mirror of https://github.com/explosion/spaCy.git
114 lines
3.5 KiB
Python
114 lines
3.5 KiB
Python
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from random import shuffle
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from examples.pipeline.wiki_entity_linking import kb_creator
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import numpy as np
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from spacy._ml import zero_init, create_default_optimizer
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from spacy.cli.pretrain import get_cossim_loss
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from thinc.v2v import Model
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from thinc.api import chain
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from thinc.neural._classes.affine import Affine
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class EntityEncoder:
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INPUT_DIM = 300 # dimension of pre-trained vectors
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DESC_WIDTH = 64
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DROP = 0
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EPOCHS = 5
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STOP_THRESHOLD = 0.05
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BATCH_SIZE = 1000
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def __init__(self, kb, nlp):
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self.nlp = nlp
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self.kb = kb
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def run(self, entity_descr_output):
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id_to_descr = kb_creator._get_id_to_description(entity_descr_output)
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processed, loss = self._train_model(entity_descr_output, id_to_descr)
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print("Trained on", processed, "entities across", self.EPOCHS, "epochs")
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print("Final loss:", loss)
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print()
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# TODO: apply and write to file afterwards !
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# self._apply_encoder(id_to_descr)
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def _train_model(self, entity_descr_output, id_to_descr):
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# TODO: when loss gets too low, a 'mean of empty slice' warning is thrown by numpy
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self._build_network(self.INPUT_DIM, self.DESC_WIDTH)
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processed = 0
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loss = 1
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for i in range(self.EPOCHS):
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entity_keys = list(id_to_descr.keys())
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shuffle(entity_keys)
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batch_nr = 0
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start = 0
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stop = min(self.BATCH_SIZE, len(entity_keys))
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while loss > self.STOP_THRESHOLD and start < len(entity_keys):
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batch = []
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for e in entity_keys[start:stop]:
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descr = id_to_descr[e]
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doc = self.nlp(descr)
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doc_vector = self._get_doc_embedding(doc)
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batch.append(doc_vector)
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loss = self.update(batch)
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print(i, batch_nr, loss)
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processed += len(batch)
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batch_nr += 1
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start = start + self.BATCH_SIZE
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stop = min(stop + self.BATCH_SIZE, len(entity_keys))
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return processed, loss
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def _apply_encoder(self, id_to_descr):
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for id, descr in id_to_descr.items():
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doc = self.nlp(descr)
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doc_vector = self._get_doc_embedding(doc)
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encoding = self.encoder(np.asarray([doc_vector]))
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@staticmethod
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def _get_doc_embedding(doc):
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indices = np.zeros((len(doc),), dtype="i")
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for i, word in enumerate(doc):
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if word.orth in doc.vocab.vectors.key2row:
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indices[i] = doc.vocab.vectors.key2row[word.orth]
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else:
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indices[i] = 0
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word_vectors = doc.vocab.vectors.data[indices]
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doc_vector = np.mean(word_vectors, axis=0) # TODO: min? max?
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return doc_vector
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def _build_network(self, orig_width, hidden_with):
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with Model.define_operators({">>": chain}):
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self.encoder = (
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Affine(hidden_with, orig_width)
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)
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self.model = self.encoder >> zero_init(Affine(orig_width, hidden_with, drop_factor=0.0))
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self.sgd = create_default_optimizer(self.model.ops)
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def update(self, vectors):
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predictions, bp_model = self.model.begin_update(np.asarray(vectors), drop=self.DROP)
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loss, d_scores = self.get_loss(scores=predictions, golds=np.asarray(vectors))
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bp_model(d_scores, sgd=self.sgd)
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return loss / len(vectors)
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@staticmethod
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def get_loss(golds, scores):
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loss, gradients = get_cossim_loss(scores, golds)
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return loss, gradients
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