237 lines
28 KiB
Plaintext
237 lines
28 KiB
Plaintext
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l#m$Z$ ddlm%Z%m&Z&m'Z' ddl(m)Z) ee*ee*e
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ee*ef f f Z+dZ, ejZ ddgdddddidddidœdd d dd œ¬ « d ed e*d!e ee.e*f d"ed#e/d$e/d%e e d&e*fd'„« Z0 ejZ d(d)ge,dddd*idddidd+d,e,d-œd.œ d/e,› d0�dd/e,› d1�d d/e,› d2�d d/e,› d3�di¬ « d ed e*d4e e* d5e ee e" e e" ge e" f d6e/d7ee e" e e" ge e" f d!e ee.e*f d"ed#e/d8e/d%e e fd9„« Z1d:e e" d;e e" d<e
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e" fd=„Z2 e'jf d>« d?„ « Z4d:e e" d;e e" d<e
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e" fd@„Z5 e'jf dA« dB„ « Z6e,dCœdDe e$ d<ee*ef fdE„Z7 e'jp d,« e,fd4e*fdF„« Z9 G dG„ dHe)« Z:y)Ié N)Úpartial)ÚPath) ÚAnyÚCallableÚDictÚIterableÚListÚOptionalÚSequenceÚSetÚTupleÚUnionÚcasté )Úutil)ÚErrorsÚWarnings)ÚLanguage)ÚMatcherÚ
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PhraseMatcher)Úlevenshtein_compare)ÚScorer)ÚDocÚSpan)ÚExample)ÚSimpleFrozenListÚ ensure_pathÚregistryé )ÚPipeÚrulerÚfuture_entity_rulerzdoc.entsFú@scorersz spacy.entity_ruler_scorer.v1Ú
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__unused__z@miscz spacy.levenshtein_compare.v1)Úphrase_matcher_attrÚvalidateÚoverwrite_entsÚscorerÚ
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ent_id_sepÚmatcher_fuzzy_compareg 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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óL — |rt }nt }t | |d d d||||d|¬« S )NTF© Ú spans_keyÚ spans_filterÚ
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annotate_entsÚ ents_filterr% r* r& Ú overwriter( )Úprioritize_new_ents_filterÚ prioritize_existing_ents_filterÚ SpanRuler) r2 r3 r% r* r& r' r( r) r9 s úZC:\Users\garci\AppData\Roaming\Python\Python312\site-packages\spacy/pipeline/span_ruler.pyÚmake_entity_rulerr? # s? € ñ8 Ü0‰ ä5ˆ Ü Ø Ø ØØØØ Ø /Ø 3ØØØô ð ó Ú
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span_rulerz doc.spansz#spacy.first_longest_spans_filter.v1Tz)spacy.overlapping_labeled_spans_scorer.v1)r# r6 r5 Úspans_Ú_fÚ_pÚ_rÚ _per_typer6 r7 r8 r9 r: c
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¬« S )Nr5 )r= ) r2 r3 r6 r7 r8 r9 r% r* r& r: r( s r> Úmake_span_rulerrH R s3 € ôJ Ø Ø ØØ!Ø#Ø Ø /Ø 3ØØØô ð r@ ÚentitiesÚspansÚreturnc ó„ ‡— d„ }t ||d¬« }t | « } g }t « Š|D ]‰ }|j }|j }t ˆfd„|D « « sŒ0|j
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|« | D �cg c]# }|j |k r|j |kD rŒ"|‘Œ% } }‰j t ||« « Œ‹ | |z S c c}w )aª Merge entities and spans into one list without overlaps by allowing
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spans to overwrite any entities that they overlap with. Intended to
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replicate the overwrite_ents=True behavior from the EntityRuler.
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entities (Iterable[Span]): The entities, already filtered for overlaps.
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spans (Iterable[Span]): The spans to merge, may contain overlaps.
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RETURNS (List[Span]): Filtered list of non-overlapping spans.
