mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 01:48:04 +03:00
5847be6022
* avoid changing original config * fix elif structure, batch with just int crashes otherwise * tok2vec example with doc2feats, encode and embed architectures * further clean up MultiHashEmbed * further generalize Tok2Vec to work with extract-embed-encode parts * avoid initializing the charembed layer with Docs (for now ?) * small fixes for bilstm config (still does not run) * rename to core layer * move new configs * walk model to set nI instead of using core ref * fix senter overfitting test to be more similar to the training data (avoid flakey behaviour)
66 lines
1.4 KiB
INI
66 lines
1.4 KiB
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_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.Doc2Feats.v1"
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columns = ["ID", "NORM", "PREFIX", "SUFFIX", "SHAPE", "ORTH"]
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[nlp.pipeline.tok2vec.model.embed]
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@architectures = "spacy.MultiHashEmbed.v1"
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columns = ${nlp.pipeline.tok2vec.model.extract:columns}
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width = 96
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rows = 2000
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use_subwords = true
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pretrained_vectors = null
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[nlp.pipeline.tok2vec.model.embed.mix]
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@architectures = "spacy.LayerNormalizedMaxout.v1"
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width = ${nlp.pipeline.tok2vec.model.embed:width}
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maxout_pieces = 3
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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.embed:width}
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window_size = 1
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maxout_pieces = 3
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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.embed:width}
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