Learn embeddings with Word2Vec.
(walks, dimensions, **skip_gram_params)
| 293 | |
| 294 | |
| 295 | def learn_embeddings(walks, dimensions, **skip_gram_params): |
| 296 | """ |
| 297 | Learn embeddings with Word2Vec. |
| 298 | """ |
| 299 | from gensim.models import Word2Vec |
| 300 | |
| 301 | walks = [list(map(str, walk)) for walk in walks] |
| 302 | |
| 303 | if "vector_size" not in skip_gram_params: |
| 304 | skip_gram_params["vector_size"] = dimensions |
| 305 | |
| 306 | model = Word2Vec(walks, **skip_gram_params) |
| 307 | |
| 308 | return model |