h}^SSKrSSKrSSKrSSKJr SSKJr SSKJrJ r J r J r J r J r Jr SSKrSSKJrJrJrJrJr SSKJr SSKJr SS KJr SS KJrJr SS KJ r SS K!J"r" SS K#J$r$J%r% SSK&J'r'J(r(J)r) SSKJ*r*J+r+ SSK,J-r- SSK.J/r/ SSK0J1r1 SSK2J3r3 Sr4Sr5\"5Rm\55Sr7Sr8Sr9"SS\35r:Sr;g)N)islice)Path)AnyCallableDictIterableListOptionalUnion)ConfigCosineDistanceModel Optimizerset_dropout_rate)Floats2d)util)Errors) Candidate KnowledgeBase)Language)Scorer)DocSpan)Examplevalidate_examplesvalidate_get_examples)SimpleFrozenListregistry)Vocab)EntityLinker_v1)deserialize_config) TrainablePipeTz [model] @architectures = "spacy.EntityLinker.v2" [model.tok2vec] @architectures = "spacy.HashEmbedCNN.v2" pretrained_vectors = null width = 96 depth = 2 embed_size = 2000 window_size = 1 maxout_pieces = 3 subword_features = true modelc R[R"U4S[R/0UD6$)Nnegative_labels)r score_links EntityLinkerNIL)exampleskwargss V/home/james-whalen/.local/lib/python3.13/site-packages/spacy/pipeline/entity_linker.pyentity_linker_scorer.-s&   h U9I9I8J Uf UUc[$N)r.r/r-make_entity_linker_scorerr31s r/c$\rSrSrSrSrS9\\SS.S\S\ S\ S \ \ S \ S \ S \ S \ S\\\/\ \4S\\\ \/\ \ \4S\\\ /\4S\ S\\S\ S\ S\\SS4"SjjjrS\ \S\ \4SjrS\\/\44SjrS:SjrSSS.S\/\ \4S\\S\\\/\44S jjrS!rS"SSS#.S\ \S$\S%\\S&\\\ \4S\\ \44 S'jjrS\ \S(\4S)jr S*\ \!S\"\ 4S+jr#S*\ \!S,\"\ SS4S-jr$\%"5S..S/jr&\%"5S..S0jr'\("5S..S1\)\ \*4S2\ \ SS4S3jjr+\("5S..S1\)\ \*4S2\ \ SS4S4jjr,SSS5.S6jr-S7r.S8r/g);r)5zVPipeline component for named entity linking. DOCS: https://spacy.io/api/entitylinker r*N) overwritescorer thresholdvocabr%namelabels_discardn_sents incl_prior incl_contextentity_vector_lengthget_candidatesget_candidates_batchgenerate_empty_kbr6r7 use_gold_entscandidates_batch_sizer8returnc  ^^ Ub7SUs=::aS::d*O [[RRSSUS95eUTlUTlUTl[U5TlUTl UTl UTl U Tl U Tl SU 0Tl[SS9TlU "TRU5TlUTlUTlUTlUS:a[[R*5eS[,[.4U U4S jjnUTlg) aInitialize an entity linker. vocab (Vocab): The shared vocabulary. model (thinc.api.Model): The Thinc Model powering the pipeline component. name (str): The component instance name, used to add entries to the losses during training. labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction. n_sents (int): The number of neighbouring sentences to take into account. incl_prior (bool): Whether or not to include prior probabilities from the KB in the model. incl_context (bool): Whether or not to include the local context in the model. entity_vector_length (int): Size of encoding vectors in the KB. get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that produces a list of candidates, given a certain knowledge base and a textual