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small fixes

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This commit is contained in:
svlandeg 2019-05-27 14:29:38 +02:00
parent cfc27d7ff9
commit 8c4aa076bc
2 changed files with 24 additions and 14 deletions
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36
examples/pipeline/wiki_entity_linking/train_el.py
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@ -29,7 +29,7 @@ from spacy.tokens import Doc
class EL_Model: class EL_Model:
PRINT_INSPECT = False PRINT_INSPECT = False
PRINT_TRAIN = False PRINT_TRAIN = True
EPS = 0.0000000005 EPS = 0.0000000005
CUTOFF = 0.5 CUTOFF = 0.5
@ -40,14 +40,15 @@ class EL_Model:
INPUT_DIM = 300 # dimension of pre-trained vectors INPUT_DIM = 300 # dimension of pre-trained vectors
# HIDDEN_1_WIDTH = 32 # 10 # HIDDEN_1_WIDTH = 32 # 10
# HIDDEN_2_WIDTH = 32 # 6 HIDDEN_2_WIDTH = 32 # 6
DESC_WIDTH = 64 # 4 DESC_WIDTH = 64 # 4
ARTICLE_WIDTH = 64 # 8 ARTICLE_WIDTH = 64 # 8
SENT_WIDTH = 64 SENT_WIDTH = 64
DROP = 0.1 DROP = 0.1
LEARN_RATE = 0.001 LEARN_RATE = 0.0001
EPOCHS = 20 EPOCHS = 20
L2 = 1e-6
name = "entity_linker" name = "entity_linker"
@ -62,7 +63,10 @@ class EL_Model:
def train_model(self, training_dir, entity_descr_output, trainlimit=None, devlimit=None, to_print=True): def train_model(self, training_dir, entity_descr_output, trainlimit=None, devlimit=None, to_print=True):
# raise errors instead of runtime warnings in case of int/float overflow # raise errors instead of runtime warnings in case of int/float overflow
np.seterr(all='raise') # (not sure if we need this. set L2 to 0 because it throws an error otherwsise)
# np.seterr(all='raise')
# alternative:
np.seterr(divide="raise", over="warn", under="ignore", invalid="raise")
train_ent, train_gold, train_desc, train_art, train_art_texts, train_sent, train_sent_texts = \ train_ent, train_gold, train_desc, train_art, train_art_texts, train_sent, train_sent_texts = \
self._get_training_data(training_dir, entity_descr_output, False, trainlimit, to_print=False) self._get_training_data(training_dir, entity_descr_output, False, trainlimit, to_print=False)
@ -159,6 +163,7 @@ class EL_Model:
stop = min(stop + self.BATCH_SIZE, len(train_ent)) stop = min(stop + self.BATCH_SIZE, len(train_ent))
if self.PRINT_TRAIN: if self.PRINT_TRAIN:
print()
self._test_dev(train_ent, train_gold, train_desc, train_art, train_art_texts, train_sent, train_sent_texts, self._test_dev(train_ent, train_gold, train_desc, train_art, train_art_texts, train_sent, train_sent_texts,
print_string="train_inter_epoch " + str(i), avg=True) print_string="train_inter_epoch " + str(i), avg=True)
@ -250,15 +255,20 @@ class EL_Model:
in_width = desc_width + article_width + sent_width in_width = desc_width + article_width + sent_width
output_layer = ( self.model = Affine(self.HIDDEN_2_WIDTH, in_width) \
zero_init(Affine(1, in_width, drop_factor=0.0)) >> logistic >> LN(Maxout(self.HIDDEN_2_WIDTH, self.HIDDEN_2_WIDTH)) \
) >> Affine(1, self.HIDDEN_2_WIDTH) \
self.model = output_layer >> logistic
# output_layer = (
# zero_init(Affine(1, in_width, drop_factor=0.0)) >> logistic
# )
# self.model = output_layer
self.model.nO = 1 self.model.nO = 1
def _encoder(self, width): def _encoder(self, width):
tok2vec = Tok2Vec(width=width, embed_size=2000, pretrained_vectors=self.nlp.vocab.vectors.name, cnn_maxout_pieces=3, tok2vec = Tok2Vec(width=width, embed_size=2000, pretrained_vectors=self.nlp.vocab.vectors.name, cnn_maxout_pieces=3,
subword_features=True, conv_depth=4, bilstm_depth=0) subword_features=False, conv_depth=4, bilstm_depth=0)
return tok2vec >> flatten_add_lengths >> Pooling(mean_pool) return tok2vec >> flatten_add_lengths >> Pooling(mean_pool)
@ -287,19 +297,19 @@ class EL_Model:
def _begin_training(self): def _begin_training(self):
self.sgd_article = create_default_optimizer(self.article_encoder.ops) self.sgd_article = create_default_optimizer(self.article_encoder.ops)
self.sgd_article.learn_rate = self.LEARN_RATE self.sgd_article.learn_rate = self.LEARN_RATE
self.sgd_article.L2 = 0 self.sgd_article.L2 = self.L2
self.sgd_sent = create_default_optimizer(self.sent_encoder.ops) self.sgd_sent = create_default_optimizer(self.sent_encoder.ops)
self.sgd_sent.learn_rate = self.LEARN_RATE self.sgd_sent.learn_rate = self.LEARN_RATE
self.sgd_sent.L2 = 0 self.sgd_sent.L2 = self.L2
self.sgd_desc = create_default_optimizer(self.desc_encoder.ops) self.sgd_desc = create_default_optimizer(self.desc_encoder.ops)
self.sgd_desc.learn_rate = self.LEARN_RATE self.sgd_desc.learn_rate = self.LEARN_RATE
self.sgd_desc.L2 = 0 self.sgd_desc.L2 = self.L2
self.sgd = create_default_optimizer(self.model.ops) self.sgd = create_default_optimizer(self.model.ops)
self.sgd.learn_rate = self.LEARN_RATE self.sgd.learn_rate = self.LEARN_RATE
self.sgd.L2 = 0 self.sgd.L2 = self.L2
@staticmethod @staticmethod
def get_loss(predictions, golds): def get_loss(predictions, golds):

2
examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py
View File

@ -111,7 +111,7 @@ if __name__ == "__main__":
print("STEP 6: training", datetime.datetime.now()) print("STEP 6: training", datetime.datetime.now())
my_nlp = spacy.load('en_core_web_md') my_nlp = spacy.load('en_core_web_md')
trainer = EL_Model(kb=my_kb, nlp=my_nlp) trainer = EL_Model(kb=my_kb, nlp=my_nlp)
trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=10000, devlimit=1000) trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=100, devlimit=20)
print() print()
# STEP 7: apply the EL algorithm on the dev dataset # STEP 7: apply the EL algorithm on the dev dataset