config.cfg

[paths]
train = null
dev = null
vectors = null
init_tok2vec = null

[system]
gpu_allocator = "pytorch"
seed = 1

[nlp]
lang = "zh"
pipeline = ["transformer","tagger","parser","attribute_ruler","ner"]
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
batch_size = 64
vectors = {"@vectors":"spacy.Vectors.v1"}

[nlp.tokenizer]
@tokenizers = "spacy.zh.ChineseTokenizer"
segmenter = "pkuseg"

[components]

[components.attribute_ruler]
factory = "attribute_ruler"
scorer = {"@scorers":"spacy.attribute_ruler_scorer.v1"}
validate = false

[components.ner]
factory = "ner"
incorrect_spans_key = null
moves = null
scorer = {"@scorers":"spacy.ner_scorer.v1"}
update_with_oracle_cut_size = 100

[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v2"
state_type = "ner"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2
use_upper = false
nO = null

[components.ner.model.tok2vec]
@architectures = "spacy-curated-transformers.LastTransformerLayerListener.v1"
width = ${components.transformer.model.hidden_width}
upstream = "transformer"
pooling = {"@layers":"reduce_mean.v1"}
grad_factor = 1.0

[components.parser]
factory = "parser"
learn_tokens = false
min_action_freq = 30
moves = null
scorer = {"@scorers":"spacy.parser_scorer.v1"}
update_with_oracle_cut_size = 100

[components.parser.model]
@architectures = "spacy.TransitionBasedParser.v2"
state_type = "parser"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2
use_upper = false
nO = null

[components.parser.model.tok2vec]
@architectures = "spacy-curated-transformers.LastTransformerLayerListener.v1"
width = ${components.transformer.model.hidden_width}
upstream = "transformer"
pooling = {"@layers":"reduce_mean.v1"}
grad_factor = 1.0

[components.tagger]
factory = "tagger"
label_smoothing = 0.0
neg_prefix = "!"
overwrite = false
scorer = {"@scorers":"spacy.tagger_scorer.v1"}

[components.tagger.model]
@architectures = "spacy.Tagger.v2"
nO = null
normalize = false

[components.tagger.model.tok2vec]
@architectures = "spacy-curated-transformers.LastTransformerLayerListener.v1"
width = ${components.transformer.model.hidden_width}
upstream = "transformer"
pooling = {"@layers":"reduce_mean.v1"}
grad_factor = 1.0

[components.transformer]
factory = "curated_transformer"
all_layer_outputs = false
frozen = false

[components.transformer.model]
@architectures = "spacy-curated-transformers.BertTransformer.v1"
vocab_size = 21128
hidden_width = 768
piece_encoder = {"@architectures":"spacy-curated-transformers.BertWordpieceEncoder.v1"}
attention_probs_dropout_prob = 0.1
hidden_act = "gelu"
hidden_dropout_prob = 0.1
intermediate_width = 3072
layer_norm_eps = 0.0
max_position_embeddings = 512
model_max_length = 512
num_attention_heads = 12
num_hidden_layers = 12
padding_idx = 0
type_vocab_size = 2
torchscript = false
mixed_precision = false
wrapped_listener = null

[components.transformer.model.grad_scaler_config]

[components.transformer.model.with_spans]
@architectures = "spacy-curated-transformers.WithStridedSpans.v1"
stride = 152
window = 208
batch_size = 384

[corpora]

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

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

[training]
train_corpus = "corpora.train"
dev_corpus = "corpora.dev"
seed = ${system:seed}
gpu_allocator = ${system:gpu_allocator}
dropout = 0.1
accumulate_gradient = 3
patience = 5000
max_epochs = 0
max_steps = 20000
eval_frequency = 1000
frozen_components = []
before_to_disk = null
annotating_components = []
before_update = null

[training.batcher]
@batchers = "spacy.batch_by_words.v1"
discard_oversize = false
size = 2000
tolerance = 0.2
get_length = null

[training.logger]
@loggers = "spacy.ConsoleLogger.v1"
progress_bar = false

[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

[training.optimizer.learn_rate]
@schedules = "warmup_linear.v1"
warmup_steps = 250
total_steps = 20000
initial_rate = 0.00005

[training.score_weights]
tag_acc = 0.32
dep_uas = 0.0
dep_las = 0.32
dep_las_per_type = null
sents_p = null
sents_r = null
sents_f = 0.04
ents_f = 0.32
ents_p = 0.0
ents_r = 0.0
ents_per_type = null
speed = 0.0

[pretraining]

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

[initialize.components]

[initialize.components.ner]

[initialize.components.ner.labels]
@readers = "spacy.read_labels.v1"
path = "corpus/labels/ner.json"
require = false

[initialize.components.parser]

[initialize.components.parser.labels]
@readers = "spacy.read_labels.v1"
path = "corpus/labels/parser.json"
require = false

[initialize.components.tagger]

[initialize.components.tagger.labels]
@readers = "spacy.read_labels.v1"
path = "corpus/labels/tagger.json"
require = false

[initialize.components.transformer]

[initialize.components.transformer.encoder_loader]
@model_loaders = "spacy-curated-transformers.HFTransformerEncoderLoader.v1"
name = "bert-base-chinese"
revision = "main"

[initialize.components.transformer.piecer_loader]
@model_loaders = "spacy-curated-transformers.HFPieceEncoderLoader.v1"
name = "bert-base-chinese"
revision = "main"

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

[initialize.tokenizer]
pkuseg_model = "assets/pkuseg_model"
pkuseg_user_dict = "default"