mirror of https://github.com/explosion/spaCy.git
119 lines
2.6 KiB
INI
119 lines
2.6 KiB
INI
[paths]
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train = ""
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dev = ""
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raw = null
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init_tok2vec = null
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[system]
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seed = 0
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use_pytorch_for_gpu_memory = false
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[nlp]
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lang = null
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pipeline = []
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load_vocab_data = true
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[nlp.tokenizer]
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@tokenizers = "spacy.Tokenizer.v1"
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[nlp.lemmatizer]
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@lemmatizers = "spacy.Lemmatizer.v1"
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[components]
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# Training hyper-parameters and additional features.
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[training]
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seed = ${system:seed}
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dropout = 0.1
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accumulate_gradient = 1
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# Extra resources for transfer-learning or pseudo-rehearsal
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init_tok2vec = ${paths:init_tok2vec}
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raw_text = ${paths:raw}
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vectors = null
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# Controls early-stopping. 0 or -1 mean unlimited.
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patience = 1600
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max_epochs = 0
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max_steps = 20000
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eval_frequency = 200
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# Control how scores are printed and checkpoints are evaluated.
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score_weights = {}
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# Names of pipeline components that shouldn't be updated during training
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frozen_components = []
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[training.train_corpus]
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@readers = "spacy.Corpus.v1"
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path = ${paths:train}
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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
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max_length = 2000
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# Limitation on number of training examples
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limit = 0
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[training.dev_corpus]
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@readers = "spacy.Corpus.v1"
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path = ${paths:dev}
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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
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max_length = 2000
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# Limitation on number of training examples
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limit = 0
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[training.batcher]
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@batchers = "batch_by_words.v1"
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discard_oversize = false
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tolerance = 0.2
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[training.batcher.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 = false
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eps = 1e-8
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[training.optimizer.learn_rate]
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@schedules = "warmup_linear.v1"
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warmup_steps = 250
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total_steps = 20000
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initial_rate = 0.001
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[pretraining]
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max_epochs = 1000
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min_length = 5
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max_length = 500
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dropout = 0.2
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n_save_every = null
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batch_size = 3000
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seed = ${system:seed}
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use_pytorch_for_gpu_memory = ${system:use_pytorch_for_gpu_memory}
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tok2vec_model = "components.tok2vec.model"
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[pretraining.objective]
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type = "characters"
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n_characters = 4
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[pretraining.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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