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Update custom_ner_model29+23-1git including iteration_data.json
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‎machine-learn/custom_ner_model29+23-1git/config.cfg

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[paths]
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train = null
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dev = null
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vectors = null
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init_tok2vec = null
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[system]
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seed = 0
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gpu_allocator = null
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[nlp]
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lang = "he"
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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 = 1000
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tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
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vectors = {"@vectors":"spacy.Vectors.v1"}
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[components]
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[components.ner]
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factory = "ner"
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incorrect_spans_key = null
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moves = null
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scorer = {"@scorers":"spacy.ner_scorer.v1"}
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update_with_oracle_cut_size = 100
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[components.ner.model]
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@architectures = "spacy.TransitionBasedParser.v2"
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state_type = "ner"
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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.ner.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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[corpora]
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[corpora.dev]
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@readers = "spacy.Corpus.v1"
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path = ${paths.dev}
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gold_preproc = false
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max_length = 0
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limit = 0
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augmenter = null
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[corpora.train]
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@readers = "spacy.Corpus.v1"
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path = ${paths.train}
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gold_preproc = false
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max_length = 0
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limit = 0
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augmenter = null
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[training]
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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 = 1600
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max_epochs = 0
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max_steps = 20000
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eval_frequency = 200
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frozen_components = []
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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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before_update = null
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[training.batcher]
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@batchers = "spacy.batch_by_words.v1"
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discard_oversize = false
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tolerance = 0.2
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get_length = null
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[training.batcher.size]
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@schedules = "compounding.v1"
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start = 100
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stop = 1000
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compound = 1.001
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t = 0.0
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[training.logger]
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@loggers = "spacy.ConsoleLogger.v1"
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progress_bar = false
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[training.optimizer]
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@optimizers = "Adam.v1"
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beta1 = 0.9
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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 = 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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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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[pretraining]
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[initialize]
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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.tokenizer]

‎machine-learn/custom_ner_model29+23-1git/iteration_data.json

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{
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"0": {
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"ner": 4802.388039036398
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},
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"1": {
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"ner": 3438.4395032675866
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},
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"2": {
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"ner": 2827.94393245209
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},
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"3": {
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"ner": 2412.4572408230288
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},
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"4": {
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"ner": 2188.735894150063
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},
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"5": {
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"ner": 1908.96445147932
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},
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"6": {
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"ner": 1894.3397682169134
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},
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"7": {
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},
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},
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"9": {
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"ner": 1567.4141720728862
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},
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"10": {
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},
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},
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"12": {
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},
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"19": {
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"20": {
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"21": {
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},
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"24": {
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},
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},
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"26": {
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"ner": 887.7737379491912
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},
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"27": {
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}
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}

‎machine-learn/custom_ner_model29+23-1git/meta.json

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{
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"lang":"he",
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"name":"singer_ner_he",
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"version":"0.0.0",
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"spacy_version":">=3.7.5,<3.8.0",
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"description":"Model for recognizing singer names in Hebrew song titles",
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"author":"nhlocal",
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"email":"[email protected]",
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"url":"",
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"license":"MIT",
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"spacy_git_version":"a6d0fc360",
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"vectors":{
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"width":0,
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"vectors":0,
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"keys":0,
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"name":null,
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"mode":"default"
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},
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"labels":{
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"ner":[
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"SINGER"
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]
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},
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"pipeline":[
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"ner"
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],
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"components":[
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"ner"
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],
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"disabled":[
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],
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"tags":[
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"NER",
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"Hebrew",
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"Singer",
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"Named Entity Recognition",
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"Text Classification"
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]
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}

‎machine-learn/custom_ner_model29+23-1git/ner/cfg

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{
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"moves":null,
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"update_with_oracle_cut_size":100,
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"multitasks":[
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],
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"min_action_freq":1,
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"learn_tokens":false,
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"beam_width":1,
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"beam_density":0.0,
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"beam_update_prob":0.0,
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"incorrect_spans_key":null
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}

‎machine-learn/custom_ner_model29+23-1git/ner/model

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‎machine-learn/custom_ner_model29+23-1git/ner/moves

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��moves�`{"0":{},"1":{"SINGER":-1},"2":{"SINGER":-1},"3":{"SINGER":-1},"4":{"":1,"SINGER":-1},"5":{"":1}}�cfg��neg_key�

‎machine-learn/custom_ner_model29+23-1git/tokenizer

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‎machine-learn/custom_ner_model29+23-1git/vocab/key2row

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�

‎machine-learn/custom_ner_model29+23-1git/vocab/lookups.bin

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�

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