Add use_auth to load_datasets for private datasets to PT and TF examples (#16521)
* fix formatting and remove use_auth * Add use_auth_token to Flax examples
This commit is contained in:
parent
b9a768b3ff
commit
24a85cca61
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@ -178,6 +178,13 @@ class ModelArguments:
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"help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
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},
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)
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use_auth_token: bool = field(
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default=False,
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metadata={
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"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
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"with private models)."
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},
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)
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@dataclass
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@ -418,6 +425,7 @@ def main():
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cache_dir=model_args.cache_dir,
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keep_in_memory=False,
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data_dir=data_args.data_dir,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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else:
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data_files = {}
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@ -430,7 +438,12 @@ def main():
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if data_args.test_file is not None:
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data_files["test"] = data_args.test_file
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extension = data_args.test_file.split(".")[-1]
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dataset = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
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dataset = load_dataset(
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extension,
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data_files=data_files,
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cache_dir=model_args.cache_dir,
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use_auth_token=True if model_args.use_auth_token else None,
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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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# https://huggingface.co/docs/datasets/loading_datasets.html.
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@ -439,12 +452,18 @@ def main():
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model_args.model_name_or_path,
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seed=training_args.seed,
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dtype=getattr(jnp, model_args.dtype),
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use_auth_token=True if model_args.use_auth_token else None,
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)
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feature_extractor = AutoFeatureExtractor.from_pretrained(
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model_args.model_name_or_path, cache_dir=model_args.cache_dir
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model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
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model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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use_fast=model_args.use_fast_tokenizer,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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tokenizer.pad_token = tokenizer.convert_ids_to_tokens(model.config.pad_token_id)
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@ -165,6 +165,13 @@ class ModelArguments:
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"help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
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},
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)
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use_auth_token: bool = field(
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default=False,
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metadata={
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"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
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"with private models)."
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},
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)
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@dataclass
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@ -363,7 +370,11 @@ def main():
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if data_args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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dataset = load_dataset(
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data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, keep_in_memory=False
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data_args.dataset_name,
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data_args.dataset_config_name,
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cache_dir=model_args.cache_dir,
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keep_in_memory=False,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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if "validation" not in dataset.keys():
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@ -372,12 +383,14 @@ def main():
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data_args.dataset_config_name,
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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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use_auth_token=True if model_args.use_auth_token else None,
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)
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dataset["train"] = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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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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use_auth_token=True if model_args.use_auth_token else None,
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)
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else:
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data_files = {}
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@ -390,7 +403,13 @@ def main():
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if extension == "txt":
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extension = "text"
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dataset_args["keep_linebreaks"] = data_args.keep_linebreaks
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dataset = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir, **dataset_args)
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dataset = load_dataset(
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extension,
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data_files=data_files,
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cache_dir=model_args.cache_dir,
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**dataset_args,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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if "validation" not in dataset.keys():
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dataset["validation"] = load_dataset(
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@ -399,6 +418,7 @@ def main():
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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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**dataset_args,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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dataset["train"] = load_dataset(
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extension,
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@ -406,6 +426,7 @@ def main():
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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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**dataset_args,
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use_auth_token=True if model_args.use_auth_token else None,
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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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# https://huggingface.co/docs/datasets/loading_datasets.html.
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@ -416,20 +437,34 @@ def main():
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# The .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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if model_args.config_name:
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config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
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config = AutoConfig.from_pretrained(
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model_args.config_name,
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cache_dir=model_args.cache_dir,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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elif model_args.model_name_or_path:
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config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
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config = AutoConfig.from_pretrained(
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model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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else:
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config = CONFIG_MAPPING[model_args.model_type]()
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logger.warning("You are instantiating a new config instance from scratch.")
