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@@ -76,9 +76,7 @@ def parse_args():
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parser.add_argument(
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parser.add_argument(
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"--preprocessing_num_workers", type=int, default=4, help="A csv or a json file containing the training data."
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"--preprocessing_num_workers", type=int, default=4, help="A csv or a json file containing the training data."
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)
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)
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parser.add_argument(
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parser.add_argument("--do_predict", action="store_true", help="Eval the question answering model")
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"--do_predict", action="store_true", help="Eval the question answering model"
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)
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parser.add_argument(
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parser.add_argument(
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"--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
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"--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
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)
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)
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@@ -396,7 +394,6 @@ def main():
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return tokenized_examples
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return tokenized_examples
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if "train" not in raw_datasets:
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if "train" not in raw_datasets:
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raise ValueError("--do_train requires a train dataset")
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raise ValueError("--do_train requires a train dataset")
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train_dataset = raw_datasets["train"]
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train_dataset = raw_datasets["train"]
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@@ -481,7 +478,6 @@ def main():
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return tokenized_examples
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return tokenized_examples
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if "validation" not in raw_datasets:
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if "validation" not in raw_datasets:
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raise ValueError("--do_eval requires a validation dataset")
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raise ValueError("--do_eval requires a validation dataset")
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eval_examples = raw_datasets["validation"]
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eval_examples = raw_datasets["validation"]
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@@ -539,11 +535,8 @@ def main():
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train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size
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train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size
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)
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)
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eval_dataset.set_format(type="torch", columns=["attention_mask", "input_ids", "token_type_ids"])
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eval_dataset.set_format(type="torch", columns=["attention_mask", "input_ids", "token_type_ids"])
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eval_dataloader = DataLoader(
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eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)
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eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
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)
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if args.do_predict:
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if args.do_predict:
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test_dataset.set_format(type="torch", columns=["attention_mask", "input_ids", "token_type_ids"])
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test_dataset.set_format(type="torch", columns=["attention_mask", "input_ids", "token_type_ids"])
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@@ -81,9 +81,7 @@ def parse_args():
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parser.add_argument(
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parser.add_argument(
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"--preprocessing_num_workers", type=int, default=4, help="A csv or a json file containing the training data."
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"--preprocessing_num_workers", type=int, default=4, help="A csv or a json file containing the training data."
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)
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)
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parser.add_argument(
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parser.add_argument("--do_predict", action="store_true", help="Eval the question answering model")
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"--do_predict", action="store_true", help="Eval the question answering model"
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)
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parser.add_argument(
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parser.add_argument(
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"--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
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"--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
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)
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)
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@@ -543,9 +541,7 @@ def main():
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)
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)
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eval_dataset.set_format(type="torch", columns=["attention_mask", "input_ids", "token_type_ids"])
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eval_dataset.set_format(type="torch", columns=["attention_mask", "input_ids", "token_type_ids"])
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eval_dataloader = DataLoader(
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eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)
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eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
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)
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if args.do_predict:
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if args.do_predict:
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test_dataset.set_format(type="torch", columns=["attention_mask", "input_ids", "token_type_ids"])
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test_dataset.set_format(type="torch", columns=["attention_mask", "input_ids", "token_type_ids"])
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