o Zh`@svdZddlZddlmZddlmZmZmZddlZddl Zddlm Z ddl m Z m Z mZddlmZmZdd lmZmZdd lmZdd lmZdd lmZmZmZd dlmZee Z!eGdddeZ"eGdddeZ#eGdddeZ$eGdddeZ%eGdddeZ&eGdddeZ'eGdddeZ(eGdddeZ)eGdd d eZ*eGd!d"d"eZ+Gd#d$d$e j,Z-Gd%d&d&e j,Z.Gd'd(d(e j,Z/Gd)d*d*e j,Z0Gd+d,d,e j,Z1Gd-d.d.e j,Z2Gd/d0d0e j,Z3Gd1d2d2e j,Z4Gd3d4d4e j,Z5Gd5d6d6e j,Z6Gd7d8d8e j,Z7Gd9d:d:e j,Z8eGd;d<dGd?d@d@e9Z:dAdBZ;GdCdDdDe j,ZGdFdGdGe9Z=edHd>GdIdJdJe9Z>edKd>GdLdMdMe9Z?edNd>GdOdPdPe9Z@edQd>GdRdSdSe9ZAedTd>GdUdVdVe9ZBeGdWdXdXe9ZCeGdYdZdZe9ZDgd[ZEdS)\zPyTorch LUKE model.N) dataclass)OptionalTupleUnion)nn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FNgelu)BaseModelOutputBaseModelOutputWithPooling)PreTrainedModel)apply_chunking_to_forward) ModelOutputauto_docstringlogging) LukeConfigc@>eZdZUdZdZeejed<dZ ee ejdfed<dS)BaseLukeModelOutputWithPoolinga Base class for outputs of the LUKE model. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. entity_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, entity_length, hidden_size)`): Sequence of entity hidden-states at the output of the last layer of the model. pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length + entity_length, sequence_length + entity_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nentity_last_hidden_state.entity_hidden_states __name__ __module__ __qualname____doc__rrtorch FloatTensor__annotations__rrr"r"U/var/www/auris/lib/python3.10/site-packages/transformers/models/luke/modeling_luke.pyr% rc@r)BaseLukeModelOutputa# Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. entity_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, entity_length, hidden_size)`): Sequence of entity hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr.rrr"r"r"r#r%Dr$r%c@seZdZUdZdZeejed<dZ eejed<dZ eejed<dZ eejed<dZ eejed<dZ eeejed<dZeeejd fed <dZeeejd fed <dS) LukeMaskedLMOutputa> Base class for model's outputs, with potential hidden states and attentions. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): The sum of masked language modeling (MLM) loss and entity prediction loss. mlm_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Masked language modeling (MLM) loss. mep_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Masked entity prediction (MEP) loss. logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). entity_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the entity prediction head (scores for each entity vocabulary token before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nlossmlm_lossmep_losslogits entity_logits hidden_states.r attentions)rrrrr'rrr r!r(r)r*r+r,rrr-r"r"r"r#r&cs r&c@eZdZUdZdZeejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<dS) EntityClassificationOutputay Outputs of entity classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr'r*.r,rr-rrrrr'rrr r!r*r,rrr-r"r"r"r#r/ r/c@r.) EntityPairClassificationOutputa~ Outputs of entity pair classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr'r*.r,rr-r0r"r"r"r#r2r1r2c@r.) EntitySpanClassificationOutputa Outputs of entity span classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, entity_length, config.num_labels)`): Classification scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr'r*.r,rr-r0r"r"r"r#r3r1r3c@r.) LukeSequenceClassifierOutputa Outputs of sentence classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr'r*.r,rr-r0r"r"r"r#r4 r4c@r.) LukeTokenClassifierOutputa Base class for outputs of token classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`): Classification scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr'r*.r,rr-r0r"r"r"r#r6 r5r6c@seZdZUdZdZeejed<dZ eejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZee ejdfed <dS) LukeQuestionAnsweringModelOutputay Outputs of question answering models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Span-start scores (before SoftMax). end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Span-end scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr' start_logits end_logits.r,rr-)rrrrr'rrr r!r8r9r,rrr-r"r"r"r#r7/s r7c@r.) LukeMultipleChoiceModelOutputa  Outputs of multiple choice models. Args: loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided): Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`): *num_choices* is the second dimension of the input tensors. (see *input_ids* above). Classification scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr'r*.r,rr-r0r"r"r"r#r:Ts r:cs:eZdZdZfddZ    d ddZddZZS) LukeEmbeddingszV Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. csttj|j|j|jd|_t|j|j|_ t|j |j|_ tj |j|j d|_ t|j|_|j|_tj|j|j|jd|_ dS)N padding_idxZeps)super__init__r Embedding vocab_size hidden_sizeZ pad_token_idword_embeddingsmax_position_embeddingsposition_embeddingstype_vocab_sizetoken_type_embeddings LayerNormlayer_norm_epsDropouthidden_dropout_probdropoutr=selfconfig __class__r"r#r@}s   zLukeEmbeddings.__init__Nc Cs|dur|durt||j|j}n||}|dur!|}n|dd}|dur8tj|tj|j jd}|durA| |}| |}| |}|||}| |}||}|S)Ndtypedevice)"create_position_ids_from_input_idsr=torV&create_position_ids_from_inputs_embedssizerzeroslong position_idsrDrFrHrIrM) rO input_idstoken_type_idsr] inputs_embeds input_shaperFrH embeddingsr"r"r#forwards"        zLukeEmbeddings.forwardcCsN|dd}|d}tj|jd||jdtj|jd}|d|S)z We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Args: inputs_embeds: torch.Tensor Returns: torch.Tensor NrSrrTr)rZrZaranger=r\rV unsqueezeexpand)rOr`raZsequence_lengthr]r"r"r#rYs  z5LukeEmbeddings.create_position_ids_from_inputs_embeds)NNNN)rrrrr@rcrY __classcell__r"r"rQr#r;xs  !r;csFeZdZdeffdd Z d dejdejdeejfdd ZZ S) LukeEntityEmbeddingsrPcst||_tj|j|jdd|_|j|jkr$tj |j|jdd|_ t|j |j|_ t|j |j|_tj|j|jd|_t|j|_dS)Nrr<Fbiasr>)r?r@rPrrAentity_vocab_sizeentity_emb_sizeentity_embeddingsrCLinearentity_embedding_denserErFrGrHrIrJrKrLrMrNrQr"r#r@s  zLukeEntityEmbeddings.__init__N entity_idsr]r_c Cs|dur t|}||}|jj|jjkr||}||jdd}|dk | d}||}tj |dd}||j ddjdd}| |}|||}| |}||}|S)Nr)minrSdimgHz>)rZ zeros_likerlrPrkrCrnrFclamptype_asrdsumrHrIrM) rOror]r_rlrFZposition_embedding_maskrHrbr"r"r#rcs       zLukeEntityEmbeddings.forwardN) rrrrr@r LongTensorrrcrfr"r"rQr#rgsrgc4eZdZfddZddZ   d ddZZS) LukeSelfAttentioncst|j|jdkrt|dstd|jd|jd|j|_t|j|j|_|j|j|_|j |_ t |j|j|_ t |j|j|_ t |j|j|_|j rpt |j|j|_t |j|j|_t |j|j|_t |j|_dS)NrZembedding_sizezThe hidden size z4 is not a multiple of the number of attention heads .)r?r@rCnum_attention_headshasattr ValueErrorintattention_head_size all_head_sizeuse_entity_aware_attentionrrmquerykeyvalue w2e_query e2w_query e2e_queryrKZattention_probs_dropout_probrMrNrQr"r#r@s&  zLukeSelfAttention.__init__cCs6|dd|j|jf}|j|}|ddddS)NrSrrr )rZr|rviewpermute)rOxZ new_x_shaper"r"r#transpose_for_scoress z&LukeSelfAttention.transpose_for_scoresNFc Cs|d}|dur |}n tj||gdd}|||}|||} |jr|dur|||} |||} || |} || |} |ddddd|ddf}|ddddd|ddf}|dddd|dddf}|dddd|dddf}t | | dd}t | | dd}t | | dd}t | | dd}tj||gdd}tj||gdd}tj||gdd}n|||}t || dd}|t |j}|dur||}tjj|dd}||}|dur||}t || }|dddd}|dd|jf}|j|}|ddd|ddf}|dur>d}n |dd|dddf}|rU|||f}|S||f}|S)NrrrrSrqr rr)rZrcatrrrrrrrrmatmulZ transposemathsqrtrr functionalZsoftmaxrMr contiguousrr) rOword_hidden_statesrattention_mask head_maskoutput_attentions word_sizeconcat_hidden_statesZ key_layerZ value_layerZw2w_query_layerZw2e_query_layerZe2w_query_layerZe2e_query_layerZ w2w_key_layerZ e2w_key_layerZ w2e_key_layerZ e2e_key_layerZw2w_attention_scoresZw2e_attention_scoresZe2w_attention_scoresZe2e_attention_scoresZword_attention_scoresZentity_attention_scoresZattention_scoresZ query_layerZattention_probsZ context_layerZnew_context_layer_shapeZoutput_word_hidden_statesZoutput_entity_hidden_statesoutputsr"r"r#rc sX           zLukeSelfAttention.forwardNNF)rrrr@rrcrfr"r"rQr#rzs  rzc8eZdZfddZdejdejdejfddZZS)LukeSelfOutputcsBtt|j|j|_tj|j|jd|_t|j |_ dSNr>) r?r@rrmrCdenserIrJrKrLrMrNrQr"r#r@_ zLukeSelfOutput.__init__r, input_tensorreturncC&||}||}|||}|SrwrrMrIrOr,rr"r"r#rce  zLukeSelfOutput.forwardrrrr@rZTensorrcrfr"r"rQr#r^ $rcry) LukeAttentioncs*tt||_t||_t|_dSrw)r?r@rzrOroutputsetZ pruned_headsrNrQr"r#r@ms    zLukeAttention.