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ru_patents_rel

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ru_patents_rel
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[paths]
train = "data/train.spacy"
dev = "data/dev.spacy"
raw = null
init_tok2vec = null
vectors = null

[system]
seed = 342
gpu_allocator = "pytorch"

[nlp]
lang = "ru"
pipeline = ["transformer","relation_extractor"]
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
batch_size = 200
vectors = {"@vectors":"spacy.Vectors.v1"}

[components]

[components.relation_extractor]
factory = "relation_extractor"
eval_frequency = ${training.eval_frequency}
threshold = 0.5

[components.relation_extractor.model]
@architectures = "rel_model.v1"

[components.relation_extractor.model.classification_layer]
@architectures = "rel_classification_layer.v1"
nI = null
nO = null

[components.relation_extractor.model.create_instance_tensor]
@architectures = "rel_instance_tensor.v1"
pooling = {"@layers":"reduce_mean.v1"}

[components.relation_extractor.model.create_instance_tensor.get_instances]
@misc = "rel_instance_generator.v1"
max_length = 100

[components.relation_extractor.model.create_instance_tensor.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
pooling = {"@layers":"reduce_mean.v1"}
upstream = "*"

[components.transformer]
factory = "transformer"
max_batch_items = 2096
set_extra_annotations = {"@annotation_setters":"spacy-transformers.null_annotation_setter.v1"}

[components.transformer.model]
@architectures = "spacy-transformers.TransformerModel.v3"
name = "DeepPavlov/rubert-base-cased"
mixed_precision = false

[components.transformer.model.get_spans]
@span_getters = "spacy-transformers.strided_spans.v1"
window = 128
stride = 96

[components.transformer.model.grad_scaler_config]

[components.transformer.model.tokenizer_config]
use_fast = true

[components.transformer.model.transformer_config]

[corpora]

[corpora.dev]
@readers = "Gold_ents_Corpus.v1"
file = ${paths.dev}

[corpora.train]
@readers = "Gold_ents_Corpus.v1"
file = ${paths.train}

[training]
seed = ${system.seed}
gpu_allocator = ${system.gpu_allocator}
dropout = 0.2
accumulate_gradient = 1
patience = 1600000
max_epochs = 0
max_steps = 5000
eval_frequency = 50
frozen_components = []
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
before_to_disk = null
annotating_components = []
before_update = null

[training.batcher]
@batchers = "spacy.batch_by_padded.v1"
discard_oversize = true
size = 2000
buffer = 256
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 = false
eps = 0.00000001

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

[training.score_weights]
rel_micro_p = 0.0
rel_micro_r = 0.0
rel_micro_f = 0.1
rel_macro_f = 0.1
rel_weighted_f = 0.1
f1_PART-OF = 0.1
f1_LOCATED-AT = 0.1
f1_CONNECTED-WITH = 0.1
f1_IN-MANNER-OF = 0.1
f1_ATTRIBUTE-FOR = 0.1
f1_macro = 0.1
f1_weighted = 0.1

[pretraining]

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

[initialize.components]

[initialize.tokenizer]