iproskurina's picture
Update spaCy pipeline
8196617
[paths]
train = "./realec/train.spacy"
dev = "./realec/dev.spacy"
vectors = null
init_tok2vec = null
[system]
gpu_allocator = "pytorch"
seed = 0
[nlp]
lang = "en"
pipeline = ["transformer","spancat"]
batch_size = 16
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
[components]
[components.spancat]
factory = "spancat"
max_positive = null
scorer = {"@scorers":"spacy.spancat_scorer.v1"}
spans_key = "errors"
threshold = 0.5
[components.spancat.model]
@architectures = "spacy.SpanCategorizer.v1"
[components.spancat.model.reducer]
@layers = "spacy.mean_max_reducer.v1"
hidden_size = 128
[components.spancat.model.scorer]
@layers = "spacy.LinearLogistic.v1"
nO = null
nI = null
[components.spancat.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
pooling = {"@layers":"reduce_mean.v1"}
upstream = "*"
[components.spancat.suggester]
@misc = "spacy.ngram_suggester.v1"
sizes = [1,2,3]
[components.transformer]
factory = "transformer"
max_batch_items = 4096
set_extra_annotations = {"@annotation_setters":"spacy-transformers.null_annotation_setter.v1"}
[components.transformer.model]
@architectures = "spacy-transformers.TransformerModel.v3"
name = "bert-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 = "spacy.Corpus.v1"
path = "./realec/dev.spacy"
max_length = 0
gold_preproc = false
limit = 0
augmenter = null
[corpora.train]
@readers = "spacy.Corpus.v1"
path = "./realec/train.spacy"
max_length = 0
gold_preproc = false
limit = 0
augmenter = null
[training]
accumulate_gradient = 3
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
seed = 0
gpu_allocator = "pytorch"
dropout = 0.1
patience = 1600
max_epochs = 0
max_steps = 20000
eval_frequency = 200
frozen_components = []
annotating_components = []
before_to_disk = null
[training.batcher]
@batchers = "spacy.batch_by_padded.v1"
discard_oversize = true
size = 2000
buffer = 256
get_length = null
[training.logger]
@loggers = "spacy.WandbLogger.v3"
project_name = "my-awesome-project"
remove_config_values = ["paths.train","paths.dev","corpora.train.path","corpora.dev.path"]
log_dataset_dir = null
entity = null
run_name = "grammar-checker"
model_log_interval = null
[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]
spans_sc_f = 0.5
spans_sc_p = 0.0
spans_sc_r = 0.0
spans_Agreement_errors_f = 0.06
spans_Articles_f = 0.03
spans_Capitalisation_f = 0.05
spans_Formational_affixes_f = 0.1
spans_Noun_number_f = 0.04
spans_Numerals_f = 0.06
spans_Prepositions_f = 0.05
spans_Punctuation_f = 0.03
spans_Spelling_f = 0.02
spans_Tense_choice_f = 0.03
spans_lex_item_choice_f = 0.03
[pretraining]
[initialize]
vectors = null
init_tok2vec = null
vocab_data = null
lookups = null
before_init = null
after_init = null
[initialize.components]
[initialize.tokenizer]