mirror of https://github.com/explosion/spaCy.git
calculate gradient for entity encoding
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2713abc651
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@ -26,9 +26,10 @@ from spacy.tokens import Doc
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class EL_Model():
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INPUT_DIM = 300
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OUTPUT_DIM = 5 # 96
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PRINT_LOSS = True
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OUTPUT_DIM = 96
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PRINT_LOSS = False
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PRINT_F = True
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EPS = 0.0000000005
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labels = ["MATCH", "NOMATCH"]
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name = "entity_linker"
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@ -71,12 +72,12 @@ class EL_Model():
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instance_count = 0
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for article_id, inst_cluster_set in train_instances.items():
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print("article", article_id)
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# print("article", article_id)
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article_doc = train_doc[article_id]
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pos_ex_list = list()
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neg_exs_list = list()
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for inst_cluster in inst_cluster_set:
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print("inst_cluster", inst_cluster)
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# print("inst_cluster", inst_cluster)
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instance_count += 1
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pos_ex_list.append(train_pos.get(inst_cluster))
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neg_exs_list.append(train_neg.get(inst_cluster, []))
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@ -143,19 +144,19 @@ class EL_Model():
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conv_depth = 1
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cnn_maxout_pieces = 3
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with Model.define_operators({">>": chain, "**": clone}):
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encoder = SpacyVectors \
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>> flatten_add_lengths \
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>> ParametricAttention(in_width)\
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>> Pooling(mean_pool) \
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>> Residual(zero_init(Maxout(in_width, in_width))) \
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>> zero_init(Affine(out_width, in_width, drop_factor=0.0))
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# encoder = SpacyVectors \
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# >> flatten_add_lengths \
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# >> with_getitem(0, Affine(in_width, in_width)) \
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# >> ParametricAttention(in_width)\
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# >> Pooling(sum_pool) \
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# >> Residual(ReLu(in_width, in_width)) ** conv_depth \
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# >> Pooling(mean_pool) \
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# >> Residual(zero_init(Maxout(in_width, in_width))) \
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# >> zero_init(Affine(out_width, in_width, drop_factor=0.0))
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encoder = SpacyVectors \
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>> flatten_add_lengths \
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>> with_getitem(0, Affine(in_width, in_width)) \
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>> ParametricAttention(in_width) \
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>> Pooling(sum_pool) \
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>> Residual(ReLu(in_width, in_width)) ** conv_depth \
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>> zero_init(Affine(out_width, in_width, drop_factor=0.0))
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# >> zero_init(Affine(nr_class, width, drop_factor=0.0))
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# >> logistic
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@ -178,20 +179,16 @@ class EL_Model():
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return sgd
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def update(self, article_doc, true_entity_list, false_entities_list, drop=0., losses=None):
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doc_encoding, article_bp = self.article_encoder.begin_update([article_doc], drop=drop)
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doc_encoding = doc_encoding[0]
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# print("doc", doc_encoding)
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for i, true_entity in enumerate(true_entity_list):
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for cnt in range(10):
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#try:
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try:
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false_vectors = list()
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false_entities = false_entities_list[i]
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if len(false_entities) > 0:
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# TODO: batch per doc
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doc_encoding, article_bp = self.article_encoder.begin_update([article_doc], drop=drop)
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doc_encoding = doc_encoding[0]
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print()
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print(cnt)
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print("doc", doc_encoding)
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for false_entity in false_entities:
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# TODO: one call only to begin_update ?
