added new invoice ml model v 0.2.0
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c4f0e9c5d6
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20 changed files with 60290 additions and 81873 deletions
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@ -5,33 +5,21 @@ vectors = null
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init_tok2vec = null
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[system]
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gpu_allocator = null
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seed = 0
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gpu_allocator = null
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[nlp]
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lang = "en"
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pipeline = ["tok2vec","tagger","parser","senter","attribute_ruler","lemmatizer","ner"]
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disabled = ["tagger","parser","senter"]
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lang = "de"
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pipeline = ["ner"]
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disabled = []
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before_creation = null
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after_creation = null
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after_pipeline_creation = null
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batch_size = 256
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batch_size = 1000
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tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
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[components]
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[components.attribute_ruler]
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factory = "attribute_ruler"
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scorer = {"@scorers":"spacy.attribute_ruler_scorer.v1"}
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validate = false
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[components.lemmatizer]
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factory = "lemmatizer"
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mode = "rule"
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model = null
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overwrite = false
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scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
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[components.ner]
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factory = "ner"
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incorrect_spans_key = null
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@ -49,106 +37,14 @@ use_upper = true
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nO = null
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[components.ner.model.tok2vec]
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@architectures = "spacy.Tok2Vec.v2"
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[components.ner.model.tok2vec.embed]
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@architectures = "spacy.MultiHashEmbed.v2"
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width = 96
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attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
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rows = [5000,1000,2500,2500,50]
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include_static_vectors = false
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[components.ner.model.tok2vec.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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width = 96
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depth = 4
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window_size = 1
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maxout_pieces = 3
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[components.parser]
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factory = "parser"
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learn_tokens = false
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min_action_freq = 30
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moves = null
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scorer = {"@scorers":"spacy.parser_scorer.v1"}
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update_with_oracle_cut_size = 100
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[components.parser.model]
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@architectures = "spacy.TransitionBasedParser.v2"
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state_type = "parser"
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extra_state_tokens = false
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hidden_width = 64
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maxout_pieces = 2
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use_upper = true
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nO = null
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[components.parser.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.encode:width}
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upstream = "tok2vec"
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[components.senter]
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factory = "senter"
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overwrite = false
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scorer = {"@scorers":"spacy.senter_scorer.v1"}
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[components.senter.model]
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@architectures = "spacy.Tagger.v2"
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nO = null
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normalize = false
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[components.senter.model.tok2vec]
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@architectures = "spacy.Tok2Vec.v2"
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[components.senter.model.tok2vec.embed]
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@architectures = "spacy.MultiHashEmbed.v2"
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width = 16
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attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
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rows = [1000,500,500,500,50]
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include_static_vectors = false
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[components.senter.model.tok2vec.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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width = 16
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depth = 2
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window_size = 1
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maxout_pieces = 2
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[components.tagger]
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factory = "tagger"
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neg_prefix = "!"
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overwrite = false
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scorer = {"@scorers":"spacy.tagger_scorer.v1"}
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[components.tagger.model]
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@architectures = "spacy.Tagger.v2"
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nO = null
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normalize = false
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[components.tagger.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.encode:width}
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upstream = "tok2vec"
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[components.tok2vec]
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factory = "tok2vec"
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[components.tok2vec.model]
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@architectures = "spacy.Tok2Vec.v2"
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[components.tok2vec.model.embed]
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@architectures = "spacy.MultiHashEmbed.v2"
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width = ${components.tok2vec.model.encode:width}
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attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
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rows = [5000,1000,2500,2500,50]
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include_static_vectors = false
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[components.tok2vec.model.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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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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[corpora]
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@ -169,19 +65,19 @@ limit = 0
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augmenter = null
