193 lines
26 KiB
Plaintext
193 lines
26 KiB
Plaintext
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l!m"Z"m#Z#m$Z$m%Z%m&Z& ddl'm(Z( dZ)ee*ee*e
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ee*ef f f Z+ ejX dg d¢dddidde)ddidœdddddœ¬ « d ed e*d e ee-e*f d ed!e.d"e.d#e*d$e e fd%„« Z/d&„ Z0 e%jb d« d'„ « Z2 G d(„ d)e(« Z3y)*é N)Ú defaultdict)ÚPath) ÚAnyÚCallableÚDictÚIterableÚListÚOptionalÚSequenceÚTupleÚUnioné )ÚErrorsÚWarnings)ÚLanguage)ÚMatcherÚ
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PhraseMatcher)Úlevenshtein_compare©Ú get_ner_prf)ÚDocÚSpan)ÚExample)ÚSimpleFrozenListÚ ensure_pathÚ from_diskÚregistryÚto_diské )ÚPipez||Ú entity_ruler)zdoc.entsztoken.ent_typez
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token.ent_iobz@miscz spacy.levenshtein_compare.v1Fz@scorersz spacy.entity_ruler_scorer.v1©Úphrase_matcher_attrÚmatcher_fuzzy_compareÚvalidateÚoverwrite_entsÚ
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ent_id_sepÚscorerg ð?g )Úents_fÚents_pÚents_rÚ
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ents_per_type)ÚassignsÚdefault_configÚdefault_score_weightsÚnlpÚnamer# r$ r% r& r' r( c
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ó( — t | |||||||¬« S )Nr" )Ú EntityRuler)r0 r1 r# r$ r% r& r' r( s ú[C:\Users\garci\AppData\Roaming\Python\Python312\site-packages\spacy/pipeline/entityruler.pyÚmake_entity_rulerr5 s) € ô8 Ø Ø Ø /Ø 3ØØ%Ø Øô ð ó c ó — t | « S ©Nr )ÚexamplesÚkwargss r4 Úentity_ruler_scorer; > s
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€ Ü �xÓ Ð r6 c ó — t S r8 )r; © r6 r4 Úmake_entity_ruler_scorerr> B s € ä Ð r6 c óx — e Zd ZdZ d-deddededœdedede e
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fd„Zdedefd„Zdefd„Zd„ Zedeedf fd„« Zdddœde g ee f de e d e e e fd„Z edee e df fd „« Z edee fd „« Z d ee ddfd „Z d.d „Z!d eddfd!„Z"d.d"„Z#dedeee e f fd#„Z$de%d e%defd$„Z& e'« d%œd&e(d'ee dd fd(„Z) e'« d%œd'ee de(fd)„Z* e'« d%œd*e
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ee+f d'ee dd fd+„Z, e'« d%œd*e
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ee+f d'ee ddfd,„Z-y)/r3 aâ The EntityRuler lets you add spans to the `Doc.ents` using token-based
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rules or exact phrase matches. It can be combined with the statistical
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`EntityRecognizer` to boost accuracy, or used on its own to implement a
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purely rule-based entity recognition system. After initialization, the
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component is typically added to the pipeline using `nlp.add_pipe`.
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DOCS: https://spacy.io/api/entityruler
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USAGE: https://spacy.io/usage/rule-based-matching#entityruler
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NF)r# r$ r% r& r' Úpatternsr( r0 r1 r# r$ r% r& r' r@ r( Úreturnc ó® — || _ || _ || _ t t « | _ t t « | _ || _ || _ t |j || j ¬« | _ || _ t |j | j |¬« | _ || _ t t « | _ |�| j% |« | | _ y)a# Initialize the entity ruler. If patterns are supplied here, they
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need to be a list of dictionaries with a `"label"` and `"pattern"`
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key. A pattern can either be a token pattern (list) or a phrase pattern
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(string). For example: `{'label': 'ORG', 'pattern': 'Apple'}`.
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nlp (Language): The shared nlp object to pass the vocab to the matchers
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and process phrase patterns.
