7a4f4148317f7274c0c88095c037e93f95b1d00d,python/mead/tasks.py,Task,_create_embeddings,#Task#Any#Any#Any#,153

Before Change

                embeddings[name] = baseline.RandomInitVecModel(dsz, vocabs[name], unif_weight=unif)

        out_vocabs = {}
        for key, value in embeddings.items():
            out_vocabs[key] = value.vocab
        return embeddings, out_vocabs

    @staticmethod
    def _log2json(log):

After Change

        logging.basicConfig(level=logging.DEBUG)

    def _create_embeddings(self, embeddings_set, vocabs, features):
        backend = self.config_params.get("backend", "tensorflow")

        if backend == "pytorch":
            import baseline.pytorch.embeddings as embeddings
        else:

Instances