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[training]
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train_corpus = "corpora.train"
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dev_corpus = "corpora.dev"
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seed = ${system:seed}
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gpu_allocator = ${system:gpu_allocator}
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seed = ${system.seed}
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gpu_allocator = ${system.gpu_allocator}
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dropout = 0.1
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accumulate_gradient = 1
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patience = 5000
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patience = 1600
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max_epochs = 0
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max_steps = 100000
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eval_frequency = 1000
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max_steps = 20000
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eval_frequency = 200
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frozen_components = []
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before_to_disk = null
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annotating_components = []
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dev_corpus = "corpora.dev"
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train_corpus = "corpora.train"
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before_to_disk = null
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[training.batcher]
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@batchers = "spacy.batch_by_words.v1"
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@ -207,60 +103,26 @@ beta2 = 0.999
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L2_is_weight_decay = true
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L2 = 0.01
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grad_clip = 1.0
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use_averages = true
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use_averages = false
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eps = 0.00000001
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learn_rate = 0.001
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[training.score_weights]
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tag_acc = 0.16
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dep_uas = 0.0
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dep_las = 0.16
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dep_las_per_type = null
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sents_p = null
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sents_r = null
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sents_f = 0.02
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lemma_acc = 0.5
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ents_f = 0.16
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ents_f = 1.0
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ents_p = 0.0
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ents_r = 0.0
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ents_per_type = null
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speed = 0.0
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[pretraining]
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[initialize]
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vocab_data = null
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vectors = ${paths.vectors}
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init_tok2vec = ${paths.init_tok2vec}
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vocab_data = null
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lookups = null
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before_init = null
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after_init = null
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[initialize.components]
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[initialize.components.ner]
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[initialize.components.ner.labels]
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@readers = "spacy.read_labels.v1"
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path = "corpus/labels/ner.json"
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require = false
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[initialize.components.parser]
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[initialize.components.parser.labels]
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@readers = "spacy.read_labels.v1"
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path = "corpus/labels/parser.json"
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require = false
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[initialize.components.tagger]
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[initialize.components.tagger.labels]
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@readers = "spacy.read_labels.v1"
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path = "corpus/labels/tagger.json"
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require = false
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[initialize.lookups]
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@misc = "spacy.LookupsDataLoader.v1"
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lang = ${nlp.lang}
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tables = ["lexeme_norm"]
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[initialize.tokenizer]
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File diff suppressed because one or more lines are too long
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@ -1,14 +1,14 @@
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{
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"lang":"en",
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"name":"core_web_sm",
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"version":"3.4.0",
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"description":"English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.",
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"author":"Explosion",
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"email":"contact@explosion.ai",
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"url":"https://explosion.ai",
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"license":"MIT",
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"spacy_version":">=3.4.0,<3.5.0",
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"spacy_git_version":"dd038b536",
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"lang":"de",
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"name":"pipeline",
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"version":"0.0.0",
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"spacy_version":">=3.4.1,<3.5.0",
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"description":"",
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"author":"",
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"email":"",
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"url":"",
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"license":"",
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"spacy_git_version":"Unknown",
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"vectors":{
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"width":0,
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"vectors":0,
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@ -17,133 +17,7 @@
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"mode":"default"
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},
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"labels":{
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"tok2vec":[
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],
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"tagger":[
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"$",
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"''",
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",",
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"-LRB-",
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"-RRB-",
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".",
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":",
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"ADD",
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"AFX",
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"CC",
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"CD",
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"DT",
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"EX",
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"FW",
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"HYPH",
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"IN",
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"JJ",
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"JJR",
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"JJS",
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"LS",