__init__cCtdNz4LUKE does not support the pruning of attention headsNotImplementedError)rOZheadsr"r"r# prune_headsszLukeAttention.prune_headsNFcCs|d}||||||}|dur|d}|} ntj|dddd}tj||gdd} ||| } | ddd|ddf} |durGd} n | dd|dddf} | | f|dd} | S)Nrrrrr)rZrOrrr)rOrrrrrrZ self_outputsZconcat_self_outputsrattention_outputZword_attention_outputZentity_attention_outputrr"r"r#rcvs(  zLukeAttention.forwardr)rrrr@rrcrfr"r"rQr#rls rc2eZdZfddZdejdejfddZZS)LukeIntermediatecsDtt|j|j|_t|jt rt |j|_ dS|j|_ dSrw) r?r@rrmrCintermediate_sizer isinstance hidden_actstrr intermediate_act_fnrNrQr"r#r@s   zLukeIntermediate.__init__r,rcCs||}||}|Srw)rrrOr,r"r"r#rcs  zLukeIntermediate.forwardrr"r"rQr#rs rcr) LukeOutputcsBtt|j|j|_tj|j|jd|_t |j |_ dSr) r?r@rrmrrCrrIrJrKrLrMrNrQr"r#r@rzLukeOutput.__init__r,rrcCrrwrrr"r"r#rcrzLukeOutput.forwardrr"r"rQr#rrrcs4eZdZfddZ   d ddZddZZS) LukeLayercs:t|j|_d|_t||_t||_t||_ dSNr) r?r@chunk_size_feed_forward seq_len_dimr attentionr intermediaterrrNrQr"r#r@s   zLukeLayer.__init__NFc Cs|d}|j|||||d}|dur|d}n tj|dddd}|dd} t|j|j|j|} | ddd|ddf} |durGd} n | dd|dddf} | | f| } | S)Nr)rrrrr)rZrrrrfeed_forward_chunkrr) rOrrrrrrZself_attention_outputsZconcat_attention_outputr layer_outputZword_layer_outputZentity_layer_outputr"r"r#rcs*    zLukeLayer.forwardcCs||}|||}|Srw)rr)rOrZintermediate_outputrr"r"r#rs  zLukeLayer.feed_forward_chunkr)rrrr@rcrrfr"r"rQr#rs   %rcs0eZdZfddZ     dddZZS) LukeEncodercs:t|_tfddtjD|_d|_dS)Ncsg|]}tqSr")r).0_rPr"r# sz(LukeEncoder.__init__..F) r?r@rPrZ ModuleListrangenum_hidden_layerslayergradient_checkpointingrNrQrr#r@s   zLukeEncoder.__init__NFTc Cs |rdnd}|r dnd} |rdnd} t|jD]I\} } |r'||f}| |f} |dur/|| nd} |jrC|jrC|| j|||| |}n| |||| |}|d}|durW|d}|r`| |df} q|rm||f}| |f} |s}tdd||| || fDSt||| || dS)Nr"rrrcs|] }|dur|VqdSrwr"rvr"r"r# (z&LukeEncoder.forward..)last_hidden_stater,r-rr) enumeraterrZtrainingZ_gradient_checkpointing_func__call__tupler%)rOrrrrroutput_hidden_states return_dictZall_word_hidden_statesZall_entity_hidden_statesZall_self_attentionsiZ layer_moduleZlayer_head_maskZ layer_outputsr"r"r#rcsb         zLukeEncoder.forward)NNFFTrrrr@rcrfr"r"rQr#rs  rcr) LukePoolercs*tt|j|j|_t|_dSrw)r?r@rrmrCrZTanh activationrNrQr"r#r@>s zLukePooler.__init__r,rcCs(|dddf}||}||}|S)Nr)rr)rOr,Zfirst_token_tensor pooled_outputr"r"r#rcCs  zLukePooler.forwardrr"r"rQr#r=s rc$eZdZfddZddZZS)EntityPredictionHeadTransformcsVtt|j|j|_t|jt rt |j|_ n|j|_ tj |j|j d|_ dSr)r?r@rrmrCrkrrrrr transform_act_fnrIrJrNrQr"r#r@Ms  z&EntityPredictionHeadTransform.__init__cCs"||}||}||}|Srw)rrrIrr"r"r#rcVs   z%EntityPredictionHeadTransform.forwardrr"r"rQr#rLs  rcr)EntityPredictionHeadcsHt||_t||_tj|j|jdd|_ t t |j|_ dS)NFrh)r?r@rPr transformrrmrkrjdecoder Parameterrr[rirNrQr"r#r@^s  zEntityPredictionHead.__init__cCs||}|||j}|Srw)rrrirr"r"r#rces zEntityPredictionHead.forwardrr"r"rQr#r]s rc@s0eZdZeZdZdZddgZdej fddZ dS) LukePreTrainedModellukeTrrgmodulecCst|tjr |jjjd|jjd|jdur|jj dSdSt|tj rO|j dkr2|jj n |jjjd|jjd|j durM|jj|j  dSdSt|tj rd|jj |jjddSdS)zInitialize the weightsg)meanZstdNr?)rrrmweightdataZnormal_rPZinitializer_rangeriZzero_rAZ embedding_dimr=rIZfill_)rOrr"r"r# _init_weightsss       z!LukePreTrainedModel._init_weightsN) rrrrZ config_classZbase_model_prefixZsupports_gradient_checkpointingZ_no_split_modulesrModulerr"r"r"r#rls rzt The bare LUKE model transformer outputting raw hidden-states for both word tokens and entities without any )Z custom_introc seZdZd$dedeffdd ZddZdd Zd d Zd d Z ddZ e             d%de e jde e jde e jde e jde e jde e jde e jde e jde e jde e jde ede ede edeeeffdd Zd!e jde e jfd"d#ZZS)& LukeModelTrPadd_pooling_layercsNt|||_t||_t||_t||_|rt |nd|_ | dS)zv add_pooling_layer (bool, *optional*, defaults to `True`): Whether to add a pooling layer N) r?r@rPr;rbrgrlrencoderrpooler post_init)rOrPrrQr"r#r@s     zLukeModel.__init__cC|jjSrwrbrDrOr"r"r#get_input_embeddingsrzLukeModel.get_input_embeddingscC ||j_dSrwrrOrr"r"r#set_input_embeddings zLukeModel.set_input_embeddingscCs|jjSrwrlrr"r"r#get_entity_embeddingsrzLukeModel.get_entity_embeddingscCs ||j_dSrwrrr"r"r#set_entity_embeddingsrzLukeModel.set_entity_embeddingscCrrr)rOZheads_to_pruner"r"r# _prune_headsrzLukeModel._prune_headsNr^rr_r]roentity_attention_maskentity_token_type_idsentity_position_idsrr`rrrrc Cs| dur| n|jj} | dur| n|jj} | dur| n|jj} |dur*| dur*td|dur9||||}n| durF| dd}ntd|\}}|durU|jn| j}|duretj ||f|d}|durrtj |tj |d}|dur|d}|durtj ||f|d}|durtj ||ftj |d}| | |jj } |j|||| d}|||}|durd}n||||}|j|||| | | | d }|d }|jdur||nd}| s||f|ddSt|||j|j|j|jd S) uz entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`): Indices of entity tokens in the entity vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*): Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`: - 1 for entity tokens that are **not masked**, - 0 for entity tokens that are **masked**. entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*): Segment token indices to indicate first and second portions of the entity token inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *portion A* entity token, - 1 corresponds to a *portion B* entity token. entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*): Indices of positions of each input entity in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. Examples: ```python >>> from transformers import AutoTokenizer, LukeModel >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-base") >>> model = LukeModel.from_pretrained("studio-ousia/luke-base") # Compute the contextualized entity representation corresponding to the entity mention "Beyoncé" >>> text = "Beyoncé lives in Los Angeles." >>> entity_spans = [(0, 7)] # character-based entity span corresponding to "Beyoncé" >>> encoding = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt") >>> outputs = model(**encoding) >>> word_last_hidden_state = outputs.last_hidden_state >>> entity_last_hidden_state = outputs.entity_last_hidden_state # Input Wikipedia entities to obtain enriched contextualized representations of word tokens >>> text = "Beyoncé lives in Los Angeles." >>> entities = [ ... "Beyoncé", ... "Los Angeles", ... ] # Wikipedia entity titles corresponding to the entity mentions "Beyoncé" and "Los Angeles" >>> entity_spans = [ ... (0, 7), ... (17, 28), ... ] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles" >>> encoding = tokenizer( ... text, entities=entities, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt" ... ) >>> outputs = model(**encoding) >>> word_last_hidden_state = outputs.last_hidden_state >>> entity_last_hidden_state = outputs.entity_last_hidden_state ```NzDYou cannot specify both input_ids and inputs_embeds at the same timerSz5You have to specify either input_ids or inputs_embeds)rVrTr)r^r]r_r`)rrrrrr)r pooler_outputr,r-rr)rPrruse_return_dictr~Z%warn_if_padding_and_no_attention_maskrZrVrZonesr[r\Z get_head_maskrrbget_extended_attention_maskrlrrrr,r-rr)rOr^rr_r]rorrrrr`rrrraZ batch_sizeZ seq_lengthrVZentity_seq_lengthZword_embedding_outputextended_attention_maskZentity_embedding_outputZencoder_outputssequence_outputrr"r"r#rcspI     zLukeModel.forwardword_attention_maskcCs|}|durtj||gdd}|dkr$|dddddddf}n|dkr7|ddddddf}n td|jd|j|jd}d |t|jj}|S) ac Makes broadcastable attention and causal masks so that future and masked tokens are ignored. Arguments: word_attention_mask (`torch.LongTensor`): Attention mask for word tokens with ones indicating tokens to attend to, zeros for tokens to ignore. entity_attention_mask (`torch.LongTensor`, *optional*): Attention mask for entity tokens with ones indicating tokens to attend to, zeros for tokens to ignore. Returns: `torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`. NrSrrr rz&Wrong shape for attention_mask (shape ))rUr) rrrsr~shaperXrUZfinforp)rOrrrrr"r"r#rGs  z%LukeModel.get_extended_attention_mask)T) NNNNNNNNNNNNN)rrrrboolr@rrrrrrrrrxr rrrrcrrfr"r"rQr#rsp       rcCs2||}tj|dd||}||S)a Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: torch.Tensor x: Returns: torch.Tensor rrr)nerrZcumsumrur\)r^r=maskZincremental_indicesr"r"r#rWfs  rWcs0eZdZdZfddZddZddZZS) LukeLMHeadz*Roberta Head for masked language modeling.csdtt|j|j|_tj|j|jd|_t|j|j |_ t t |j |_|j|j _dSr)r?r@rrmrCrrIrJ layer_normrBrrrr[rirNrQr"r#r@zs zLukeLMHead.__init__cKs*||}t|}||}||}|Srw)rr r r)rOfeatureskwargsrr"r"r#rcs   zLukeLMHead.forwardcCs,|jjjjdkr|j|j_dS|jj|_dS)Nmeta)rrirVtyperr"r"r# _tie_weightsszLukeLMHead._tie_weights)rrrrr@rcrrfr"r"rQr#r ws    r z The LUKE model with a language modeling head and entity prediction head on top for masked language modeling and masked entity prediction. c$s eZdZgdZfddZfddZddZdd Ze dd e e j d e e j d e e j de e j de e j de e j de e j de e j de e j de e j de e j de e j de e de e de e deeeff ddZZS)LukeForMaskedLM)zlm_head.decoder.weightzlm_head.decoder.biasz!entity_predictions.decoder.weightcs@t|t||_t||_t||_t |_ | dSrw) r?r@rrr lm_headrentity_predictionsrrloss_fnrrNrQr"r#r@s      zLukeForMaskedLM.