rhdZddlZddlmZddlmZmZddlZddlZddlm Z ddl m Z m Z m Z ddlmZmZdd lmZdd lmZmZdd lmZdd lmZdd lmZmZmZddlmZej@e!Z"eedGddeZ#eedGddeZ$eedGddeZ%eedGddeZ&eedGddeZ'eedGd d!eZ(eed"Gd#d$eZ)eed%Gd&d'eZ*eed(Gd)d*eZ+eed+Gd,d-eZ,Gd.d/e jZZ.Gd0d1e jZZ/Gd2d3e jZZ0Gd4d5e jZZ1Gd6d7e jZZ2Gd8d9e jZZ3Gd:d;e jZZ4Gd<d=eZ5Gd>d?e jZZ6Gd@dAe jZZ7GdBdCe jZZ8GdDdEe jZZ9eGdFdGeZ:edHGdIdJe:Z;dKZ<GdLdMe jZZ=edNGdOdPe:Z>edQGdRdSe:Z?edTGdUdVe:Z@edWGdXdYe:ZAedZGd[d\e:ZBed]Gd^d_e:ZCeGd`dae:ZDeGdbdce:ZEgddZFy)ezPyTorch LUKE model.N) dataclass)OptionalUnion)nn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FNgelu)GradientCheckpointingLayer)BaseModelOutputBaseModelOutputWithPooling)PreTrainedModel)apply_chunking_to_forward) ModelOutputauto_docstringlogging) LukeConfigz3 Base class for outputs of the LUKE model. ) custom_introcleZdZUdZdZeejed<dZ ee ejdfed<y)BaseLukeModelOutputWithPoolingax 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. 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. 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. Nentity_last_hidden_state.entity_hidden_states __name__ __module__ __qualname____doc__rrtorch FloatTensor__annotations__rtupley/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/luke/modeling_luke.pyrr&s@ =Ahu'8'89@DH(5):):C)?#@AHr&rzV Base class for model's outputs, with potential hidden states and attentions. cleZdZUdZdZeejed<dZ ee ejdfed<y)BaseLukeModelOutputa 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. 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. Nr.rrr%r&r'r)r)=s@=Ahu'8'89@DH(5):):C)?#@AHr&r)cleZdZUdZdZeejed<dZ eejed<dZ eejed<dZ eejed<dZ eejed<dZ eeejed<dZeeejd fed <dZeeejd fed <y) LukeMaskedLMOutputa: 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). 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. Nlossmlm_lossmep_losslogits entity_logits hidden_states.r attentions)rrrr r,rr!r"r#r-r.r/r0r1r$rr2r%r&r'r+r+Qs")-D(5$$ %,,0Hhu(()0,0Hhu(()0*.FHU&& '.15M8E--.58Ju00#567>r&r+z2 Outputs of entity classification models. ceZdZUdZdZeejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y) EntityClassificationOutput loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification scores (before SoftMax). entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. Nr,r/.r1rr2rrrr r,rr!r"r#r/r1r$rr2r%r&r'r4r4s )-D(5$$ %,*.FHU&& '.=AM8E%"3"3S"89:ADH(5):):C)?#@AH:>Ju00#567>r&r4z7 Outputs of entity pair classification models. ceZdZUdZdZeejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y) EntityPairClassificationOutputr5Nr,r/.r1rr2r6r%r&r'r9r9r7r&r9z7 Outputs of entity span classification models. ceZdZUdZdZeejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y) EntitySpanClassificationOutputa 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). 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. Nr,r/.r1rr2r6r%r&r'r;r;r7r&r;z4 Outputs of sentence classification models. ceZdZUdZdZeejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y) LukeSequenceClassifierOutputa 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). 