h,ODSSKrSSKrSSKrSSKJr SSKJr SSKJrJ r J r J r J r J r JrJrJr SSKrSSKJrJr SSKJr SSKJrJr SS KJr SS KJr SS KJrJ r SS K!J"r" SS K#J$r$J%r%J&r&J'r'J(r( SSK)J*r* Sr+\ \,\\,\ \ \,\444r-Sr.Sr/"SS\*5r0Sr1g)N) defaultdict)Path) AnyCallableDictIterableListOptionalSequenceTupleUnion)ErrorsWarnings)Language)Matcher PhraseMatcher)levenshtein_compare get_ner_prf)DocSpan)Example)SimpleFrozenList ensure_path from_diskregistryto_disk)Pipez||c [U5$Nr)exampleskwargss T/home/james-whalen/.local/lib/python3.13/site-packages/spacy/pipeline/entityruler.pyentity_ruler_scorer&s x  c[$r")r&r'r%make_entity_ruler_scorerr*s r'c\rSrSrSrS/S\SS\S\S.S\S\ S\ \ \ \ 4S \ S \S \S \ S \ \\S\ \ SS4SjjjrS\ 4SjrS\ S\4SjrS\S\4SjrS\4SjrSr\S\\ S44Sj5rSSS.S\ /\\4S\ \S \ \\4Sjjr\S\\ \ S44Sj5r\S\\4Sj5r S \\SS4Sjr!S0S jr"S!\ SS4S"jr#S0S#jr$S\ S\\ \ \ 44S$jr%S\&S!\&S\ 4S%jr'\("5S&.S'\)S(\\ SS4S)jjr*\("5S&.S(\\ S\)4S*jjr+\("5S&.S+\ \ \,4S(\\ SS4S,jjr-\("5S&.S+\ \ \,4S(\\ SS4S-jjr.S.r/g)1 EntityRuler aThe EntityRuler lets you add spans to the `Doc.ents` using token-based rules or exact phrase matches. It can be combined with the statistical `EntityRecognizer` to boost accuracy, or used on its own to implement a purely rule-based entity recognition system. After initialization, the component is typically added to the pipeline using `nlp.add_pipe`. DOCS: https://spacy.io/api/entityruler USAGE: https://spacy.io/usage/rule-based-matching#entityruler NF)phrase_matcher_attrmatcher_fuzzy_comparevalidateoverwrite_ents ent_id_seppatternsscorernlpnamer.r/r0r1r2r3r4returncXlX lX`l[[5Ul[[5UlXPlX@l[URXPRS9Ul X0l [URURUS9UlXpl[[ 5UlUbUR%U5 Xlg)acInitialize the entity ruler. If patterns are supplied here, they need to be a list of dictionaries with a `"label"` and `"pattern"` key. A pattern can either be a token pattern (list) or a phrase pattern (string). For example: `{'label': 'ORG', 'pattern': 'Apple'}`. nlp (Language): The shared nlp object to pass the vocab to the matchers and process phrase patterns. name (str): Instance name of the current pipeline component. Typically passed in automatically from the factory when the component is added. Used to disable the current entity ruler while creating phrase patterns with the nlp object. phrase_matcher_attr (int / str): Token attribute to match on, passed to the internal PhraseMatcher as `attr`. matcher_fuzzy_compare (Callable): The fuzzy comparison method for the internal Matcher. Defaults to spacy.matcher.levenshtein.levenshtein_compare. validate (bool): Whether patterns should be validated, passed to Matcher and PhraseMatcher as `validate` patterns (iterable): Optional patterns to load in. overwrite_ents (bool): If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. ent_id_sep (str): Separator used internally for entity IDs. scorer (Optional[Callable]): The scoring method. Defaults to spacy.scorer.get_ner_prf. DOCS: https://spacy.io/api/entityruler#init r0 fuzzy_compareattrr0N)r5r6 overwriterlisttoken_patternsphrase_patterns _validater/rvocabmatcherr.rphrase_matcherr2tuple_ent_ids add_patternsr4) selfr5r6r.r/r0r1r2r3r4s r%__init__EntityRuler.