__init__cs$t||jj|jjjdSrw)r? tie_weightsZ_tie_or_clone_weightsrrrrlrrQr"r#rs zLukeForMaskedLM.tie_weightscCrrwrrrr"r"r#get_output_embeddingsrz%LukeForMaskedLM.get_output_embeddingscCrrwr)rOZnew_embeddingsr"r"r#set_output_embeddingsrz%LukeForMaskedLM.set_output_embeddingsNr^rr_r]rorrrlabels entity_labelsrr`rrrrcCs.|dur|n|jj}|j||||||||| | | |dd }d}d}||j}| durE| |j} ||d|jj | d}|durE|}d}d}|j durr| |j }| durr||d|jj | d}|durn|}n||}|st dd||||||j|j|jfDSt||||||j|j|jdS)aC entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`): Indices of entity tokens in the entity vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*): Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`: - 1 for entity tokens that are **not masked**, - 0 for entity tokens that are **masked**. entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*): Segment token indices to indicate first and second portions of the entity token inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *portion A* entity token, - 1 corresponds to a *portion B* entity token. entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*): Indices of positions of each input entity in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` entity_labels (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` NT r^rr_r]rorrrrr`rrrrScsrrwr"rr"r"r#rs  z*LukeForMaskedLM.forward..)r'r(r)r*r+r,rr-)rPrrrrrXrVrrrBrrrjrr,rr-r&)rOr^rr_r]rorrrrrrr`rrrrr'r(r*r)r+r"r"r#rcsn1    zLukeForMaskedLM.forwardNNNNNNNNNNNNNNN)rrrZ_tied_weights_keysr@rrrrrrrxr r rrr&rcrfr"r"rQr#rsn        rz The LUKE model with a classification head on top (a linear layer on top of the hidden state of the first entity token) for entity classification tasks, such as Open Entity. c"eZdZfddZe              ddeejdeejdeejdeejdeejd eejd eejd eejd eejd eejdeejdee dee dee de e e ffddZ ZS)LukeForEntityClassificationcsJt|t||_|j|_t|j|_t |j |j|_ | dSrw r?r@rr num_labelsrrKrLrMrmrC classifierrrNrQr"r#r@2s   z$LukeForEntityClassification.__init__Nr^rr_r]rorrrrr`rrrrrcCs|dur|n|jj}|j||||||||| | | | dd }|jdddddf}||}||}d}| dur[| |j} | jdkrKt j || }nt j | d| d|}|sntdd|||j|j|jfDSt|||j|j|jd S) u entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`): Indices of entity tokens in the entity vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*): Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`: - 1 for entity tokens that are **not masked**, - 0 for entity tokens that are **masked**. entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*): Segment token indices to indicate first and second portions of the entity token inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *portion A* entity token, - 1 corresponds to a *portion B* entity token. entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*): Indices of positions of each input entity in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*): Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is used for the single-label classification. In this case, labels should contain the indices that should be in `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0 and 1 indicate false and true, respectively. Examples: ```python >>> from transformers import AutoTokenizer, LukeForEntityClassification >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-open-entity") >>> model = LukeForEntityClassification.from_pretrained("studio-ousia/luke-large-finetuned-open-entity") >>> text = "Beyoncé lives in Los Angeles." >>> entity_spans = [(0, 7)] # character-based entity span corresponding to "Beyoncé" >>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> predicted_class_idx = logits.argmax(-1).item() >>> print("Predicted class:", model.config.id2label[predicted_class_idx]) Predicted class: person ```NTrrrrScsrrwr"rr"r"r#rz6LukeForEntityClassification.forward..r'r*r,rr-)rPrrrrMr#rXrVndimrr cross_entropy binary_cross_entropy_with_logitsrrurr,rr-r/rOr^rr_r]rorrrrr`rrrrrfeature_vectorr*r'r"r"r#rc>sH>     z#LukeForEntityClassification.forwardNNNNNNNNNNNNNN)rrrr@rrrrxr r rrr/rcrfr"r"rQr#r +`        r z The LUKE model with a classification head on top (a linear layer on top of the hidden states of the two entity tokens) for entity pair classification tasks, such as TACRED. c"seZdZfddZe              ddeejdeejdeejdeejdeejd eejd eejd eejd eejd eejdeejdee dee dee de e e ffddZ ZS)LukeForEntityPairClassificationcsPt|t||_|j|_t|j|_t |j d|jd|_ | dS)NrFr!rNrQr"r#r@s   z(LukeForEntityPairClassification.