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. Nr,r/.r1rr2r6r%r&r'r=r=r7r&r=z@ Base class for outputs of token classification models. ceZdZUdZdZeejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y) LukeTokenClassifierOutputa 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). 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. Nr,r/.r1rr2r6r%r&r'r?r?r7r&r?z/ Outputs of question answering models. c eZdZUdZdZeejed<dZ eejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZee ejdfed <y) LukeQuestionAnsweringModelOutputa 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. 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. Nr, start_logits end_logits.r1rr2)rrrr r,rr!r"r#rBrCr1r$rr2r%r&r'rArAs)-D(5$$ %,04L(5,,-4.2J**+2=AM8E%"3"3S"89:ADH(5):):C)?#@AH:>Ju00#567>r&rAz, Outputs of multiple choice models. ceZdZUdZdZeejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y) LukeMultipleChoiceModelOutputa loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided): Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`): *num_choices* is the second dimension of the input tensors. (see *input_ids* above). Classification scores (before SoftMax). entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. Nr,r/.r1rr2r6r%r&r'rErEs )-D(5$$ %,*.FHU&& '.=AM8E%"3"3S"89:ADH(5):):C)?#@AH:>Ju00#567>r&rEc8eZdZdZfdZ ddZdZxZS)LukeEmbeddingszV Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. ct|tj|j|j |j |_tj|j|j |_ tj|j|j |_ tj|j |j|_ tj|j|_|j |_tj|j|j |j"|_ y)N padding_idxeps)super__init__r Embedding vocab_size hidden_size pad_token_idword_embeddingsmax_position_embeddingsposition_embeddingstype_vocab_sizetoken_type_embeddings LayerNormlayer_norm_epsDropouthidden_dropout_probdropoutrJselfconfig __class__s r'rNzLukeEmbeddings.__init__(s !||F,=,=v?Q?Q_e_r_rs#%<<0N0NPVPbPb#c %'\\&2H2H&J\J\%]"f&8&8f>S>STzz&"<"<= "..#%<<  * *F,>,>DL\L\$  r&c|C|0t||jj|j}n|j |}||j }n|j dd}|:t j|t j|jj}||j|}|j|}|j|}||z|z}|j|}|j|}|S)Ndtypedevice)"create_position_ids_from_input_idsrJtore&create_position_ids_from_inputs_embedssizer!zeroslong position_idsrSrUrWrXr\) r^ input_idstoken_type_idsrl inputs_embeds input_shaperUrW embeddingss r'forwardzLukeEmbeddings.forward9s  $A)TM]M]^aabkbrbrs #JJ=Y  #..*K',,.s3K  !"[[EJJtO`O`OgOghN   00;M"66|D $ : :> J"%88;PP ^^J/ \\*- r&c|jdd}|d}tj|jdz||jzdztj|j }|j dj|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 Nrbrrcr)rir!arangerJrkre unsqueezeexpand)r^rorpsequence_lengthrls r'rhz5LukeEmbeddings.create_position_ids_from_inputs_embedsZs$((*3B/ %a.||   q /D4D4D"Dq"HPUPZPZcpcwcw %%a(// <S>STzz&"<"<= r& entity_idsrlrncR|tj|}|j|}|jj|jj k7r|j |}|j|jd}|dk7j|jd}||z}tj|d}||jdjdz }|j|}||z|z}|j|}|j|}|S)Nr)minrbdimgHz>)r! zeros_likerr_rrQrrUclamptype_asrusumrWrXr\) r^rrlrnrrUposition_embedding_maskrWrqs r'rrzLukeEntityEmbeddings.forward{s'  !"--j9N 22:> ;; & &$++*A*A A $ ; ;">?R"S"]"]^`"a14KK#ii(;D14K4O4OTV4O4W4]4]bf4]4gg $ : :> J&)<z >$6: $$&&!!1!12 r&r{c2eZdZfdZdZ ddZxZS)LukeSelfAttentionc t||j|jzdk7r2t |ds&t d|jd|jd|j|_t |j|jz |_|j|jz|_|j|_ tj|j|j|_ tj|j|j|_ tj|j|j|_|jrtj|j|j|_tj|j|j|_tj|j|j|_tj$|j&|_y)Nrembedding_sizezThe hidden size z4 is not a multiple of the number of attention heads .)rMrNrQnum_attention_headshasattr ValueErrorintattention_head_size all_head_sizeuse_entity_aware_attentionrrquerykeyvalue w2e_query e2w_query e2e_queryrZattention_probs_dropout_probr\r]s r'rNzLukeSelfAttention.