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if model_args.tokenizer_name:
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
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model_args.tokenizer_name,
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cache_dir=model_args.cache_dir,
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use_fast=model_args.use_fast_tokenizer,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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elif model_args.model_name_or_path:
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
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model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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use_fast=model_args.use_fast_tokenizer,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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else:
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raise ValueError(
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@ -439,11 +474,18 @@ def main():
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if model_args.model_name_or_path:
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model = FlaxAutoModelForCausalLM.from_pretrained(
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model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
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model_args.model_name_or_path,
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config=config,
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seed=training_args.seed,
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dtype=getattr(jnp, model_args.dtype),
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use_auth_token=True if model_args.use_auth_token else None,
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)
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else:
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model = FlaxAutoModelForCausalLM.from_config(
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config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
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config,
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seed=training_args.seed,
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dtype=getattr(jnp, model_args.dtype),
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use_auth_token=True if model_args.use_auth_token else None,
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)
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# Preprocessing the datasets.
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@ -163,6 +163,13 @@ class ModelArguments:
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"help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
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},
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)
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use_auth_token: bool = field(
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default=False,
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metadata={
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"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
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"with private models)."
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},
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)
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@dataclass
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@ -396,7 +403,12 @@ def main():
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# download the dataset.
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if data_args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
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datasets = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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cache_dir=model_args.cache_dir,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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if "validation" not in datasets.keys():
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datasets["validation"] = load_dataset(
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@ -404,12 +416,14 @@ def main():
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data_args.dataset_config_name,
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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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use_auth_token=True if model_args.use_auth_token else None,
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)
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datasets["train"] = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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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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use_auth_token=True if model_args.use_auth_token else None,
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)
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else:
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data_files = {}
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@ -420,7 +434,12 @@ def main():
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extension = data_args.train_file.split(".")[-1]
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if extension == "txt":
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extension = "text"
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datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
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datasets = load_dataset(
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extension,
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data_files=data_files,
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cache_dir=model_args.cache_dir,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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if "validation" not in datasets.keys():
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datasets["validation"] = load_dataset(
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@ -428,12 +447,14 @@ def main():
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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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use_auth_token=True if model_args.use_auth_token else None,
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)
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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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use_auth_token=True if model_args.use_auth_token else None,
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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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# https://huggingface.co/docs/datasets/loading_datasets.html.
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@ -444,20 +465,34 @@ def main():
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# The .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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if model_args.config_name:
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config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
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config = AutoConfig.from_pretrained(
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model_args.config_name,
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cache_dir=model_args.cache_dir,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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elif model_args.model_name_or_path:
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config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
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config = AutoConfig.from_pretrained(
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model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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else:
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config = CONFIG_MAPPING[model_args.model_type]()
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logger.warning("You are instantiating a new config instance from scratch.")
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if model_args.tokenizer_name:
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
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model_args.tokenizer_name,
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cache_dir=model_args.cache_dir,
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use_fast=model_args.use_fast_tokenizer,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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elif model_args.model_name_or_path:
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
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model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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use_fast=model_args.use_fast_tokenizer,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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else:
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raise ValueError(
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@ -572,11 +607,18 @@ def main():
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if model_args.model_name_or_path:
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model = FlaxAutoModelForMaskedLM.from_pretrained(
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model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
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model_args.model_name_or_path,
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config=config,
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seed=training_args.seed,
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dtype=getattr(jnp, model_args.dtype),
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use_auth_token=True if model_args.use_auth_token else None,
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)
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else:
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model = FlaxAutoModelForMaskedLM.from_config(
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config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
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config,
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seed=training_args.seed,
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dtype=getattr(jnp, model_args.dtype),
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use_auth_token=True if model_args.use_auth_token else None,
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)
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# Store some constant
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|
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@ -162,6 +162,13 @@ class ModelArguments:
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"help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
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},
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)
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use_auth_token: bool = field(
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default=False,
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metadata={
|
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"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
|
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"with private models)."
|
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},
|
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)
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@dataclass
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|
@ -525,7 +532,12 @@ def main():
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# 'text' is found. You can easily tweak this behavior (see below).
