[training]
use_gpu = -1
limit = 0
dropout = 0.2
patience = 10000
eval_frequency = 200
scores = ["ents_f"]
score_weights = {"ents_f": 1}
orth_variant_level = 0.0
gold_preproc = true
max_length = 0
batch_size = 25
seed = 0
accumulate_gradient = 2

[optimizer]
@optimizers = "Adam.v1"
learn_rate = 0.001
beta1 = 0.9
beta2 = 0.999

[nlp]
lang = "en"
vectors = null

[nlp.pipeline.tok2vec]
factory = "tok2vec"

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

[nlp.pipeline.tok2vec.model.extract]
@architectures = "spacy.CharacterEmbed.v1"
width = 96
nM = 64
nC = 8
rows = 2000
columns = ["ID", "NORM", "PREFIX", "SUFFIX", "SHAPE", "ORTH"]

[nlp.pipeline.tok2vec.model.extract.features]
@architectures = "spacy.Doc2Feats.v1"
columns = ${nlp.pipeline.tok2vec.model.extract:columns}

[nlp.pipeline.tok2vec.model.embed]
@architectures = "spacy.LayerNormalizedMaxout.v1"
width = ${nlp.pipeline.tok2vec.model.extract:width}
maxout_pieces = 4

[nlp.pipeline.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
width = ${nlp.pipeline.tok2vec.model.extract:width}
window_size = 1
maxout_pieces = 2
depth = 2

[nlp.pipeline.ner]
factory = "ner"

[nlp.pipeline.ner.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 6
hidden_width = 64
maxout_pieces = 2

[nlp.pipeline.ner.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model.extract:width}