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1import torch
2model: ValidatorInterface = (torch.load(args.metre_model_path_full, map_location=torch.device('cpu')))1tokenizer = = AutoTokenizer.from_pretrained('roberta-base')
2model.validate(input_ids=datum["input_ids"], metre=datum["metre"])['acc']1meter_model = MeterValidator(pretrained_model=args.pretrained_model)
2tokenizer = AutoTokenizer.from_pretrained(args.tokenizer)
3
4training_args = TrainingArguments(
5 save_strategy = "no",
6 logging_steps = 500,
7 warmup_steps = args.worm_up,
8 weight_decay = 0.0,
9 num_train_epochs = args.epochs,
10 learning_rate = args.learning_rate,
11 fp16 = True if torch.cuda.is_available() else False,
12 ddp_backend = "nccl",
13 lr_scheduler_type="cosine",
14 logging_dir = './logs',
15 output_dir = './results',
16 per_device_train_batch_size = args.batch_size)
17
18Trainer(model = rhyme_model,
19 args = training_args,
20 train_dataset= train_data.pytorch_dataset_body,
21 data_collator=collate).train()
22