__init__s     : : :a ?PVXhHi"6#5#5"67334A7  $*#=#= #&v'9'9FS"S* **,CD $1!ZiZ2B$C!  '*. '*79:q8H*I ' 02M_G12MNGr&NNF)rrrrNrrrrxrys@r'rrsG0% Pr&rcneZdZfdZdej dej dej fdZxZS)LukeSelfOutputc(t|tj|j|j|_tj |j|j|_tj|j|_ yNrK) rMrNrrrQdenserXrYrZr[r\r]s r'rNzLukeSelfOutput.__init__ s` YYv1163E3EF f&8&8f>S>STzz&"<"<= r&r1 input_tensorreturncr|j|}|j|}|j||z}|Srrr\rXr^r1rs r'rrzLukeSelfOutput.forward7 =1  ]3 }|'CD r&rrrrNr!Tensorrrrxrys@r'rr 1> U\\RWR^R^r&rc2eZdZfdZdZ ddZxZS) LukeAttentionct|t||_t ||_t |_yr)rMrNrr^routputset pruned_headsr]s r'rNzLukeAttention.__init__s0 %f- $V, Er&ctdNz4LUKE does not support the pruning of attention headsNotImplementedError)r^headss r' prune_headszLukeAttention.prune_heads!"XYYr&cF|jd}|j|||||}||d}|} n3tj|ddd}tj||gd} |j || } | ddd|ddf} |d} n| dd|dddf} | | f|ddz} | S)Nrrrr)rir^r!rr)r^rrrrrr self_outputsconcat_self_outputsrattention_outputword_attention_outputentity_attention_outputrs r'rrzLukeAttention.forward!s'++A. yy       '".q/ #5 "'))L!,S>STzz&"<"<= r&r1rrcr|j|}|j|}|j||z}|Srrrs r'rrzLukeOutput.forward^rr&rrys@r'rrWrr&rc2eZdZfdZ ddZdZxZS) LukeLayerct||j|_d|_t ||_t ||_t||_ yNr) rMrNchunk_size_feed_forward seq_len_dimr attentionr intermediaterrr]s r'rNzLukeLayer.__init__fsI '-'E'E$&v.,V4 ( r&cJ|jd}|j|||||}||d}ntj|ddd}|dd} t |j |j |j|} | ddd|ddf} |d} n| dd|dddf} | | f| z} | S)Nr)rrrr)rirr!rrfeed_forward_chunkrr) r^rrrrrrself_attention_outputsconcat_attention_outputr layer_outputword_layer_outputentity_layer_outputs r'rrzLukeLayer.forwardns'++A. !%   / "0"  '&z&LukeEncoder.forward.. = )last_hidden_stater1r2rr) enumeraterr$r))r^rrrrroutput_hidden_states return_dictall_word_hidden_statesall_entity_hidden_statesall_self_attentionsi layer_modulelayer_head_mask layer_outputss r'rrzLukeEncoder.forwards/(<)=24 $5b4(4 POA|#)?CUBW)W&+CG[F]+](.7.CilO("$! M"/q!1 #/'4Q'7$ &9]1=MS%S "'?CWBY'Y $ '*'(,    #00*%9!9   r&)NNFFTrrrrNrrrxrys@r'rrs,": r&rcVeZdZfdZdej dej fdZxZS) LukePoolerct|tj|j|j|_tj |_yr)rMrNrrrQrTanh activationr]s r'rNzLukePooler.__init__s9 YYv1163E3EF '')r&r1rc\|dddf}|j|}|j|}|S)Nr)rr4)r^r1first_token_tensor pooled_outputs r'rrzLukePooler.forwards6+1a40 #56  6 r&rrys@r'r1r1s#$ U\\ellr&r1c$eZdZfdZdZxZS)EntityPredictionHeadTransformcht|tj|j|j |_t|jtrt|j|_ n|j|_ tj|j |j|_ yr)rMrNrrrQrrrrrr transform_act_fnrXrYr]s r'rNz&EntityPredictionHeadTransform.__init__s{ YYv1163I3IJ f'' -$*6+<+<$=D !$*$5$5D !f&<&<&BWBWXr&cl|j|}|j|}|j|}|Sr)rr;rXrs r'rrz%EntityPredictionHeadTransform.forwards4 =1 --m< }5 r&r/rys@r'r9r9sYr&r9c$eZdZfdZdZxZS)EntityPredictionHeadc t|||_t||_t j |j|jd|_ t jtj|j|_ y)NFr})rMrNr_r9 transformrrrrdecoder Parameterr!rjr~r]s r'rNzEntityPredictionHead.