h6^SSKrSSKrSSKJr SSKJrJrJrJrJ r J r J r SSK r SSK JrJrJrJrJr SSKJr SSKJr SSKJr SS KJr SS KJr SS KJrJrJ r SS K!J"r" SS K#J$r$ SSK%J&r& Sr'\"5RQ\'5Sr)Sr*Sr+S\\S\\,\44Sjr-Sr."SS\&5r/Sr0g)N)islice)AnyCallableDictIterableListOptionalTuple)ConfigModel Optimizerget_array_moduleset_dropout_rate)Floats2d)Errors)Language)Scorer)Doc)Examplevalidate_examplesvalidate_get_examples)registry)Vocab) TrainablePipeaW [model] @architectures = "spacy.TextCatEnsemble.v2" [model.tok2vec] @architectures = "spacy.Tok2Vec.v2" [model.tok2vec.embed] @architectures = "spacy.MultiHashEmbed.v2" width = 64 rows = [2000, 2000, 500, 1000, 500] attrs = ["NORM", "LOWER", "PREFIX", "SUFFIX", "SHAPE"] include_static_vectors = false [model.tok2vec.encode] @architectures = "spacy.MaxoutWindowEncoder.v2" width = ${model.tok2vec.embed.width} window_size = 1 maxout_pieces = 3 depth = 2 [model.linear_model] @architectures = "spacy.TextCatBOW.v3" exclusive_classes = true length = 262144 ngram_size = 1 no_output_layer = false modelz [model] @architectures = "spacy.TextCatBOW.v3" exclusive_classes = true length = 262144 ngram_size = 1 no_output_layer = false a` [model] @architectures = "spacy.TextCatReduce.v1" exclusive_classes = true use_reduce_first = false use_reduce_last = false use_reduce_max = false use_reduce_mean = true [model.tok2vec] @architectures = "spacy.HashEmbedCNN.v2" pretrained_vectors = null width = 96 depth = 4 embed_size = 2000 window_size = 1 maxout_pieces = 3 subword_features = true examplesreturnc 6[R"US4SS0UD6$)Ncats multi_labelF)r score_cats)rkwargss P/home/james-whalen/.local/lib/python3.13/site-packages/spacy/pipeline/textcat.py textcat_scorer&Os/        c[$N)r&r'r%make_textcat_scorerr+Xs r'cJ\rSrSrSrS&\S.S\S\S\S\ S\ \ S S 4 S jjjr \ S 5r\ S \\4S j5r\ S \\4Sj5rS\\4SjrS\\S S 4SjrSS S S.S\\S\ S\ \S\ \\\ 4S \\\ 44 SjjrSS S S.S\\S\ S\ \S\ \\\ 4S \\\ 44 SjjrS\\S \\R:\R:44SjrS\\S \\ \ 44SjrS\S \ 4Sjr!S S S S.S\ /\\4S \ \"S!\ \\S"\ \S S 4 S#jjr#S\\4S$jr$S%r%g )'TextCategorizer\zePipeline component for single-label text classification. DOCS: https://spacy.io/api/textcategorizer )scorervocabrname thresholdr/rNcnXlX lX0lSUl/USS.n[ U5UlXPlg)aInitialize a text categorizer for single-label classification. 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. threshold (float): Unused, not needed for single-label (exclusive classes) classification. scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_cats for the attribute "cats". DOCS: https://spacy.io/api/textcategorizer#init N)labelsr2positive_label)r0rr1_rehearsal_modeldictcfgr/)selfr0rr1r2r/r8s r%__init__TextCategorizer.