mirror of
https://github.com/explosion/spaCy.git
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333b1a308b
* Draft layer for BILUO actions * Fixes to biluo layer * WIP on BILUO layer * Add tests for BILUO layer * Format * Fix transitions * Update test * Link in the simple_ner * Update BILUO tagger * Update __init__ * Import simple_ner * Update test * Import * Add files * Add config * Fix label passing for BILUO and tagger * Fix label handling for simple_ner component * Update simple NER test * Update config * Hack train script * Update BILUO layer * Fix SimpleNER component * Update train_from_config * Add biluo_to_iob helper * Add IOB layer * Add IOBTagger model * Update biluo layer * Update SimpleNER tagger * Update BILUO * Read random seed in train-from-config * Update use of normal_init * Fix normalization of gradient in SimpleNER * Update IOBTagger * Remove print * Tweak masking in BILUO * Add dropout in SimpleNER * Update thinc * Tidy up simple_ner * Fix biluo model * Unhack train-from-config * Update setup.cfg and requirements * Add tb_framework.py for parser model * Try to avoid memory leak in BILUO * Move ParserModel into spacy.ml, avoid need for subclass. * Use updated parser model * Remove incorrect call to model.initializre in PrecomputableAffine * Update parser model * Avoid divide by zero in tagger * Add extra dropout layer in tagger * Refine minibatch_by_words function to avoid oom * Fix parser model after refactor * Try to avoid div-by-zero in SimpleNER * Fix infinite loop in minibatch_by_words * Use SequenceCategoricalCrossentropy in Tagger * Fix parser model when hidden layer * Remove extra dropout from tagger * Add extra nan check in tagger * Fix thinc version * Update tests and imports * Fix test * Update test * Update tests * Fix tests * Fix test Co-authored-by: Ines Montani <ines@ines.io>
45 lines
736 B
INI
45 lines
736 B
INI
[training]
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use_gpu = -1
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limit = 0
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dropout = 0.2
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patience = 10000
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eval_frequency = 200
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scores = ["ents_p", "ents_r", "ents_f"]
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score_weights = {"ents_f": 1}
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orth_variant_level = 0.0
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gold_preproc = true
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max_length = 0
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[training.batch_size]
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@schedules = "compounding.v1"
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start = 3000
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stop = 3000
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compound = 1.001
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[optimizer]
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@optimizers = "Adam.v1"
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learn_rate = 0.001
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beta1 = 0.9
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beta2 = 0.999
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[nlp]
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lang = "en"
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vectors = null
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[nlp.pipeline.ner]
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factory = "simple_ner"
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[nlp.pipeline.ner.model]
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@architectures = "spacy.BiluoTagger.v1"
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[nlp.pipeline.ner.model.tok2vec]
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@architectures = "spacy.HashEmbedCNN.v1"
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width = 128
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depth = 4
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embed_size = 7000
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maxout_pieces = 3
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window_size = 1
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subword_features = true
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pretrained_vectors = null
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