__init__+sP ')$/*40!%:" II8R8R  $7 + IID44x %#E*     h ' r'c[SURR555n[SURR555nX-$)z5The number of all patterns added to the entity ruler.c38# UHn[U5v M g7fr"len.0ps r% &EntityRuler.__len__..isL/K!s1vv/Kc38# UHn[U5v M g7fr"rMrOs r%rRrSjsN0M1A0MrT)sumr?valuesr@)rHn_token_patternsn_phrase_patternss r%__len__EntityRuler.__len__gsKLt/B/B/I/I/KLLN0D0D0K0K0MNN33r'labelcHXR;=(d XR;$)z+Whether a label is present in the patterns.)r?r@)rHr\s r% __contains__EntityRuler.__contains__ms+++Lu8L8L/LLr'doccUR5nURU5nURX5 U$![anU"URX/U5sSnA$SnAff=f)zFind matches in document and add them as entities. doc (Doc): The Doc object in the pipeline. RETURNS (Doc): The Doc with added entities, if available. DOCS: https://spacy.io/api/entityruler#call N)get_error_handlermatchset_annotations Exceptionr6)rHr` error_handlermatcheses r%__call__EntityRuler.__call__qs\..0  <jjoG   .J < D%; ; #EntityRuler.match..s!A$1+!u!5r'T)keyreverse) _require_patternswarningscatch_warningsfilterwarningsr>rCrDsetsorted)rHr`rgm_idstartend final_matches get_sort_keys r%rcEntityRuler.matchs   $ $ &  # #Hi @4<<,-T5H5H5M0NNG'8? P$4DC< d3  P 6 }M ' & QsA B4 C C 4 Cc [UR5n/n[5nUHupgn[SXU55(aUR(dM2Xu;dM9US- U;dMDX`R ;aUR Uup[ XXU S9n O [ XXS9n URU 5 UV s/sH&oRU:aU RU:aM$U PM( nn UR[Xx55 M X4-Ulgs sn f)zModify the document in placec38# UHoRv M g7fr")ent_type)rPts r%rR.EntityRuler.set_annotations..s6~!::~rTr)r\span_id)r\N) r>entsrxanyr=rFrappendr{r|updaterange) rHr`rgentities new_entities seen_tokensmatch_idr{r|r\ent_idspanrhs r%rdEntityRuler.set_annotationss> e $+ HS6s~666t~~'C!G;,F}},$(MM($;MECfMDC@D##D)''!# !%%%-Ax""5#45%,* s 3#D D .cv[URR55nURURR55 [5nUHJnUR U;a&UR U5upEURU5 M9URU5 ML [[U55$)z|All labels present in the match patterns. RETURNS (set): The string labels. DOCS: https://spacy.io/api/entityruler#labels ) rxr?keysrr@r2 _split_labeladdrEry)rHr all_labelslr\_s r%labelsEntityRuler.labelss4&&++-. D((--/0U A!#,,Q/u%q!  VJ'((r')r5r3 get_examplescVUR5 U(aURU5 gg)a[Initialize the pipe for training. 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. patterns Optional[Iterable[PatternType]]: The list of patterns. DOCS: https://spacy.io/api/entityruler#initialize N)clearrG)rHrr5r3s r% initializeEntityRuler.initializes#    h ' r'cB[URR55nURURR55 [5nUH9nUR U;dMUR U5upEURU5 M; [U5$)zAll entity ids present in the match patterns `id` properties RETURNS (set): The string entity ids. DOCS: https://spacy.io/api/entityruler#ent_ids ) rxr?rrr@r2rrrE)rHr all_ent_idsrrrs r%ent_idsEntityRuler.ent_idss4&&++-. D((--/0e A!