spaCy/spacy/default_config.cfg

[paths]
train = ""
dev = ""
raw = null
init_tok2vec = null

[system]
seed = 0
use_pytorch_for_gpu_memory = false

[nlp]
lang = null
pipeline = []
load_vocab_data = true
before_creation = null
after_creation = null
after_pipeline_creation = null

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

[components]

# Training hyper-parameters and additional features.
[training]
seed = ${system:seed}
dropout = 0.1
accumulate_gradient = 1
# Extra resources for transfer-learning or pseudo-rehearsal
init_tok2vec = ${paths:init_tok2vec}
raw_text = ${paths:raw}
vectors = null
# Controls early-stopping. 0 or -1 mean unlimited.
patience = 1600
max_epochs = 0
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 = []

[training.train_corpus]
@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 = 2000
# Limitation on number of training examples
limit = 0

[training.dev_corpus]
@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 = 2000
# Limitation on number of training examples
limit = 0

[training.batcher]
@batchers = "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

[training.optimizer.learn_rate]
@schedules = "warmup_linear.v1"
warmup_steps = 250
total_steps = 20000
initial_rate = 0.001

[pretraining]
max_epochs = 1000
min_length = 5
max_length = 500
dropout = 0.2
n_save_every = null
batch_size = 3000
seed = ${system:seed}
use_pytorch_for_gpu_memory = ${system:use_pytorch_for_gpu_memory}
tok2vec_model = "components.tok2vec.model"

[pretraining.objective]
type = "characters"
n_characters = 4

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