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"MD",
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"NFP",
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"NN",
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"NNP",
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"NNPS",
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"NNS",
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"PDT",
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"POS",
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"PRP",
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"PRP$",
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"RB",
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"RBR",
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"RBS",
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"RP",
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"SYM",
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"TO",
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"UH",
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"VB",
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"VBD",
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"VBG",
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"VBN",
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"VBP",
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"VBZ",
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"WDT",
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"WP",
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"WP$",
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"WRB",
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"XX",
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"_SP",
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"``"
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],
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"parser":[
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"ROOT",
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"acl",
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"acomp",
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"advcl",
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"advmod",
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"agent",
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"amod",
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"appos",
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"attr",
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"aux",
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"auxpass",
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"case",
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"cc",
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"ccomp",
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"compound",
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"conj",
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"csubj",
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"csubjpass",
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"dative",
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"dep",
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"det",
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"dobj",
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"expl",
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"intj",
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"mark",
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"meta",
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"neg",
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"nmod",
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"npadvmod",
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"nsubj",
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"nsubjpass",
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"nummod",
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"oprd",
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"parataxis",
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"pcomp",
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"pobj",
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"poss",
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"preconj",
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"predet",
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"prep",
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"prt",
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"punct",
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"quantmod",
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"relcl",
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"xcomp"
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],
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"attribute_ruler":[
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],
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"lemmatizer":[
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],
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"ner":[
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"CARDINAL",
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"DATE",
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"EVENT",
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"FAC",
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"GPE",
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"LANGUAGE",
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"LAW",
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"LOC",
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"MONEY",
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"NORP",
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"ORDINAL",
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"ORG",
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"PERCENT",
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"PERSON",
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"PRODUCT",
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"QUANTITY",
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"TIME",
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"WORK_OF_ART",
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"cdtr.bic",
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"cdtr.iban",
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"cdtr.name",
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@ -153,376 +27,12 @@
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]
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},
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"pipeline":[
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"tok2vec",
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"attribute_ruler",
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"lemmatizer",
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"ner"
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],
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"components":[
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"tok2vec",
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"tagger",
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"parser",
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"senter",
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"attribute_ruler",
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"lemmatizer",
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"ner"
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],
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"disabled":[
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"tagger",
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"parser",
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"senter"
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],
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"performance":{
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"token_acc":0.9993092439,
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"token_p":0.9956819193,
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"token_r":0.9957659295,
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"token_f":0.9957239226,
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"tag_acc":0.9726250474,
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"sents_p":0.9191788296,
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"sents_r":0.8890428129,
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"sents_f":0.9038596962,
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"dep_uas":0.9165718428,
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"dep_las":0.8978441095,
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"dep_las_per_type":{
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"prep":{
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"p":0.8546376267,
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"r":0.8635553026,
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"f":0.8590733226
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},
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"det":{
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"p":0.9768701389,
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"r":0.9781048683,
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"f":0.9774871137
