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https://github.com/explosion/spaCy.git
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95c0833656
* Add training option to set annotations on update Add a `[training]` option called `set_annotations_on_update` to specify a list of components for which the predicted annotations should be set on `example.predicted` immediately after that component has been updated. The predicted annotations can be accessed by later components in the pipeline during the processing of the batch in the same `update` call. * Rename to annotates / annotating_components * Add test for `annotating_components` when training from config * Add documentation
134 lines
4.0 KiB
INI
134 lines
4.0 KiB
INI
[paths]
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train = null
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dev = null
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vectors = null
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init_tok2vec = null
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[system]
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seed = 0
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gpu_allocator = null
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[nlp]
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lang = null
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# List of pipeline component names, in order. The names should correspond to
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# components defined in the [components block]
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pipeline = []
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# Components that are loaded but disabled by default
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disabled = []
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# Optional callbacks to modify the nlp object before it's initialized, after
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# it's created and after the pipeline has been set up
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before_creation = null
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after_creation = null
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after_pipeline_creation = null
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# Default batch size to use with nlp.pipe and nlp.evaluate
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batch_size = 1000
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[nlp.tokenizer]
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@tokenizers = "spacy.Tokenizer.v1"
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# The pipeline components and their models
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[components]
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# Readers for corpora like dev and train.
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[corpora]
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[corpora.train]
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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 = 0
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# Limitation on number of training examples
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limit = 0
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# Apply some simply data augmentation, where we replace tokens with variations.
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# This is especially useful for punctuation and case replacement, to help
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# generalize beyond corpora that don't/only have smart quotes etc.
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augmenter = null
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[corpora.dev]
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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 = 0
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# Limitation on number of training examples
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limit = 0
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# Optional callback for data augmentation
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augmenter = null
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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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gpu_allocator = ${system.gpu_allocator}
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dropout = 0.1
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accumulate_gradient = 1
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# Controls early-stopping. 0 disables early stopping.
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patience = 1600
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# Number of epochs. 0 means unlimited. If >= 0, train corpus is loaded once in
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# memory and shuffled within the training loop. -1 means stream train corpus
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# rather than loading in memory with no shuffling within the training loop.
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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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# Names of pipeline components that should set annotations during training
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annotating_components = []
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# Location in the config where the dev corpus is defined
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dev_corpus = "corpora.dev"
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# Location in the config where the train corpus is defined
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train_corpus = "corpora.train"
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# Optional callback before nlp object is saved to disk after training
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before_to_disk = null
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[training.logger]
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@loggers = "spacy.ConsoleLogger.v1"
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[training.batcher]
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@batchers = "spacy.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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learn_rate = 0.001
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# These settings are used when nlp.initialize() is called (typically before
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# training or pretraining). Components and the tokenizer can each define their
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# own arguments via their initialize methods that are populated by the config.
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# This lets them gather data resources, build label sets etc.
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[initialize]
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vectors = ${paths.vectors}
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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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# Data and lookups for vocabulary
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vocab_data = null
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lookups = null
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# Arguments passed to the tokenizer's initialize method
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tokenizer = {}
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# Arguments for initialize methods of the components (keyed by component)
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components = {}
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before_init = null
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after_init = null
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