Returns: Returns `modelscope.outputs.AttentionBackboneModelOutputWithEmbedding` Examples: >>> from modelscope.models import Model >>> from modelscope.preprocessors import Preprocessor >>> model = Model.from_pretrained('damo/nlp_veco_f
(self, *args, **kwargs)
| 60 | super(Model, self).__init__(config) |
| 61 | |
| 62 | def forward(self, *args, **kwargs): |
| 63 | """ |
| 64 | Returns: |
| 65 | Returns `modelscope.outputs.AttentionBackboneModelOutputWithEmbedding` |
| 66 | |
| 67 | Examples: |
| 68 | >>> from modelscope.models import Model |
| 69 | >>> from modelscope.preprocessors import Preprocessor |
| 70 | >>> model = Model.from_pretrained('damo/nlp_veco_fill-mask-large', task='backbone') |
| 71 | >>> preprocessor = Preprocessor.from_pretrained('damo/nlp_veco_fill-mask-large') |
| 72 | >>> print(model(**preprocessor('这是个测试'))) |
| 73 | |
| 74 | """ |
| 75 | kwargs['return_dict'] = True |
| 76 | outputs = super(Model, self).forward(*args, **kwargs) |
| 77 | return AttentionBackboneModelOutput( |
| 78 | last_hidden_state=outputs.last_hidden_state, |
| 79 | pooler_output=outputs.pooler_output, |
| 80 | past_key_values=outputs.past_key_values, |
| 81 | hidden_states=outputs.hidden_states, |
| 82 | attentions=outputs.attentions, |
| 83 | cross_attentions=outputs.cross_attentions, |
| 84 | ) |
| 85 | |
| 86 | @classmethod |
| 87 | def _instantiate(cls, **kwargs): |
nothing calls this directly
no test coverage detected