tok2vec = cfg['pipeline'][0].model
|
||||
self.model = self.Model(self.moves.n_moves, tok2vec=tok2vec, **cfg)
|
||||
|
||||
|
||||
class ParserStateError(ValueError):
|
||||
def __init__(self, doc):
|
||||
|
|
|
|||
|
|
@ -3,12 +3,14 @@ from __future__ import absolute_import, unicode_literals
|
|||
|
||||
import random
|
||||
import tqdm
|
||||
from cytoolz import partition_all
|
||||
|
||||
from thinc.neural.optimizers import Adam
|
||||
from thinc.neural.ops import NumpyOps, CupyOps
|
||||
|
||||
from .gold import GoldParse, merge_sents
|
||||
from .scorer import Scorer
|
||||
from .tokens.doc import Doc
|
||||
|
||||
|
||||
class Trainer(object):
|
||||
|
|
@ -19,6 +21,7 @@ class Trainer(object):
|
|||
self.nlp = nlp
|
||||
self.gold_tuples = gold_tuples
|
||||
self.nr_epoch = 0
|
||||
self.optimizer = Adam(NumpyOps(), 0.001)
|
||||
|
||||
def epochs(self, nr_epoch, augment_data=None, gold_preproc=False):
|
||||
cached_golds = {}
|
||||
|
|
@ -75,9 +78,9 @@ class Trainer(object):
|
|||
|
||||
def make_docs(self, raw_text, paragraph_tuples):
|
||||
if raw_text is not None:
|
||||
return [self.nlp.tokenizer(raw_text)]
|
||||
return [self.nlp.make_doc(raw_text)]
|
||||
else:
|
||||
return [self.nlp.tokenizer.tokens_from_list(sent_tuples[0][1])
|
||||
return [Doc(self.nlp.vocab, words=sent_tuples[0][1])
|
||||
for sent_tuples in paragraph_tuples]
|
||||
|
||||
def make_golds(self, docs, paragraph_tuples):
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user