mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-22 03:49:17 +03:00
c0f4a1e43b
* verbose and tag_map options * adding init_tok2vec option and only changing the tok2vec that is specified * adding omit_extra_lookups and verifying textcat config * wip * pretrain bugfix * add replace and resume options * train_textcat fix * raw text functionality * improve UX when KeyError or when input data can't be parsed * avoid unnecessary access to goldparse in TextCat pipe * save performance information in nlp.meta * add noise_level to config * move nn_parser's defaults to config file * multitask in config - doesn't work yet * scorer offering both F and AUC options, need to be specified in config * add textcat verification code from old train script * small fixes to config files * clean up * set default config for ner/parser to allow create_pipe to work as before * two more test fixes * small fixes * cleanup * fix NER pickling + additional unit test * create_pipe as before
111 lines
3.6 KiB
Python
111 lines
3.6 KiB
Python
import numpy
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from thinc.api import chain, Maxout, LayerNorm, Softmax, Linear, zero_init, Model
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def build_multi_task_model(tok2vec, maxout_pieces, token_vector_width, nO=None):
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softmax = Softmax(nO=nO, nI=token_vector_width * 2)
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model = chain(
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tok2vec,
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Maxout(nO=token_vector_width * 2, nI=token_vector_width, nP=maxout_pieces, dropout=0.0),
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LayerNorm(token_vector_width * 2),
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softmax,
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)
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model.set_ref("tok2vec", tok2vec)
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model.set_ref("output_layer", softmax)
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return model
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def build_cloze_multi_task_model(vocab, tok2vec, maxout_pieces, nO=None):
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# nO = vocab.vectors.data.shape[1]
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output_layer = chain(
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Maxout(
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nO=nO, nI=tok2vec.get_dim("nO"), nP=maxout_pieces, normalize=True, dropout=0.0
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),
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Linear(nO=nO, nI=nO, init_W=zero_init),
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)
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model = chain(tok2vec, output_layer)
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model = build_masked_language_model(vocab, model)
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model.set_ref("tok2vec", tok2vec)
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model.set_ref("output_layer", output_layer)
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return model
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def build_masked_language_model(vocab, wrapped_model, mask_prob=0.15):
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"""Convert a model into a BERT-style masked language model"""
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random_words = _RandomWords(vocab)
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def mlm_forward(model, docs, is_train):
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mask, docs = _apply_mask(docs, random_words, mask_prob=mask_prob)
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mask = model.ops.asarray(mask).reshape((mask.shape[0], 1))
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output, backprop = model.get_ref("wrapped-model").begin_update(docs) # drop=drop
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def mlm_backward(d_output):
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d_output *= 1 - mask
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return backprop(d_output)
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return output, mlm_backward
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mlm_model = Model("masked-language-model", mlm_forward, layers=[wrapped_model])
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mlm_model.set_ref("wrapped-model", wrapped_model)
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return mlm_model
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class _RandomWords(object):
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def __init__(self, vocab):
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self.words = [lex.text for lex in vocab if lex.prob != 0.0]
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self.probs = [lex.prob for lex in vocab if lex.prob != 0.0]
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self.words = self.words[:10000]
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self.probs = self.probs[:10000]
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self.probs = numpy.exp(numpy.array(self.probs, dtype="f"))
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self.probs /= self.probs.sum()
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self._cache = []
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def next(self):
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if not self._cache:
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self._cache.extend(
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numpy.random.choice(len(self.words), 10000, p=self.probs)
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)
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index = self._cache.pop()
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return self.words[index]
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def _apply_mask(docs, random_words, mask_prob=0.15):
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# This needs to be here to avoid circular imports
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from ...tokens import Doc
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N = sum(len(doc) for doc in docs)
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mask = numpy.random.uniform(0.0, 1.0, (N,))
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mask = mask >= mask_prob
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i = 0
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masked_docs = []
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for doc in docs:
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words = []
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for token in doc:
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if not mask[i]:
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word = _replace_word(token.text, random_words)
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else:
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word = token.text
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words.append(word)
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i += 1
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spaces = [bool(w.whitespace_) for w in doc]
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# NB: If you change this implementation to instead modify
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# the docs in place, take care that the IDs reflect the original
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# words. Currently we use the original docs to make the vectors
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# for the target, so we don't lose the original tokens. But if
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# you modified the docs in place here, you would.
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masked_docs.append(Doc(doc.vocab, words=words, spaces=spaces))
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return mask, masked_docs
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def _replace_word(word, random_words, mask="[MASK]"):
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roll = numpy.random.random()
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if roll < 0.8:
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return mask
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elif roll < 0.9:
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return random_words.next()
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else:
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return word
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