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},
|
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"pobj":{
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"p":0.9605592002,
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"r":0.9659532692,
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"f":0.9632486833
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},
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"nsubj":{
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"p":0.9545715675,
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"r":0.9463745893,
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"f":0.9504554055
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},
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"aux":{
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"p":0.9798401421,
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"r":0.9821953174,
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"f":0.9810163162
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},
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"advmod":{
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"p":0.8545653823,
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"r":0.8527679623,
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"f":0.8536657262
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},
|
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"relcl":{
|
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"p":0.7656695157,
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"r":0.7801161103,
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"f":0.7728253055
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},
|
||||
"root":{
|
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"p":0.91776518,
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"r":0.8864041164,
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"f":0.9018120805
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},
|
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"xcomp":{
|
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"p":0.882290562,
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"r":0.895908112,
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"f":0.889047195
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},
|
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"amod":{
|
||||
"p":0.9154102213,
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"r":0.908649174,
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"f":0.9120171674
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},
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"compound":{
|
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"p":0.9121588361,
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"r":0.9287703275,
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"f":0.9203896355
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},
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"poss":{
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"p":0.9735258724,
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"r":0.9770531401,
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"f":0.9752863171
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},
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"ccomp":{
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"p":0.7657557167,
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"r":0.8389002037,
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"f":0.8006609
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},
|
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"attr":{
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"p":0.9032126881,
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"r":0.9339781329,
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"f":0.9183378127
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},
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"case":{
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"p":0.9773063641,
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"r":0.9914914915,
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"f":0.9843478261
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},
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"mark":{
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"p":0.9002893975,
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"r":0.9067302597,
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"f":0.9034983498
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},
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"intj":{
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"p":0.6514555468,
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"r":0.6065934066,
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"f":0.6282245827
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},
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"advcl":{
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"p":0.6653050804,
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"r":0.6562578696,
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"f":0.6607505071
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},
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"cc":{
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"p":0.8285611165,
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"r":0.8237052984,
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"f":0.8261260721
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},
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"neg":{
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"p":0.9452191235,
|
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
"name":"WordNet 3.0",
|
||||
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||||
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||||
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||||
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||||
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@ -1 +1 @@
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|
||||
‚¥movesÚÄ{"0":{},"1":{"invoice.total":-1,"invoice.number":-2,"invoice.date":-3,"cdtr.name":-4,"cdtr.iban":-5,"cdtr.bic":-6},"2":{"invoice.total":-1,"invoice.number":-2,"invoice.date":-3,"cdtr.name":-4,"cdtr.iban":-5,"cdtr.bic":-6},"3":{"invoice.total":-1,"invoice.number":-2,"invoice.date":-3,"cdtr.name":-4,"cdtr.iban":-5,"cdtr.bic":-6},"4":{"":1,"invoice.total":-1,"invoice.number":-2,"invoice.date":-3,"cdtr.name":-4,"cdtr.iban":-5,"cdtr.bic":-6},"5":{"":1}}£cfg<66>§neg_keyÀ
|
||||
|
|
@ -1,13 +0,0 @@
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|||
{
|
||||
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|
||||
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|
||||
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|
||||
|
||||
],
|
||||
"min_action_freq":30,
|
||||
"learn_tokens":false,
|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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@ -1 +0,0 @@
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|||
‚¥movesÚ {"0":{"":994332},"1":{"":999432},"2":{"det":172595,"nsubj":165748,"compound":116623,"amod":105184,"aux":86667,"punct":65478,"advmod":62763,"poss":36443,"mark":27941,"nummod":22598,"auxpass":15594,"prep":14001,"nsubjpass":13856,"neg":12357,"cc":10739,"nmod":9562,"advcl":9062,"npadvmod":8168,"quantmod":7101,"intj":6464,"ccomp":5896,"dobj":3427,"expl":3360,"dep":2871,"predet":1944,"parataxis":1837,"csubj":1428,"preconj":621,"pobj||prep":616,"attr":578,"meta":376,"advmod||conj":368,"dobj||xcomp":352,"acomp":284,"nsubj||ccomp":224,"dative":206,"advmod||xcomp":149,"dobj||ccomp":70,"csubjpass":64,"dobj||conj":62,"prep||conj":51,"acl":48,"prep||nsubj":41,"prep||dobj":36,"xcomp":34,"advmod||ccomp":32,"oprd":31},"3":{"punct":183790,"pobj":182191,"prep":174008,"dobj":89615,"conj":59687,"cc":51930,"ccomp":30385,"advmod":22861,"xcomp":21021,"relcl":20969,"advcl":19828,"attr":17741,"acomp":16922,"appos":15265,"case":13388,"acl":12085,"pcomp":10324,"dep":10116,"npadvmod":9796,"prt":8179,"agent":3903,"dative":3866,"nsubj":3470,"neg":2906,"amod":2839,"intj":2819,"nummod":2732,"oprd":2301,"parataxis":1261,"quantmod":319,"nmod":294,"acl||dobj":200,"prep||dobj":190,"prep||nsubj":162,"acl||nsubj":159,"appos||nsubj":145,"relcl||dobj":134,"relcl||nsubj":111,"aux":103,"expl":96,"meta":92,"appos||dobj":86,"preconj":71,"csubj":65,"prep||nsubjpass":55,"prep||advmod":54,"prep||acomp":53,"det":51,"nsubjpass":45,"relcl||pobj":42,"acl||nsubjpass":42,"mark":40,"auxpass":39,"prep||pobj":36,"relcl||nsubjpass":32,"appos||nsubjpass":31},"4":{"ROOT":111664}}£cfg<66>§neg_keyÀ
|
||||
|
|
@ -1,3 +0,0 @@
|
|||
{
|
||||
"overwrite":false
|
||||
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|
||||
Binary file not shown.
|
|
@ -1,56 +0,0 @@
|
|||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"PRP$",
|
||||
"RB",
|
||||
"RBR",
|
||||
"RBS",
|
||||
"RP",
|
||||
"SYM",
|
||||
"TO",
|
||||
"UH",
|
||||
"VB",
|
||||
"VBD",
|
||||
"VBG",
|
||||
"VBN",
|
||||
"VBP",
|
||||
"VBZ",
|
||||
"WDT",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
Binary file not shown.
|
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@ -1,3 +0,0 @@
|
|||
{
|
||||
|
||||
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|
||||
Binary file not shown.
