[paths] train = null dev = null vectors = null init_tok2vec = null [system] gpu_allocator = "pytorch" seed = 0 [nlp] lang = "en" pipeline = ["transformer","textcat_multilabel"] batch_size = 128 disabled = [] before_creation = null after_creation = null after_pipeline_creation = null tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"} vectors = {"@vectors":"spacy.Vectors.v1"} [components] [components.textcat_multilabel] factory = "textcat_multilabel" scorer = {"@scorers":"spacy.textcat_multilabel_scorer.v2"} threshold = 0.5 [components.textcat_multilabel.model] @architectures = "spacy.TextCatEnsemble.v2" nO = null [components.textcat_multilabel.model.linear_model] @architectures = "spacy.TextCatBOW.v3" exclusive_classes = false length = 262144 ngram_size = 1 no_output_layer = false nO = null [components.textcat_multilabel.model.tok2vec] @architectures = "spacy-transformers.TransformerListener.v1" grad_factor = 1.0 pooling = {"@layers":"reduce_mean.v1"} upstream = "*" [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 = "roberta-base" 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 = ${paths.dev} max_length = 0 gold_preproc = false limit = 0 augmenter = null [corpora.train] @readers = "spacy.Corpus.v1" path = ${paths.train} max_length = 0 gold_preproc = false limit = 0 augmenter = null [training] accumulate_gradient = 3 dev_corpus = "corpora.dev" train_corpus = "corpora.train" seed = ${system.seed} gpu_allocator = ${system.gpu_allocator} dropout = 0.1 patience = 1600 max_epochs = 0 max_steps = 20000 eval_frequency = 200 frozen_components = [] annotating_components = [] before_to_disk = null 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] cats_score = 1.0 cats_score_desc = null cats_micro_p = null cats_micro_r = null cats_micro_f = null cats_macro_p = null cats_macro_r = null cats_macro_f = null cats_macro_auc = null cats_f_per_type = null [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]