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if data_args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
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datasets = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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cache_dir=model_args.cache_dir,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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if "validation" not in datasets.keys():
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datasets["validation"] = load_dataset(
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|
@ -533,12 +545,14 @@ def main():
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data_args.dataset_config_name,
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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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use_auth_token=True if model_args.use_auth_token else None,
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)
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datasets["train"] = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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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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use_auth_token=True if model_args.use_auth_token else None,
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)
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else:
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data_files = {}
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|
@ -549,7 +563,12 @@ def main():
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extension = data_args.train_file.split(".")[-1]
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if extension == "txt":
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extension = "text"
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datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
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datasets = load_dataset(
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extension,
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data_files=data_files,
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cache_dir=model_args.cache_dir,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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if "validation" not in datasets.keys():
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datasets["validation"] = load_dataset(
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|
@ -557,12 +576,14 @@ def main():
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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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use_auth_token=True if model_args.use_auth_token else None,
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)
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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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use_auth_token=True if model_args.use_auth_token else None,
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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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# https://huggingface.co/docs/datasets/loading_datasets.html.
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|
@ -571,11 +592,17 @@ def main():
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if model_args.tokenizer_name:
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tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
||||
model_args.tokenizer_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_fast=model_args.use_fast_tokenizer,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
elif model_args.model_name_or_path:
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
||||
model_args.model_name_or_path,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_fast=model_args.use_fast_tokenizer,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
|
@ -585,10 +612,17 @@ def main():
|
|||
|
||||
if model_args.config_name:
|
||||
config = T5Config.from_pretrained(
|
||||
model_args.config_name, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer)
|
||||
model_args.config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
vocab_size=len(tokenizer),
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
elif model_args.model_name_or_path:
|
||||
config = T5Config.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
|
||||
config = T5Config.from_pretrained(
|
||||
model_args.model_name_or_path,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
config = CONFIG_MAPPING[model_args.model_type]()
|
||||
logger.warning("You are instantiating a new config instance from scratch.")
|
||||
|
@ -678,11 +712,20 @@ def main():
|
|||
|
||||
if model_args.model_name_or_path:
|
||||
model = FlaxT5ForConditionalGeneration.from_pretrained(
|
||||
model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
|
||||
model_args.model_name_or_path,
|
||||
config=config,
|
||||
seed=training_args.seed,
|
||||
dtype=getattr(jnp, model_args.dtype),
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
config.vocab_size = len(tokenizer)
|
||||
model = FlaxT5ForConditionalGeneration(config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype))
|
||||
model = FlaxT5ForConditionalGeneration(
|
||||
config,
|
||||
seed=training_args.seed,
|
||||
dtype=getattr(jnp, model_args.dtype),
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
|
||||
# Data collator
|
||||
# This one will take care of randomly masking the tokens.
|
||||
|
|
|
@ -448,7 +448,10 @@ def main():
|
|||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
# Loading the dataset from local csv or json file.
|
||||
|
@ -463,7 +466,13 @@ def main():
|
|||
if data_args.test_file is not None:
|
||||
data_files["test"] = data_args.test_file
|
||||
extension = data_args.test_file.split(".")[-1]
|
||||
raw_datasets = load_dataset(extension, data_files=data_files, field="data", cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
field="data",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
# endregion
|
||||
|
|
|
@ -176,6 +176,13 @@ class ModelArguments:
|
|||
"help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
|
||||
},
|
||||
)
|
||||
use_auth_token: bool = field(
|
||||
default=False,
|
||||
metadata={
|
||||
"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
|
||||
"with private models)."
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
|
@ -421,7 +428,11 @@ def main():
|
|||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
dataset = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, keep_in_memory=False
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
keep_in_memory=False,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
|
@ -434,27 +445,46 @@ def main():
|
|||
if data_args.test_file is not None:
|
||||
data_files["test"] = data_args.test_file
|
||||
extension = data_args.test_file.split(".")[-1]
|
||||
dataset = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
|
||||
dataset = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
# Load pretrained model and tokenizer
|
||||
|
||||
if model_args.config_name:
|
||||
config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
|
||||
config = AutoConfig.from_pretrained(
|
||||
model_args.config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
elif model_args.model_name_or_path:
|
||||
config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
|
||||
config = AutoConfig.from_pretrained(
|
||||
model_args.model_name_or_path,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
config = CONFIG_MAPPING[model_args.model_type]()
|
||||
logger.warning("You are instantiating a new config instance from scratch.")