__init__Nr^rr_r]rorrrrr`rrrrrcCs|dur|n|jj}|j||||||||| | | | dd }tj|jdddddf|jdddddfgdd}||}||}d}| durl| |j } | j dkr\t j || }nt j |d| d|}|stdd |||j|j|jfDSt|||j|j|jd S) u entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`): Indices of entity tokens in the entity vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*): Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`: - 1 for entity tokens that are **not masked**, - 0 for entity tokens that are **masked**. entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*): Segment token indices to indicate first and second portions of the entity token inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *portion A* entity token, - 1 corresponds to a *portion B* entity token. entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*): Indices of positions of each input entity in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*): Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is used for the single-label classification. In this case, labels should contain the indices that should be in `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0 and 1 indicate false and true, respectively. Examples: ```python >>> from transformers import AutoTokenizer, LukeForEntityPairClassification >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-tacred") >>> model = LukeForEntityPairClassification.from_pretrained("studio-ousia/luke-large-finetuned-tacred") >>> text = "Beyoncé lives in Los Angeles." >>> entity_spans = [ ... (0, 7), ... (17, 28), ... ] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles" >>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> predicted_class_idx = logits.argmax(-1).item() >>> print("Predicted class:", model.config.id2label[predicted_class_idx]) Predicted class: per:cities_of_residence ```NTrrrrrrScsrrwr"rr"r"r#r%r$z:LukeForEntityPairClassification.forward..r%)rPrrrrrrMr#rXrVr&rrr'r(rrurr,rr-r2r)r"r"r#rcsLA0     z'LukeForEntityPairClassification.forwardr+)rrrr@rrrrxr r rrr2rcrfr"r"rQr#r-r,r-z The LUKE model with a span classification head on top (a linear layer on top of the hidden states output) for tasks such as named entity recognition. c&seZdZfddZe                ddeejdeejdeejdeejdeejd eejd eejd eejd eejd eejdeejdeejdeejdee dee dee de e e ff"ddZ ZS)LukeForEntitySpanClassificationcsNt|t||_|j|_t|j|_t |j d|j|_ | dS)Nr r!rNrQr"r#r@;s   z(LukeForEntitySpanClassification.__init__Nr^rr_r]rorrrentity_start_positionsentity_end_positionsrr`rrrrrcCs|dur|n|jj}|j||||||||| | ||dd }|jd}| ddd|} | j|jjkr:| |jj} t |jd| }| ddd|} | j|jjkrZ| |jj} t |jd| }t j |||j gdd}| |}||}d}| dur| |j} | jdkrtj|d|j| d}ntj|d| d|}|stdd |||j|j|jfDSt|||j|j|jd S) u entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`): Indices of entity tokens in the entity vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*): Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`: - 1 for entity tokens that are **not masked**, - 0 for entity tokens that are **masked**. entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*): Segment token indices to indicate first and second portions of the entity token inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *portion A* entity token, - 1 corresponds to a *portion B* entity token. entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*): Indices of positions of each input entity in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. entity_start_positions (`torch.LongTensor`): The start positions of entities in the word token sequence. entity_end_positions (`torch.LongTensor`): The end positions of entities in the word token sequence. labels (`torch.LongTensor` of