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@ -201,6 +198,7 @@ class EL_Model():
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true_entity_encoding, true_entity_bp = self.entity_encoder.begin_update([true_entity], drop=drop)
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true_entity_encoding = true_entity_encoding[0]
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# true_gradient = self._calculate_true_gradient(doc_encoding, true_entity_encoding)
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all_vectors = [true_entity_encoding]
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all_vectors.extend(false_vectors)
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@ -208,29 +206,37 @@ class EL_Model():
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# consensus_encoding = self._calculate_consensus(doc_encoding, true_entity_encoding)
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true_prob = self._calculate_probability(doc_encoding, true_entity_encoding, all_vectors)
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print("true", true_prob, true_entity_encoding)
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# print("true", true_prob, true_entity_encoding)
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# print("true gradient", true_gradient)
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# print()
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all_probs = [true_prob]
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for false_vector in false_vectors:
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false_prob = self._calculate_probability(doc_encoding, false_vector, all_vectors)
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print("false", false_prob, false_vector)
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# print("false", false_prob, false_vector)
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# print("false gradient", false_gradient)
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# print()
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all_probs.append(false_prob)
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loss = self._calculate_loss(true_prob, all_probs).astype(np.float32)
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if self.PRINT_LOSS:
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print("loss", round(loss, 5))
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print(round(loss, 5))
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doc_gradient = self._calculate_doc_gradient(loss, doc_encoding, true_entity_encoding, false_vectors)
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print("doc_gradient", doc_gradient)
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article_bp([doc_gradient.astype(np.float32)], sgd=self.sgd_article)
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#except Exception as e:
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#pass
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#doc_gradient = self._calculate_doc_gradient(loss, doc_encoding, true_entity_encoding, false_vectors)
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entity_gradient = self._calculate_entity_gradient(doc_encoding, true_entity_encoding, false_vectors)
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# print("entity_gradient", entity_gradient)
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# print("doc_gradient", doc_gradient)
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# article_bp([doc_gradient.astype(np.float32)], sgd=self.sgd_article)
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true_entity_bp([entity_gradient.astype(np.float32)], sgd=self.sgd_entity)
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#true_entity_bp([true_gradient.astype(np.float32)], sgd=self.sgd_entity)
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except Exception as e:
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pass
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# TODO: FIX
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def _calculate_consensus(self, vector1, vector2):
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if len(vector1) != len(vector2):
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raise ValueError("To calculate consenus, both vectors should be of equal length")
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raise ValueError("To calculate consensus, both vectors should be of equal length")
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avg = (vector2 + vector1) / 2
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return avg
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@ -246,12 +252,11 @@ class EL_Model():
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for v in allvectors:
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e_sum += self._calculate_dot_exp(v, vector1_t)
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return float(e / e_sum)
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return float(e / (self.EPS + e_sum))
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@staticmethod
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def _calculate_loss(true_prob, all_probs):
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def _calculate_loss(self, true_prob, all_probs):
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""" all_probs should include true_prob ! """
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return -1 * np.log(true_prob / sum(all_probs))
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return -1 * np.log((self.EPS + true_prob) / (self.EPS + sum(all_probs)))
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@staticmethod
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def _calculate_doc_gradient(loss, doc_vector, true_vector, false_vectors):
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@ -276,9 +281,53 @@ class EL_Model():
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return gradient
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def _calculate_true_gradient(self, doc_vector, entity_vector):
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# sum_entity_vector = sum(entity_vector)
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# gradient = [-sum_entity_vector/(self.EPS + np.exp(doc_vector[i] * entity_vector[i])) for i in range(len(doc_vector))]
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gradient = [1 / (self.EPS + np.exp(doc_vector[i] * entity_vector[i])) for i in range(len(doc_vector))]
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return np.asarray(gradient)
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def _calculate_entity_gradient(self, doc_vector, true_vector, false_vectors):
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entity_gradient = list()
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prob_true = list()
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false_prob_list = list()
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for i in range(len(true_vector)):
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doc_i = np.asarray([doc_vector[i]])
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true_i = np.asarray([true_vector[i]])
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falses_i = np.asarray([[fv[i]] for fv in false_vectors])
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all_i = [true_i]
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all_i.extend(falses_i)
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prob_true_i = self._calculate_probability(doc_i, true_i, all_i)
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prob_true.append(prob_true_i)
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false_list = list()
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all_probs_i = [prob_true_i]
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for false_vector in falses_i:
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false_prob_i = self._calculate_probability(doc_i, false_vector, all_i)
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all_probs_i.append(false_prob_i)
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false_list.append(false_prob_i)
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false_prob_list.append(false_list)
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sign_loss_i = 1
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if doc_vector[i] * true_vector[i] < 0:
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sign_loss_i = -1
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loss_i = sign_loss_i * self._calculate_loss(prob_true_i, all_probs_i).astype(np.float32)
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entity_gradient.append(loss_i)
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# print("prob_true", prob_true)
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# print("false_prob_list", false_prob_list)
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return np.asarray(entity_gradient)
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@staticmethod
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def _calculate_dot_exp(vector1, vector2_transposed):
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e = np.exp(vector1.dot(vector2_transposed))
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dot_product = vector1.dot(vector2_transposed)
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dot_product = min(50, dot_product)
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# dot_product = max(-10000, dot_product)
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# print("DOT", dot_product)
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e = np.exp(dot_product)
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# print("E", e)
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return e
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def _get_training_data(self, training_dir, entity_descr_output, dev, limit, to_print):
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@ -111,7 +111,7 @@ if __name__ == "__main__":
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print("STEP 6: training ", datetime.datetime.now())
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my_nlp = spacy.load('en_core_web_md')
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trainer = EL_Model(kb=my_kb, nlp=my_nlp)
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trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=1, devlimit=5)
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trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=1500, devlimit=50)
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print()
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# STEP 7: apply the EL algorithm on the dev dataset
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