o *j@sddlmZddlZddlmZddlmZddlmZddlm Z ddl m Z m Z dd l mZmZmZd gZe je jejd Gd d d eZdS) )deepcopyN)nn)Models) TorchModel)MODELS) ModelFileTasks) BertEncoder BertModelBertPreTrainedModelSiameseUieModel) module_namecsXeZdZdZfddZdddZddZd d Z dd dZddZ ddZ Z S)r aSiameseUIE general information extraction model, based on the construction idea of prompt (Prompt) + text (Text), uses pointer network (Pointer Network) to realize segment extraction (Span Extraction), so as to realize named entity recognition (NER), relation extraction (RE), Extraction of various tasks such as event extraction (EE), attribute sentiment extraction (ABSA), etc. Different from the existing general information extraction tasks on the market: c s\t|||_t|jdd|_t|j|_t |j d|_ t |j d|_ | dS)NT)Zadd_pooling_layerr )super__init__configr plmrZDropoutZhidden_dropout_probZdropoutZLinearZ hidden_size head_clsf tail_clsfset_crossattention_layer)selfrkwargs __class__g/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/modelscope/models/nlp/bert/siamese_uie.pyrs  zSiameseUieModel.__init__cCsdt|j}||_|jj|8_t||_|jjj|jjd|j_|jjjd|jj|jj_dS)N)rrnum_hidden_layersr crossattentionrencoderlayer)rrZcrossattention_configrrrr%s    z(SiameseUieModel.set_crossattention_layerc Cs|d}||d}||d}dd||}||d}|d|d}t|ddddf}tj||gdd}tj||gdd}tj|dd}tj|dd}||S)Nrr gmBdim)sizeviewtorchZ zeros_likecatZ logsumexpmean) rZy_predZy_trueZ batch_sizeZ y_pred_negZ y_pred_posZzerosZneg_lossZpos_lossrrr circle_loss/s     zSiameseUieModel.circle_losscCsrtj||gdd}tj||gdd}|j||dd}|j||ddddd|dddf}|S)Nr r#r") hidden_statesattention_maskr)r'r(rZget_extended_attention_maskr%r)rr+r,Zencoder_hidden_statesZencoder_attention_maskZcat_hidden_statesZcat_attention_maskrrrget_cross_attention_output=s$ z*SiameseUieModel.get_cross_attention_outputNFcCs,|rt|nd}|j||||dd}|S)N)r,token_type_ids position_idsr)r'Z ones_liker)r input_idsr,r/is_hintr.sequence_outputrrrget_plm_sequence_outputLsz'SiameseUieModel.get_plm_sequence_outputcCs|||}|d|ddksJt|dd|d}||j}|j|||dd} |||| |}|| d} | | d} |j } | | |} | | |}d| |iS)atrain forward Args: input_ids (Tensor): input token ids of text. attention_masks (Tensor): attention_masks of text. hint_ids (Tensor): input token ids of prompt. cross_attention_masks (Tensor): attention_masks of prompt. head_labels (Tensor): labels of start position. tail_labels (Tensor): labels of end position. Returns: Dict[str, float]: the loss Example: {"loss": 0.5091743} r ir r!Tr1r!Zloss) r3r%r'arangeexpandtodevicer-rsqueezerr*)rr0attention_maskshint_idscross_attention_masksZ head_labelsZ tail_labelsr2r/hint_sequence_output head_logits tail_logitsZ loss_funcZ head_lossZ tail_lossrrrforwardYs.    zSiameseUieModel.forwardc Cst|dd|d}||j}|j|||dd}|||||}|| d}| | d}|d|d} |d|d} | | fS)ao Args: sequence_output(tensor): 3-dimension tensor (batch size, sequence length, hidden size) attention_masks(tensor): attention mask, 2-dimension tensor (batch size, sequence length) hint_ids(tensor): token ids of prompt 2-dimension tensor (batch size, sequence length) cross_attention_masks(tensor): cross attention mask, 2-dimension tensor (batch size, sequence length) Default Returns: head_probs(tensor): 2-dimension tensor(batch size, sequence length) tail_probs(tensor): 2-dimension tensor(batch size, sequence length) r r4Tr5r!i) r'r6r%r7r8r9r3r-rr:r) rr2r;r<r=r/r>r?r@Z head_probsZ tail_probsrrrfast_inference}s$  zSiameseUieModel.fast_inference)r)NF) __name__ __module__ __qualname____doc__rrr*r-r3rArB __classcell__rrrrr s    $)copyrr'rZmodelscope.metainforZmodelscope.models.baserZmodelscope.models.builderrZmodelscope.utils.constantrrZbackboner r r __all__Zregister_moduleZ siamese_uieZbertr rrrrs