o ߥi@sddlmZmZddlZddlmZddlmZddl m Z ddl m Z m Z ddlmZdgZejejejd Gd dde ZdS) )AnyDictN) TaskModels)MODELS) EncoderModel)FeatureExtractionOutput OutputKeys)TasksModelForFeatureExtraction) module_namec@s4eZdZejZdZ          dddZdS)r Nc Ks(|j|||||||| | d } t| jdS)a< Args: input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using :class:`~modelscope.models.nlp.structbert.SbertTokenizer`. See :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for details. attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0, 1]``: - 0 corresponds to a `sentence A` token, - 1 corresponds to a `sentence B` token. position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0, config.max_position_embeddings - 1]``. head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`): Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert :obj:`input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (:obj:`bool`, `optional`): Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned tensors for more detail. output_hidden_states (:obj:`bool`, `optional`): Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for more detail. return_dict (:obj:`bool`, `optional`): Whether or not to return a :class:`~transformers.ModelOutput` instead of a plain tuple. labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ..., config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss), If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Returns `modelscope.outputs.AttentionTextClassificationModelOutput` Examples: >>> from modelscope.models import Model >>> from modelscope.preprocessors import Preprocessor >>> model = Model.from_pretrained('damo/nlp_structbert_sentence-similarity_chinese-base') >>> preprocessor = Preprocessor.from_pretrained('damo/nlp_structbert_sentence-similarity_chinese-base') >>> print(model(**preprocessor(('This is a test', 'This is also a test')))) ) input_idsattention_masktoken_type_ids position_ids head_mask inputs_embedsoutput_attentionsoutput_hidden_states return_dict)text_embedding)extract_featurerlast_hidden_state) selfr r rrrrlabelsrrrkwargsfeaturerh/home/ubuntu/.local/lib/python3.10/site-packages/modelscope/models/nlp/task_models/feature_extraction.pyforwardsI z!ModelForFeatureExtraction.forward) NNNNNNNNNN)__name__ __module__ __qualname__r feature_extractiontask head_typerrrrrr s)typingrrnumpynpmodelscope.metainformodelscope.models.builderr,modelscope.models.nlp.task_models.task_modelrmodelscope.outputsrrmodelscope.utils.constantr __all__register_moduler"r rrrrs