* Validation split percentage to be used for custom data files also Issue same as https://github.com/huggingface/transformers/issues/12406 fixed for pytorch branch run_mlm.py * Validation split added in the right place * Update run_clm.py * validation split added for custom files * Validation split added for custom files * Update run_plm.py * fixed validation split for custom files as input for pytorch examples in lm * Update run_clm_no_trainer.py * args modified
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@@ -278,6 +278,21 @@ def main():
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if extension == "txt":
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if extension == "txt":
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extension = "text"
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extension = "text"
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raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
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raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
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# If no validation data is there, validation_split_percentage will be used to divide the dataset.
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if "validation" not in raw_datasets.keys():
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raw_datasets["validation"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[:{data_args.validation_split_percentage}%]",
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cache_dir=model_args.cache_dir,
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)
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raw_datasets["train"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[{data_args.validation_split_percentage}%:]",
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cache_dir=model_args.cache_dir,
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)
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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@@ -253,6 +253,19 @@ def main():
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if extension == "txt":
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if extension == "txt":
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extension = "text"
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extension = "text"
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raw_datasets = load_dataset(extension, data_files=data_files)
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raw_datasets = load_dataset(extension, data_files=data_files)
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# If no validation data is there, validation_split_percentage will be used to divide the dataset.
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if "validation" not in raw_datasets.keys():
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raw_datasets["validation"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[:{args.validation_split_percentage}%]",
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)
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raw_datasets["train"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[{args.validation_split_percentage}%:]",
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)
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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@@ -285,6 +285,22 @@ def main():
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if extension == "txt":
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if extension == "txt":
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extension = "text"
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extension = "text"
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raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
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raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
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# If no validation data is there, validation_split_percentage will be used to divide the dataset.
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if "validation" not in raw_datasets.keys():
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raw_datasets["validation"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[:{data_args.validation_split_percentage}%]",
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cache_dir=model_args.cache_dir,
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)
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raw_datasets["train"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[{data_args.validation_split_percentage}%:]",
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cache_dir=model_args.cache_dir,
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)
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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@@ -265,6 +265,19 @@ def main():
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if extension == "txt":
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if extension == "txt":
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extension = "text"
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extension = "text"
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raw_datasets = load_dataset(extension, data_files=data_files)
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raw_datasets = load_dataset(extension, data_files=data_files)
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# If no validation data is there, validation_split_percentage will be used to divide the dataset.
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if "validation" not in raw_datasets.keys():
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raw_datasets["validation"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[:{args.validation_split_percentage}%]",
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)
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raw_datasets["train"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[{args.validation_split_percentage}%:]",
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)
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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@@ -280,6 +280,21 @@ def main():
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if extension == "txt":
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if extension == "txt":
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extension = "text"
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extension = "text"
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raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
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raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
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# If no validation data is there, validation_split_percentage will be used to divide the dataset.
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if "validation" not in raw_datasets.keys():
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raw_datasets["validation"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[:{data_args.validation_split_percentage}%]",
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cache_dir=model_args.cache_dir,
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)
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raw_datasets["train"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[{data_args.validation_split_percentage}%:]",
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cache_dir=model_args.cache_dir,
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)
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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