mention. get_candidates_batch ( Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]], Iterable[Candidate]] ): Function that produces a list of candidates, given a certain knowledge base and several textual mentions. generate_empty_kb (Callable[[Vocab, int], KnowledgeBase]): Callable returning empty KnowledgeBase. scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_links. use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another component must provide entity annotations. candidates_batch_size (int): Size of batches for entity candidate generation. threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the threshold, prediction is discarded. If None, predictions are not filtered by any threshold. DOCS: https://spacy.io/api/entitylinker#init Nrr!) range_start range_endvaluer6F) normalizer+c>T(dT$TR(d T"U40UD6$TRU5nTRSU55n[X5H up4XClM T"U40UD6$)Nc38# UHoRv M g7fr1) predicted).0egs r- FEntityLinker.__init__.._score_with_ents_set..s5Hb\\Hs)rC _ensure_entspipeziprM)r+r,docsrOdocr7selfs r-_score_with_ents_set3EntityLinker.__init__.._score_with_ents_setst %%h1&11,,X6yy5H5 #82GB#&L 3h1&11r/) ValueErrorrE1043formatr9r%r:listr;r<r=r>r@rAcfgr distancekbrCrDr8E1044rrr7)rWr9r%r:r;r<r=r>r?r@rArBr6r7rCrDr8rXs` ` r-__init__EntityLinker.__init__=sb  !y*=A*= ## !#$    ">2 $(,$8!$/#;&7 #DJJ0DE*%:"" 1 $V\\* * 28G+< 2 2 + r/r+cUR(dU$/nUHFnUR5upEUR5nXFRlUR U5 MH U$)zLIf use_gold_ents is true, set the gold entities to (a copy of) eg.predicted.)rCget_aligned_ents_and_nercopyrMentsappend)rWr+ new_examplesrOrg_new_egs r-rREntityLinker._ensure_entss\!!O B113GDWWYF$(   !    '  r/ kb_loaderc[U5(d/[[RR [ U5S95eU"UR 5Ulg)zaDefine the KB of this pipe by providing a function that will create it using this object's vocab.)arg_typeN)callablerZrE885r\typer9r`)rWrms r-set_kbEntityLinker.set_kbs@ ""V[[//i/IJ JDJJ'r/cTURc0[[RR UR S95e[ URS5(aPURR5(a0[[RR UR S95egg)Nr:is_empty) r`rZrE1018r\r:hasattrrwE139rWs r- validate_kbEntityLinker.validate_kbsx 77?V\\00dii0@A A 477J ' 'DGG,<,<,>,>V[[//TYY/?@ @-? 'r/)nlprm get_examplesr~c[US5 UbURU5 UR5 URRn/n/nUR [ U"5S55nUHTnURn URU 5 URURRRU55 MV [U5S:d,[RRUR S95e[U5S:d,[RRUR S95e[#UV s/sHoR$PM sn 5n U (dUSn U SSn SU lU 4U lURR)XPRRR+USS 9S 9 U (d/W lggs sn f) aJInitialize the pipe for training, using a representative set of data examples. get_examples (Callable[[], Iterable[Example]]): Function that returns a representative sample of gold-standard Example objects. nlp (Language): The current nlp object the component is part of. kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance. Note that providing this