119 lines
8.4 KiB
Plaintext
119 lines
8.4 KiB
Plaintext
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lmZmZ dd lmZ dd lm Z d
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dl m Z dZ e
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« jA e « d Z!dZ"dZ# ejH ddgde!ddidœdddddddddddœ
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¬ « d ed e%de ee ee
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f d e&d ee d!d"f d#„« Z'd$ee d!ee%ef fd%„Z( ejR d« d&„ « Z* G d'„ d"e « Z+y)(é )Úislice)ÚAnyÚCallableÚDictÚIterableÚListÚOptional)ÚConfigÚModel)ÚFloats2dé )ÚErrors)ÚLanguage)ÚScorer)ÚDoc)ÚExampleÚvalidate_get_examples)Úregistry)ÚVocabé )ÚTextCategorizeraX
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[model]
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@architectures = "spacy.TextCatEnsemble.v2"
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[model.tok2vec]
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@architectures = "spacy.Tok2Vec.v2"
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[model.tok2vec.embed]
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@architectures = "spacy.MultiHashEmbed.v2"
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width = 64
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rows = [2000, 2000, 500, 1000, 500]
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attrs = ["NORM", "LOWER", "PREFIX", "SUFFIX", "SHAPE"]
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include_static_vectors = false
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[model.tok2vec.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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width = ${model.tok2vec.embed.width}
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window_size = 1
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maxout_pieces = 3
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depth = 2
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[model.linear_model]
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@architectures = "spacy.TextCatBOW.v3"
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exclusive_classes = false
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length = 262144
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ngram_size = 1
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no_output_layer = false
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Úmodelzq
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[model]
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@architectures = "spacy.TextCatBOW.v3"
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exclusive_classes = false
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ngram_size = 1
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no_output_layer = false
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aa
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[model]
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@architectures = "spacy.TextCatReduce.v1"
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exclusive_classes = false
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use_reduce_first = false
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use_reduce_last = false
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use_reduce_max = false
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use_reduce_mean = true
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[model.tok2vec]
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@architectures = "spacy.HashEmbedCNN.v2"
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pretrained_vectors = null
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width = 96
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depth = 4
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embed_size = 2000
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window_size = 1
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maxout_pieces = 3
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subword_features = true
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Útextcat_multilabelzdoc.catsg à?z@scorersz"spacy.textcat_multilabel_scorer.v2)Ú thresholdr Úscorerç ð?N)
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Ú
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cats_scoreÚcats_score_descÚ cats_micro_pÚ cats_micro_rÚ cats_micro_fÚ cats_macro_pÚ cats_macro_rÚ cats_macro_fÚcats_macro_aucÚcats_f_per_type)ÚassignsÚdefault_configÚdefault_score_weightsÚnlpÚnamer r ÚreturnÚMultiLabel_TextCategorizerc ó6 — t | j ||||¬« S )a Create a MultiLabel_TextCategorizer component. The text categorizer predicts categories
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over a whole document. It can learn one or more labels, and the labels are considered
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to be non-mutually exclusive, which means that there can be zero or more labels
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per doc).
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model (Model[List[Doc], List[Floats2d]]): A model instance that predicts
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scores for each category.
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threshold (float): Cutoff to consider a prediction "positive".
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scorer (Optional[Callable]): The scoring method.
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)r r )r- Úvocab)r* r+ r r r s úbC:\Users\garci\AppData\Roaming\Python\Python312\site-packages\spacy/pipeline/textcat_multilabel.pyÚmake_multilabel_textcatr1 K s! € ôL &Ø � ‰ �5˜$¨)¸Fô ð ó Úexamplesc ó4 — t j | dfddi|¤ŽS )NÚcatsÚ multi_labelT)r Ú
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score_cats)r3 Úkwargss r0 Útextcat_multilabel_scorer9 v s/ € Ü × Ñ ØØñ ð ð ð ñ ð r2 c ó — t S )N)r9 © r2 r0 Ú make_textcat_multilabel_scorerr< s € ä #Ð#r2 c ó — e Zd ZdZ dedœdedededede e
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dd f d
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„Z e d „ « Z
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d d d œd
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e
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g ee f de e de ee fd„Zdee fd„Zy )r- zlPipeline component for multi-label text classification.
