| [paths] | |
| train = "assets/clausecat/train.spacy" | |
| dev = "assets/clausecat/dev.spacy" | |
| vectors = ${paths.ner_model} | |
| init_tok2vec = "assets/pretrained_weights_clausecat.bin" | |
| ner_model = "training/ner/config_tok2vec/model-best" | |
| [system] | |
| gpu_allocator = "pytorch" | |
| seed = 0 | |
| [nlp] | |
| lang = "en" | |
| pipeline = ["sentencizer","tok2vec","ner","benepar","segmentation","clausecat","aggregation"] | |
| batch_size = 128 | |
| disabled = [] | |
| before_creation = null | |
| after_creation = null | |
| after_pipeline_creation = null | |
| tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"} | |
| [components] | |
| [components.aggregation] | |
| factory = "healthsea.aggregation.v1" | |
| [components.benepar] | |
| factory = "benepar" | |
| disable_tagger = false | |
| model = "benepar_en3" | |
| subbatch_max_tokens = 500 | |
| [components.clausecat] | |
| factory = "healthsea.clausecat.v1" | |
| threshold = 0.5 | |
| [components.clausecat.model] | |
| @architectures = "healthsea.clausecat_model.v1" | |
| blinder = {"@layers":"healthsea.blinder.v1"} | |
| [components.clausecat.model.textcat] | |
| @architectures = "spacy.TextCatEnsemble.v2" | |
| nO = null | |
| [components.clausecat.model.textcat.linear_model] | |
| @architectures = "spacy.TextCatBOW.v2" | |
| exclusive_classes = true | |
| ngram_size = 1 | |
| no_output_layer = false | |
| nO = null | |
| [components.clausecat.model.textcat.tok2vec] | |
| @architectures = "spacy.Tok2Vec.v2" | |
| [components.clausecat.model.textcat.tok2vec.embed] | |
| @architectures = "spacy.MultiHashEmbed.v2" | |
| width = 64 | |
| rows = [2000,2000,1000,1000,1000,1000] | |
| attrs = ["ORTH","LOWER","PREFIX","SUFFIX","SHAPE","ID"] | |
| include_static_vectors = false | |
| [components.clausecat.model.textcat.tok2vec.encode] | |
| @architectures = "spacy.MaxoutWindowEncoder.v2" | |
| width = ${components.clausecat.model.textcat.tok2vec.embed.width} | |
| window_size = 1 | |
| maxout_pieces = 3 | |
| depth = 2 | |
| [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.Tok2VecListener.v1" | |
| width = 256 | |
| upstream = "tok2vec" | |
| [components.segmentation] | |
| factory = "healthsea.segmentation.v1" | |
| [components.sentencizer] | |
| factory = "sentencizer" | |
| overwrite = false | |
| punct_chars = null | |
| scorer = {"@scorers":"spacy.senter_scorer.v1"} | |
| [components.tok2vec] | |
| factory = "tok2vec" | |
| [components.tok2vec.model] | |
| @architectures = "spacy.Tok2Vec.v2" | |
| [components.tok2vec.model.embed] | |
| @architectures = "spacy.MultiHashEmbed.v2" | |
| width = 256 | |
| attrs = ["ORTH","SHAPE"] | |
| rows = [5000,2500] | |
| include_static_vectors = true | |
| [components.tok2vec.model.encode] | |
| @architectures = "spacy.MaxoutWindowEncoder.v2" | |
| width = 256 | |
| depth = 8 | |
| window_size = 1 | |
| maxout_pieces = 3 | |
| [corpora] | |
| [corpora.dev] | |
| @readers = "healthsea.clausecat_reader.v1" | |
| path = ${paths.dev} | |
| [corpora.train] | |
| @readers = "healthsea.clausecat_reader.v1" | |
| path = ${paths.train} | |
| [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 = ["tok2vec","ner"] | |
| annotating_components = [] | |
| before_to_disk = 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 = true | |
| [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] | |
| sents_f = null | |
| sents_p = null | |
| sents_r = null | |
| ents_f = null | |
| ents_p = null | |
| ents_r = null | |
| ents_per_type = null | |
| 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 | |
| cats_macro_auc_per_type = null | |
| [pretraining] | |
| max_epochs = 100 | |
| dropout = 0.2 | |
| n_save_every = null | |
| n_save_epoch = 1 | |
| component = "clausecat" | |
| layer = "tok2vec" | |
| corpus = "corpora.pretrain" | |
| [pretraining.batcher] | |
| @batchers = "spacy.batch_by_words.v1" | |
| size = 10000 | |
| discard_oversize = false | |
| tolerance = 0.2 | |
| get_length = null | |
| [pretraining.objective] | |
| @architectures = "spacy.PretrainVectors.v1" | |
| maxout_pieces = 3 | |
| hidden_size = 300 | |
| loss = "cosine" | |
| [pretraining.optimizer] | |
| @optimizers = "Adam.v1" | |
| beta1 = 0.9 | |
| beta2 = 0.999 | |
| L2_is_weight_decay = true | |
| L2 = 0.01 | |
| grad_clip = 1.0 | |
| use_averages = true | |
| eps = 0.00000001 | |
| learn_rate = 0.001 | |
| [initialize] | |
| vectors = ${paths.vectors} | |
| init_tok2vec = ${paths.init_tok2vec} | |
| vocab_data = null | |
| lookups = null | |
| before_init = null | |
| after_init = null | |
| [initialize.components] | |
| [initialize.tokenizer] |