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local env = import "../env.jsonnet"; |
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local base = import "ace.jsonnet"; |
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local pretrained_path = env.str("PRETRAINED_PATH", "cache/ace/best"); |
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local lr = env.json("FT_LR", 5e-5); |
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# training |
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local cuda_devices = base.cuda_devices; |
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{ |
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dataset_reader: base.dataset_reader, |
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train_data_path: base.train_data_path, |
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validation_data_path: base.validation_data_path, |
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test_data_path: base.test_data_path, |
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datasets_for_vocab_creation: ["train"], |
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data_loader: base.data_loader, |
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validation_data_loader: base.validation_data_loader, |
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model: { |
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type: "from_archive", |
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archive_file: pretrained_path |
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}, |
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vocabulary: { |
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type: "from_files", |
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directory: pretrained_path + "/vocabulary" |
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}, |
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trainer: { |
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num_epochs: base.trainer.num_epochs, |
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patience: base.trainer.patience, |
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[if std.length(cuda_devices) == 1 then "cuda_device"]: cuda_devices[0], |
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validation_metric: "+arg-c_f", |
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num_gradient_accumulation_steps: base.trainer.num_gradient_accumulation_steps, |
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optimizer: { |
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type: "transformer", |
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base: { |
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type: "adam", |
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lr: lr, |
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}, |
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embeddings_lr: 0.0, |
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encoder_lr: 1e-5, |
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pooler_lr: 1e-5, |
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layer_fix: base.trainer.optimizer.layer_fix, |
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} |
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}, |
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[if std.length(cuda_devices) > 1 then "distributed"]: { |
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"cuda_devices": cuda_devices |
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}, |
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[if std.length(cuda_devices) == 1 then "evaluate_on_test"]: true |
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} |
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