|
||||
|
||||
if model_args.tokenizer_name:
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
||||
model_args.tokenizer_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_fast=model_args.use_fast_tokenizer,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
elif model_args.model_name_or_path:
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
||||
model_args.model_name_or_path,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_fast=model_args.use_fast_tokenizer,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
|
@ -464,11 +494,18 @@ def main():
|
|||
|
||||
if model_args.model_name_or_path:
|
||||
model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
|
||||
model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
|
||||
model_args.model_name_or_path,
|
||||
config=config,
|
||||
seed=training_args.seed,
|
||||
dtype=getattr(jnp, model_args.dtype),
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
model = FlaxAutoModelForSeq2SeqLM.from_config(
|
||||
config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
|
||||
config,
|
||||
seed=training_args.seed,
|
||||
dtype=getattr(jnp, model_args.dtype),
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
|
||||
if model.config.decoder_start_token_id is None:
|
||||
|
|
|
@ -337,7 +337,11 @@ def main():
|
|||
# download the dataset.
|
||||
if data_args.task_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset("glue", data_args.task_name)
|
||||
raw_datasets = load_dataset(
|
||||
"glue",
|
||||
data_args.task_name,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
# Loading the dataset from local csv or json file.
|
||||
data_files = {}
|
||||
|
@ -346,7 +350,11 @@ def main():
|
|||
if data_args.validation_file is not None:
|
||||
data_files["validation"] = data_args.validation_file
|
||||
extension = (data_args.train_file if data_args.train_file is not None else data_args.valid_file).split(".")[-1]
|
||||
raw_datasets = load_dataset(extension, data_files=data_files)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
|
@ -372,12 +380,21 @@ def main():
|
|||
|
||||
# Load pretrained model and tokenizer
|
||||
config = AutoConfig.from_pretrained(
|
||||
model_args.model_name_or_path, num_labels=num_labels, finetuning_task=data_args.task_name
|
||||
model_args.model_name_or_path,
|
||||
num_labels=num_labels,
|
||||
finetuning_task=data_args.task_name,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_args.model_name_or_path, use_fast=not model_args.use_slow_tokenizer
|
||||
model_args.model_name_or_path,
|
||||
use_fast=not model_args.use_slow_tokenizer,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
model = FlaxAutoModelForSequenceClassification.from_pretrained(
|
||||
model_args.model_name_or_path,
|
||||
config=config,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
model = FlaxAutoModelForSequenceClassification.from_pretrained(model_args.model_name_or_path, config=config)
|
||||
|
||||
# Preprocessing the datasets
|
||||
if data_args.task_name is not None:
|
||||
|
|
|
@ -391,7 +391,10 @@ def main():
|
|||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
# Loading the dataset from local csv or json file.
|
||||
|
@ -401,7 +404,12 @@ def main():
|
|||
if data_args.validation_file is not None:
|
||||
data_files["validation"] = data_args.validation_file
|
||||
extension = (data_args.train_file if data_args.train_file is not None else data_args.valid_file).split(".")[-1]
|
||||
raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
|
|
|
@ -154,6 +154,13 @@ class ModelArguments:
|
|||
"help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
|
||||
},
|
||||
)
|
||||
use_auth_token: bool = field(
|
||||
default=False,
|
||||
metadata={
|
||||
"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
|
||||
"with private models)."
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
|
@ -315,6 +322,7 @@ def main():
|
|||
num_labels=len(train_dataset.classes),
|
||||
image_size=data_args.image_size,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
elif model_args.model_name_or_path:
|
||||
config = AutoConfig.from_pretrained(
|
||||
|
@ -322,6 +330,7 @@ def main():
|
|||
num_labels=len(train_dataset.classes),
|
||||
image_size=data_args.image_size,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
config = CONFIG_MAPPING[model_args.model_type]()
|
||||
|
@ -329,11 +338,18 @@ def main():
|
|||
|
||||
if model_args.model_name_or_path:
|
||||
model = FlaxAutoModelForImageClassification.from_pretrained(
|
||||
model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
|
||||
model_args.model_name_or_path,
|
||||
config=config,
|
||||
seed=training_args.seed,
|
||||
dtype=getattr(jnp, model_args.dtype),
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
model = FlaxAutoModelForImageClassification.from_config(
|
||||
config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
|
||||
config,
|
||||
seed=training_args.seed,
|
||||
dtype=getattr(jnp, model_args.dtype),
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
|
||||
# Store some constant
|
||||
|
|
|
@ -227,10 +227,16 @@ def main():