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DOCS: https://spacy.io/api/textcategorizer
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)r r/ r r+ r r r, Nc ót — || _ || _ || _ d| _ g |dœ}t |« | _ || _ y)að Initialize a text categorizer for multi-label classification.
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vocab (Vocab): The shared vocabulary.
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model (thinc.api.Model): The Thinc Model powering the pipeline component.
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name (str): The component instance name, used to add entries to the
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losses during training.
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threshold (float): Cutoff to consider a prediction "positive".
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scorer (Optional[Callable]): The scoring method.
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DOCS: https://spacy.io/api/textcategorizer#init
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N)Úlabelsr )r/ r r+ Ú_rehearsal_modelÚdictÚcfgr )Úselfr/ r r+ r r rB s r0 Ú__init__z#MultiLabel_TextCategorizer.__init__Š s= € ð( ˆŒ
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؈Œ
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؈Œ Ø $ˆÔ ب)Ñ4ˆÜ˜“9ˆŒØ ˆ� r2 c ó — y)NTr; )rC s r0 Úsupport_missing_valuesz1MultiLabel_TextCategorizer.support_missing_values¦ s € àr2 )r* r? Ú get_examplesr* r? c óÊ — t |d« |€9 |« D ]. }|j j D ] }| j |« Œ Œ0 n|D ] }| j |« Œ t t |« d« « }| j
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|« |D �cg c] }|j ‘Œ } }| j |« \ }
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} | j « t | « dkD s/J t j j | j ¬« « ‚t |
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« dkD s/J t j j | j ¬« « ‚| j j! | |
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¬« yc c}w )a\ Initialize the pipe for training, using a representative set
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of data examples.
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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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labels: The labels to add to the component, typically generated by the
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`init labels` command. If no labels are provided, the get_examples
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callback is used to extract the labels from the data.
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DOCS: https://spacy.io/api/textcategorizer#initialize
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z%MultiLabel_TextCategorizer.initializeNé
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r )r+ )ÚXÚY)r Úyr5 Ú add_labelÚlistr Ú_validate_categoriesÚ referenceÚ_examples_to_truthÚ_require_labelsÚlenr ÚE923Úformatr+ r Ú
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initialize) rC rG r* r? ÚexampleÚcatÚlabelÚsubbatchÚegÚ
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doc_sampleÚ label_sampleÚ_s r0 rV z%MultiLabel_TextCategorizer.initializeª s& € ô& ˜lÐ,SÔTØ ˆ>Ù'ž>�Ø"Ÿ9™9Ÿ>œ>�CØ—N‘N 3Õ'ñ *ñ *ó �Ø—‘˜uÕ%ð 䜙|›~¨rÓ2Ó3ˆØ ×!Ñ! (Ô+á-5Ó6©X r�b—l“l¨Xˆ
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Ð6Ø ×1Ñ1°(Ó;‰ˆ �aØ × Ñ Ô Ü�:‹ Ò"ÐF¤F§K¡K×$6Ñ$6¸D¿I¹IÐ$6Ó$FÓFÐ"Ü�<Ó 1Ò$ÐH¤f§k¡k×&8Ñ&8¸d¿i¹iÐ&8Ó&HÓHÐ$Ø �
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‰
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× Ñ
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¨lÐ Õ;ùò 7s ÂE r3 c óÌ — |D ]_ }|j j j « D ]6 }|dk( rŒ |dk( rŒt t j
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j
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|¬« « ‚ Œa y)zˆThis component allows any type of single- or multi-label annotations.
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This method overwrites the more strict one from 'textcat'.r g )ÚvalN)rP r5 ÚvaluesÚ
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