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name (str): Instance name of the current pipeline component. Typically
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passed in automatically from the factory when the component is
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added. Used to disable the current entity ruler while creating
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phrase patterns with the nlp object.
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phrase_matcher_attr (int / str): Token attribute to match on, passed
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to the internal PhraseMatcher as `attr`.
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matcher_fuzzy_compare (Callable): The fuzzy comparison method for the
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internal Matcher. Defaults to
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spacy.matcher.levenshtein.levenshtein_compare.
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validate (bool): Whether patterns should be validated, passed to
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Matcher and PhraseMatcher as `validate`
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patterns (iterable): Optional patterns to load in.
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overwrite_ents (bool): If existing entities are present, e.g. entities
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added by the model, overwrite them by matches if necessary.
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ent_id_sep (str): Separator used internally for entity IDs.
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scorer (Optional[Callable]): The scoring method. Defaults to
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spacy.scorer.get_ner_prf.
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DOCS: https://spacy.io/api/entityruler#init
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©r% Ú
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fuzzy_compare©Úattrr% N)r0 r1 Ú overwriter ÚlistÚtoken_patternsÚphrase_patternsÚ _validater$ r ÚvocabÚmatcherr# r Úphrase_matcherr' ÚtupleÚ_ent_idsÚ add_patternsr( )
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Úselfr0 r1 r# r$ r% r& r' r@ r( s
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r4 Ú__init__zEntityRuler.__init__R s¸ € ðP ˆŒØˆŒ Ø'ˆŒÜ )¬$Ó /ˆÔÜ *¬4Ó 0ˆÔ Ø!ˆŒØ%:ˆÔ"Ü Ø �I‰I ¸×8RÑ8Rô
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ˆŒ ð $7ˆÔ Ü +Ø �I‰I˜D× 4Ñ 4¸xô
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ˆÔð %ˆŒÜ#¤EÓ*ˆŒ
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Ø Ð Ø × Ñ ˜hÔ 'Ø ˆ� r6 c ó´ — t d„ | j j « D « « }t d„ | j j « D « « }||z S )z5The number of all patterns added to the entity ruler.c 3 ó2 K — | ] }t |« –— Œ y wr8 ©Úlen©Ú.0Úps r4 ú <genexpr>z&EntityRuler.__len__.<locals>.<genexpr>� s è ø€ Ð LÑ/K¨!œs 1ŸvÑ/Kùó ‚c 3 ó2 K — | ] }t |« –— Œ y wr8 rV rX s r4 r[ z&EntityRuler.__len__.<locals>.<genexpr>‘ s è ø€ Ð NÑ0M¨1¤ A§Ñ0Mùr\ )ÚsumrI ÚvaluesrJ )rR Ún_token_patternsÚn_phrase_patternss r4 Ú__len__zEntityRuler.__len__Ž sN € ä Ñ L¨t×/BÑ/B×/IÑ/IÔ/KÓ LÓLÐÜ Ñ N°×0DÑ0D×0KÑ0KÔ0MÓ NÓ NÐØ Ð"3Ñ3Ð3r6 Úlabelc ó> — || j v xs || j v S )z+Whether a label is present in the patterns.)rI rJ )rR rc s r4 Ú __contains__zEntityRuler.__contains__” s# € à˜×+Ñ+Ð+ÒL¨u¸×8LÑ8LÐ/LÐLr6 Údocc óÆ — | j « } | j |« }| j ||« |S # t $ r } || j | |g|« cY d}~S d}~ww xY w)zæFind matches in document and add them as entities.
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doc (Doc): The Doc object in the pipeline.
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RETURNS (Doc): The Doc with added entities, if available.
