spaCy/spacy/default_config.cfg

[paths]
train = null
dev = null
vectors = null
init_tok2vec = null

[system]
seed = 0
gpu_allocator = null

[nlp]
lang = null
# List of pipeline component names, in order. The names should correspond to
# components defined in the [components block]
pipeline = []
# Components that are loaded but disabled by default
disabled = []
# Optional callbacks to modify the nlp object before it's initialized, after
# it's created and after the pipeline has been set up
before_creation = null
after_creation = null
after_pipeline_creation = null
# Default batch size to use with nlp.pipe and nlp.evaluate
batch_size = 1000

[nlp.tokenizer]
@tokenizers = "spacy.Tokenizer.v1"

[nlp.vectors]
@vectors = "spacy.Vectors.v1"

# The pipeline components and their models
[components]

# Readers for corpora like dev and train.
[corpora]

[corpora.train]
@readers = "spacy.Corpus.v1"
path = ${paths.train}
# Whether to train on sequences with 'gold standard' sentence boundaries
# and tokens. If you set this to true, take care to ensure your run-time
# data is passed in sentence-by-sentence via some prior preprocessing.
gold_preproc = false
# Limitations on training document length
max_length = 0
# Limitation on number of training examples
limit = 0
# Apply some simply data augmentation, where we replace tokens with variations.
# This is especially useful for punctuation and case replacement, to help
# generalize beyond corpora that don't/only have smart quotes etc.
augmenter = null

[corpora.dev]
@readers = "spacy.Corpus.v1"
path = ${paths.dev}
# Whether to train on sequences with 'gold standard' sentence boundaries
# and tokens. If you set this to true, take care to ensure your run-time
# data is passed in sentence-by-sentence via some prior preprocessing.
gold_preproc = false
# Limitations on training document length
max_length = 0
# Limitation on number of training examples
limit = 0
# Optional callback for data augmentation
augmenter = null

# Training hyper-parameters and additional features.
[training]
seed = ${system.seed}
gpu_allocator = ${system.gpu_allocator}
dropout = 0.1
accumulate_gradient = 1
# Controls early-stopping, i.e., the number of steps to continue without
# improvement before stopping. 0 disables early stopping.
patience = 1600
# Number of epochs. 0 means unlimited. If >= 0, train corpus is loaded once in
# memory and shuffled within the training loop. -1 means stream train corpus
# rather than loading in memory with no shuffling within the training loop.
max_epochs = 0
# Maximum number of update steps to train for. 0 means an unlimited number of steps.
max_steps = 20000
eval_frequency = 200
# Control how scores are printed and checkpoints are evaluated.
score_weights = {}
# Names of pipeline components that shouldn't be updated during training
frozen_components = []
# Names of pipeline components that should set annotations during training
annotating_components = []
# Location in the config where the dev corpus is defined
dev_corpus = "corpora.dev"
# Location in the config where the train corpus is defined
train_corpus = "corpora.train"
# Optional callback before nlp object is saved to disk after training
before_to_disk = null
# Optional callback that is invoked at the start of each training step
before_update = null

[training.logger]
@loggers = "spacy.ConsoleLogger.v1"

[training.batcher]
@batchers = "spacy.batch_by_words.v1"
discard_oversize = false
tolerance = 0.2

[training.batcher.size]
@schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001

[training.optimizer]
@optimizers = "Adam.v1"
beta1 = 0.9
beta2 = 0.999
L2_is_weight_decay = true
L2 = 0.01
grad_clip = 1.0
use_averages = false
eps = 1e-8
learn_rate = 0.001

# These settings are used when nlp.initialize() is called (typically before
# training or pretraining). Components and the tokenizer can each define their
# own arguments via their initialize methods that are populated by the config.
# This lets them gather data resources, build label sets etc.
[initialize]
vectors = ${paths.vectors}
# Extra resources for transfer-learning or pseudo-rehearsal
init_tok2vec = ${paths.init_tok2vec}
# Data and lookups for vocabulary
vocab_data = null
lookups = null
# Arguments passed to the tokenizer's initialize method
tokenizer = {}
# Arguments for initialize methods of the components (keyed by component)
components = {}
before_init = null
after_init = null