| [paths] | |
| train = "data/engagement_spl_train.spacy" | |
| dev = "data/engagement_spl_dev.spacy" | |
| vectors = null | |
| init_tok2vec = null | |
| source = "en_core_web_trf" | |
| [system] | |
| gpu_allocator = "pytorch" | |
| seed = 0 | |
| [nlp] | |
| lang = "en" | |
| pipeline = ["transformer","tagger","parser","ner","trainable_transformer","span_finder","spancat"] | |
| batch_size = 16 | |
| disabled = [] | |
| before_creation = null | |
| after_creation = null | |
| after_pipeline_creation = null | |
| tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"} | |
| [components] | |
| [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-transformers.TransformerListener.v1" | |
| grad_factor = 1.0 | |
| upstream = "transformer" | |
| pooling = {"@layers":"reduce_mean.v1"} | |
| [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-transformers.TransformerListener.v1" | |
| grad_factor = 1.0 | |
| upstream = "transformer" | |
| pooling = {"@layers":"reduce_mean.v1"} | |
| [components.span_finder] | |
| factory = "experimental_span_finder" | |
| max_length = 0 | |
| min_length = 0 | |
| predicted_key = "span_candidates" | |
| threshold = 0.2 | |
| training_key = ${vars.spans_key} | |
| [components.span_finder.model] | |
| @architectures = "spacy-experimental.SpanFinder.v1" | |
| [components.span_finder.model.scorer] | |
| @layers = "spacy.LinearLogistic.v1" | |
| nO = 2 | |
| nI = null | |
| [components.span_finder.model.tok2vec] | |
| @architectures = "spacy-transformers.TransformerListener.v1" | |
| grad_factor = 1.0 | |
| upstream = "trainable_transformer" | |
| pooling = {"@layers":"reduce_mean.v1"} | |
| [components.span_finder.scorer] | |
| @scorers = "spacy-experimental.span_finder_scorer.v1" | |
| predicted_key = ${components.span_finder.predicted_key} | |
| training_key = ${vars.spans_key} | |
| [components.spancat] | |
| factory = "spancat" | |
| max_positive = 2 | |
| scorer = {"@scorers":"spacy.spancat_scorer.v1"} | |
| spans_key = ${vars.spans_key} | |
| threshold = 0.5 | |
| [components.spancat.model] | |
| @architectures = "spacy.SpanCategorizer.v1" | |
| [components.spancat.model.reducer] | |
| @layers = "mean_max_reducer.v1.5" | |
| hidden_size = 128 | |
| dropout = 0.2 | |
| [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 | |
| upstream = "trainable_transformer" | |
| pooling = {"@layers":"reduce_mean.v1"} | |
| [components.spancat.suggester] | |
| @misc = "spacy-experimental.span_finder_suggester.v1" | |
| candidates_key = ${components.span_finder.predicted_key} | |
| [components.tagger] | |
| factory = "tagger" | |
| 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-transformers.TransformerListener.v1" | |
| grad_factor = 1.0 | |
| upstream = "transformer" | |
| pooling = {"@layers":"reduce_mean.v1"} | |
| [components.trainable_transformer] | |
| factory = "transformer" | |
| max_batch_items = 4096 | |
| set_extra_annotations = {"@annotation_setters":"spacy-transformers.null_annotation_setter.v1"} | |
| [components.trainable_transformer.model] | |
| @architectures = "spacy-transformers.TransformerModel.v1" | |
| name = "egumasa/roberta-base-finetuned-academic" | |
| [components.trainable_transformer.model.get_spans] | |
| @span_getters = "spacy-transformers.strided_spans.v1" | |
| window = 196 | |
| stride = 147 | |
| [components.trainable_transformer.model.tokenizer_config] | |
| use_fast = true | |
| [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 = 2000 | |
| gold_preproc = false | |
| limit = 0 | |
| augmenter = null | |
| [training] | |
| dev_corpus = "corpora.dev" | |
| train_corpus = "corpora.train" | |
| seed = ${system.seed} | |
| gpu_allocator = ${system.gpu_allocator} | |
| dropout = 0.1 | |
| accumulate_gradient = 1 | |
| patience = 2000 | |
| max_epochs = 0 | |
| max_steps = 20000 | |
| eval_frequency = 100 | |
| frozen_components = ["transformer","parser","tagger","ner"] | |
| annotating_components = ["span_finder"] | |
| before_to_disk = null | |
| [training.batcher] | |
| @batchers = "spacy.batch_by_words.v1" | |
| discard_oversize = false | |
| tolerance = 0.3 | |
| get_length = null | |
| [training.batcher.size] | |
| @schedules = "compounding.v1" | |
| start = 200 | |
| stop = 500 | |
| compound = 1.0005 | |
| t = 0.0 | |
| [training.logger] | |
| @loggers = "spacy.WandbLogger.v3" | |
| project_name = "spnacat_engagementv2" | |
| remove_config_values = ["paths.train","paths.dev","corpora.train.path","corpora.dev.path"] | |
| model_log_interval = 100 | |
| entity = "e-masaki0101" | |
| run_name = "OS_AdapR_max1-128do0.2_Cyc1000_RAdam_20221030" | |
| log_dataset_dir = null | |
| [training.optimizer] | |
| @optimizers = "RAdam.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 = "cyclic_triangular.v1" | |
| min_lr = 0.00001 | |
| max_lr = 0.0001 | |
| period = 500 | |
| [training.score_weights] | |
| tag_acc = null | |
| dep_uas = null | |
| dep_las = null | |
| dep_las_per_type = null | |
| sents_p = null | |
| sents_r = null | |
| sents_f = null | |
| ents_f = null | |
| ents_p = null | |
| ents_r = null | |
| ents_per_type = null | |
| span_finder_span_candidates_f = 0.0 | |
| span_finder_span_candidates_p = 0.0 | |
| span_finder_span_candidates_r = 0.18 | |
| spans_sc_f = 0.64 | |
| spans_sc_p = 0.09 | |
| spans_sc_r = 0.09 | |
| lemma_acc = 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] | |
| [vars] | |
| spans_key = "sc" |