|
|||
# Initialize our dataset and prepare it for the audio classification task.
|
||||
raw_datasets = DatasetDict()
|
||||
raw_datasets["train"] = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
split=data_args.train_split_name,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
raw_datasets["eval"] = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
split=data_args.eval_split_name,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
|
||||
if data_args.audio_column_name not in raw_datasets["train"].column_names:
|
||||
|
|
|
@ -276,6 +276,7 @@ def main():
|
|||
cache_dir=model_args.cache_dir,
|
||||
keep_in_memory=False,
|
||||
data_dir=data_args.data_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
|
@ -288,7 +289,12 @@ def main():
|
|||
if data_args.test_file is not None:
|
||||
data_files["test"] = data_args.test_file
|
||||
extension = data_args.test_file.split(".")[-1]
|
||||
dataset = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
|
||||
dataset = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
|
|
|
@ -207,6 +207,7 @@ def main():
|
|||
data_files=data_args.data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
task="image-classification",
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
|
||||
# If we don't have a validation split, split off a percentage of train as validation.
|
||||
|
|
|
@ -207,6 +207,7 @@ def main():
|
|||
data_args.dataset_config_name,
|
||||
data_files=data_args.data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
|
||||
# If we don't have a validation split, split off a percentage of train as validation.
|
||||
|
|
|
@ -266,6 +266,7 @@ def main():
|
|||
data_args.dataset_config_name,
|
||||
data_files=data_args.data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
|
||||
# If we don't have a validation split, split off a percentage of train as validation.
|
||||
|
|
|
@ -254,7 +254,10 @@ def main():
|
|||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
if "validation" not in raw_datasets.keys():
|
||||
raw_datasets["validation"] = load_dataset(
|
||||
|
@ -262,12 +265,14 @@ def main():
|
|||
data_args.dataset_config_name,
|
||||
split=f"train[:{data_args.validation_split_percentage}%]",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
raw_datasets["train"] = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
split=f"train[{data_args.validation_split_percentage}%:]",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
|
@ -284,7 +289,13 @@ def main():
|
|||
if extension == "txt":
|
||||
extension = "text"
|
||||
dataset_args["keep_linebreaks"] = data_args.keep_linebreaks
|
||||
raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir, **dataset_args)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
**dataset_args,
|
||||
)
|
||||
# If no validation data is there, validation_split_percentage will be used to divide the dataset.
|
||||
if "validation" not in raw_datasets.keys():
|
||||
raw_datasets["validation"] = load_dataset(
|
||||
|
@ -292,6 +303,7 @@ def main():
|
|||
data_files=data_files,
|
||||
split=f"train[:{data_args.validation_split_percentage}%]",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
**dataset_args,
|
||||
)
|
||||
raw_datasets["train"] = load_dataset(
|
||||
|
@ -299,6 +311,7 @@ def main():
|
|||
data_files=data_files,
|
||||
split=f"train[{data_args.validation_split_percentage}%:]",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
**dataset_args,
|
||||
)
|
||||
|
||||
|
|
|
@ -263,7 +263,10 @@ def main():
|
|||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
if "validation" not in raw_datasets.keys():
|
||||
raw_datasets["validation"] = load_dataset(
|
||||
|
@ -271,12 +274,14 @@ def main():
|
|||
data_args.dataset_config_name,
|
||||
split=f"train[:{data_args.validation_split_percentage}%]",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
raw_datasets["train"] = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
split=f"train[{data_args.validation_split_percentage}%:]",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
|
@ -288,7 +293,12 @@ def main():
|
|||
extension = data_args.validation_file.split(".")[-1]
|
||||
if extension == "txt":
|
||||
extension = "text"