# --a0 '[!!r'c/nURR5HAup#UH6nURU5upVXTS.nU(aXgS'URU5 M8 MC URR5HKup#UH@nURU5upVXTR S.nU(aXgS'URU5 MB MM U$)zGet all patterns that were added to the entity ruler. RETURNS (list): The original patterns, one dictionary per pattern. DOCS: https://spacy.io/api/entityruler#patterns r\patternid)r?itemsrrr@text)rH all_patternsr\r3r ent_labelrrQs r%r3EntityRuler.patternss #2288:OE#$($5$5e$<! '<$dG##A& $ ; $3399;OE#$($5$5e$<! 'LLA$dG##A& $ <r'cSn[URR5HunupEX:XdMUn O URRUSVs/sHoUPM nnURR US9 /n/n/n /n UHn [ U S[5(aJURU S5 U RU S5 U RU RS55 Me[ U S[5(dMURU 5 M /n [UURRU 5U 5H'upnXS.nU(aUUS'U RU5 M) X|-GHn U Sn SU ;aEU nURXS5n URRU 5nUU S4UR U'U Sn[ U["5(a<UR$U RU5 UR&R)X/5 M[ U[5(a<UR*U RU5 URR)X/5 M[ [,R.R1US95e SSS5 gs snf![a /nGNOf=f!,(df  g=f) aNAdd patterns to the entity ruler. A pattern can either be a token pattern (list of dicts) or a phrase pattern (string). For example: {'label': 'ORG', 'pattern': 'Apple'} {'label': 'GPE', 'pattern': [{'lower': 'san'}, {'lower': 'francisco'}]} patterns (list): The patterns to add. DOCS: https://spacy.io/api/entityruler#add_patterns N)disablerr\rr)r) enumerater5pipeline pipe_names ValueError select_pipes isinstancestrrgetr>zippipe _create_labelrC_normalize_keyrFrr@rDrr?rE097format)rHr3 current_indexir6rsubsequent_pipesr?phrase_pattern_labelsphrase_pattern_textsphrase_pattern_idsentryr@r\rrphrase_patternrrrs r%rGEntityRuler.add_patternss "M#,TXX->->#?i 0$77"))%0 "!O*-% 23"+& ,1!E+1N4(&&~6+(9g5= %I ..uDkBE,,55eLL$$UI6$V[[%7%7%7%HII:-= < V "!  " < r?r@rErFrr5rBrAr/rCrr.rDrHs r%rEntityRuler.clear3sw)$/*40#E*  HHNN^^44  , HHNN!9!9DNN r'rc URR5VVs/sHup#X1:XdM X#4PM nnnU(d1[[RR SXR S95eUVVs/sHup#URX#5PM nnn[[URR5VVs0sHup&X%;dM X&_M snn5Ul [[URR5VVs0sHup&X%;dM X&_M snn5Ul UHJnX R;aURRU5 M/URRU5 ML gs snnfs snnfs snnfs snnf)zRemove a pattern by its ent_id if a pattern with this ent_id was added before ent_id (str): id of the pattern to be removed RETURNS: None DOCS: https://spacy.io/api/entityruler#remove ID) attr_typer\ componentN)rFrWrrE1024rr6rrr>r@rr?rDremoverC)rHrr\eidlabel_id_pairscreated_labelsvals r%rEntityRuler.removeAsx.2]]-A-A-C -C\es}LUL-C   ##d&II#V @N ?M|D  u *~   + %)$8$8$>$>$@ $@LU. $@  * %)$7$7$=$=$? $?LU. $?  $E+++##**51 ##E* $;    s( E7E74E=; F F  F F c[U5S:Xa<[R"[RR UR S95 gg)z:Raise a warning if this component has no patterns defined.r)r6N)rNruwarnrW036rr6rs r%rtEntityRuler._require_patternsks4 t9> MM(--..DII.