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DOCS: https://spacy.io/api/entityruler#call
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N)Úget_error_handlerÚmatchÚset_annotationsÚ Exceptionr1 )rR rf Ú
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error_handlerÚmatchesÚes r4 Ú__call__zEntityRuler.__call__˜ s` € ð ×.Ñ.Ó0ˆ
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ð <Ø—j‘j “oˆGØ × Ñ gÔ .؈JøÜò <Ù §¡¨D°3°%¸Ó;Õ ;ûð <ús ’$7 · A Á AÁA ÁA c
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| d fS )Nr r r= )Úms r4 ú<lambda>z#EntityRuler.match.<locals>.<lambda>± s € ! A¡$¨¨1©¡+°°!±¨uÑ!5r6 T)ÚkeyÚreverse) Ú_require_patternsÚwarningsÚcatch_warningsÚfilterwarningsrH rM rN ÚsetÚsorted)rR rf rm Úm_idÚstartÚendÚ
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final_matchesÚ get_sort_keys r4 ri zEntityRuler.match¨ s² € Ø × Ñ Ô Ü
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||||¬« } |j | « |D � cg c]# } | j |k r| j |kD rŒ"| ‘Œ% }} |j t ||« « ŒÑ ||z |_ yc c} w )z Modify the document in placec 3 ó4 K — | ] }|j –— Œ y wr8 )Úent_type)rY Úts r4 r[ z.EntityRuler.set_annotations.<locals>.<genexpr>» s è ø€ Ð6¡~ !�1—:•:¡~ùs ‚r )rc Úspan_id)rc N) rH Úentsr| ÚanyrG rP r Úappendr r€ ÚupdateÚrange)
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r4 rj zEntityRuler.set_annotationsµ s € 䘟™“>ˆØˆ Ü“eˆ Û$+Ñ ˆH�e˜SÜÑ6 s¨5°¡~Ó6Ô6¸t¿~º~Øà˜KÒ'¨C°!©G¸;Ò,FؘtŸ}™}Ñ,Ø$(§M¡M°(Ñ$;‘M�E˜6Ü U¨C°uÀfÔM‘Dä U¨C°xÔ@�DØ ×#Ñ# DÔ)á 'ó Ù '˜!°·±¸#²
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À!Ç%Á%È%Ã-’A˜xð ð ð ×"Ñ"¤5¨°Ó#4Õ5ð %,ð ˜lÑ*ˆ�ùò s Â/#DÃD.c ón — t | j j « « }|j | j j « « t « }|D ]G }| j
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|« \ }}|j |« Œ7|j |« ŒI t t |« « S )z”All labels present in the match patterns.
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RETURNS (set): The string labels.
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DOCS: https://spacy.io/api/entityruler#labels
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)
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r| rI Úkeysr‹ rJ r' Ú _split_labelÚaddrO r} )rR r” Ú
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all_labelsÚlrc Ú_s r4 ÚlabelszEntityRuler.labelsË s“ € ô �4×&Ñ&×+Ñ+Ó-Ó.ˆØ � ‰ �D×(Ñ(×-Ñ-Ó/Ô0Ü“Uˆ
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ãˆAØ�‰ !Ñ#Ø ×,Ñ,¨QÓ/‘��qØ—‘˜uÕ%à—‘˜qÕ!ð ô ”V˜JÓ'Ó(Ð(r6 )r0 r@ Ú get_examplesc óL — | j « |r| j |« yy)a‹ Initialize the pipe for training.
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get_examples (Callable[[], Iterable[Example]]): Function that
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returns a representative sample of gold-standard Example objects.
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nlp (Language): The current nlp object the component is part of.
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patterns Optional[Iterable[PatternType]]: The list of patterns.
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DOCS: https://spacy.io/api/entityruler#initialize
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N)ÚclearrQ )rR r› r0 r@ s r4 Ú
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initializezEntityRuler.initializeß s# € ð
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|« \ }}|j |« Œ8 t |« S )z¬All entity ids present in the match patterns `id` properties
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RETURNS (set): The string entity ids.
