update the examples, docs and template
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@@ -41,7 +41,7 @@ from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
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from transformers import (OpenAIGPTDoubleHeadsModel, OpenAIGPTTokenizer,
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AdamW, cached_path, WEIGHTS_NAME, CONFIG_NAME,
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WarmupLinearSchedule)
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get_linear_schedule_with_warmup)
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ROCSTORIES_URL = "https://s3.amazonaws.com/datasets.huggingface.co/ROCStories.tar.gz"
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@@ -211,7 +211,7 @@ def main():
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{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
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]
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optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
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scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
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scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
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if args.do_train:
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nb_tr_steps, tr_loss, exp_average_loss = 0, 0, None
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@@ -42,7 +42,7 @@ from tqdm import tqdm, trange
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from transformers import (WEIGHTS_NAME, BertConfig,
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BertForMultipleChoice, BertTokenizer)
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from transformers import AdamW, WarmupLinearSchedule
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from transformers import AdamW, get_linear_schedule_with_warmup
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logger = logging.getLogger(__name__)
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@@ -322,7 +322,7 @@ def train(args, train_dataset, model, tokenizer):
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{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
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]
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optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
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scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
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scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
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if args.fp16:
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try:
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from apex import amp
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