__init__sa  6v>yy!7!79Q9QX]^ LLV-E-E!FG r&cd|j|}|j||jz}|Sr)r@rAr~rs r'rrzEntityPredictionHead.forwards-}5  ]3dii? r&r/rys@r'r>r>sHr&r>cHeZdZUeed<dZdZddgZdejfdZ y) LukePreTrainedModelr_lukeTrr{modulecjt|tjrm|jjj d|j j|j%|jjjyyt|tjr|jdk(r%|jjjn;|jjj d|j j|j2|jj|jjyyt|tjrJ|jjj|jjjdyy)zInitialize the weightsg)meanstdNr?)rrrweightdatanormal_r_initializer_ranger~zero_rO embedding_dimrJrXfill_)r^rGs r' _init_weightsz!LukePreTrainedModel._init_weightss( fbii ( MM   & &CT[[5R5R & S{{&   &&('  -##q( ""((* ""**9V9V*W!!- ""6#5#56<<>.  - KK   " " $ MM   $ $S ).r&N) rrrrr#base_model_prefixsupports_gradient_checkpointing_no_split_modulesrModulerSr%r&r'rErE s0 &*#(*@A*BII*r&rEzt The bare LUKE model transformer outputting raw hidden-states for both word tokens and entities without any c eZdZddedeffd ZdZdZdZdZ dZ e dd e e jd e e jd e e jd e e jd e e jde e jde e jde e jde e jde e jde ede ede edeeeffdZde jde e jfdZxZS) LukeModelr_add_pooling_layerct||||_t||_t ||_t||_|r t|nd|_ |jy)zv add_pooling_layer (bool, *optional*, defaults to `True`): Whether to add a pooling layer N) rMrNr_rGrqr{rrencoderr1pooler post_init)r^r_rZr`s r'rNzLukeModel.__init__,sZ   (0!5f!="6* ,=j(4  r&c.|jjSrrqrSr^s r'get_input_embeddingszLukeModel.get_input_embeddings=s...r&c&||j_yrr`r^rs r'set_input_embeddingszLukeModel.set_input_embeddings@s*/'r&c.|jjSrrras r'get_entity_embeddingszLukeModel.get_entity_embeddingsCs%%777r&c&||j_yrrgrds r'set_entity_embeddingszLukeModel.set_entity_embeddingsFs380r&ctdrr)r^heads_to_prunes r' _prune_headszLukeModel._prune_headsIrr&rmrrnrlrentity_attention_maskentity_token_type_idsentity_position_idsrrorr&r'rc | | n|jj} | | n|jj} | | n|jj} | | t d|#|j |||j }n!| | j dd}n t d|\}}| |jn | j}|tj||f|}|&tj|tj|}|V|j d}|tj||f|}|(tj||ftj|}|j| |jj} |j|||| }|j||}|d}n|j!|||}|j#|||| | | | }|d }|j$|j%|nd}| s ||f|ddzSt'|||j(|j*|j,|j. 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 timerbz5You have to specify either input_ids or inputs_embeds)rercr)rmrlrnro)rrrr&r'r)r$ pooler_outputr1r2rr)r_rr&use_return_dictr%warn_if_padding_and_no_attention_maskrirer!onesrjrk get_head_maskrrqget_extended_attention_maskrr\r]rr1r2rr)r^rmrrnrlrrnrorprrorr&r'rp batch_size seq_lengthreentity_seq_lengthword_embedding_outputextended_attention_maskentity_embedding_outputencoder_outputssequence_outputr7s r'rrzLukeModel.forwardLsR2C1N-TXT_T_TqTq$8$D $++JjJj &1%cd d  "  6 6y. Q#..*K  &',,.s3KTU U!, J%.%:!! @T@T  !"ZZZ(@PN  !"[[EJJvVN  ! * 2 $,(- J@Q3R[a(b%$,(- ZAR4S[`[e[ent(u%&&y$++2O2OP !%%)' !0! #'"B"B>Sh"i  &* #&*&<&1559,0/3&*Y E,,-Y !!2!23Y !!1!12 Y u//0 Y U--. Y  ((9(9:Y  ((8(89Y &e&6&67Y E--.Y   1 12Y $D>Y 'tnY d^Y  u44 5Y Y v'#(#3#3'LTUZUeUeLf'r&rYc|j|j}tj|dj ||z}|j |zS)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 rr)nerr!cumsumrrk)rmrJmaskincremental_indicess r'rfrfsP << $ ( ( *D <<!4<->!?@  II r&c |j|}t|}|j|}|j|}|Sr)rr rrA)r^featureskwargsrs r'rrzLukeLMHead.forward$s; JJx  G OOA  LLOr&c|jjjjdk(r|j|j_y|jj|_y)Nmeta)rAr~retyperas r' _tie_weightszLukeLMHead._tie_weights.sC <<   # # ( (F 2 $ DLL  ))DIr&)rrrr rNrrrrxrys@r'rrs4&*r&rz The LUKE model with a language modeling head and entity prediction head on top for masked language modeling and masked entity prediction. c$eZdZgdZfdZfdZdZdZe dde e jde e jde e jd e e jd e e jd e e jd e e jd e e jde e jde e jde e jde e jde e de e de e deeeff dZxZS)LukeForMaskedLM)zlm_head.decoder.weightzlm_head.decoder.biasz!entity_predictions.decoder.weightct||t||_t ||_t ||_tj|_ |jyr) rMrNrYrFrlm_headr>entity_predictionsrrloss_fnr^r]s r'rNzLukeForMaskedLM.