File diff suppressed because one or more lines are too long
Binary file not shown.
File diff suppressed because it is too large
Load diff
|
|
@ -7,11 +7,6 @@
|
|||
<item name="workflow.userid"><value xsi:type="xs:string">admin</value></item>
|
||||
<item name="workflow.password"><value xsi:type="xs:string">imixs4.null</value></item>
|
||||
|
||||
<!-- Erledigte Rechnungen" -->
|
||||
<item name="workflow.query"><value xsi:type="xs:string">($workflowgroup:"Rechnungseingang" OR $workflowgroup:"Sachrechnung") AND ($taskid:5900)</value></item>
|
||||
|
||||
<item name="workflow.pagesize"><value xsi:type="xs:int">10</value></item>
|
||||
<item name="workflow.pageindex"><value xsi:type="xs:int">0</value></item>
|
||||
<item name="workflow.entities">
|
||||
<value xsi:type="xs:string">cdtr.name</value>
|
||||
<value xsi:type="xs:string">cdtr.iban</value>
|
||||
|
|
@ -39,13 +34,19 @@
|
|||
<item name="ml.training.filepattern"><value xsi:type="xs:string">.pdf|.PDF</value></item>
|
||||
<!-- LOW | GOOD -->
|
||||
<item name="ml.training.quality"><value xsi:type="xs:string">LOW</value></item>
|
||||
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|
||||
<item name="ml.training.dropoutrate"><value xsi:type="xs:string">0.25</value></item>
|
||||
|
||||
|
||||
<!-- ML Max Accuracy - should be between 1 and 0 -->
|
||||
<item name="ml.options"><value xsi:type="xs:string">min_losses=0</value></item>
|
||||
|
||||
|
||||
<!-- Define the training set and taining mode -->
|
||||
<item name="workflow.query"><value xsi:type="xs:string">($workflowgroup:"Rechnungseingang" OR $workflowgroup:"Sachrechnung") AND ($taskid:5900)</value></item>
|
||||
<item name="workflow.pagesize"><value xsi:type="xs:int">1000</value></item>
|
||||
<item name="workflow.pageindex"><value xsi:type="xs:int">0</value></item>
|
||||
<item name="ml.training.iterations"><value xsi:type="xs:string">10</value></item>
|
||||
<item name="ml.training.dropoutrate"><value xsi:type="xs:string">0.0</value></item>
|
||||
<item name="ml.options">
|
||||
<value xsi:type="xs:string">min_losses=0.0&retrain_rate=100</value>
|
||||
</item>
|
||||
|
||||
|
||||
</document>
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -384,7 +384,7 @@ Betrag: <itemvalue>_amount</itemvalue> (Brutto € <itemvalue>_amount_brutto</it
|
|||
</imixs:item>
|
||||
<imixs:item name="txtactivityresult" type="xs:string">
|
||||
<imixs:value><![CDATA[<ml-config name="model">invoice-de-0.2.0</ml-config>
|
||||
<ml-config name="options">min_losses=0.001</ml-config>
|
||||
<ml-config name="options">min_losses=0.1&retrain_rate=25</ml-config>
|
||||
<ml-config name="filename.pattern">((PDF|pdf)$)</ml-config>
|
||||
<ml-config name="entity">
|
||||
<name>invoice.total</name>
|
||||
|
|
@ -635,6 +635,7 @@ Betrag: <itemvalue>_amount</itemvalue> (Brutto € <itemvalue>_amount_brutto</it
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|||
<bpmn2:sequenceFlow id="SequenceFlow_17" sourceRef="IntermediateCatchEvent_7" targetRef="IntermediateCatchEvent_5"/>
|
||||
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|
||||
<bpmn2:text>min_losses = 1.0 - 22.12.2021 geändert auf 0.001
|
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retrain_rate=25
|
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filename.pattern = (PDF|pdf)$</bpmn2:text>
|
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</bpmn2:textAnnotation>
|
||||
<bpmn2:association id="Association_1" sourceRef="TextAnnotation_2" targetRef="IntermediateCatchEvent_4"/>
|
||||
|
|
|
|||
Loading…
Reference in a new issue