9i!SSKrSSKJr SSKJr SSKJrJr SSKJ r SSK J r SSK J r SSKJr S S KJr \ R$"\ R&\R(S 9"S S \\55rg)N)RobertaForTokenClassification)Models)Model TorchModel)MODELS)'AttentionTokenClassificationModelOutput)Tasks)parse_labels_in_order) VecoConfig) module_namecR^\rSrSrSr\rU4SjrU4Sjr\ U4Sj5r Sr U=r $)VecoForTokenClassificationaDVeco Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`VecoConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. This class overrides [`RobertaForTokenClassification`]. Please check the superclass for the appropriate documentation alongside usage examples. c b>[TU]"UR40UD6 [[U]U5 g)N)super__init__ name_or_pathr)selfconfigkwargs __class__s o/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/modelscope/models/nlp/veco/token_classification.pyr#VecoForTokenClassification.__init__7s* ,,77 eT#F+c>SUS'[[U] "U0UD6n[URUR UR URS9$)NT return_dict)losslogits hidden_states attentions)rrforwardrrrr r!)rargsroutputsrs rr""VecoForTokenClassification.forward;sP $}t,d=f=6>>!//))   rc >URSS5nURSS5n[X#40UD6nUc[S0UD6nU"U5nU$[[U]"SSU0UD6nU$)a$Instantiate the model. Args: kwargs: Input args. model_dir: The model dir used to load the checkpoint and the label information. num_labels: An optional arg to tell the model how many classes to initialize. Method will call utils.parse_label_mapping if num_labels is not input. label2id: An optional label2id mapping, which will cover the label2id in configuration (if exists). Returns: The loaded model, which is initialized by transformers.PreTrainedModel.from_pretrained model_dirNcfgpretrained_model_name_or_path)popr r rrfrom_pretrained)clsrr'r( model_argsrmodelrs r _instantiate'VecoForTokenClassification._instantiateFsJJ{D1 jj%*9DVD  -*-FKE %5G.7G;EGE rr*) __name__ __module__ __qualname____firstlineno____doc__r config_classrr" classmethodr0__static_attributes__ __classcell__)rs@rrrs-*L,  rr)torch transformersrmodelscope.metainformodelscope.modelsrrmodelscope.models.builderrmodelscope.outputsrmodelscope.utils.constantr modelscope.utils.nlp.utilsr configurationr register_moduletoken_classificationvecorr*rrrGs[$ 6&/,F+<%22 L@-J@M@r