a947d66d28baaae1302363556a8a18b04fa6aa40,gluonnlp/embedding/evaluation.py,ThreeCosMul,__init__,#ThreeCosMul#Any#Any#Any#,249

Before Change

        self.k = k
        self.eps = eps

        self._vocab_size, self._embed_size = idx_to_vec.shape

        idx_to_vec = mx.nd.L2Normalization(idx_to_vec, eps=self.eps)
        with self.name_scope():
            self.weight = self.params.get_constant("weight", idx_to_vec)

    def hybrid_forward(self, F, words1, words2, words3, weight):  // pylint: disable=arguments-differ
        Implement forward computation.
        words123 = F.concat(words1, words2, words3, dim=0)
        embeddings_words123 = F.Embedding(words123, weight,

After Change

    Parameters
    ----------
    kind : ["similarity", "analogy", None]
        Return only valid names for similarity, analogy or both kinds of functions.

    Returns
    -------
    dict or list:
        A list of all the valid evaluation function names for the specified
        kind. If kind is set to None, returns a dict mapping each valid name to
        its respective output list. The valid names can be plugged in
        `gluonnlp.model.word_evaluation_model.create(name)`.

    

Instances