(self, embed_size=128, window_size=5, workers=3, iter=5)
| 107 | pd.to_pickle(gamma, self.temp_path + 'gamma.pkl') |
| 108 | |
| 109 | def train(self, embed_size=128, window_size=5, workers=3, iter=5): |
| 110 | |
| 111 | # pd.read_pickle(self.temp_path+'walks.pkl') |
| 112 | sentences = self.sentences |
| 113 | |
| 114 | print("Learning representation...") |
| 115 | model = Word2Vec(sentences, vector_size=embed_size, window=window_size, min_count=0, hs=1, sg=1, |
| 116 | workers=workers, |
| 117 | epochs=iter) |
| 118 | print("Learning representation done!") |
| 119 | self.w2v_model = model |
| 120 | |
| 121 | return model |
| 122 | |
| 123 | def get_embeddings(self, ): |
| 124 | if self.w2v_model is None: |
no outgoing calls