__init__@sQ  f% !&) "6v">**,  r&ct||j|jj|j j j yr)rM tie_weights_tie_or_clone_weightsrrArFr)r^r`s r'rzLukeForMaskedLM.tie_weightsMs:  ""4#:#:#B#BDIID_D_DqDqrr&c.|jjSrrrAras r'get_output_embeddingsz%LukeForMaskedLM.get_output_embeddingsQs||###r&c&||j_yrr)r^new_embeddingss r'set_output_embeddingsz%LukeForMaskedLM.set_output_embeddingsTs- r&rmrrnrlrrnrorplabels entity_labelsrrorr&r'rcJ||n|jj}|j||||||||| | | |d }d}d}|j|j}| d| j |j } |j|jd|jj| jd}||}d}d}|jl|j|j}| O|j|jd|jj| jd}||}n||z}|s8td||||||j|j|j fDSt#||||||j|j|j S)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 rmrrnrlrrnrorprrorr&r'rbc3$K|]}|| ywrr%rs r'r!z*LukeForMaskedLM.forward..s = r#)r,r-r.r/r0r1rr2)r_rsrFrr$rgrerrrPrrrr$r1rr2r+)r^rmrrnrlrrnrorprrrrorr&r'rr,r-r/r.r0s r'rrzLukeForMaskedLM.forwardWsb&1%-1481559,0/3&*!q E,,-q !!2!23q !!1!12 q u//0 q U--. q  ((8(89q  ((8(89q &e&6&67q ))*q   0 01q E--.q   1 12q $D>q 'tnq d^!q " u(( )#q q r&rz The LUKE model with a classification head on top (a linear layer on top of the hidden state of the first entity token) for entity classification tasks, such as Open Entity. c"eZdZfdZe ddeejdeejdeejdeejdeejdeejdeejd eejd eejd eejd eejd ee dee dee de e e ffdZ xZS)LukeForEntityClassificationc,t||t||_|j|_t j |j|_t j|j|j|_ |jyr rMrNrYrF num_labelsrrZr[r\rrQ classifierr^r]s r'rNz$LukeForEntityClassification.__init__si  f%  ++zz&"<"<= ))F$6$68I8IJ r&rmrrnrlrrnrorprrorrr&r'rc||n|jj}|j||||||||| | | | d }|jdddddf}|j |}|j |}d}| | j |j} | jdk(r!tjj|| }nMtjj|jd| jdj|}|s5td|||j |j"|j$fDSt'|||j |j"|j$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 ```NTrrrrbc3$K|]}|| ywrr%rs r'r!z6LukeForEntityClassification.forward..?=r#r,r/r1rr2)r_rsrFrr\rrgrendimrr cross_entropy binary_cross_entropy_with_logitsrrr$r1rr2r4r^rmrrnrlrrnrorprrorrr&r'rfeature_vectorr/r,s r'rrz#LukeForEntityClassification.forwardsm|&1%1559.2,0/3&*k E,,-k !!2!23k !!1!12 k u//0 k U--. k  ((9(9:k  ((8(89k &e&6&67k E--.k   1 12k **+k $D>k 'tnk d^k u00 1!k k r&rz 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"eZdZfdZe ddeejdeejdeejdeejdeejdeejdeejd eejd eejd eejd eejd ee dee dee de e e ffdZ xZS)LukeForEntityPairClassificationc4t||t||_|j|_t j |j|_t j|jdz|jd|_ |jy)NrFrr]s r'rNz(LukeForEntityPairClassification.__init__Usp  f%  ++zz&"<"<= ))F$6$6$:F>> 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 ```NTrrrrrbc3$K|]}|| ywrr%rs r'r!z:LukeForEntityPairClassification.forward..rr#r)r_rsrFr!rrr\rrgrerrrrrrrr$r1rr2r9rs r'rrz'LukeForEntityPairClassification.forwardasB&1%1559-1,0/3&*p E,,-p !!2!23p !!1!12 p u//0 p U--. p  ((9(9:p  ((8(89p &e&6&67p E--.p   1 12p ))*p $D>p 'tnp d^p u44 5!p p r&rz 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&eZdZfdZe ddeejdeejdeejdeejdeejdeejdeejd eejd eejd eejd eejd eejdeejdee dee dee de e e ff"dZ xZS)LukeForEntitySpanClassificationc2t||t||_|j|_t j |j|_t j|jdz|j|_ |jy)Nr rr]s r'rNz(LukeForEntitySpanClassification.