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https://github.com/explosion/spaCy.git
synced 2025-01-26 09:14:32 +03:00
sentence encoder only (removing article/mention encoder)
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svlandeg
parent
6332af40de
commit
ffae7d3555
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@ -294,7 +294,6 @@ def read_training(nlp, training_dir, dev, limit):
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# we assume the data is written sequentially
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current_article_id = None
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current_doc = None
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gold_entities = list()
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ents_by_offset = dict()
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skip_articles = set()
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total_entities = 0
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@ -302,8 +301,6 @@ def read_training(nlp, training_dir, dev, limit):
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with open(entityfile_loc, mode='r', encoding='utf8') as file:
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for line in file:
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if not limit or len(data) < limit:
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if len(data) > 0 and len(data) % 50 == 0:
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print("Read", total_entities, "entities in", len(data), "articles")
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fields = line.replace('\n', "").split(sep='|')
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article_id = fields[0]
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alias = fields[1]
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@ -313,34 +310,42 @@ def read_training(nlp, training_dir, dev, limit):
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if dev == is_dev(article_id) and article_id != "article_id" and article_id not in skip_articles:
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if not current_doc or (current_article_id != article_id):
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# store the data from the previous article
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if gold_entities and current_doc:
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gold = GoldParse(doc=current_doc, links=gold_entities)
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data.append((current_doc, gold))
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total_entities += len(gold_entities)
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# parse the new article text
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file_name = article_id + ".txt"
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try:
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with open(os.path.join(training_dir, file_name), mode="r", encoding='utf8') as f:
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text = f.read()
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current_doc = nlp(text)
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for ent in current_doc.ents:
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ents_by_offset[str(ent.start_char) + "_" + str(ent.end_char)] = ent.text
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if len(text) < 30000: # threshold for convenience / speed of processing
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current_doc = nlp(text)
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current_article_id = article_id
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ents_by_offset = dict()
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for ent in current_doc.ents:
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ents_by_offset[str(ent.start_char) + "_" + str(ent.end_char)] = ent
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else:
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skip_articles.add(current_article_id)
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current_doc = None
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except Exception as e:
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print("Problem parsing article", article_id, e)
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current_article_id = article_id
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gold_entities = list()
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# repeat checking this condition in case an exception was thrown
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if current_doc and (current_article_id == article_id):
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found_ent = ents_by_offset.get(start + "_" + end, None)
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if found_ent:
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if found_ent != alias:
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if found_ent.text != alias:
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skip_articles.add(current_article_id)
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current_doc = None
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else:
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gold_entities.append((int(start), int(end), wp_title))
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sent = found_ent.sent.as_doc()
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# currently feeding the gold data one entity per sentence at a time
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gold_start = int(start) - found_ent.sent.start_char
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gold_end = int(end) - found_ent.sent.start_char
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gold_entities = list()
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gold_entities.append((gold_start, gold_end, wp_title))
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gold = GoldParse(doc=current_doc, links=gold_entities)
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data.append((sent, gold))
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total_entities += 1
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if len(data) % 500 == 0:
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print(" -read", total_entities, "entities")
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print("Read", total_entities, "entities in", len(data), "articles")
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print(" -read", total_entities, "entities")
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return data
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@ -9,7 +9,6 @@ from examples.pipeline.wiki_entity_linking import wikipedia_processor as wp, kb_
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from examples.pipeline.wiki_entity_linking.kb_creator import DESC_WIDTH
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import spacy
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from spacy.vocab import Vocab
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from spacy.kb import KnowledgeBase
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import datetime
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@ -64,8 +63,8 @@ def run_pipeline():
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to_test_pipeline = True
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# write the NLP object, read back in and test again
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to_write_nlp = True
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to_read_nlp = True
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to_write_nlp = False
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to_read_nlp = False
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# STEP 1 : create prior probabilities from WP
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# run only once !
