CountVectorizer - text-col - default -#

Fitted on a problem type text-col (see find_suitable_problem), method transform matches output .

CountVectorizer()

index

0

skl_nop

1

onx_size

1046

onx_nnodes

6

onx_ninits

1

onx_doc_string

onx_ir_version

8

onx_domain

ai.onnx

onx_model_version

0

onx_producer_name

skl2onnx

onx_producer_version

1.11.1

onx_

14

onx_com.microsoft

1

onx_op_Identity

1

onx_op_Reshape

1

onx_size_optim

1006

onx_nnodes_optim

5

onx_ninits_optim

1
%0 X X str((0,)) Reshape Reshape (Reshape) X->Reshape variable variable float((0, 18)) shape_tensor shape_tensor int64((1,)) [-1] shape_tensor->Reshape flattened flattened StringNormalizer StringNormalizer (StringNormalizer) case_change_action=b'LOWER' is_case_sensitive=0 flattened->StringNormalizer Reshape->flattened normalized normalized Tokenizer Tokenizer (Tokenizer) mark=0 mincharnum=1 pad_value=b'#' tokenexp=b'[a-zA-Z0-9_]+' normalized->Tokenizer StringNormalizer->normalized tokenized tokenized Flatten Flatten (Flatten) tokenized->Flatten Tokenizer->tokenized flattened1 flattened1 TfIdfVectorizer TfIdfVectorizer (TfIdfVectorizer) max_gram_length=1 max_skip_count=0 min_gram_length=1 mode=b'TF' ngram_counts=[0] ngram_indexes=[ 0  1  2  3  4  ... pool_strings=[b'dix' b'eighteen... weights=[1. 1. 1. 1. 1. 1. 1. 1... flattened1->TfIdfVectorizer Flatten->flattened1 output output Identity Identity (Identity) output->Identity TfIdfVectorizer->output Identity->variable

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