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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
}
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