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@ -134,8 +133,8 @@ def run_pipeline():
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if train_pipe:
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print("STEP 6: training Entity Linking pipe", datetime.datetime.now())
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train_limit = 5
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dev_limit = 2
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train_limit = 25000
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dev_limit = 1000
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train_data = training_set_creator.read_training(nlp=nlp_2,
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training_dir=TRAINING_DIR,
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@ -345,7 +344,11 @@ def calculate_acc(correct_by_label, incorrect_by_label):
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acc_by_label = dict()
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total_correct = 0
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total_incorrect = 0
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for label, correct in correct_by_label.items():
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all_keys = set()
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all_keys.update(correct_by_label.keys())
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all_keys.update(incorrect_by_label.keys())
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for label in sorted(all_keys):
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correct = correct_by_label.get(label, 0)
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incorrect = incorrect_by_label.get(label, 0)
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total_correct += correct
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total_incorrect += incorrect
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@ -1079,36 +1079,39 @@ class EntityLinker(Pipe):
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embed_width = cfg.get("embed_width", 300)
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hidden_width = cfg.get("hidden_width", 32)
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article_width = cfg.get("article_width", 128)
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sent_width = cfg.get("sent_width", 64)
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entity_width = cfg.get("entity_width") # no default because this needs to correspond with the KB
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sent_width = entity_width
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article_encoder = build_nel_encoder(in_width=embed_width, hidden_width=hidden_width, end_width=article_width, **cfg)
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sent_encoder = build_nel_encoder(in_width=embed_width, hidden_width=hidden_width, end_width=sent_width, **cfg)
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model = build_nel_encoder(in_width=embed_width, hidden_width=hidden_width, end_width=sent_width, **cfg)
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# dimension of the mention encoder needs to match the dimension of the entity encoder
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mention_width = article_width + sent_width
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mention_encoder = Affine(entity_width, mention_width, drop_factor=0.0)
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# article_width = cfg.get("article_width", 128)
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# sent_width = cfg.get("sent_width", 64)
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# article_encoder = build_nel_encoder(in_width=embed_width, hidden_width=hidden_width, end_width=article_width, **cfg)
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# mention_width = article_width + sent_width
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# mention_encoder = Affine(entity_width, mention_width, drop_factor=0.0)
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# return article_encoder, sent_encoder, mention_encoder
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return article_encoder, sent_encoder, mention_encoder
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return model
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def __init__(self, **cfg):
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self.article_encoder = True
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self.sent_encoder = True
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self.mention_encoder = True
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# self.article_encoder = True
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# self.sent_encoder = True
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# self.mention_encoder = True
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self.model = True
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self.kb = None
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self.cfg = dict(cfg)
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self.doc_cutoff = self.cfg.get("doc_cutoff", 5)
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self.sgd_article = None
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self.sgd_sent = None
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self.sgd_mention = None
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# self.sgd_article = None
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# self.sgd_sent = None
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# self.sgd_mention = None
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def set_kb(self, kb):
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self.kb = kb
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def require_model(self):
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# Raise an error if the component's model is not initialized.
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if getattr(self, "mention_encoder", None) in (None, True, False):
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if getattr(self, "model", None) in (None, True, False):
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raise ValueError(Errors.E109.format(name=self.name))
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def require_kb(self):
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@ -1121,12 +1124,19 @@ class EntityLinker(Pipe):
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self.require_kb()
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self.cfg["entity_width"] = self.kb.entity_vector_length
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if self.mention_encoder is True:
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self.article_encoder, self.sent_encoder, self.mention_encoder = self.Model(**self.cfg)
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self.sgd_article = create_default_optimizer(self.article_encoder.ops)
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self.sgd_sent = create_default_optimizer(self.sent_encoder.ops)
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self.sgd_mention = create_default_optimizer(self.mention_encoder.ops)
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return self.sgd_article
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if self.model is True:
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self.model = self.Model(**self.cfg)
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if sgd is None:
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sgd = self.create_optimizer()
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return sgd
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# if self.mention_encoder is True:
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# self.article_encoder, self.sent_encoder, self.mention_encoder = self.Model(**self.cfg)
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# self.sgd_article = create_default_optimizer(self.article_encoder.ops)
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# self.sgd_sent = create_default_optimizer(self.sent_encoder.ops)
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# self.sgd_mention = create_default_optimizer(self.mention_encoder.ops)
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# return self.sgd_article
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def update(self, docs, golds, state=None, drop=0.0, sgd=None, losses=None):
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self.require_model()
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@ -1146,7 +1156,7 @@ class EntityLinker(Pipe):
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docs = [docs]
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golds = [golds]
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article_docs = list()
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# article_docs = list()
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sentence_docs = list()
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entity_encodings = list()
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@ -1173,34 +1183,32 @@ class EntityLinker(Pipe):
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if kb_id == gold_kb:
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prior_prob = c.prior_prob
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entity_encoding = c.entity_vector
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entity_encodings.append(entity_encoding)
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article_docs.append(first_par)
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# article_docs.append(first_par)
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sentence_docs.append(sentence)
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if len(entity_encodings) > 0:
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doc_encodings, bp_doc = self.article_encoder.begin_update(article_docs, drop=drop)