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c óN — | j | j z
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| j fS ©N©ÚendÚstart©Úspans r> ú<lambda>z,prioritize_new_ents_filter.<locals>.<lambda>‘ ó € §¡¨D¯J©JÑ!6¸¿¹¸ Ñ Dr@ T©ÚkeyÚreversec 3 ó: •K — | ] }|j ‰v–— Œ y wrN ©Úi©Ú.0ÚtokenÚ seen_tokenss €r> ú <genexpr>z-prioritize_new_ents_filter.<locals>.<genexpr>™ ó øè ø€ Ð<±t¨eˆu�w‰w˜kÔ)±tùó ƒ) ÚsortedÚlistÚsetrQ rP ÚallÚappendÚupdateÚrange) rI rJ Ú get_sort_keyÚ new_entitiesrS rQ rP Úer_ s @r> r; r; † s¶ ø€ ñ E€LÜ �5˜l°DÔ 9€EÜ�H‹~€HØ€LÜ ›E€Kۈؗ
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ˆØ�h‰hˆÜ Ó<±tÓ<Õ <Ø × Ñ Ô %Ù#+ÓU¡8˜a°A·G±G¸c²MÀaÇeÁeÈeÃmš 8ˆHÐUØ × Ñ œu U¨CÓ 0Õ 1ð
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ð �lÑ "Ð"ùò Vs Á1#B=ÂB=z#spacy.prioritize_new_ents_filter.v1c ó — t S rN )r; © r@ r> Ú make_prioritize_new_ents_filterro s € ä %Ð%r@ c óJ ‡— d„ }t ||d¬« }t | « } g }t « Š ‰j d„ | D « Ž |D ][ }|j }|j
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}t
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ˆfd„|D « « sŒ0|j |« ‰j t ||« « Œ] | |z S )aŠ Merge entities and spans into one list without overlaps by prioritizing
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existing entities. Intended to replicate the overwrite_ents=False behavior
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from the EntityRuler.
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entities (Iterable[Span]): The entities, already filtered for overlaps.
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spans (Iterable[Span]): The spans to merge, may contain overlaps.
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RETURNS (List[Span]): Filtered list of non-overlapping spans.
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c óN — | j | j z
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| j fS rN rO rR s r> rT z1prioritize_existing_ents_filter.<locals>.<lambda>° rU r@ TrV c 3 ó\ K — | ]$ }t |j |j « –— Œ& y wrN )ri rQ rP )r] Úents r> r` z2prioritize_existing_ents_filter.<locals>.<genexpr>µ s è ø€ ÐG¹h°sœ˜sŸy™y¨#¯'©'×2¹hùs ‚*,c 3 ó: •K — | ] }|j ‰v–— Œ y wrN rZ r\ s €r> r` z2prioritize_existing_ents_filter.<locals>.<genexpr>¹ ra rb ) rc rd re rh rQ rP rf rg ri )rI rJ rj rk rS rQ rP r_ s @r> r< r< ¥ sž ø€ ñ E€LÜ �5˜l°DÔ 9€EÜ�H‹~€HØ€LÜ ›E€KØ€K×ÑÑG¹hÓGÑHۈؗ
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ˆØ�h‰hˆÜ Ó<±tÓ<Õ <Ø × Ñ Ô %Ø × Ñ œu U¨CÓ 0Õ 1ð ð �lÑ "Ð"r@ z(spacy.prioritize_existing_ents_filter.v1c ó — t S rN )r< rn r@ r> Ú"make_preserve_existing_ents_filterrv ¿ s € ä *Ð*r@ ©r6 Úexamplesc ó ‡‡— t |« }dŠ|j d‰› ‰› �« |j dd« |j dd« |j dˆfd„« |j dˆfd „« t j | fi |¤ŽS )
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NrB ÚattrÚ
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allow_overlapTÚlabeledÚgetterc óT •— | j j |t ‰« d g « S rN )rJ ÚgetÚlen)ÚdocrW Ú attr_prefixs €r> rT z1overlapping_labeled_spans_score.<locals>.<lambda>Í s! ø€ 3§9¡9§=¡=°´S¸Ó5EÐ5GÐ1HÈ"Ô#Mr@ Úhas_annotationc ó •— ‰| j v S rN )rJ )r� r6 s €r> rT z1overlapping_labeled_spans_score.<locals>.<lambda>Ï s ø€ °IÀÇÁÑ4Jr@ )ÚdictÚ
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setdefaultr Ú score_spans)rx r6 Úkwargsr‚ s ` @r> Ú overlapping_labeled_spans_scorer‰ Ä sŠ ù€ ô �&‹\€FØ€KØ
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×ÑØÓMôð ×ÑÐ&Ó(JÔKÜ × Ñ ˜hÑ 1¨&Ñ 1Ð1r@ c ó$ — t t | ¬« S )Nrw )r r‰ rw s r> Ú%make_overlapping_labeled_spans_scorerr‹ Ó s € ä Ô2¸iÔ HÐHr@ c óÚ — e Zd ZdZ d1eddej dedd ee e¬« dœ de
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de de e d e e
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ddfd„Zdefd„Zde defd„Zede e fd„« Zdedefd„Zdefd„Zd„ Zede e df fd „« Z ede e df fd „« Z ddd œd e
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d e e e! fd!„Z"ede#e! fd"„« Z$d e#e! ddfd#„Z%d2d$„Z&de ddfd%„Z'd&e ddfd'„Z(d2d(„Z) e*« d)œd*e+d+ee dd fd,„Z, e*« d)œd+ee de+fd-„Z- e*« d)œd.ee e.f d+ee dd fd/„Z/ e*« d)œd.ee e.f d+ee ddfd0„Z0y)3r= z×The SpanRuler lets you add spans to the `Doc.spans` using token-based
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rules or exact phrase matches.
