NER trained in 3 iterations
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NER/config.cfg
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128
NER/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 = "en"
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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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[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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[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]
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36
NER/meta.json
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NER/meta.json
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{
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"lang":"en",
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"name":"pipeline",
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"version":"0.0.0",
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"spacy_version":">=3.2.4,<3.3.0",
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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":"b50fe5ec6",
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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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"effective_date",
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"jurisdiction",
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"party",
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"term"
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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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}
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13
NER/ner/cfg
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13
NER/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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}
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NER/ner/model
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NER/ner/model
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NER/ner/moves
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NER/ner/moves
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‚¥movesÚ{"0":{},"1":{"effective_date":-1,"jurisdiction":-2,"party":-3,"term":-4},"2":{"effective_date":-1,"jurisdiction":-2,"party":-3,"term":-4},"3":{"effective_date":-1,"jurisdiction":-2,"party":-3,"term":-4},"4":{"":1,"effective_date":-1,"jurisdiction":-2,"party":-3,"term":-4},"5":{"":1}}£cfg<66>§neg_keyÀ
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3
NER/tokenizer
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NER/tokenizer
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NER/vocab/key2row
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NER/vocab/key2row
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<EFBFBD>
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1
NER/vocab/lookups.bin
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NER/vocab/lookups.bin
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<EFBFBD>
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75798
NER/vocab/strings.json
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75798
NER/vocab/strings.json
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NER/vocab/vectors
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NER/vocab/vectors
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NER/vocab/vectors.cfg
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NER/vocab/vectors.cfg
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{
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"mode":"default"
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}
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963
main.ipynb
963
main.ipynb
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