mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 03:19:13 +03:00
74 lines
1.4 KiB
INI
74 lines
1.4 KiB
INI
[training]
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patience = 10000
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eval_frequency = 200
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dropout = 0.2
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init_tok2vec = null
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vectors = null
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max_epochs = 100
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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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use_gpu = 0
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scores = ["tags_acc", "uas", "las"]
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score_weights = {"las": 0.8, "tags_acc": 0.2}
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limit = 0
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seed = 0
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accumulate_gradient = 2
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discard_oversize = false
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[training.batch_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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[training.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 = ${training:vectors}
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[nlp.pipeline.tok2vec]
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factory = "tok2vec"
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[nlp.pipeline.tagger]
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factory = "tagger"
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[nlp.pipeline.parser]
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factory = "parser"
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learn_tokens = false
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min_action_freq = 1
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beam_width = 1
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beam_update_prob = 1.0
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[nlp.pipeline.tagger.model]
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@architectures = "spacy.Tagger.v1"
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[nlp.pipeline.tagger.model.tok2vec]
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@architectures = "spacy.Tok2VecTensors.v1"
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width = ${nlp.pipeline.tok2vec.model:width}
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[nlp.pipeline.parser.model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 8
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hidden_width = 64
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maxout_pieces = 3
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[nlp.pipeline.parser.model.tok2vec]
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@architectures = "spacy.Tok2VecTensors.v1"
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width = ${nlp.pipeline.tok2vec.model:width}
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[nlp.pipeline.tok2vec.model]
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@architectures = "spacy.HashEmbedBiLSTM.v1"
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pretrained_vectors = ${nlp:vectors}
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width = 96
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depth = 4
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embed_size = 2000
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subword_features = true
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maxout_pieces = 3
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dropout = null
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