tiny fix
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f2aaebdbde
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@ -73,11 +73,11 @@ class Evaluator:
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return [chr(ord("A") + offset.item()) for offset in torch.argmax(choice_probs, dim=-1)]
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def eval(self) -> None:
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task = self.eval_args.task.split("_")[0]
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split = self.eval_args.task.split("_")[1]
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eval_task = self.eval_args.task.split("_")[0]
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eval_split = self.eval_args.task.split("_")[1]
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mapping = cached_file(
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path_or_repo_id=os.path.join(self.eval_args.task_dir, task),
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path_or_repo_id=os.path.join(self.eval_args.task_dir, eval_task),
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filename="mapping.json",
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cache_dir=self.model_args.cache_dir,
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token=self.model_args.hf_hub_token,
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@ -91,7 +91,7 @@ class Evaluator:
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results = {}
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for subject in pbar:
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dataset = load_dataset(
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path=os.path.join(self.eval_args.task_dir, task),
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path=os.path.join(self.eval_args.task_dir, eval_task),
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name=subject,
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cache_dir=self.model_args.cache_dir,
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download_mode=self.eval_args.download_mode,
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@ -100,12 +100,12 @@ class Evaluator:
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)
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pbar.set_postfix_str(categorys[subject]["name"])
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inputs, outputs, labels = [], [], []
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for i in trange(len(dataset[split]), desc="Formatting batches", position=1, leave=False):
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for i in trange(len(dataset[eval_split]), desc="Formatting batches", position=1, leave=False):
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support_set = (
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dataset["train"].shuffle().select(range(min(self.eval_args.n_shot, len(dataset["train"]))))
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)
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messages = self.eval_template.format_example(
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target_data=dataset[split][i],
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target_data=dataset[eval_split][i],
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support_set=support_set,
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subject_name=categorys[subject]["name"],
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)
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