| [paths] | |
| train = null | |
| dev = null | |
| raw = null | |
| init_tok2vec = null | |
| [system] | |
| seed = 342 | |
| gpu_allocator = null | |
| [nlp] | |
| lang = "en" | |
| pipeline = ["tok2vec", "relation_extractor"] | |
| disabled = [] | |
| before_creation = null | |
| after_creation = null | |
| after_pipeline_creation = null | |
| tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"} | |
| batch_size = 512 | |
| [components] | |
| [components.tok2vec] | |
| factory = "tok2vec" | |
| [components.tok2vec.model] | |
| @architectures = "spacy.HashEmbedCNN.v1" | |
| pretrained_vectors = null | |
| width = 96 | |
| depth = 2 | |
| embed_size = 2000 | |
| window_size = 1 | |
| maxout_pieces = 3 | |
| subword_features = true | |
| [components.relation_extractor] | |
| factory = "relation_extractor" | |
| threshold = 0.5 | |
| [components.relation_extractor.model] | |
| @architectures = "rel_model.v1" | |
| [components.relation_extractor.model.create_instance_tensor] | |
| @architectures = "rel_instance_tensor.v1" | |
| [components.relation_extractor.model.create_instance_tensor.tok2vec] | |
| @architectures = "spacy.Tok2VecListener.v1" | |
| width = ${components.tok2vec.model.width} | |
| [components.relation_extractor.model.create_instance_tensor.pooling] | |
| @layers = "reduce_mean.v1" | |
| [components.relation_extractor.model.create_instance_tensor.get_instances] | |
| @misc = "rel_instance_generator.v1" | |
| max_length = 1000 | |
| [components.relation_extractor.model.classification_layer] | |
| @architectures = "rel_classification_layer.v1" | |
| nI = null | |
| nO = null | |
| [initialize] | |
| [initialize.components] | |
| [corpora] | |
| [corpora.dev] | |
| @readers = "Gold_ents_Corpus.v1" | |
| file = ${paths.dev} | |
| [corpora.train] | |
| @readers = "Gold_ents_Corpus.v1" | |
| file = ${paths.train} | |
| [training] | |
| seed = ${system.seed} | |
| gpu_allocator = ${system.gpu_allocator} | |
| dropout = 0.1 | |
| accumulate_gradient = 1 | |
| patience = 160000000 | |
| max_epochs = 0 | |
| max_steps = 10000000 | |
| eval_frequency = 500 | |
| frozen_components = [] | |
| dev_corpus = "corpora.dev" | |
| train_corpus = "corpora.train" | |
| before_to_disk = null | |
| logger = {"@loggers":"spacy.ConsoleLogger.v1"} | |
| [training.batcher] | |
| @batchers = "spacy.batch_by_words.v1" | |
| discard_oversize = false | |
| tolerance = 0.2 | |
| [training.batcher.size] | |
| @schedules = "compounding.v1" | |
| start = 100 | |
| stop = 1000 | |
| compound = 1.001 | |
| [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] | |
| rel_micro_p = 0.0 | |
| rel_micro_r = 0.0 | |
| rel_micro_f = 1.0 | |