def gen_embedding(word): ''' Generates embedding of the word from the model trained. ''' try: with open('./embeddings.pickle', 'rb') as f: embeddings = pickle.load(f) return embeddings[word] except Exception as e: print ("Exception: Model file not found, please train the model first by runing train")
def gen_embedding(word): ''' Generates embedding of the word from the model trained. ''' try: with open('./embeddings.pickle', 'rb') as f: embeddings = pickle.load(f) return embeddings[word] except Exception as e: print( "Exception: Model file not found, please train the model first by runing train" )
def closest_words(word,topn=10): ''' Returns top 10 closest words to word provided by user. ''' try: with open("./embeddings.pickle", "rb") as f: embeddings = pickle.load(f) words = embeddings.keys() closest = [] # Embedding of word provided by user vec = embeddings[word] for word in words: heapq.heappush(closest, (1 - cosine(vec, embeddings[word]), word)) closest_words = heapq.nlargest(topn, closest) return closest_words except Exception as e: print ("Exception: Check if model file is present, if not them train the model first, if present, then vocabulary issue, queried word not present in vocab")
def closest_words(word,topn=10): ''' Returns top 10 closest words to word provided by user. ''' try: with open("./embeddings.pickle", "rb") as f: embeddings = pickle.load(f) words = embeddings.keys() closest = [] # Embedding of word provided by user vec = embeddings[word] for word in words: heapq.heappush(closest, (1 - cosine(vec, embeddings[word]), word)) closest_words = heapq.nlargest(topn, closest) return closest_words except Exception as e: print ("Exception: Model file not found, please train the model by running train function")
def closest_words(word, topn=10): ''' Returns top 10 closest words to word provided by user. ''' try: with open("./embeddings.pickle", "rb") as f: embeddings = pickle.load(f) words = embeddings.keys() closest = [] # Embedding of word provided by user vec = embeddings[word] for word in words: heapq.heappush(closest, (1 - cosine(vec, embeddings[word]), word)) closest_words = heapq.nlargest(topn, closest) return closest_words except Exception as e: print( "Exception: Check if model file is present, if not them train the model first, if present, then vocabulary issue, queried word not present in vocab" )
def closest_words(word, topn=10): ''' Returns top 10 closest words to word provided by user. ''' try: with open("./embeddings.pickle", "rb") as f: embeddings = pickle.load(f) words = embeddings.keys() closest = [] # Embedding of word provided by user vec = embeddings[word] for word in words: heapq.heappush(closest, (1 - cosine(vec, embeddings[word]), word)) closest_words = heapq.nlargest(topn, closest) return closest_words except Exception as e: print( "Exception: Model file not found, please train the model by running train function" )