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spacy
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xx_sent_ud_sm

spaCy
multilingual
Eval Results
Model card Files Community
xx_sent_ud_sm
File size: 2,487 Bytes 
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[paths]
train = "corpus/xx-sent-ud/train.spacy"
dev = "corpus/xx-sent-ud/dev.spacy"
vectors = null
raw = null
init_tok2vec = null
vocab_data = null

[system]
gpu_allocator = null
seed = 0

[nlp]
lang = "xx"
pipeline = ["senter"]
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
batch_size = 256
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}

[components]

[components.senter]
factory = "senter"

[components.senter.model]
@architectures = "spacy.Tagger.v1"
nO = null

[components.senter.model.tok2vec]
@architectures = "spacy.Tok2Vec.v2"

[components.senter.model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = ${components.senter.model.tok2vec.encode:width}
attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
rows = [1000,500,500,500]
include_static_vectors = false

[components.senter.model.tok2vec.encode]
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = 16
depth = 2
window_size = 1
maxout_pieces = 2

[corpora]

[corpora.dev]
@readers = "spacy.Corpus.v1"
limit = 0
max_length = 0
path = ${paths:dev}
gold_preproc = false
augmenter = null

[corpora.train]
@readers = "spacy.Corpus.v1"
path = ${paths:train}
max_length = 5000
gold_preproc = false
limit = 0

[corpora.train.augmenter]
@augmenters = "spacy.lower_case.v1"
level = 0.1

[training]
train_corpus = "corpora.train"
dev_corpus = "corpora.dev"
seed = ${system:seed}
gpu_allocator = ${system:gpu_allocator}
dropout = 0.1
accumulate_gradient = 1
patience = 5000
max_epochs = 0
max_steps = 0
eval_frequency = 1000
frozen_components = []
before_to_disk = null
annotating_components = []

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

[training.batcher.size]
@schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001
t = 0.0

[training.logger]
@loggers = "spacy.WandbLogger.v1"
project_name = "spacy-v3.0.0a2"
remove_config_values = []

[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 = true
eps = 0.00000001
learn_rate = 0.001

[training.score_weights]
sents_f = 1.0
sents_p = 0.0
sents_r = 0.0

[pretraining]

[initialize]
vocab_data = ${paths.vocab_data}
vectors = ${paths.vectors}
init_tok2vec = ${paths.init_tok2vec}
before_init = null
after_init = null

[initialize.components]

[initialize.lookups]
@misc = "spacy.LookupsDataLoader.v1"
lang = ${nlp.lang}
tables = []

[initialize.tokenizer]