num_words: int,
    embedding_dim: int,
    num_samples: int,
    sparse: bool = False,
    unk_id: int = None,
    use_character_inputs: bool = True,
    use_fast_sampler: bool = False,
) -> None

Based on the default log_uniform_candidate_sampler in tensorflow.

NOTE: num_words DOES NOT include padding id.

NOTE: In all cases except (tie_embeddings=True and use_character_inputs=False) the weights are dimensioned as num_words and do not include an entry for the padding (0) id. For the (tie_embeddings=True and use_character_inputs=False) case, then the embeddings DO include the extra 0 padding, to be consistent with the word embedding layer.


num_words, int, required The number of words in the vocabulary embedding_dim, int, required The dimension to softmax over num_samples, int, required During training take this many samples. Must be less than num_words. sparse, bool, optional (default = False) If this is true, we use a sparse embedding matrix. unk_id, int, optional (default = None) If provided, the id that represents unknown characters. use_character_inputs, bool, optional (default = True) Whether to use character inputs use_fast_sampler, bool, optional (default = False) Whether to use the fast cython sampler.