shape `(batch_size, entity_length)` or `(batch_size, entity_length, num_labels)`, *optional*): Labels for computing the classification loss. If the shape is `(batch_size, entity_length)`, the cross entropy loss is used for the single-label classification. In this case, labels should contain the indices that should be in `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, entity_length, num_labels)`, the binary cross entropy loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0 and 1 indicate false and true, respectively. Examples: ```python >>> from transformers import AutoTokenizer, LukeForEntitySpanClassification >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003") >>> model = LukeForEntitySpanClassification.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003") >>> text = "Beyoncé lives in Los Angeles" # List all possible entity spans in the text >>> word_start_positions = [0, 8, 14, 17, 21] # character-based start positions of word tokens >>> word_end_positions = [7, 13, 16, 20, 28] # character-based end positions of word tokens >>> entity_spans = [] >>> for i, start_pos in enumerate(word_start_positions): ... for end_pos in word_end_positions[i:]: ... entity_spans.append((start_pos, end_pos)) >>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> predicted_class_indices = logits.argmax(-1).squeeze().tolist() >>> for span, predicted_class_idx in zip(entity_spans, predicted_class_indices): ... if predicted_class_idx != 0: ... print(text[span[0] : span[1]], model.config.id2label[predicted_class_idx]) Beyoncé PER Los Angeles LOC ```NTrrSrqrrrcsrrwr"rr"r"r#rr$z:LukeForEntitySpanClassification.forward..r%)rPrrrrZrdrerVrXrgatherrrrMr#r&rrr'rr"r(rurr,rr-r3)rOr^rr_r]rorrrr/r0rr`rrrrrrCZ start_statesZ end_statesr*r*r'r"r"r#rcGsZO       z'LukeForEntitySpanClassification.forward)NNNNNNNNNNNNNNNN)rrrr@rrrrxr r rrr3rcrfr"r"rQr#r.4sl        r.z The LUKE Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. c"r)LukeForSequenceClassificationcsZt||j|_t||_t|jdur|jn|j|_ t |j |j|_ | dSrwr?r@r"rrrrKclassifier_dropoutrLrMrmrCr#rrNrQr"r#r@s   z&LukeForSequenceClassification.__init__Nr^rr_r]rorrrrr`rrrrrcCs|dur|n|jj}|j||||||||| | | | dd }|j}||}||}d}| dur| |j} |jjdur_|j dkrEd|j_n|j dkr[| j t j ksV| j t j kr[d|j_nd|j_|jjdkr}t}|j dkrw||| }n+||| }n%|jjdkrt}||d|j | d}n|jjdkrt}||| }|std d |||j|j|jfDSt|||j|j|jd S) a entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`): Indices of entity tokens in the entity vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*): Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`: - 1 for entity tokens that are **not masked**, - 0 for entity tokens that are **masked**. entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*): Segment token indices to indicate first and second portions of the entity token inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *portion A* entity token, - 1 corresponds to a *portion B* entity token. entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*): Indices of positions of each input entity in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). NTrrZ regressionZsingle_label_classificationZmulti_label_classificationrScsrrwr"rr"r"r#rBr$z8LukeForSequenceClassification.forward..r%)rPrrrrMr#rXrVZ problem_typer"rUrr\rr squeezerrrrr,rr-r4)rOr^rr_r]rorrrrr`rrrrrrr*r'loss_fctr"r"r#rcsd+      "       z%LukeForSequenceClassification.forwardr+)rrrr@rrrrxr r rrr4rcrfr"r"rQr#r2r,r2z The LUKE Model with a token classification head on top (a linear layer on top of the hidden-states output). To solve Named-Entity Recognition (NER) task using LUKE, `LukeForEntitySpanClassification` is more suitable than this class. c"r)LukeForTokenClassificationcs^t||j|_t|dd|_t|jdur|jn|j|_ t |j |j|_ | dSNF)rr3rNrQr"r#r@Ys  z#LukeForTokenClassification.