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DOCS: https://spacy.io/api/spanruler
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USAGE: https://spacy.io/usage/rule-based-matching#spanruler
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NFrw r5 r2 r3 r6 r7 r8 r9 r% r* r& r: r( rK c óÌ — || _ || _ || _ || _ || _ | | _ |
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| _ || _ || _ | | _ || _
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i | _ | j « y)a¿ Initialize the span 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 span ruler while creating
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phrase patterns with the nlp object.
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spans_key (Optional[str]): The spans key to save the spans under. If
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`None`, no spans are saved. Defaults to "ruler".
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spans_filter (Optional[Callable[[Iterable[Span], Iterable[Span]], List[Span]]):
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The optional method to filter spans before they are assigned to
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doc.spans. Defaults to `None`.
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annotate_ents (bool): Whether to save spans to doc.ents. Defaults to
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`False`.
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ents_filter (Callable[[Iterable[Span], Iterable[Span]], List[Span]]):
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The method to filter spans before they are assigned to doc.ents.
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Defaults to `util.filter_chain_spans`.
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phrase_matcher_attr (Optional[Union[int, str]]): Token attribute to
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match on, passed to the internal PhraseMatcher as `attr`. Defaults
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to `None`.
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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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overwrite (bool): Whether to remove any existing spans under this spans
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key if `spans_key` is set, and/or to remove any ents under `doc.ents` if
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`annotate_ents` is set. Defaults to `True`.
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scorer (Optional[Callable]): The scoring method. Defaults to
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spacy.pipeline.span_ruler.overlapping_labeled_spans_score.
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DOCS: https://spacy.io/api/spanruler#init
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N)
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r2 r3 r6 r8 r% r& r: r7 r9 r( r* Ú_match_label_id_mapÚclear) Úselfr2 r3 r6 r7 r8 r9 r% r* r& r: r( s r> Ú__init__zSpanRuler.__init__à sh € ðt ˆŒØˆŒ Ø"ˆŒØ *ˆÔØ#6ˆÔ Ø ˆŒ
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Ø"ˆŒØ (ˆÔØ&ˆÔØ ˆŒ Ø%:ˆÔ"Ø>@ˆÔ Ø �
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‰
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� r@ c ó, — t | j « S )z1The number of all labels added to the span ruler.)r€ Ú _patterns©r� s r> Ú__len__zSpanRuler.__len__( s € ä�4—>‘>Ó"Ð"r@ Úlabelc óV — | j j « D ] }|d |k( sŒ y y)z+Whether a label is present in the patterns.r– TF)rŽ Úvalues)r� r– Úlabel_ids r> Ú __contains__zSpanRuler.__contains__, s0 € à ×0Ñ0×7Ñ7Ö9ˆHØ˜Ñ EÓ)Ùð :ð r@ c ó — | j S )z2Key of the doc.spans dict to save the spans under.rw r” s r> rW z
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SpanRuler.key3 s € ð �~‰~Ð r@ r� c óÆ — | j « } | j |« }| j ||« |S # t $ r } || j | |g|« cY d}~S d}~ww xY w)zäFind 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/spanruler#call
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N)Úget_error_handlerÚmatchÚset_annotationsÚ Exceptionr3 )r� r� Ú
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error_handlerÚmatchesrl s r> Ú__call__zSpanRuler.__call__8 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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ó° ‡ ‡— ‰ j « t j « 5 t j dd¬« t t
