spaCy/examples/experiments/ptb-joint-pos-dep/defaults.cfg

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

[system]
seed = 0
use_pytorch_for_gpu_memory = false

[training]
seed = ${system:seed}
dropout = 0.2
init_tok2vec = ${paths:init_tok2vec}
vectors = null
accumulate_gradient = 1
max_steps = 0
max_epochs = 0
patience = 10000
eval_frequency = 200
score_weights = {"dep_las": 0.8, "tag_acc": 0.2}

[training.read_train]
@readers = "spacy.Corpus.v1"
path = ${paths:train}
gold_preproc = true
max_length = 0
limit = 0

[training.read_dev]
@readers = "spacy.Corpus.v1"
path = ${paths:dev}
gold_preproc = ${training.read_train:gold_preproc}
max_length = 0
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"
learn_rate = 0.001
beta1 = 0.9
beta2 = 0.999

[nlp]
lang = "en"
pipeline = ["tok2vec", "tagger", "parser"]
load_vocab_data = false

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

[nlp.lemmatizer]
@lemmatizers = "spacy.Lemmatizer.v1"

[components]

[components.tok2vec]
factory = "tok2vec"

[components.tagger]
factory = "tagger"

[components.parser]
factory = "parser"
learn_tokens = false
min_action_freq = 1

[components.tagger.model]
@architectures = "spacy.Tagger.v1"

[components.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode:width}

[components.parser.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 8
hidden_width = 64
maxout_pieces = 3

[components.parser.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode:width}

[components.tok2vec.model]
@architectures = "spacy.Tok2Vec.v1"

[components.tok2vec.model.embed]
@architectures = "spacy.MultiHashEmbed.v1"
width = ${components.tok2vec.model.encode:width}
rows = 2000
also_embed_subwords = true
also_use_static_vectors = false

[components.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
width = 96
depth = 4
window_size = 1
maxout_pieces = 3