2eb4c5f77bd8da9b1e23851b0acb84543e442953,python/baseline/pytorch/classify/model.py,WordClassifierModelBase,create,#Any#Any#Any#,27

Before Change

        model.pdrop = kwargs.get("pdrop", 0.5)
        model.embeddings = dict()
        model.embeddings["word"] = pytorch_embeddings(embeddings_set["word"], **kwargs)
        input_sz = model.embeddings["word"].get_dsz()
        if "char" in embeddings_set:
            model.embeddings["char"] = pytorch_embeddings(embeddings_set["char"],
                                                          DefaultType=PyTorchCharConvEmbeddings,

After Change

            elif "x" in embeddings:
                model.lengths_key = "x"

        if model.lengths_key is not None:
            // This allows user to short-hand the field to use
            if not model.lengths_key.endswith("_lengths"):
                model.lengths_key += "_lengths"
        input_sz = model._init_embed(embeddings)
        model.gpu = not bool(kwargs.get("nogpu", False))
        model.labels = labels
        model.log_softmax = nn.LogSoftmax(dim=1)

Instances