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t t t t f t ‰ j ‰« « t ‰ j ‰« « z « }d d d « t ˆˆ fd„D « « }t t |« « S # 1 sw Y Œ2xY w)NÚignorez\[W036)Úmessagec 3 ó� •K — | ]= \ }}}||k7 r2t ‰||‰j | d ‰j | d ¬« –— Œ? yw)r– Úid)r– Úspan_idN)r rŽ )r] Úm_idrQ rP r� r� s €€r> r` z"SpanRuler.match.<locals>.<genexpr>P sd øè ø€ ð
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Ú_require_patternsÚwarningsÚcatch_warningsÚfilterwarningsr r r
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Úintrd ÚmatcherÚphrase_matcherre rc )r� r� r¢ Údeduplicated_matchess `` r> rž zSpanRuler.matchH s« ù€ Ø × Ñ Ô Ü
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rGg }| j st
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|j « }| j ||« } t |« |_ yy# t $ r t t j « ‚w xY w)z Modify the document in placeN)
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rW rJ r: Úextendr7 r8 rd Úentsr9 rc Ú
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ValueErrorr ÚE854)r� r� r¢ rJ s r> rŸ zSpanRuler.set_annotations] sÛ € ð �8Š8؈EØ�x‰x˜3Ÿ9™9Ñ$¨T¯^ª^ØŸ ™ $§(¡(Ñ+�Ø �L‰LØ59×5FÒ5F�×!Ñ! %¨Ô1ÈGô
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ð #(ˆC�I‰I�d—h‘hÑ à × Ò ØˆEØ—>’>Ü ˜SŸX™X›�Ø×$Ñ$ U¨GÓ4ˆEð
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øô ò
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.Ü ¤§¡Ó-Ð-ð
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.ús ÃC( Ã(#D .c ó˜ — t t t | j D �cg c] }t t
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|d « ‘Œ c}« « « S c c}w )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/spanruler#labels
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r– )Útuplerc re r“ r Ústr©r� Úps r> ÚlabelszSpanRuler.labelss s: € ô ”VœCÀÇÂÓ OÁ¸1¤¤c¨1¨W©:Õ!6ÀÑ OÓ PÓQÓRÐRùÒ Os žAc óÊ — t t t | j D �cg c]! }t t
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|j
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d« « ‘Œ# c}« t dg« z
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« « S c c}w )z‰All IDs present in the match patterns.
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RETURNS (set): The string IDs.
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DOCS: https://spacy.io/api/spanruler#ids
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r¨ N)r¹ rc re r“ r rº r r» s r> Úidsz
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SpanRuler.ids} sO € ô Ü ”3¸¿ºÓG¹°1œœS !§%¡%¨£+Õ.¸ÑGÓHÌ3ÐPTÈvË;ÑVÓ Wó
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ùÚGs ž&A )r2 ÚpatternsÚ get_examplesrÀ c 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/spanruler#initialize
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N)r� Ú add_patterns)r� rÁ r2 rÀ s r> Ú
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initializezSpanRuler.initialize‰ s# € ð
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�
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‰
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Œ Ù Ø × Ñ ˜hÕ 'ð r@ c ó — | j S )z¿Get all patterns that were added to the span ruler.
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RETURNS (list): The original patterns, one dictionary per pattern.
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DOCS: https://spacy.io/api/spanruler#patterns
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)r“ r” s r> rÀ zSpanRuler.patterns� s € ð �~‰~Ð r@ c ób — d}t | j j « D ] \ }\ }}| |k( sŒ|} n | j j |d D �cg c] }|‘Œ }}| j j |¬« 5 g }g }|D �]6 } t
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t | d « }
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t
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t | j dd« « } t |
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| dœ| j | j j j j | « < t | d t « r&|j | « |j | d « n`t | d t « r!| j" j% | | d g« n,t t& j( j+ | d ¬ « « ‚| j, j | « �Œ9 t/ || j j1 |« « D ]" \ } }
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g« Œ$ ddd« yc c}w # t $ r g }Y �ŒÄw xY w# 1 sw Y yxY w)
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a¶ Add patterns to the span 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': 'ORG', 'pattern': 'Apple', 'id': '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/spanruler#add_patterns
