| local env = import "../env.jsonnet"; | |
| local base = import "basic.jsonnet"; | |
| local fn_path = "data/framenet/full/full.jsonl"; | |
| local mapping_path = "data/basic/framenet2better/"; | |
| local debug = false; | |
| # training | |
| local lr = env.json("PT_LR", "5e-5"); | |
| local cuda_devices = base.cuda_devices; | |
| # mapping | |
| local min_weight = env.json("MIN_WEIGHT", '0.0'); | |
| local max_weight = env.json("MAX_WEIGHT", '5.0'); | |
| { | |
| dataset_reader: { | |
| type: "semantic_role_labeling", | |
| debug: debug, | |
| pretrained_model: base.dataset_reader.pretrained_model, | |
| ignore_label: false, | |
| [ if debug then "max_instances" ]: 128, | |
| ontology_mapping_path: mapping_path + '/ontology_mapping.json', | |
| min_weight: min_weight, | |
| max_weight: max_weight, | |
| }, | |
| validation_dataset_reader: base.dataset_reader, | |
| train_data_path: fn_path, | |
| validation_data_path: base.validation_data_path, | |
| test_data_path: base.test_data_path, | |
| vocabulary: { | |
| type: "extend", | |
| directory: mapping_path + "/vocabulary" | |
| }, | |
| datasets_for_vocab_creation: ["train"], | |
| data_loader: base.data_loader, | |
| validation_data_loader: base.validation_data_loader, | |
| model: base.model, | |
| trainer: { | |
| num_epochs: base.trainer.num_epochs, | |
| patience: base.trainer.patience, | |
| [if std.length(cuda_devices) == 1 then "cuda_device"]: cuda_devices[0], | |
| validation_metric: "+arg-c_f", | |
| num_gradient_accumulation_steps: base.trainer.num_gradient_accumulation_steps, | |
| optimizer: { | |
| type: "transformer", | |
| base: { | |
| type: "adam", | |
| lr: lr, | |
| }, | |
| embeddings_lr: 0.0, | |
| encoder_lr: 1e-5, | |
| pooler_lr: 1e-5, | |
| layer_fix: base.trainer.optimizer.layer_fix, | |
| } | |
| }, | |
| [if std.length(cuda_devices) > 1 then "distributed"]: { | |
| "cuda_devices": cuda_devices | |
| }, | |
| [if std.length(cuda_devices) == 1 then "evaluate_on_test"]: true | |
| } | |