> ? r'crURU;a!URURS5up#X#4$UnSnX#4$)zSplit Entity label into ent_label and ent_id if it contains self.ent_id_sep label (str): The value of label in a pattern entry RETURNS (tuple): ent_label, ent_id rN)r2rsplit)rHr\rrs r%rEntityRuler._split_labelpsG ??e # % T__a @ I  IF  r'cT[U[5(aUURU3nU$)aJoin Entity label with ent_id if the pattern has an `id` attribute If ent_id is not a string, the label is returned as is. label (str): The label to set for ent.label_ ent_id (str): The label RETURNS (str): The ent_label joined with configured `ent_id_sep` )rrr2)rHr\rs r%rEntityRuler._create_label}s- fc " "gdoo.vh7E r')excludepatterns_bytesrc[R"U5nUR5 [U[5(aUR UR SU55 UR SS5UlUR SS5Ul[URRURS9Ul UR S[5UlU$UR U5 U$)zLoad the entity ruler from a bytestring. patterns_bytes (bytes): The bytestring to load. RETURNS (EntityRuler): The loaded entity ruler. DOCS: https://spacy.io/api/entityruler#from_bytes r3r=Fr.Nr<r2)srsly msgpack_loadsrrdictrGrr=r.rr5rBrDDEFAULT_ENT_ID_SEPr2)rHrrcfgs r% from_bytesEntityRuler.from_bytess!!.1 c4   cggj#6 7 WW[%8DN'*ww/Dd'KD $"/--#D "ggl4FGDO    c " r'cURURURURS.n[R "U5$)zSerialize the entity ruler patterns to a bytestring. RETURNS (bytes): The serialized patterns. DOCS: https://spacy.io/api/entityruler#to_bytes )r=r2r.r3)r=r2r.r3r msgpack_dumps)rHrserials r%to_bytesEntityRuler.to_bytess<//#'#;#;   ""6**r'pathcr^^[U5nTR5 URS5nURS:Xa`UR(a)[ R "U5nTRU5 T$[[RRUS95eUR 5(a)[ R "U5nTRU5 T$UR5(a0mSU4Sj0nSU4Sj0n[X05 TRSS5TlTRS 5TlTRS ["5Tl['TR(R*TR S 9Tl[X05 T$[[R.RUS95e) aLoad the entity ruler from a file. Expects a file containing newline-delimited JSON (JSONL) with one entry per line. path (str / Path): The JSONL file to load. RETURNS (EntityRuler): The loaded entity ruler. DOCS: https://spacy.io/api/entityruler#from_disk .jsonl)rr3cl>TR[R"URS555$Nr)rGr read_jsonl with_suffixrQrHs r%rp'EntityRuler.from_disk..s'd&7&7$$Q]]8%<='r'rcN>TR[R"U55$r")rr read_jsonrQrs r%rprs#**U__Q=O2Pr'r=Fr.r2r)rrrsuffixis_filerrrGrrE1023ris_dirrrr=r.rr2rr5rBrDE146)rHrrdepr_patterns_pathr3deserializers_patternsdeserializers_cfgrs` @r%rEntityRuler.from_disksx4  !--h7 ;;( "|| ++D1!!(+2 /!!4!4$!4!?@@  ' ' ) )''(:;H   h '( '[[]]C& " "'(P Q  dr 2 WW[%8DN'*ww/D'ED $!ggl4FGDO"/T%=%=#D  dB 7 V[[//T/:; ;r'c^^[U5nTRTRTRS.mU4SjU4SjS.nURS:Xa"[ R "UTR5 g[X05 g)zSave the entity ruler patterns to a directory. The patterns will be saved as newline-delimited JSON (JSONL). path (str / Path): The JSONL file to save. DOCS: https://spacy.io/api/entityruler#to_disk )r=r.r2cd>[R"URS5TR5$r)r write_jsonlrr3rs r%rp%EntityRuler.to_disk..s"%"3"3 h'#r'c2>[R"UT5$r")r write_jsonrs r%rprsU--a5r')r3rrN) rr=r.r2rrrr3r)rHrr serializersrs` @r%rEntityRuler.to_disksj4 #'#;#;//  6  ;;( "   dDMM 2 Dr *r') rFrAr2rCr/r6r5r=rDr.r@r4r?) entity_ruler)r7N)0__name__ __module__ __qualname____firstlineno____doc__rrr&rrr r intrboolr PatternTyperIrZr^rrircrdpropertyr rrrr rrr3rGrrrtrrrrbytesrrrrr__static_attributes__r)r'r%r,r, s#: :>*=$,04%7: :: &eCHo6 : ( ::::4 ,-:": :x44 M#M$MtfE FFr')2rsysru collectionsrpathlibrtypingrrrrr r r r r rerrorsrrlanguagerrCrrmatcher.levenshteinrr4rtokensrrtrainingrutilrrrrrrr rrrr&r*r,rr)r'r%r(s #XXX %,5 NN3c4S#X#77889 !V+$V+tGr'