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DOCS: https://spacy.io/api/entityruler#ent_ids
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) r| rI r” r‹ rJ r' r• r– rO )rR r” Ú all_ent_idsr˜ r™ r‘ s r4 Úent_idszEntityRuler.ent_idsó s‚ € ô �4×&Ñ&×+Ñ+Ó-Ó.ˆØ � ‰ �D×(Ñ(×-Ñ-Ó/Ô0Ü“eˆ ãˆAØ�‰ !Ò#Ø × -Ñ -¨aÓ 0‘ ��6Ø—‘ Õ'ð ô �[Ó!Ð!r6 c ó† — g }| j j « D ]= \ }}|D ]3 }| j |« \ }}||dœ}|r||d< |j |« Œ5 Œ? | j j « D ]G \ }}|D ]= }| j |« \ }}||j
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dœ}|r||d< |j |« Œ? ŒI |S )zÃGet all patterns that were added to the entity ruler.
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RETURNS (list): The original patterns, one dictionary per pattern.
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DOCS: https://spacy.io/api/entityruler#patterns
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©rc ÚpatternÚid)rI Úitemsr• rŠ rJ Útext)rR Ú all_patternsrc r@ r¤ Ú ent_labelr‘ rZ s r4 r@ zEntityRuler.patterns s× € ð ˆ Ø #× 2Ñ 2× 8Ñ 8Ö :‰OˆE�8Û#�Ø$(×$5Ñ$5°eÓ$<Ñ!� ˜6Ø '°GÑ<�ÙØ $�A�d‘GØ ×#Ñ# AÕ&ñ $ð ;ð $× 3Ñ 3× 9Ñ 9Ö ;‰OˆE�8Û#�Ø$(×$5Ñ$5°eÓ$<Ñ!� ˜6Ø '°G·L±LÑA�ÙØ $�A�d‘GØ ×#Ñ# AÕ&ñ $ð <ð Ðr6 c óH — d}t | j j « D ] \ }\ }}| |k( sŒ|} n | j j |d D �cg c] }|‘Œ }}| j j |¬« 5 g }g }g } g }
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|g« Œðt t, j. j1 |¬« « ‚ ddd« yc c}w # t $ r g }Y �Œ7w xY w# 1 sw Y yxY w) a~ Add patterns to the entity ruler. A pattern can either be a token
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pattern (list of dicts) or a phrase pattern (string). For example:
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{'label': 'ORG', 'pattern': 'Apple'}
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{'label': 'GPE', 'pattern': [{'lower': 'san'}, {'lower': 'francisco'}]}
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patterns (list): The patterns to add.
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DOCS: https://spacy.io/api/entityruler#add_patterns
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éÿÿÿÿN)Údisabler¤ rc r¥ r£ )r¤ )Ú enumerater0 ÚpipelineÚ
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| j j | j | j ¬« | _ t | j j | j | j ¬« | _ y)zReset all patterns.rC rE N)r rH rI rJ rO rP r r0 rL rK r$ rM r r# rN ©rR s r4 r� zEntityRuler.clearZ sw € ä )¬$Ó /ˆÔÜ *¬4Ó 0ˆÔ Ü#¤EÓ*ˆŒ
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ˆÕr6 r‘ c óþ — | j j « D ��cg c] \ }}||k( sŒ ||f‘Œ }}}|s5t t j j d|| j ¬« « ‚|D ��cg c] \ }}| j ||« ‘Œ }}}t t | j j « D ��ci c] \ }}||vr||“Œ c}}« | _
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t t | j j « D ��ci c] \ }}||vr||“Œ c}}« | _ |D ]G }|| j v r | j j |« Œ-| j j |« ŒI yc c}}w c c}}w c c}}w c c}}w )zÙRemove a pattern by its ent_id if a pattern with this ent_id was added before
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ent_id (str): id of the pattern to be removed
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RETURNS: None
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DOCS: https://spacy.io/api/entityruler#remove
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ÚID)Ú attr_typerc Ú componentN)rP r_ r° r ÚE1024rº r1 r· r rH rJ r¦ rI rN ÚremoverM )rR r‘ rc ÚeidÚlabel_id_pairsÚcreated_labelsÚvals r4 rÊ zEntityRuler.removeh sŒ € ð .2¯]©]×-AÑ-AÔ-Cô
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