i ddlmZmZmZddlmZmZmZmZm Z m Z m Z m Z m Z mZmZmZmZddlmZmZddlmZddlmZdd eeeffd Zd ed eeeffd Zd eeeeefdeeefdeeefd eeeeefeffdZy))ListTuplecast) LinearLogisticMaxoutModelchain concatenateglorot_uniform_init list2ragged reduce_first reduce_last reduce_max reduce_mean with_getitem)Floats2dRagged)Doc) extract_spansNreturncJtt||ttS)zrAn output layer for multi-label classification. It uses a linear layer followed by a logistic activation. )nOnIinit_W)r rr r)rrs h/var/www/vps2.regionflexible.com/Desarrollo/venv/lib/python3.12/site-packages/spacy/ml/models/spancat.pybuild_linear_logisticrs 2"-@A8: NN hidden_sizec tttttt ft tttt ftttt|ddS)zReduce sequences by concatenating their mean and max pooled vectors, and then combine the concatenated vectors with a hidden layer. Tg)r normalizedropout) r r rr rrrrrrr)r!s rbuild_mean_max_reducerr%s]  vx'(+- 8 vx'(,. 9 M L  +s; r tok2vecreducerscorerctttttt t ftt t fftdt|ttttt ftt||}|jd||jd||jd||S)aBuild a span categorizer model, given a token-to-vector model, a reducer model to map the sequence of vectors for each span down to a single vector, and a scorer model to map the vectors to probabilities. tok2vec (Model[List[Doc], List[Floats2d]]): The tok2vec model. reducer (Model[Ragged, Floats2d]): The reducer model. scorer (Model[Floats2d, Floats2d]): The scorer model. rr&r'r() r rr rrrrrrr rset_ref)r&r'r(models rbuild_spancat_modelr,.s  %S 6)*E&&.,AA B 5$uT(^V-C'Dkm"TU   E MM)W% MM)W% MM(F# Lr )NN)typingrrr thinc.apirrrr r r r r rrrrr thinc.typesrrtokensrrrintr%r,r rr3s$$))OuXx5G/HO  fh6F0G  49d8n, - 68# $ (H$ % 5cF" #X -. r