| [paths] | |
| tagger_model = "models/hu_core_news_trf_xl-tagger-3.5.2/model-best" | |
| parser_model = "models/hu_core_news_trf_xl-parser-3.5.2/model-best" | |
| ner_model = "models/hu_core_news_trf_xl-ner-3.5.2/model-best" | |
| lemmatizer_lookups = "models/hu_core_news_trf_xl-lookup-lemmatizer-3.5.2" | |
| train = null | |
| dev = null | |
| vectors = null | |
| init_tok2vec = null | |
| [system] | |
| seed = 0 | |
| gpu_allocator = null | |
| [nlp] | |
| lang = "hu" | |
| pipeline = ["transformer","senter","tagger","morphologizer","lookup_lemmatizer","trainable_lemmatizer","experimental_arc_predicter","experimental_arc_labeler","ner"] | |
| tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"} | |
| disabled = [] | |
| before_creation = null | |
| after_creation = null | |
| after_pipeline_creation = null | |
| batch_size = 1000 | |
| [components] | |
| [components.experimental_arc_labeler] | |
| factory = "experimental_arc_labeler" | |
| scorer = {"@scorers":"spacy-experimental.biaffine_parser_scorer.v1"} | |
| [components.experimental_arc_labeler.model] | |
| @architectures = "spacy-experimental.Bilinear.v1" | |
| hidden_width = 256 | |
| mixed_precision = true | |
| nO = null | |
| dropout = 0.1 | |
| grad_scaler = null | |
| [components.experimental_arc_labeler.model.tok2vec] | |
| @architectures = "spacy-transformers.TransformerListener.v1" | |
| grad_factor = 1.0 | |
| upstream = "transformer" | |
| pooling = {"@layers":"reduce_mean.v1"} | |
| [components.experimental_arc_predicter] | |
| factory = "experimental_arc_predicter" | |
| scorer = {"@scorers":"spacy-experimental.biaffine_parser_scorer.v1"} | |
| [components.experimental_arc_predicter.model] | |
| @architectures = "spacy-experimental.PairwiseBilinear.v1" | |
| hidden_width = 64 | |
| nO = 1 | |
| mixed_precision = false | |
| dropout = 0.1 | |
| grad_scaler = null | |
| [components.experimental_arc_predicter.model.tok2vec] | |
| @architectures = "spacy-transformers.TransformerListener.v1" | |
| grad_factor = 1.0 | |
| upstream = "transformer" | |
| pooling = {"@layers":"reduce_mean.v1"} | |
| [components.lookup_lemmatizer] | |
| factory = "hu.lookup_lemmatizer" | |
| scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"} | |
| source = ${paths.lemmatizer_lookups} | |
| [components.morphologizer] | |
| factory = "morphologizer" | |
| extend = false | |
| overwrite = true | |
| scorer = {"@scorers":"spacy.morphologizer_scorer.v1"} | |
| [components.morphologizer.model] | |
| @architectures = "spacy.Tagger.v1" | |
| nO = null | |
| [components.morphologizer.model.tok2vec] | |
| @architectures = "spacy-transformers.TransformerListener.v1" | |
| grad_factor = 1.0 | |
| pooling = {"@layers":"reduce_mean.v1"} | |
| upstream = "*" | |
| [components.ner] | |
| factory = "beam_ner" | |
| beam_density = 0.01 | |
| beam_update_prob = 1 | |
| beam_width = 32 | |
| 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 = true | |
| hidden_width = 64 | |
| maxout_pieces = 3 | |
| use_upper = false | |
| nO = null | |
| [components.ner.model.tok2vec] | |
| @architectures = "spacy-transformers.Tok2VecTransformer.v3" | |
| name = "xlm-roberta-large" | |
| mixed_precision = false | |
| pooling = {"@layers":"reduce_mean.v1"} | |
| grad_factor = 1.0 | |
| [components.ner.model.tok2vec.get_spans] | |
| @span_getters = "spacy-transformers.strided_spans.v1" | |
| window = 128 | |
| stride = 96 | |
| [components.ner.model.tok2vec.grad_scaler_config] | |
