mirror of https://github.com/explosion/spaCy.git
74 lines
1.7 KiB
INI
74 lines
1.7 KiB
INI
# Training hyper-parameters and additional features.
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[training]
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# Whether to train on sequences with 'gold standard' sentence boundaries
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# and tokens. If you set this to true, take care to ensure your run-time
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# data is passed in sentence-by-sentence via some prior preprocessing.
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gold_preproc = false
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# Limitations on training document length or number of examples.
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max_length = 3000
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limit = 0
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# Data augmentation
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orth_variant_level = 0.0
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dropout = 0.1
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# Controls early-stopping. 0 or -1 mean unlimited.
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patience = 100000
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max_epochs = 0
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max_steps = 0
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eval_frequency = 1000
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# Other settings
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seed = 0
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accumulate_gradient = 1
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use_pytorch_for_gpu_memory = false
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# Control how scores are printed and checkpoints are evaluated.
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scores = ["speed", "ents_p", "ents_r", "ents_f"]
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score_weights = {"ents_f": 1.0}
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# These settings are invalid for the transformer models.
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init_tok2vec = null
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discard_oversize = false
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omit_extra_lookups = false
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batch_by = "words"
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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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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 = true
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eps = 1e-8
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learn_rate = 0.001
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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 = "ner"
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learn_tokens = false
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min_action_freq = 1
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[nlp.pipeline.ner.model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 3
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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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[nlp.pipeline.ner.model.tok2vec]
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@architectures = "spacy.HashEmbedCNN.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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window_size = 1
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embed_size = 2000
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maxout_pieces = 3
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subword_features = true
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dropout = ${training:dropout}
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