argument will overwrite all data accumulated in the current KB. Use this only when loading a KB as-such from file. DOCS: https://spacy.io/api/entitylinker#initialize zEntityLinker.initializeN rrvr!XXXfloat32dtype)XY)rrsr|r`r?rRrxrhr%opsalloc1flenrE923r\r:anyrglabel_ initializeasarray) rWrr~rmnO doc_sample vector_sampler+rOrVhas_annotationsents r-rEntityLinker.initializes& l,EF  KK "  WW ) )  $$VLNB%?@B$$C   c "  !7!7!; <:"FFKK$6$6DII$6$FF"=!A%Iv{{'9'9tyy'9'II%:>:Cxx:>?Q-Ca(CCJvCH JJNN22= 2R  CH?sG cUHOnURRH2n[URURU55nU(dM1 g MQ g)zpCheck if a batch contains a learnable example. If one isn't present, then the update step needs to be skipped. TF)rMrgr]r@r`)rWr+rOr candidatess r-batch_has_learnable_example(EntityLinker.batch_has_learnable_examplesJ B||((!$"5"5dggs"CD :) r/)dropsgdlossesrrrcUR5 Uc0nURURS5 U(dU$URU5n[ US5 UR U5(dU$[ URU5 UVs/sHoURPM nnURRU5upxURXqS9upU"U 5 UbURU5 X@R==U - ss'U$s snf)aLearn from a batch of documents and gold-standard information, updating the pipe's model. Delegates to predict and get_loss. examples (Iterable[Example]): A batch of Example objects. drop (float): The dropout rate. sgd (thinc.api.Optimizer): The optimizer. losses (Dict[str, float]): Optional record of the loss during training. Updated using the component name as the key. RETURNS (Dict[str, float]): The updated losses dictionary. DOCS: https://spacy.io/api/entitylinker#update rzEntityLinker.update)sentence_encodingsr+) r| setdefaultr:rRrrrr%rM begin_updateget_loss finish_update) rWr+rrrrOrUr bp_contextlossd_scoress r-updateEntityLinker.updates(  >F$))S)M$$X.($9://99MT*'/0x x0)-)@)@)F&1'  8 ?   s #yyT! 1s Drc~[US5 /nSn/nUHnURSSS9nUR5HZnXxRn U (a=URR U 5n UR U 5 UR U5 US- nM\ M URRRUSS9nX%n U(d1URRR"UR6n SU 4$U RUR:wa)[RRS S S 9n [U 5eUR R#X5nURRR"UR6n XU'UR R%X5nU['U5- n[)U5U 4$) NzEntityLinker.get_lossr ENT_KB_IDT) as_stringr!rrrzgold entities do not match upmethodmsg)r get_alignedget_matching_entsstartr` get_vectorrhr%r asarray2falloc2fshaperE147r\ RuntimeErrorr_get_gradrrfloat)rWr+rentity_encodingseidx keep_entsrOkb_idsrkb_identity_encodingselected_encodingsouterr gradientsrs r-rEntityLinker.get_loss,s($;< B^^K4^@F++-yy)&*gg&8&8&?O$++O<$$T* . ::>>334DI3V/:**..((*<*B*BCCc6M  # #'7'='= =++$$!'F%Cs# #MM**+=P jjnn$$&8&>&>?"I}}%%&8Kc*++T{Cr/rUc  UR5 Sn/nURRRnU(dU$[ U[ 5(aU/n[ U5GHupV[U5S:XaMURVs/sHowPM nn[S[UR5UR5GHn URXUR-n [[U 55V s/sH#n XRUR;dM!U PM% n n [URS:a/URUR U V s/sHoU PM sn 5O.U V s/sH!n UR#UR X5PM# sn 5n [ U 5GHup[%US5(de[UR5nUR'US5UR'US54nUSUSs=:aS:de eUR((a[+SUSUR,- 5n[/[U5S- USUR,-5nUUR0nUUR2nUUUR55nURR7U/5SnUR8nUR:R=U5nUS- nURUR;aUR?UR@5 GMw[X5nU(dUR?UR@5 GM[U5S:Xa.URBc!UR?USRD5 GM[FRH"U5 URKUVs/sHnURLPM sn5nURN(d"URKUVs/sHnSPM sn5nUnUR((aURKUVs/sHnURPPM sn5nUR:R=USS9n [U5[U5:wa'[S[TRVRYSSS 95eUR[UW5WU -- n!U!R\UR\:wa[_[TR`5eUU!