__init__bs=,   $""  9 r'cg)NFr*r9s r%support_missing_values&TextCategorizer.support_missing_valuess r'c2[URS5$)zvRETURNS (Tuple[str]): The labels currently added to the component. DOCS: https://spacy.io/api/textcategorizer#labels r4)tupler8r=s r%r4TextCategorizer.labelss TXXh'((r'cUR$)zvRETURNS (List[str]): Information about the component's labels. DOCS: https://spacy.io/api/textcategorizer#label_data )r4r=s r% label_dataTextCategorizer.label_datas {{r'docsc[SU55(duUVs/sHo"RPM nnURRRnUR [ [U55[ UR545nU$URRU5nURRRU5nU$s snf)zApply the pipeline's model to a batch of docs, without modifying them. docs (Iterable[Doc]): The documents to predict. RETURNS: The models prediction for each document. DOCS: https://spacy.io/api/textcategorizer#predict c38# UHn[U5v M g7fr)len.0docs r% *TextCategorizer.predict..,t3s88t) anytensorropsxpzerosrJlistr4predictasarray)r9rFrMtensorsrUscoress r%rXTextCategorizer.predicts,t,,,-12TczzTG2""BXXs4:DKK0@ABFM##D)''/ 3sCc[U5H?up4[UR5H!upV[X#U45URU'M# MA g)zModify a batch of Doc objects, using pre-computed scores. docs (Iterable[Doc]): The documents to modify. scores: The scores to set, produced by TextCategorizer.predict. DOCS: https://spacy.io/api/textcategorizer#set_annotations N) enumerater4floatr!)r9rFr[irMjlabels r%set_annotationsTextCategorizer.set_annotationssA oFA%dkk2"'!t "53&r')dropsgdlossesrrfrgrhcUc0nURURS5 [US5 URU5 [ SU55(dU$[ UR U5 UR RUVs/sHoURPM sn5upgURX5upU"U 5 UbURU5 X@R==U- ss'U$s snf)aLearn from a batch of documents and gold-standard information, updating the pipe's model. Delegates to predict and get_loss. examples (Iterable[Example]): A batch of Example objects. drop (float): The dropout rate. sgd (thinc.api.Optimizer): The optimizer. losses (Dict[str, float]): Optional record of the loss during training. Updated using the component name as the key. RETURNS (Dict[str, float]): The updated losses dictionary. DOCS: https://spacy.io/api/textcategorizer#update rezTextCategorizer.updatec3p# UH,oR(a[UR5OSv M. g7f)rN) predictedrJ)rLegs r%rN)TextCategorizer.update..s"Oh 3r||$!;hs46) setdefaultr1r_validate_categoriesrRrr begin_updaterkget_loss finish_update) r9rrfrgrhrlr[ bp_scoreslossd_scoress r%updateTextCategorizer.updates( >F$))S)($<= !!(+OhOOOMT* JJ33H4UHb\\H4UVx8( ?   s #yyT! 5VsC,c@Uc0nURURS5 URcU$[US5 UR U5 UVs/sHoUR PM nn[ SU55(dU$[URU5 URRU5upxURRU5upXy- n U"U 5 UbURU5 X@R==U S-R5- ss'U$s snf)aSPerform a "rehearsal" update from a batch of data. Rehearsal updates teach the current model to make predictions similar to an initial model, to try to address the "catastrophic forgetting" problem. This feature is experimental. examples (Iterable[Example]): A batch of Example objects. drop (float): The dropout rate. sgd (thinc.api.Optimizer): The optimizer. losses (Dict[str, float]): Optional record of the loss during training. Updated using the component name as the key. RETURNS (Dict[str, float]): The updated losses dictionary. DOCS: https://spacy.io/api/textcategorizer#rehearse rezTextCategorizer.rehearsec38# UHn[U5v M g7fr)rIrKs r%rN+TextCategorizer.rehearse..rPrQr) rnr1r6rrorkrRrrrprrsum) r9rrfrgrhrlrFr[rstarget_gradients r%rehearseTextCategorizer.rehearses, >F$))S)  (M($>? !!(+'/0x x0,t,,,MT* JJ33D9))66t< ?( ?   s #yyhk..00 1sDc([[U55n[R"U[UR54SS9n[R "U[UR54SS9n[ U5HtupV[ UR5HVupxXRR;aURRUX5U4'M=UR(dMPSXEU4'MX Mv URRRU5nX44$)Nf)dtypere) rJrWnumpyrVr4onesr^ referencer!r>rrTrY) r9r nr_examplestruths not_missingr`rlrarbs r%_examples_to_truth"TextCategorizer._examples_to_truth s$x.) k3t{{+;? !!