__init__sn  f%  ++zz&"<"<= ))F$6$6$:F>> 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 ```NTrrbrrrc3$K|]}|| ywrr%rs r'r!z:LukeForEntitySpanClassification.forward..err#r)r_rsrFr$rirurvrergr!gatherrrr\rrrrrrrrrr$r1rr2r;)r^rmrrnrlrrnrorprrrrorrr&r'rrQ start_states end_statesrr/r,s r'rrz'LukeForEntitySpanClassification.forwards^&1%%>w?X?X?_?_%` "||G$=$=rCYZ 3==bAHHRQ\]  & &'*C*C*J*J J#7#:#:7;T;T;[;[#\ \\'";";RAUV L*g>^>^#_efgn50  YYv}}-F{{a}}226;;r4??3SU[U`U`acUde}}EEfkkRToW]WbWbceWfWnWnouWvw(=(=w?[?[]d]o]op  .!//!(!=!=))   r&)NNNNNNNNNNNNNNNN)rrrrNrrr!rr"rrr$r;rrrxrys@r'rrs 156:593715<@<@:>=A;?1559-1,0/3&*#H E,,-H !!2!23H !!1!12 H u//0 H U--. H  ((8(89H  ((8(89H &e&6&67H !))9)9 :H 'u'7'78H E--.H   1 12H ))*H $D>H 'tn!H "d^#H $ u44 5%H H r&rz 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"eZdZfdZe ddeejdeejdeejdeejdeejdeejdeejd eejd eejd eejd eejd ee dee dee de e e ffdZ xZS)LukeForSequenceClassificationc\t|||j|_t||_t j |j |jn |j|_ t j|j|j|_ |jyrrMrNrrYrFrrZclassifier_dropoutr[r\rrQrr^r]s r'rNz&LukeForSequenceClassification.__init__{s   ++f% zz)/)B)B)NF % %TZTnTn  ))F$6$68I8IJ r&rmrrnrlrrnrorprrorrr&r'rc||n|jj}|j||||||||| | | | d }|j}|j |}|j |}d}| | j |j} |jj|jdk(rd|j_nl|jdkDrL| jtjk(s| jtjk(rd|j_nd|j_|jjdk(rIt}|jdk(r&||j| j}n||| }n|jjdk(r=t!}||j#d|j| j#d}n,|jjdk(rt%}||| }|s5t'd |||j(|j*|j,fDSt/|||j(|j*|j, S) a entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`): Indices of entity tokens in the entity vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*): Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`: - 1 for entity tokens that are **not masked**, - 0 for entity tokens that are **masked**. entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*): Segment token indices to indicate first and second portions of the entity token inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *portion A* entity token, - 1 corresponds to a *portion B* entity token. entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*): Indices of positions of each input entity in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). NTrr regressionsingle_label_classificationmulti_label_classificationrbc3$K|]}|| ywrr%rs r'r!z8LukeForSequenceClassification.forward..rr#r)r_rsrFrrr\rrgre problem_typerrdr!rkrr squeezerrrr$r1rr2r=)r^rmrrnrlrrnrorprrorrr&r'rr7r/r,loss_fcts r'rrz%LukeForSequenceClassification.forwards#V&1%1559.2,0/3&*g E,,-g !!2!23g !!1!12 g u//0 g U--. g  ((9(9:g  ((8(89g &e&6&67g E--.g   1 12g **+g $D>g 'tng d^g u22 3!g g r&rz 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"eZdZfdZe ddeejdeejdeejdeejdeejdeejdeejd eejd eejd eejd eejd ee dee dee de e e ffdZ xZS)LukeForTokenClassificationc`t|||j|_t|d|_t j |j |jn |j|_ t j|j|j|_ |jyNF)rZrr]s r'rNz#LukeForTokenClassification.