| [components.ner.model.tok2vec.tokenizer_config] | |
| use_fast = true | |
| model_max_length = 512 | |
| [components.ner.model.tok2vec.transformer_config] | |
| [components.senter] | |
| factory = "senter" | |
| overwrite = false | |
| scorer = {"@scorers":"spacy.senter_scorer.v1"} | |
| [components.senter.model] | |
| @architectures = "spacy.Tagger.v1" | |
| nO = null | |
| [components.senter.model.tok2vec] | |
| @architectures = "spacy-transformers.TransformerListener.v1" | |
| grad_factor = 1.0 | |
| upstream = "transformer" | |
| pooling = {"@layers":"reduce_mean.v1"} | |
| [components.tagger] | |
| factory = "tagger" | |
| neg_prefix = "!" | |
| overwrite = false | |
| scorer = {"@scorers":"spacy.tagger_scorer.v1"} | |
| [components.tagger.model] | |
| @architectures = "spacy.Tagger.v1" | |
| nO = null | |
| [components.tagger.model.tok2vec] | |
| @architectures = "spacy-transformers.TransformerListener.v1" | |
| grad_factor = 1.0 | |
| pooling = {"@layers":"reduce_mean.v1"} | |
| upstream = "*" | |
| [components.trainable_lemmatizer] | |
| factory = "trainable_lemmatizer_v2" | |
| backoff = "orth" | |
| min_tree_freq = 1 | |
| overwrite = false | |
| overwrite_labels = true | |
| scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"} | |
| top_k = 3 | |
| [components.trainable_lemmatizer.model] | |
| @architectures = "spacy.Tagger.v1" | |
| nO = null | |
| [components.trainable_lemmatizer.model.tok2vec] | |
| @architectures = "spacy-transformers.Tok2VecTransformer.v3" | |
| name = "xlm-roberta-large" | |
| mixed_precision = false | |
| pooling = {"@layers":"reduce_mean.v1"} | |
| grad_factor = 1.0 | |
| [components.trainable_lemmatizer.model.tok2vec.get_spans] | |
| @span_getters = "spacy-transformers.strided_spans.v1" | |
| window = 128 | |
| stride = 96 | |
| [components.trainable_lemmatizer.model.tok2vec.grad_scaler_config] | |
| [components.trainable_lemmatizer.model.tok2vec.tokenizer_config] | |
| use_fast = true | |
| model_max_length = 512 | |
| [components.trainable_lemmatizer.model.tok2vec.transformer_config] | |
| [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 = "xlm-roberta-large" | |
| 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 | |
| model_max_length = 512 | |
| [components.transformer.model.transformer_config] | |
| [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] | |
| seed = ${system.seed} | |
| gpu_allocator = ${system.gpu_allocator} | |
| dropout = 0.1 | |
| accumulate_gradient = 1 | |
| patience = 1600 | |
| max_epochs = 0 | |
| max_steps = 20000 | |
| eval_frequency = 200 | |
| frozen_components = [] | |
| annotating_components = [] | |
| dev_corpus = "corpora.dev" | |
| train_corpus = "corpora.train" | |
| before_to_disk = null | |
| before_update = null | |
| [training.batcher] | |
| @batchers = "spacy.batch_by_words.v1" | |
| discard_oversize = false | |
| tolerance = 0.2 | |
| get_length = null | |
| [training.batcher.size] | |
| @schedules = "compounding.v1" | |
| start = 100 | |
| stop = 1000 | |
| compound = 1.001 | |
| t = 0.0 | |
| [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 | |
| learn_rate = 0.001 | |
| [training.score_weights] | |
| sents_f = 0.2 | |
| sents_p = 0.0 | |
| sents_r = 0.0 | |
| tag_acc = 0.2 | |
| pos_acc = 0.1 | |
| morph_acc = 0.1 | |
| morph_per_feat = null | |
| lemma_acc = 0.2 | |
| ents_f = 0.2 | |
| ents_p = 0.0 | |
| ents_r = 0.0 | |
| ents_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] |