(+"55h?jjnn,,[9 ?%q[..0&'11r'rbc[U[5(d[[R5eXR ;agUR 5 URSRU5 UR(a\SURR;aBURRS"UR[UR 55Ul URRRU5 g)zAdd a new label to the pipe. label (str): The label to add. RETURNS (int): 0 if label is already present, otherwise 1. DOCS: https://spacy.io/api/textcategorizer#add_label rr4 resize_outputr) isinstancestr ValueErrorrE187r4_allow_extra_labelr8appendrattrsrJr0stringsadd)r9rbs r% add_labelTextCategorizer.add_label+s%%%V[[) ) KK  ! !!%( ::/TZZ-=-==))/:4::s4;;GWXDJ u%r')nlpr4r5 get_examplesrr4r5cr[US5 URU"55 Uc=U"5H1nURRHnUR U5 M M3 OUHnUR U5 M [ UR 5S:a[[R5eUbX@R ;a2[RRX@R S9n[U5e[ UR 5S:wa2[RRX@R S9n[U5eX@RS'[[U"5S55n U V s/sHoR PM n n UR#U 5upUR%5 [ U 5S:d,[R&RUR(S95e[ U 5S:d,[R&RUR(S95eUR*R-XS 9 gs sn f) aInitialize the pipe for training, using a representative set of data examples. get_examples (Callable[[], Iterable[Example]]): Function that returns a representative sample of gold-standard Example objects. nlp (Language): The current nlp object the component is part of. labels (Optional[Iterable[str]]): The labels to add to the component, typically generated by the `init labels` command. If no labels are provided, the get_examples callback is used to extract the labels from the data. positive_label (Optional[str]): The positive label for a binary task with exclusive classes, `None` otherwise and by default. DOCS: https://spacy.io/api/textcategorizer#initialize zTextCategorizer.initializeNr) pos_labelr4r5 r)r1)XY)rroyr!rrJr4rrE867E920formatE919r8rWrrr_require_labelsE923r1r initialize)r9rrr4r5examplecatrberrsubbatchrl doc_sample label_sampler}s r%rTextCategorizer.initialize>s, l,HI !!,.1 >'>"99>>CNN3'** u% t{{ a V[[) )  %[[0kk((>++(V o%4;;1$kk((>++(V o%%3!"|~r23-56XrllX 611(;  :"FFKK$6$6DII$6$FF"< 1$Hfkk&8&8dii&8&HH$  ; 7s*H4cUHn[URRR55nUR S5S:a:[ [ RRURRS95eUH7nUS:XaM US:XaM[ [ RRUS95e M g)zKCheck whether the provided examples all have single-label cats annotations.g?r)valuere)valN) rWrr!valuescountrrE895rE851)r9rexvalsrs r%ro$TextCategorizer._validate_categoriesosB ))0023Dzz#" !3!3",,:K:K!3!LMMs cSj$V[[%7%7C%7%@AA r')r6r8rr1r/r0)textcat)&__name__ __module__ __qualname____firstlineno____doc__r&rr rr_r rr:propertyr>r r4rrDrrrXrcrr rrvrrndarrayrrqintrrrro__static_attributes__r*r'r%r-r-\sm &3      "   D )c ))DIHSM$ 6HSM 6d 6 #'-1 #7## # i # c5j)* # c5j #R#'-1 )7#) ) i ) c5j)* ) c5j )V # ) # u}}emm+ , #2'!22uUE\?R2&ss.#'*.(, /<r8G#445/<h  /< #' /< ! /< /Br'r-c|US:Xa"[R"S5nUR$[S[SU35e)N make_textcatzspacy.pipeline.factorieszmodule z has no attribute ) importlib import_modulerAttributeErrorr)r1modules r% __getattr__r{s@ ~(()CD""" 78*,>tfE FFr')1rsys itertoolsrtypingrrrrrr r r thinc.apir r r rr thinc.typesrerrorsrlanguagerr/rtokensrtrainingrrrutilrr0rtrainable_pipersingle_label_default_configfrom_strDEFAULT_SINGLE_TEXTCAT_MODELsingle_label_bow_configsingle_label_cnn_configrr&r+r-rr*r'r%rs GGG RR HH)8 &x001LMgV*HW-DcN[Bm[B~Gr'