-UU!-- nUR?URBbUR+5URB:a+UURc5Re5RDO[fR@5 GM GM GM [U5U:Xd)[TRVRYSS S 9n"[SU"5eU$s snfs sn fs sn fs sn fs snfs snfs snf) a@Apply the pipeline's model to a batch of docs, without modifying them. Returns the KB IDs for each entity in each doc, including NIL if there is no prediction. docs (Iterable[Doc]): The documents to predict. RETURNS (List[str]): The models prediction for each document. DOCS: https://spacy.io/api/entitylinker#predict rr!sentsr)axispredictzvectors not of equal lengthrz$result variables not of equal length)4r|r%rxp isinstancer enumeraterrrangergrDrr;r]rAr`r@ryindexr>maxr<minrendas_docrTlinalgnormrhr*r8entity_randomshuffler prior_probr= entity_vectorrrrr\dotrrZE161argmaxitemr))#rWrU entity_count final_kb_idsrirVs sentencesent_idx ent_batchidx valid_ent_idxbatch_candidatesjrr sent_indicesstart_sentence end_sentence start_token end_tokensent_docsentence_encodingsentence_encoding_t sentence_normrc prior_probsrjscoresr entity_normsimsrs# r-rEntityLinker.predictTs  "$ ZZ^^    dC 6DoFA3x1}$'II.IqII.!CM43M3MNHHW9S9S/ST %S^4!4 ~,,D4G4GG4! $(11A5--M!JMSC.M!J $1#0C++DGGY^D#0 $ ( 2FA"30000 OE!a1!b 2$L(?l1oBBBBBB((),Q Q$,,0N)O'* NQ. Q$,,0N( '0&?&E&E $-l$;$?$? #&{9#=#D#D#F-1JJ,>,>z,J1,M).?.A.A+(* 7J(K  A%LzzT%8%88$++DHH5%)*:*=%> )(//9 _1dnn6L(// 1 0E0EF"NN:6*,**J5WJqallJ5W*XK#'??.0jjz9Rz!#z9R.S %0F#0035::>H$IjQ__j$I4" 0/1iinn=MTUn.V #&'7#8C @S'T$1K$?("$(::1B1B#B*4V[[*A$A)4t);{T?Q)R(//#'>>#9#)::<4>>#A!+6==?+?+?+A B J J&2%5%5 }3-O &BL!\1++$$ &L%Cs# #G/!"KZ6X9R %Js0 V'2 V,V, V1&(V6 +V;( WWrc[UVVs/sHo3RHoDPM M snn5nU[U5:wa0[[RR U[U5S95eSnUR SnUHGnURH4nX&nUS- nUH"n U RS:Xd U(dMXlM$ M6 MI gs snnf)zModify a batch of documents, using pre-computed scores. docs (Iterable[Doc]): The documents to modify. kb_ids (List[str]): The IDs to set, produced by EntityLinker.predict. DOCS: https://spacy.io/api/entitylinker#set_annotations )rgidsrr6r!N) rrgrZrE148r\r^ ent_kb_id ent_kb_id_) rWrUrrVr count_entsrr6rtokens r-set_annotationsEntityLinker.set_annotationssB####BC V $V[[//ZS[/QR R HH[) Cxx Q E!+yy+0(!  