1d18d40da194082c2eda8a343ffae85b6c5c634e,pytext/contrib/pytext_lib/models/intent_slot_model.py,IntentSlotJointModel,forward,#IntentSlotJointModel#Any#Any#,100

Before Change

        all_intents: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        tokens = inputs["token_ids"]
        slots_batch = torch.tensor([])
        intent_batch = torch.tensor([])
        word_embedding_output = self.word_embedding(tokens)
        sentence_encodings = self.doc_output.encode(word_embedding_output)
        word_encodings = self.slot_output.encode(word_embedding_output)

After Change

    ) -> Tuple[torch.Tensor, torch.Tensor]:
        tokens = inputs["token_ids"]
        if len(list(tokens.size())) == 1:
            tokens = tokens.unsqueeze(0)
        empty_list: List[float] = []
        slots_batch: torch.Tensor = torch.tensor(empty_list)
        intent_batch: torch.Tensor = torch.tensor(empty_list)
        word_embedding_output = self.word_embedding(tokens)

Instances