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SSK/J0r0 SSK1J2r2 Sr3Sr4Sr5\"5Rm\35Sr7\"5Rm\45Sr8\"SS\55r9SS.S\ \'S\ \:S\ \S\4Sjjr;SS.S\ \'S\S!\:S"\:S\94S#jr?S\\rSrSrSS.S\\S\\S\4Sjjr Sr g) SuggesterQNopsdocsr-returncgN)selfr.r-s P/home/james-whalen/.local/lib/python3.13/site-packages/spacy/pipeline/spancat.py__call__Suggester.__call__Ss r2) __name__ __module__ __qualname____firstlineno__rrr rrr5__static_attributes__r2r7r4r*r*Qs,DH Xc] HSM V  r7r*r,r.sizesr-r/c Uc [5n/n/nUHnURR[U5SS9nUR S5nSnUHnU[U5::aVUS[U5US- - n UR URR XU-455 XsSRS- nU(dMqUSRS:XaMUSR5e UR U5 M URU5n [U5S:a&[URRU5U 5n O$[URRSSS9U 5n U RRS:XdeU $) Nidtype)r%rr%rBrrr)rxparangelenreshapeappendhstackshapendim asarray1irvstackzerosdataXd) r.r=r-spanslengthsdocstartslengthsize starts_size lengths_arrayoutputs r4ngram_suggesterrYWs\ { EGs3xs3(Ds3x$%URRU[T5S/5$r1)rPgetrF)rRkey attr_prefixs r4spancat_score..s!399==S5E5G1H"#Mr7has_annotationc">TUR;$r1)rP)rRrus r4rwrxsC3994Dr7)dict setdefaultr score_spans)rmkwargsrvrus @@r4 spancat_scorers &\FK  C f cU34 ot, M &(DE   h 1& 11r7c[$r1)rr2r7r4make_spancat_scorerrs r7c*\rSrSrSrSrSrSrSrg) _Intervalsz2 Helper class to avoid storing overlapping spans. c"[5Ulgr1)setrangesr3s r4__init___Intervals.__init__s e r7c^[X5HnURRU5 M gr1)rhradd)r3r?jes r4r_Intervals.adds qA KKOOA r7cRUup#[X#5HnX@R;dM g g)NTF)rhr)r3rangr?rrs r4 __contains___Intervals.__contains__s)qAKKr7)rN) r8r9r:r;__doc__rrrr<r2r7r4rrsr7rc\rSrSrSrS>SSSSSS\S .S \S \\\ \ \ 4\ 4S \ S \S\S\S\\S\\S\\S\\S\\SS4Sjjjr\S\4Sj5rS?SjrS\S\4Sjr\S\\4Sj5r\S\ \4Sj5r\S\\\44Sj5r\S\4Sj5r\S\\S44Sj5rS \ \ 4S!jr!S"S#.S \ \ S$\SS4S%jjr"S \ \ SS4S&jr#S'SSS(.S)\ \$S*\S+\\%S,\\\\4S\\\44 S-jjr&S)\ \$S.\\ \ 4S\\\44S/jr'SSS0.S1\/\ \$4S2\\(S3\\ \SS4S4jjr)S)\ \$4S5jr*S6\$4S7jr+S8\ S9\,S:\ S\-4S;jr.S@S8\ S9\,S:\ S\S\-4 S<jjr/S=r0g)ASpanCategorizerzWPipeline component to label spans of text. DOCS: https://spacy.io/api/spancategorizer FrP?TNg?)add_negative_labelrZnegative_weightrq max_positive thresholdscorervocabr( suggesternamerrZrrqrrrr/c/UU U UUS.UlXlX0lX lX@lXlXPlU(d1U b-U S:a&[[RRU S95eggg)a}Initialize the multi-label or multi-class span categorizer. vocab (Vocab): The shared vocabulary. model (thinc.api.Model): The Thinc Model powering the pipeline component. For multi-class classification (single label per span) we recommend using a Softmax classifier as a the final layer, while for multi-label classification (multiple possible labels per span) we recommend Logistic. suggester (Callable[[Iterable[Doc], Optional[Ops]], Ragged]): A function that suggests spans. Spans are returned as a ragged array with two integer columns, for the start and end positions. name (str): The component instance name, used to add entries to the losses during training. spans_key (str): Key of the Doc.spans dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"spans"`. add_negative_label (bool): Learn to predict a special 'negative_label' when a Span is not annotated. threshold (Optional[float]): Minimum probability to consider a prediction