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Training loss is not initialized if only do_eval is specified
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@ -430,7 +430,7 @@ def main():
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if not args.do_train and not args.do_eval:
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raise ValueError("At least one of `do_train` or `do_eval` must be True.")
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if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
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raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
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os.makedirs(args.output_dir, exist_ok=True)
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@ -503,6 +503,7 @@ def main():
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t_total=t_total)
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global_step = 0
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tr_loss = 0
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if args.do_train:
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train_features = convert_examples_to_features(
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train_examples, label_list, args.max_seq_length, tokenizer)
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@ -581,7 +582,8 @@ def main():
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model.eval()
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eval_loss, eval_accuracy = 0, 0
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nb_eval_steps, nb_eval_examples = 0, 0
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for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
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for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
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input_ids = input_ids.to(device)
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input_mask = input_mask.to(device)
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segment_ids = segment_ids.to(device)
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@ -603,11 +605,11 @@ def main():
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eval_loss = eval_loss / nb_eval_steps
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eval_accuracy = eval_accuracy / nb_eval_examples
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loss = tr_loss/nb_tr_steps if args.do_train else None
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result = {'eval_loss': eval_loss,
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'eval_accuracy': eval_accuracy,
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'global_step': global_step,
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'loss': tr_loss/nb_tr_steps}
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'loss': loss}
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output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
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with open(output_eval_file, "w") as writer:
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