CsC excludec^^TR5 0n[TS5(aTRb U4SjUS'UU4SjUS'TRRUS'TR RUS'[ R"UT5$)zSerialize the pipe to a bytestring. exclude (Iterable[str]): String names of serialization fields to exclude. RETURNS (bytes): The serialized object. DOCS: https://spacy.io/api/entitylinker#to_bytes r^cD>[R"TR5$r1)srsly json_dumpsr^r{sr-'EntityLinker.to_bytes..su'7'7'Ar/c6>TRRTS9$Nr)r9to_bytes)rrWsr-r r sTZZ%8%8%8%Ir/r9r`r%)_validate_serialization_attrsryr^r`r r%r)rWr serializes`` r-r EntityLinker.to_bytessx **, 4  DHH$8AIe I '''** $!ZZ00 '}}Y00r/c^^TR5 U4Sjn0n[TS5(aTRb U4SjUS'UU4SjUS'U4SjUS'X4S'[R"XT5 T$) zLoad the pipe from a bytestring. exclude (Iterable[str]): String names of serialization fields to exclude. RETURNS (TrainablePipe): The loaded object. DOCS: https://spacy.io/api/entitylinker#from_bytes c>TRRU5 g![a [[R 5Sef=fr1)r% from_bytesAttributeErrorrZrE149brWs r- load_model+EntityLinker.from_bytes..load_models: 8 %%a(! 8 -47 8s %Ar^cb>TRR[R"U55$r1)r^rr json_loadsrs r-r )EntityLinker.from_bytes..s488??5;K;KA;N+Or/c8>TRRUTS9$r )r9r)rrrWs r-r rs)>)>q')>)Rr/r9c:>TRRU5$r1)r`rrs r-r rsdgg&8&8&;r/r`r%)rryr^rr)rW bytes_datarr deserializes` ` r-rEntityLinker.from_bytessl **, 8  4  DHH$8!OK R G; D)G  9 r/pathrc^^0nUU4SjUS'U4SjUS'U4SjUS'U4SjUS'[R"XT5 g ) zSerialize the pipe to disk. path (str / Path): Path to a directory. exclude (Iterable[str]): String names of serialization fields to exclude. DOCS: https://spacy.io/api/entitylinker#to_disk c8>TRRUTS9$r )r9to_diskprrWs r-r &EntityLinker.to_disk..stzz'9'9!W'9'Mr/r9cF>[R"UTR5$r1)r write_jsonr^r'rWs r-r r(sU%5%5a%Br/r^c:>TRRU5$r1)r`r%r+s r-r r(sDGGOOA$6r/r`c:>TRRU5$r1)r%r%r+s r-r r(stzz'9'9!'URS5nTRRUR55 SSS5 g!,(df  g=f![a [ [ R5Sef=f)Nrb)openr%rreadrrZrr)r'infilerWs r-r*EntityLinker.from_disk..load_model*sV 8VVD\VJJ))&++-8"\\! 8 -47 8s'A*AA AAA%A=cL>TRR[U55$r1)r^rr#r+s r-r (EntityLinker.from_disk..2stxx7I!7L'Mr/r^c8>TRRUTS9$r )r9 from_diskr&s r-r r73s)=)=a)=)Qr/r9c:>TRRU5$r1)r`r9r+s r-r r74sdgg&7&7&:r/r`r%)rr9)rWr"rrr s` ` r-r9EntityLinker.from_disksH 88: M EQ G: D)G t'2 r/)rrc [er1NotImplementedError)rWr+rrconfigs r-rehearseEntityLinker.rehearse9!!r/c[er1r=)rWlabels r- add_labelEntityLinker.add_label<rBr/)rDr^r_r@rAr>r=r`r;r%r<r:r7r8rCr9) entity_linker)rEN)0__name__ __module__ __qualname____firstlineno____doc__r*BACKWARD_OVERWRITEr.r rstrrintboolrrrrr rrbrrRrsr|rrrrrrrrrr rrtupler rrr rr%r9r@rE__static_attributes__r2r/r-r)r)5s C $ ^+ -%8&*)^+^+^+ ^+ ! ^+^+^+^+"^+!-!68K!KL^+' HTN +Xhy6I-J J ^+$UCL-$?@^+ !^+""#^+$%^+& #'^+(E?)^+* +^+@ Xg%6 8G;L (%-)? @(A#'@D 4r8G#4454h  4 HeWm%;<= 4l $#'-1 -7#- - i - c5j)* - c5j -^& '!2& & PxHSMxd3ixt1HSM14911*#('1"164CSBT/#t)$/2:3-/ /$CSBT#t)$2:3- 6)-T""r/r)c|US:Xa"[R"S5nUR$[S[SU35e)Nmake_entity_linkerzspacy.pipeline.factorieszmodule z has no attribute ) importlib import_modulerTrrH)r:modules r- __getattr__rXAsA ##(()CD((( 78*,>tfE FFr/)ris GGG PP )HH-1$) H%%&:;GDVH"=H"XGr/