positive. Defaults to 0.5. Spans with a positive prediction will be saved on the Doc. max_positive (Optional[int]): Maximum number of labels to consider positive per span. Defaults to None, indicating no limit. negative_weight (float): Multiplier for the loss terms. Can be used to downweight the negative samples if there are too many when add_negative_label is True. Otherwise its unused. allow_overlap (bool): If True the data is assumed to contain overlapping spans. Otherwise it produces non-overlapping spans greedily prioritizing higher assigned label scores. Only used when max_positive is 1. scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_spans for the Doc.spans[spans_key] with overlapping spans allowed. DOCS: https://spacy.io/api/spancategorizer#init )labelsrZrrrrqNr%)r) cfgrrr(rrr ValueErrorrE1051format) r3rr(rrrrZrrqrrrs r4rSpanCategorizer.__init__sxf""(.*   "   "4!9lQ>NV\\00l0KL L?O!9}r7c2[URS5$)zKey of the doc.spans dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. rZ)strrrs r4ruSpanCategorizer.keys 488K())r7cPSnURRS5(aURRS5nOyURRS5(aYURR S5RS5(a*URR S5RS5nUblXR :Xa\UR (dJ[[RRURURRS5S95eggg)zbNN2$$ KK&&DII$**:L:LT:R&S%3>r7labelc&[U[5(d[[R5eXR ;agUR 5 URSRU5 URRRU5 g)zAdd a new label to the pipe. label (str): The label to add. RETURNS (int): 0 if label is already present, otherwise 1. DOCS: https://spacy.io/api/spancategorizer#add_label rrr%) isinstancerrrE187rrrrHrstringsr)r3rs r4 add_labelSpanCategorizer.add_label(sk%%%V[[) ) KK  ! !!%( u%r7c2[URS5$)zvRETURNS (Tuple[str]): The labels currently added to the component. DOCS: https://spacy.io/api/spancategorizer#labels r)tuplerrs r4rSpanCategorizer.labels9s TXXh'((r7c,[UR5$)zvRETURNS (List[str]): Information about the component's labels. DOCS: https://spacy.io/api/spancategorizer#label_data )rgrrs r4 label_dataSpanCategorizer.label_dataAs DKK  r7cb[UR5VVs0sHupX!_M snn$s snnf)z(RETURNS (Dict[str, int]): The label map.) enumerater)r3r?rs r4 _label_mapSpanCategorizer._label_mapIs+*34;;)?@)?XQ)?@@@s+c~UR(a[UR5S-$[UR5$)z RETURNS (int): Number of labels.r%)rrFrrs r4rSpanCategorizer._n_labelsNs0  " "t{{#a' 't{{# #r7cPUR(a[UR5$g)z8RETURNS (Union[int, None]): Index of the negative label.N)rrFrrs r4_negative_label_i!SpanCategorizer._negative_label_iVs  " "t' 'r7r.cURXRRS9nURR 5S:Xa)URRR SS5nX#4$URR X45nX#4$)zApply the pipeline's model to a batch of docs, without modifying them. docs (Iterable[Doc]): The documents to predict. RETURNS: The models prediction for each document. DOCS: https://spacy.io/api/spancategorizer#predict r,r)rr(r-rQsumalloc2fpredict)r3r.indicesscoress r4rSpanCategorizer.predict^su..::>>.: ??   