|
||||
raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
|
||||
# If no validation data is there, validation_split_percentage will be used to divide the dataset.
|
||||
if "validation" not in raw_datasets.keys():
|
||||
|
@ -297,12 +307,14 @@ def main():
|
|||
data_files=data_files,
|
||||
split=f"train[:{data_args.validation_split_percentage}%]",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
raw_datasets["train"] = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
split=f"train[{data_args.validation_split_percentage}%:]",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
|
|
|
@ -256,7 +256,10 @@ def main():
|
|||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
if "validation" not in raw_datasets.keys():
|
||||
raw_datasets["validation"] = load_dataset(
|
||||
|
@ -264,12 +267,14 @@ def main():
|
|||
data_args.dataset_config_name,
|
||||
split=f"train[:{data_args.validation_split_percentage}%]",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
raw_datasets["train"] = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
split=f"train[{data_args.validation_split_percentage}%:]",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
|
@ -288,12 +293,14 @@ def main():
|
|||
data_files=data_files,
|
||||
split=f"train[:{data_args.validation_split_percentage}%]",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
raw_datasets["train"] = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
split=f"train[{data_args.validation_split_percentage}%:]",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
|
|
|
@ -269,10 +269,20 @@ def main():
|
|||
if data_args.validation_file is not None:
|
||||
data_files["validation"] = data_args.validation_file
|
||||
extension = data_args.train_file.split(".")[-1]
|
||||
raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
# Downloading and loading the swag dataset from the hub.
|
||||
raw_datasets = load_dataset("swag", "regular", cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
"swag",
|
||||
"regular",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
|
|
|
@ -262,7 +262,10 @@ def main():
|
|||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
|
@ -276,7 +279,13 @@ def main():
|
|||
if data_args.test_file is not None:
|
||||
data_files["test"] = data_args.test_file
|
||||
extension = data_args.test_file.split(".")[-1]
|
||||
raw_datasets = load_dataset(extension, data_files=data_files, field="data", cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
field="data",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
|
|
|
@ -260,7 +260,10 @@ def main():
|
|||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
|
@ -273,7 +276,13 @@ def main():
|
|||
if data_args.test_file is not None:
|
||||
data_files["test"] = data_args.test_file
|
||||
extension = data_args.test_file.split(".")[-1]
|
||||
raw_datasets = load_dataset(extension, data_files=data_files, field="data", cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
field="data",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
|
|
|
@ -403,7 +403,10 @@ def main():
|
|||
for dataset_config_name, train_split_name in zip(args.dataset_config_names, args.dataset_split_names):
|
||||
# load dataset
|
||||
dataset_split = load_dataset(
|
||||
args.dataset_name, dataset_config_name, split=train_split_name, cache_dir=args.cache_dir
|
||||
args.dataset_name,
|
||||
dataset_config_name,
|
||||
split=train_split_name,
|
||||
cache_dir=args.cache_dir,
|
||||
)
|
||||
datasets_splits.append(dataset_split)
|
||||
|
||||
|
|
|
@ -278,12 +278,18 @@ def main():
|
|||
|
||||
if training_args.do_train:
|
||||
raw_datasets["train"] = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
split=data_args.train_split_name,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
|
||||
if training_args.do_eval:
|
||||
raw_datasets["eval"] = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
split=data_args.eval_split_name,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
|
||||
if data_args.audio_column_name not in next(iter(raw_datasets.values())).column_names:
|
||||
|
|
|
@ -341,7 +341,10 @@ def main():
|
|||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
|
@ -354,7 +357,12 @@ def main():
|
|||
if data_args.test_file is not None:
|
||||
data_files["test"] = data_args.test_file
|
||||
extension = data_args.test_file.split(".")[-1]
|
||||
raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
|
|
|
@ -252,11 +252,19 @@ def main():
