FS-TFP/federatedscope/nlp/hetero_tasks/baseline/config_pretrain.yaml

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YAML

use_gpu: True
device: 0
seed: 12345
outdir: exp/
federate:
mode: standalone
method: fedavg
total_round_num: 200
client_num: 18
save_to: ckpt/
data:
type: hetero_nlp_tasks
root: datasets/
hetero_data_name: ['imdb', 'agnews', 'squad', 'newsqa', 'cnndm', 'msqg']
num_of_client_for_data: [1, 3, 3, 2, 5, 4]
max_seq_len: 384
max_tgt_len: 128
batch_size: 16
num_workers: 0
cache_dir: cache/
model:
type: atc_model
model_type: google/bert_uncased_L-2_H-128_A-2
stage: assign
task: pretrain
pretrain_tasks: ['mlm', 'denoise']
aggregator:
num_agg_groups: 5
inside_weight: 1.0
outside_weight: 0.0
personalization:
local_param: ['classifier']
trainer:
type: atc_trainer
train:
batch_or_epoch: batch
local_update_steps: 50
optimizer:
type: AdamW
lr: 5e-4
weight_decay: 0.01
scheduler:
type: warmup_step
warmup_ratio: 0.1
grad:
grad_clip: 1.0
grad_accum_count: 4
eval:
metrics: ['acc']
split: ['test']
report: ['raw']
freq: 100000000 # eval freq across rounds