| | local env = import "../env.jsonnet"; |
| | local base = import "ace.jsonnet"; |
| |
|
| | local pretrained_path = env.str("PRETRAINED_PATH", "cache/ace/best"); |
| | local lr = env.json("FT_LR", 5e-5); |
| |
|
| | # training |
| | local cuda_devices = base.cuda_devices; |
| |
|
| | { |
| | dataset_reader: base.dataset_reader, |
| | train_data_path: base.train_data_path, |
| | validation_data_path: base.validation_data_path, |
| | test_data_path: base.test_data_path, |
| | datasets_for_vocab_creation: ["train"], |
| | data_loader: base.data_loader, |
| | validation_data_loader: base.validation_data_loader, |
| | |
| | model: { |
| | type: "from_archive", |
| | archive_file: pretrained_path |
| | }, |
| | vocabulary: { |
| | type: "from_files", |
| | directory: pretrained_path + "/vocabulary" |
| | }, |
| | |
| | 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 |
| | } |
| |
|