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sent_encodings, bp_sent = self.sent_encoder.begin_update(sentence_docs, drop=drop)
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# doc_encodings, bp_doc = self.article_encoder.begin_update(article_docs, drop=drop)
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# sent_encodings, bp_sent = self.sent_encoder.begin_update(sentence_docs, drop=drop)
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concat_encodings = [list(doc_encodings[i]) + list(sent_encodings[i]) for i in
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range(len(article_docs))]
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mention_encodings, bp_mention = self.mention_encoder.begin_update(np.asarray(concat_encodings), drop=drop)
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# concat_encodings = [list(doc_encodings[i]) + list(sent_encodings[i]) for i in range(len(article_docs))]
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# mention_encodings, bp_mention = self.mention_encoder.begin_update(np.asarray(concat_encodings), drop=drop)
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sent_encodings, bp_sent = self.model.begin_update(sentence_docs, drop=drop)
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entity_encodings = np.asarray(entity_encodings, dtype=np.float32)
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loss, d_scores = self.get_loss(scores=mention_encodings, golds=entity_encodings, docs=None)
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mention_gradient = bp_mention(d_scores, sgd=self.sgd_mention)
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loss, d_scores = self.get_loss(scores=sent_encodings, golds=entity_encodings, docs=None)
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bp_sent(d_scores, sgd=sgd)
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# gradient : concat (doc+sent) vs. desc
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sent_start = self.article_encoder.nO
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sent_gradients = list()
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doc_gradients = list()
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for x in mention_gradient:
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doc_gradients.append(list(x[0:sent_start]))
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sent_gradients.append(list(x[sent_start:]))
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bp_doc(doc_gradients, sgd=self.sgd_article)
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bp_sent(sent_gradients, sgd=self.sgd_sent)
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# sent_start = self.article_encoder.nO
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# sent_gradients = list()
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# doc_gradients = list()
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# for x in mention_gradient:
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# doc_gradients.append(list(x[0:sent_start]))
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# sent_gradients.append(list(x[sent_start:]))
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# bp_doc(doc_gradients, sgd=self.sgd_article)
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# bp_sent(sent_gradients, sgd=self.sgd_sent)
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if losses is not None:
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losses[self.name] += loss
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@ -1262,14 +1270,17 @@ class EntityLinker(Pipe):
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first_par_end = sent.end
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first_par = doc[0:first_par_end].as_doc()
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doc_encoding = self.article_encoder([first_par])
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# doc_encoding = self.article_encoder([first_par])
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for ent in doc.ents:
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sent_doc = ent.sent.as_doc()
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if len(sent_doc) > 0:
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sent_encoding = self.sent_encoder([sent_doc])
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concat_encoding = [list(doc_encoding[0]) + list(sent_encoding[0])]
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mention_encoding = self.mention_encoder(np.asarray([concat_encoding[0]]))
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mention_enc_t = np.transpose(mention_encoding)
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# sent_encoding = self.sent_encoder([sent_doc])
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# concat_encoding = [list(doc_encoding[0]) + list(sent_encoding[0])]
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# mention_encoding = self.mention_encoder(np.asarray([concat_encoding[0]]))
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# mention_enc_t = np.transpose(mention_encoding)
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sent_encoding = self.model([sent_doc])
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sent_enc_t = np.transpose(sent_encoding)
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candidates = self.kb.get_candidates(ent.text)
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if candidates:
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@ -1278,7 +1289,7 @@ class EntityLinker(Pipe):
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prior_prob = c.prior_prob * self.prior_weight
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kb_id = c.entity_
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entity_encoding = c.entity_vector
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sim = float(cosine(np.asarray([entity_encoding]), mention_enc_t)) * self.context_weight
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sim = float(cosine(np.asarray([entity_encoding]), sent_enc_t)) * self.context_weight
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score = prior_prob + sim - (prior_prob*sim) # put weights on the different factors ?
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scores.append(score)
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@ -1299,34 +1310,20 @@ class EntityLinker(Pipe):
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serialize = OrderedDict()
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serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
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serialize["kb"] = lambda p: self.kb.dump(p)
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if self.mention_encoder not in (None, True, False):
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serialize["article_encoder"] = lambda p: p.open("wb").write(self.article_encoder.to_bytes())
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serialize["sent_encoder"] = lambda p: p.open("wb").write(self.sent_encoder.to_bytes())
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serialize["mention_encoder"] = lambda p: p.open("wb").write(self.mention_encoder.to_bytes())
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if self.model not in (None, True, False):
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serialize["model"] = lambda p: p.open("wb").write(self.model.to_bytes())
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exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
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util.to_disk(path, serialize, exclude)
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def from_disk(self, path, exclude=tuple(), **kwargs):
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def load_article_encoder(p):
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if self.article_encoder is True:
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self.article_encoder, _, _ = self.Model(**self.cfg)
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self.article_encoder.from_bytes(p.open("rb").read())
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def load_sent_encoder(p):
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if self.sent_encoder is True:
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_, self.sent_encoder, _ = self.Model(**self.cfg)
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self.sent_encoder.from_bytes(p.open("rb").read())
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def load_mention_encoder(p):
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if self.mention_encoder is True:
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_, _, self.mention_encoder = self.Model(**self.cfg)
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self.mention_encoder.from_bytes(p.open("rb").read())
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def load_model(p):
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if self.model is True:
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self.model = self.Model(**self.cfg)
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self.model.from_bytes(p.open("rb").read())
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deserialize = OrderedDict()
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deserialize["cfg"] = lambda p: self.cfg.update(_load_cfg(p))
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deserialize["article_encoder"] = load_article_encoder
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deserialize["sent_encoder"] = load_sent_encoder
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deserialize["mention_encoder"] = load_mention_encoder
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deserialize["model"] = load_model
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exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
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util.from_disk(path, deserialize, exclude)
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return self
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