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éÿÿÿÿN)Údisabler– r¨ Ú )r– r¨ Úpattern)rÊ )Ú enumerater2 ÚpipelineÚ
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pipe_namesr¶ Ú select_pipesr rº r ÚreprrŽ ÚvocabÚstringsÚas_intÚ
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isinstancerg rd r° Úaddr ÚE097Úformatr“ ÚzipÚpiper± )r� rÀ Ú
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current_indexr[ r3 rØ Úsubsequent_pipesÚphrase_pattern_labelsÚphrase_pattern_textsÚentryÚp_labelÚp_idr– rÊ s r> rà zSpanRuler.add_patterns§ sú € ð "Ø ˆMÜ#,¨T¯X©X×->Ñ->Ö#?‘�‘<�D˜$ؘ4“<Ø$%�MÙð $@ð 26·±×1DÑ1DÀ]À^Ñ1TÓ UÑ1T¨¢Ð1TÐ Ð Uð �X‰X×
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"Ñ
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"Ð+;Ð
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"Õ
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<Ø$&Ð !Ø#%Ð Ü!�Ü œs E¨'¡NÓ3�ÜœC §¡¨4°Ó!4Ó5�Ü ˜g t˜_Ó-�à $Ø ñR�×(Ñ(¨¯©¯ © ×)?Ñ)?×)FÑ)FÀuÓ)MÑNô ˜e IÑ .´Ô4Ø)×0Ñ0°Ô7Ø(×/Ñ/°°iÑ0@ÕAÜ iÑ 0´$Ô7Ø—L‘L×$Ñ$ U¨U°9Ñ-=Ð,>Õ?ä$¤V§[¡[×%7Ñ%7ÀÀiÑ@PÐ%7Ó%QÓRÐRØ—‘×%Ñ% eÖ,ð "ô #&Ø%Ø—‘—
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‘
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Ð 2Ó3ö#‘��wð ×#Ñ#×'Ñ'¨°¨yÕ9ñ #÷' =Ð
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<ùò VøÜò "Ø !Ó ð "úç
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<Ð
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<ús5 ‚/H ² H Á HÁH Á9F H%ÈH È H"È!H"È%H.c óþ — g | _ t | j j | j | j
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¬« | _ t | j j | j | j ¬« | _ y)zfReset all patterns.
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RETURNS: None
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DOCS: https://spacy.io/api/spanruler#clear
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)r& Ú
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fuzzy_compare)rz r& N)
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r“ r r2 rÐ r& r* r° r r% r± r” s r> r� zSpanRuler.clear× s\ € ð -/ˆŒÜ 'Ø �H‰H�N‰NØ—]‘]Ø ×4Ñ4ô!
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ˆŒ ô
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.;Ø �H‰H�N‰NØ×)Ñ)Ø—]‘]ô.
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ˆÕr@ c ó" — || vr5t t j j d|| j ¬« « ‚| j
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D �cg c]
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}|d |k7 sŒ |‘Œ c}| _ | j D ]š }| j | d |k( sŒ| j j j j |« }|| j v r| j j |« || j v sŒ€| j j |« Œœ yc c}w )z«Remove a pattern by its label.
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label (str): Label of the pattern to be removed.
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RETURNS: None
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DOCS: https://spacy.io/api/spanruler#remove
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r– ©Ú attr_typer– Ú componentN)r¶ r ÚE1024rÖ r3 r“ rŽ r2 rÐ rÑ Ú as_stringr± Úremover° )r� r– r¼ Úm_labelÚ m_label_strs r> rè zSpanRuler.removeé sé € ð ˜Ñ Ü Ü— ‘ ×#Ñ#¨g¸UÈdÏiÉiÐ#ÓXóð
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ð &*§^¢^ÓK¡^ °q¸±zÀUÓ7Jš! ^ÑKˆŒØ×/Ô/ˆGØ×'Ñ'¨Ñ0°Ñ9¸UÓBØ "Ÿh™hŸn™n× 4Ñ 4× >Ñ >¸wÓ G� Ø $×"5Ñ"5Ñ5Ø×'Ñ'×.Ñ.¨{Ô;Ø $§,¡,Ò.Ø—L‘L×'Ñ'¨ Õ4ñ
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0ùò Ls Á
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D ÁD Ú
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pattern_idc ód — t | « }| j D �cg c] }|j d« |k7 sŒ|‘Œ c}| _ |t | « k( r5t t j
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j
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d|| j ¬« « ‚| j D ]š }| j | d |k( sŒ| j j j j |« }|| j v r| j j |« || j v sŒ€| j j |« Œœ yc c}w )z¸Remove a pattern by its pattern ID.
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pattern_id (str): ID of the pattern to be removed.
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RETURNS: None
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DOCS: https://spacy.io/api/spanruler#remove_by_id
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r¨ ÚIDrã N)r€ r“ r r¶ r ræ rÖ r3 rŽ r2 rÐ rÑ rç r± rè r° )r� rë Úorig_lenr¼ ré rê s r> Ú remove_by_idzSpanRuler.remove_by_idý sÿ € ô �t“9ˆØ%)§^¢^ÓQ¡^ °q·u±u¸T³{ÀjÓ7Pš! ^ÑQˆŒØ ”s˜4“yÒ Ü Ü— ‘ ×#Ñ#Ø "¨*ÀÇ Á ð $ó óð
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