[training]
max_steps = 0
patience = 10000
eval_frequency = 200
dropout = 0.2
init_tok2vec = null
vectors = "tmp/fasttext_vectors/vocab"
max_epochs = 100
orth_variant_level = 0.0
gold_preproc = true
max_length = 0
scores = ["tag_acc", "dep_uas", "dep_las", "speed"]
score_weights = {"dep_las": 0.8, "tag_acc": 0.2}
limit = 0
seed = 0
accumulate_gradient = 1
discard_oversize = false
raw_text = null
tag_map = null
morph_rules = null
base_model = null

eval_batch_size = 128
use_pytorch_for_gpu_memory = false
batch_by = "words"

[training.batch_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 = true

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