A %ZZ^^++Aq1FZZ''8Fr7 candidates)candidates_keyrcURXRRS9n[X15HMupE/URU'UR H)nURUR XVSUS5 M+ MO g)aGUse the spancat suggester to add a list of span candidates to a list of docs. This method is intended to be used for debugging purposes. docs (Iterable[Doc]): The documents to modify. candidates_key (str): Key of the Doc.spans dict to save the candidate spans under. DOCS: https://spacy.io/api/spancategorizer#set_candidates r,rr%N)rr(r-ziprPrOrH)r3r.rsuggester_outputrrRindexs r4set_candidatesSpanCategorizer.set_candidatesmsq >>$JJNN>C"#3:OJ(*CIIn %#** .)001Xa1IJ+ ;r7c Uup4Sn[U5HupgX6Rn[[URS5n URSS:Xa=UR UUXEXSR U-U 5URUR'O;URUUXEXSR U-5URUR'XSR U- nM g)zModify a batch of Doc objects, using pre-computed scores. docs (Iterable[Doc]): The documents to modify. scores: The scores to set, produced by SpanCategorizer.predict. DOCS: https://spacy.io/api/spancategorizer#set_annotations rrqrr%N) rrOr boolr_make_span_group_singlelabelrQrPru_make_span_group_multilabel) r3r.indices_scoresrroffsetr?rR indices_irqs r4set_annotationsSpanCategorizer.set_annotationss)oFA ))I txx'@AMxx'1,&*&G&GF__Q-?$?@! ' $((#'+&F&FF__Q-?$?@' $((# ooa( (F!&r7)dropsgdlossesrmrrrcpUc0nURURS5 [US5 URU5 [ SU55(dU$UVs/sHoUR PM nnUR X`RRS9nURR5S:XaU$[URU5 URRXg45upURXU45upU "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/spancategorizer#update rzSpanCategorizer.updatec3p# UH,oR(a[UR5OSv M. g7f)rN) predictedrF).0egs r4 )SpanCategorizer.update..s"Oh 3r||$!;hs46r,r)r|rr"_validate_categoriesanyrrr(r-rQrr begin_updateget_loss finish_update) r3rmrrrrr.rPrbackprop_scoreslossd_scoress r4updateSpanCategorizer.updates ( >F$))S)($<= !!(+OhOOOM'/0x x0t8 ==   ! #MT*"&**"9"94-"HxA! ?   s #yyT! 1sD3 spans_scorescUup4[URRRUR5URRRUR 55n[ R"URURS9nUR(a#[ R"URS5nSnURn[U5Hup0n X9Rn [UR U 5H)n [!XS45n[!XS45nX}-XU4'M+ UR#U 5HVnUR$UR&4nUU ;dM#U UnUUR(nSUUU4'UR(dMQSWU'MX XsR U - nM URRR+USS9nUR(a*[ R,"W5SnSUUUR.4'XE- nUR(a0[1[2UR4S5nUS:wa UW==U-ss'[3US-R755nUU4$) a;Find the loss and gradient of loss for the batch of documents and their predicted scores. examples (Iterable[Examples]): The batch of examples. spans_scores: Scores representing the model's predictions. RETURNS (Tuple[float, float]): The loss and the gradient. DOCS: https://spacy.io/api/spancategorizer#get_loss r@rr%rrfrr)rr(r-to_numpydatarQnumpyrNrJrAronesrrrOrhint_get_aligned_spansr\r]label_r^nonzerorr floatrr)r3rmrrPrtargetnegative_spansr label_mapr?r spans_indexspans_irr\r] gold_spanrurowknegative_samplesr neg_weightrs r4rSpanCategorizer.get_losss-%  JJNN # #EJJ /1H1H1W V\\>  " ""ZZa:NOO x(EAKhooG5==+,GqDM*'Q$-(,2J CL)-"44R8   6+%%c*C!)"2"23A%(F36N....1s+9 mmA& &F+),''c':  " "$}}^vx?O9P< ue| <D#'&* $$r8G#445$$h  $$ c# $$  $$L Xg->  W ' '' '  '\# 5 55 5  5  55r7rcUS:Xa"[R"S5nUR$US:Xa"[R"S5nUR$[ S[ SU35e)N make_spancatzspacy.pipeline.factoriesmake_spancat_singlelabelzmodule z has no attribute ) importlib import_moduler?r@AttributeErrorr8)rmodules r4 __getattr__rEsf ~(()CD""" + +(()CD... 78*,>tfE FFr7)FrAsys dataclassesr functoolsrtypingrrrrr r r r r r thinc.apirrrrrr thinc.typesrrrrcompatrrerrorsrlanguagerrrtokensrrr trainingr!r"utilr#rr$trainable_piper&spancat_default_config"spancat_singlelabel_default_configDEFAULT_SPANS_KEYfrom_strDEFAULT_SPANCAT_MODEL!DEFAULT_SPANCAT_SINGLELABEL_MODELr*rrYrr`rcrirlrrrrrEr2r7r4rYs !TTT VV880))1)6&"4))*@A'J$*H$5$5&% % !    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