|
local env = import "../env.jsonnet"; |
|
|
|
#local dataset_path = env.str("DATA_PATH", "data/framenet/full"); |
|
local dataset_path = "/home/p289731/cloned/lome/preproc/evalita_jsonl"; |
|
local ontology_path = "data/framenet/ontology.tsv"; |
|
|
|
local debug = false; |
|
|
|
# reader |
|
local pretrained_model = env.str("ENCODER", "xlm-roberta-large"); |
|
local smoothing_factor = env.json("SMOOTHING", "0.1"); |
|
|
|
# model |
|
local label_dim = env.json("LABEL_DIM", "64"); |
|
local dropout = env.json("DROPOUT", "0.2"); |
|
local bio_dim = env.json("BIO_DIM", "512"); |
|
local bio_layers = env.json("BIO_LAYER", "2"); |
|
local span_typing_dims = env.json("TYPING_DIMS", "[256, 256]"); |
|
local typing_loss_factor = env.json("LOSS_FACTOR", "8.0"); |
|
|
|
# loader |
|
local exemplar_ratio = env.json("EXEMPLAR_RATIO", "0.05"); |
|
local max_training_tokens = 512; |
|
local max_inference_tokens = 1024; |
|
|
|
# training |
|
local layer_fix = env.json("LAYER_FIX", "0"); |
|
local grad_acc = env.json("GRAD_ACC", "1"); |
|
#local cuda_devices = env.json("CUDA_DEVICES", "[-1]"); |
|
local cuda_devices = [0]; |
|
local patience = 32; |
|
|
|
{ |
|
dataset_reader: { |
|
type: "semantic_role_labeling", |
|
debug: debug, |
|
pretrained_model: pretrained_model, |
|
ignore_label: false, |
|
[ if debug then "max_instances" ]: 128, |
|
event_smoothing_factor: smoothing_factor, |
|
arg_smoothing_factor: smoothing_factor, |
|
}, |
|
train_data_path: dataset_path + "/evalita_plus_fn_train.jsonl", |
|
validation_data_path: dataset_path + "/evalita_dev.jsonl", |
|
test_data_path: dataset_path + "/evalita_test.jsonl", |
|
|
|
datasets_for_vocab_creation: ["train"], |
|
|
|
data_loader: { |
|
batch_sampler: { |
|
type: "mix_sampler", |
|
max_tokens: max_training_tokens, |
|
sorting_keys: ['tokens'], |
|
sampling_ratios: { |
|
'exemplar': 1.0, |
|
'full text': 0.0, |
|
} |
|
} |
|
}, |
|
|
|
validation_data_loader: { |
|
batch_sampler: { |
|
type: "max_tokens_sampler", |
|
max_tokens: max_inference_tokens, |
|
sorting_keys: ['tokens'] |
|
} |
|
}, |
|
|
|
model: { |
|
type: "span", |
|
word_embedding: { |
|
token_embedders: { |
|
"pieces": { |
|
type: "pretrained_transformer", |
|
model_name: pretrained_model, |
|
} |
|
}, |
|
}, |
|
span_extractor: { |
|
type: 'combo', |
|
sub_extractors: [ |
|
{ |
|
type: 'self_attentive', |
|
}, |
|
{ |
|
type: 'bidirectional_endpoint', |
|
} |
|
] |
|
}, |
|
span_finder: { |
|
type: "bio", |
|
bio_encoder: { |
|
type: "lstm", |
|
hidden_size: bio_dim, |
|
num_layers: bio_layers, |
|
bidirectional: true, |
|
dropout: dropout, |
|
}, |
|
no_label: false, |
|
}, |
|
span_typing: { |
|
type: 'mlp', |
|
hidden_dims: span_typing_dims, |
|
}, |
|
metrics: [{type: "srl"}], |
|
|
|
typing_loss_factor: typing_loss_factor, |
|
ontology_path: null, |
|
label_dim: label_dim, |
|
max_decoding_spans: 128, |
|
max_recursion_depth: 2, |
|
debug: debug, |
|
}, |
|
|
|
trainer: { |
|
num_epochs: 128, |
|
patience: patience, |
|
[if std.length(cuda_devices) == 1 then "cuda_device"]: cuda_devices[0], |
|
validation_metric: "+em_f", |
|
grad_norm: 10, |
|
grad_clipping: 10, |
|
num_gradient_accumulation_steps: grad_acc, |
|
optimizer: { |
|
type: "transformer", |
|
base: { |
|
type: "adam", |
|
lr: 1e-3, |
|
}, |
|
embeddings_lr: 0.0, |
|
encoder_lr: 1e-5, |
|
pooler_lr: 1e-5, |
|
layer_fix: layer_fix, |
|
} |
|
}, |
|
|
|
cuda_devices:: cuda_devices, |
|
[if std.length(cuda_devices) > 1 then "distributed"]: { |
|
"cuda_devices": cuda_devices |
|
}, |
|
[if std.length(cuda_devices) == 1 then "evaluate_on_test"]: true |
|
} |
|
|
