B An]Z @slddlmZddlmZmZmZddlmZmZddl m Z ddl m Z Gdd d e ZGd d d e Zd S) )unicode_literals)Poolingmax_pool mean_pool)SiameseCauchySimilarity)Pipe)link_vectors_to_modelsc@s2eZdZdZdZd ddZddZedd ZdS) SentenceSegmenteraA simple spaCy hook, to allow custom sentence boundary detection logic (that doesn't require the dependency parse). To change the sentence boundary detection strategy, pass a generator function `strategy` on initialization, or assign a new strategy to the .strategy attribute. Sentence detection strategies should be generators that take `Doc` objects and yield `Span` objects for each sentence. DOCS: https://spacy.io/api/sentencesegmenter sentencizerNcCs&||_|dks|dkr|j}||_dS)NZon_punct)vocabsplit_on_punctstrategy)selfrrrw/home/app_decipher_dev_19-4/dev/decipher-analysis/serverless-application/helper/df_spacy/python/spacy/pipeline/hooks.py__init__szSentenceSegmenter.__init__cCs|j|jd<|S)Nsents)r user_hooks)rdocrrr__call__s zSentenceSegmenter.__call__ccstd}d}xHt|D]<\}}|r@|js@|||jV|j}d}q|jdkrd}qW|t|krp||t|VdS)NrF).!?T) enumerateis_punctitextlen)rstartZ seen_periodrwordrrrr"s   z SentenceSegmenter.split_on_punct)N) __name__ __module__ __qualname____doc__namerr staticmethodrrrrrr s   r c@s^eZdZdZdZdddZeddZdd Zd d Z d d Z dddZ e ddfddZ dS)SimilarityHooka. Experimental: A pipeline component to install a hook for supervised similarity into `Doc` objects. Requires a `Tensorizer` to pre-process documents. The similarity model can be any object obeying the Thinc `Model` interface. By default, the model concatenates the elementwise mean and elementwise max of the two tensors, and compares them using the Cauchy-like similarity function from Chen (2013): >>> similarity = 1. / (1. + (W * (vec1-vec2)**2).sum()) Where W is a vector of dimension weights, initialized to 1. similarityTcKs||_||_t||_dS)N)rmodeldictcfg)rrr+r-rrrrAszSimilarityHook.__init__cCsttttt|S)N)rrrrr)clslengthrrrModelFszSimilarityHook.ModelcCs|j|jd<|S)zInstall similarity hookr*)predictr)rrrrrrJs zSimilarityHook.__call__cksx|D]}||VqWdS)Nr)rdocskwargsrrrrpipeOs zSimilarityHook.pipecCs||j||fgS)N) require_modelr+r1)rdoc1Zdoc2rrrr1SszSimilarityHook.predictNcCs ||jj||d\}}dS)N)drop)r5r+ begin_update)rZ doc1_doc2goldssgdr8ZsimsZbp_simsrrrupdateWszSimilarityHook.updatecKs<|jdkr(||djj|_t|j|dkr8|}|S)zAllocate model, using width from tensorizer in pipeline. gold_tuples (iterable): Gold-standard training data. pipeline (list): The pipeline the model is part of. TrN)r+r0nOr rcreate_optimizer)r_pipeliner;r3rrrbegin_training[s   zSimilarityHook.begin_training)T)Nr7)r#r$r%r&r'r classmethodr0rr4r1r<tuplerArrrrr)1s    r)N) __future__r thinc.t2vrrrZ thinc.neural._classes.differencerrpipesr _mlr objectr r)rrrrs   &