__init__Nr^rr_r]rorrrrr`rrrrrcCs|dur|n|jj}|j||||||||| | | | dd }|j}||}||}d}| durE| |j} t}|| d|j | d}|sXt dd|||j |j |jfDSt|||j |j |jdS)aM entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`): Indices of entity tokens in the entity vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*): Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`: - 1 for entity tokens that are **not masked**, - 0 for entity tokens that are **masked**. entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*): Segment token indices to indicate first and second portions of the entity token inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *portion A* entity token, - 1 corresponds to a *portion B* entity token. entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*): Indices of positions of each input entity in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) NTrrScsrrwr"rr"r"r#rr$z5LukeForTokenClassification.forward..r%)rPrrrrMr#rXrVrrr"rr,rr-r6)rOr^rr_r]rorrrrr`rrrrrrr*r'r6r"r"r#rcfsF+   z"LukeForTokenClassification.forwardr+)rrrr@rrrrxr r rrr6rcrfr"r"rQr#r7Qs`        r7c$seZdZfddZe               ddeejdeejdeejdeejdeejd eejd eejd eejd eejd eejdeejdeejdee dee dee de e e ff ddZ ZS)LukeForQuestionAnsweringcs@t||j|_t|dd|_t|j|j|_| dSr8) r?r@r"rrrrmrC qa_outputsrrNrQr"r#r@s  z!LukeForQuestionAnswering.__init__Nr^rr_r]rorrrrr`start_positions end_positionsrrrrcCsD|dur|n|jj}|j||||||||| | | |dd }|j}||}|jddd\}}|d}|d}d}| dur| durt| dkrN| d} t| dkr[| d} |d}| d|| d|t |d}||| }||| }||d }|st d d ||||j |j |jfDSt||||j |j |jd S) a  entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`): Indices of entity tokens in the entity vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*): Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`: - 1 for entity tokens that are **not masked**, - 0 for entity tokens that are **masked**. entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*): Segment token indices to indicate first and second portions of the entity token inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *portion A* entity token, - 1 corresponds to a *portion B* entity token. entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*): Indices of positions of each input entity in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. NTrrrSrrr)Z ignore_indexrcsrrwr"rr"r"r#rs z3LukeForQuestionAnswering.forward..)r'r8r9r,rr-)rPrrrr:splitr5lenrZZclamp_rrr,rr-r7)rOr^rr_r]rorrrrr`r;r<rrrrrr*r8r9Z total_lossZ ignored_indexr6Z start_lossZend_lossr"r"r#rcsh(             z LukeForQuestionAnswering.forwardr)rrrr@rrrrxr r rrr7rcrfr"r"rQr#r9sf        r9c"r)LukeForMultipleChoicecsPt|t||_t|jdur|jn|j|_t |j d|_ | dSr) r?r@rrrrKr4rLrMrmrCr#rrNrQr"r#r@8s   zLukeForMultipleChoice.__init__Nr^rr_r]rorrrrr`rrrrrcCs|dur|n|jj}|dur|jdn| jd}|dur%|d|dnd}|dur4|d|dnd}|durC|d|dnd}|durR|d|dnd}| dure| d| d| dnd} |durt|d|dnd}|dur|d|dnd}|dur|d|dnd}|dur|d|d|dnd}|j||||||||| | | | dd }|j}||}||}|d|}d}| dur| |j } t }||| }|st dd|||j |j|jfDSt|||j |j|jd S) a^ input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, 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. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, 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]`. [What are position IDs?](../glossary#position-ids) entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`): Indices of entity tokens in the entity vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*): Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`: - 1 for entity tokens that are **not masked**, - 0 for entity tokens that are **masked**. entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*): Segment token indices to indicate first and second portions of the entity token inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *portion A* entity token, - 1 corresponds to a *portion B* entity token. entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*): Indices of positions of each input entity in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) NrrSrqTrcsrrwr"rr"r"r#rrz0LukeForMultipleChoice.forward..r%)rPrrrrZrrrMr#rXrVrrr,rr-r:)rOr^rr_r]rorrrrr`rrrrZ num_choicesrrr*Zreshaped_logitsr'r6r"r"r#rcDs~C      zLukeForMultipleChoice.forwardr+)rrrr@rrrrxr r rrr:rcrfr"r"rQr#r?6s`        r?) r r-r.r?r9r2r7rrr)Frr dataclassesrtypingrrrrZtorch.utils.checkpointrZtorch.nnrrr Z activationsr r Zmodeling_outputsr rZmodeling_utilsrZ pytorch_utilsrutilsrrrZconfiguration_lukerZ get_loggerrloggerrr%r&r/r2r3r4r6r7r:rr;rgrzrrrrrrrrrrrrWr rr r-r.r2r7r9r?__all__r"r"r"r#s      *!!$#I+r04O]|xgv "