__init__s   ++f> zz)/)B)B)NF % %TZTnTn  ))F$6$68I8IJ r&rmrrnrlrrnrorprrorrr&r'rcN||n|jj}|j||||||||| | | | d }|j}|j |}|j |}d}| W| j |j} t}||jd|j| jd}|s5td|||j|j|jfDSt|||j|j|jS)aM entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`): Indices of entity tokens in the entity vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*): Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`: - 1 for entity tokens that are **not masked**, - 0 for entity tokens that are **masked**. entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*): Segment token indices to indicate first and second portions of the entity token inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *portion A* entity token, - 1 corresponds to a *portion B* entity token. entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*): Indices of positions of each input entity in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) NTrrbc3$K|]}|| ywrr%rs r'r!z5LukeForTokenClassification.forward..Qrr#r)r_rsrFr$r\rrgrerrrr$r1rr2r?)r^rmrrnrlrrnrorprrorrr&r'rrr/r,rs r'rrz"LukeForTokenClassification.forwards1V&1%1559.2,0/3&*U E,,-U !!2!23U !!1!12 U u//0 U U--. U  ((9(9:U  ((8(89U &e&6&67U E--.U   1 12U **+U $D>U 'tnU d^U u// 0!U U r&rc$eZdZfdZe ddeejdeejdeejdeejdeejdeejdeejd eejd eejd eejd eejd eejdee dee dee de e e ff dZ xZS)LukeForQuestionAnsweringct|||j|_t|d|_t j |j|j|_|jyr) rMrNrrYrFrrrQ qa_outputsr^r]s r'rNz!LukeForQuestionAnswering.__init__bsU   ++f> ))F$6$68I8IJ r&rmrrnrlrrnrorprrostart_positions end_positionsrr&r'rc`||n|jj}|j||||||||| | | |d }|j}|j |}|j dd\}}|j d}|j d}d}| | t| jdkDr| j d} t| jdkDr| j d} |jd}| jd|| jd|t|}||| }||| }||zd z }|s6td ||||j|j|jfDSt||||j|j|j S) a  entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`): Indices of entity tokens in the entity vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*): Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`: - 1 for entity tokens that are **not masked**, - 0 for entity tokens that are **masked**. entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*): Segment token indices to indicate first and second portions of the entity token inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *portion A* entity token, - 1 corresponds to a *portion B* entity token. entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*): Indices of positions of each input entity in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. NTrrrbrr) ignore_indexrc3$K|]}|| ywrr%rs r'r!z3LukeForQuestionAnswering.forward..s = r#)r,rBrCr1rr2)r_rsrFr$rsplitrlenriclamp_rr$r1rr2rA)r^rmrrnrlrrnrorprrorrrr&r'rrr/rBrC total_loss ignored_indexr start_lossend_losss r'rrz LukeForQuestionAnswering.forwardmsP&1%15596:48,0/3&*!f E,,-f !!2!23f !!1!12 f u001 f U--. f  ((9(9:f  ((8(89f &e&6&67f E--.f   1 12f "%"2"23f   0 01f $D>f 'tnf d^!f " u66 7#f f r&rc"eZdZfdZe ddeejdeejdeejdeejdeejdeejdeejd eejd eejd eejd eejd ee dee dee de e e ffdZ xZS)LukeForMultipleChoicec&t||t||_t j |j |j n |j|_t j|jd|_ |jyr) rMrNrYrFrrZrr[r\rrQrr^r]s r'rNzLukeForMultipleChoice.__init__so  f% zz)/)B)B)NF % %TZTnTn  ))F$6$6: r&rmrrnrlrrnrorprrorrr&r'rc:||n|jj}||jdn| jd}|!|jd|j dnd}|!|jd|j dnd}|!|jd|j dnd}|!|jd|j dnd}| 1| jd| j d| j dnd} |!|jd|j dnd}|!|jd|j dnd}|!|jd|j dnd}|1|jd|j d|j dnd}|j ||||||||| | | | d }|j }|j|}|j|}|jd|}d}| .| j|j} t}||| }|s5td|||j|j|jfDSt!|||j|j|jS) 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. 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