mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-14 05:37:03 +03:00
66 lines
1.4 KiB
INI
66 lines
1.4 KiB
INI
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[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_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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batch_size = 25
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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.tok2vec]
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factory = "tok2vec"
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[nlp.pipeline.tok2vec.model]
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@architectures = "spacy.Tok2Vec.v1"
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[nlp.pipeline.tok2vec.model.extract]
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@architectures = "spacy.CharacterEmbed.v1"
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width = 96
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nM = 64
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nC = 8
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rows = 2000
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columns = ["ID", "NORM", "PREFIX", "SUFFIX", "SHAPE", "ORTH"]
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[nlp.pipeline.tok2vec.model.extract.features]
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@architectures = "spacy.Doc2Feats.v1"
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columns = ${nlp.pipeline.tok2vec.model.extract:columns}
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[nlp.pipeline.tok2vec.model.embed]
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@architectures = "spacy.LayerNormalizedMaxout.v1"
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width = ${nlp.pipeline.tok2vec.model.extract:width}
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maxout_pieces = 4
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[nlp.pipeline.tok2vec.model.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v1"
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width = ${nlp.pipeline.tok2vec.model.extract:width}
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window_size = 1
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maxout_pieces = 2
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depth = 2
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[nlp.pipeline.ner]
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factory = "ner"
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[nlp.pipeline.ner.model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 6
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hidden_width = 64
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maxout_pieces = 2
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[nlp.pipeline.ner.model.tok2vec]
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@architectures = "spacy.Tok2VecTensors.v1"
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width = ${nlp.pipeline.tok2vec.model.extract:width}
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