gluon_nlp_blog

gluonnlp.py

import mxnet as mx
import gluonnlp as nlp

# Load GloVe word embeddings
glove = nlp.embedding.create('glove', source='glove.6B.50d')

# Compute 'baby' and 'infant' word embeddings
baby_glove, infant_glove = glove['baby'], glove['infant']

# Load pre-trained AWD LSTM language model and get the embedding
lm_model, lm_vocab = nlp.model.get_model(name='awd_lstm_lm_1150',
                                         dataset_name='wikitext-2',
                                         pretrained=True)
baby_idx, infant_idx = lm_vocab['baby', 'infant']
lm_embedding = lm_model.embedding[0]

# Get the word embeddings of 'baby' and 'infant'
baby_lm, infant_lm = lm_embedding(mx.nd.array([baby_idx, infant_idx]))

# cosine similarity
def cos_similarity(vec1, vec2):
    return mx.nd.dot(vec1, vec2) / (vec1.norm() * vec2.norm())

print(cos_similarity(baby_glove, infant_glove)) # 0.74056691
print(cos_similarity(baby_lm, infant_lm)) # 0.3729561