|
|||
# download the dataset.
|
||||
if data_args.task_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
"glue",
|
||||
data_args.task_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
elif data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
# Loading a dataset from your local files.
|
||||
|
@ -281,10 +289,20 @@ def main():
|
|||
|
||||
if data_args.train_file.endswith(".csv"):
|
||||
# Loading a dataset from local csv files
|
||||
raw_datasets = load_dataset("csv", data_files=data_files, cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
"csv",
|
||||
data_files=data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
# Loading a dataset from local json files
|
||||
raw_datasets = load_dataset("json", data_files=data_files, cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
"json",
|
||||
data_files=data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
|
|
|
@ -213,19 +213,41 @@ def main():
|
|||
# Downloading and loading xnli dataset from the hub.
|
||||
if training_args.do_train:
|
||||
if model_args.train_language is None:
|
||||
train_dataset = load_dataset("xnli", model_args.language, split="train", cache_dir=model_args.cache_dir)
|
||||
train_dataset = load_dataset(
|
||||
"xnli",
|
||||
model_args.language,
|
||||
split="train",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
train_dataset = load_dataset(
|
||||
"xnli", model_args.train_language, split="train", cache_dir=model_args.cache_dir
|
||||
"xnli",
|
||||
model_args.train_language,
|
||||
split="train",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
label_list = train_dataset.features["label"].names
|
||||
|
||||
if training_args.do_eval:
|
||||
eval_dataset = load_dataset("xnli", model_args.language, split="validation", cache_dir=model_args.cache_dir)
|
||||
eval_dataset = load_dataset(
|
||||
"xnli",
|
||||
model_args.language,
|
||||
split="validation",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
label_list = eval_dataset.features["label"].names
|
||||
|
||||
if training_args.do_predict:
|
||||
predict_dataset = load_dataset("xnli", model_args.language, split="test", cache_dir=model_args.cache_dir)
|
||||
predict_dataset = load_dataset(
|
||||
"xnli",
|
||||
model_args.language,
|
||||
split="test",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
label_list = predict_dataset.features["label"].names
|
||||
|
||||
# Labels
|
||||
|
|
|
@ -249,7 +249,10 @@ def main():
|
|||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
|
|
|
@ -306,7 +306,10 @@ def main():
|
|||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
|
@ -319,7 +322,12 @@ def main():
|
|||
if data_args.test_file is not None:
|
||||
data_files["test"] = data_args.test_file
|
||||
extension = data_args.test_file.split(".")[-1]
|
||||
raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
|
|
|
@ -280,17 +280,23 @@ def main():
|
|||
# download the dataset.
|
||||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
if "validation" not in raw_datasets.keys():
|
||||
raw_datasets["validation"] = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
split=f"train[:{data_args.validation_split_percentage}%]",
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
raw_datasets["train"] = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
split=f"train[{data_args.validation_split_percentage}%:]",
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
|
@ -303,7 +309,12 @@ def main():
|
|||
if extension == "txt":
|
||||
extension = "text"
|
||||
dataset_args["keep_linebreaks"] = data_args.keep_linebreaks
|
||||
raw_datasets = load_dataset(extension, data_files=data_files, **dataset_args)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
**dataset_args,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
# endregion
|
||||
|
|
|
@ -292,17 +292,23 @@ def main():
|
|||
# download the dataset.
|
||||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
if "validation" not in raw_datasets.keys():
|
||||
raw_datasets["validation"] = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
split=f"train[:{data_args.validation_split_percentage}%]",
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
raw_datasets["train"] = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
split=f"train[{data_args.validation_split_percentage}%:]",
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
|
@ -313,7 +319,11 @@ def main():
|
|||
extension = data_args.train_file.split(".")[-1]
|
||||
if extension == "txt":
|
||||
extension = "text"
|
||||
raw_datasets = load_dataset(extension, data_files=data_files)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
|
|
@ -290,10 +290,20 @@ def main():
|
|||
if data_args.validation_file is not None:
|
||||
data_files["validation"] = data_args.validation_file
|
||||
extension = data_args.train_file.split(".")[-1]
|
||||
raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
# Downloading and loading the swag dataset from the hub.
|
||||
raw_datasets = load_dataset("swag", "regular", cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
"swag",
|
||||
"regular",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
|
|
|
@ -278,7 +278,12 @@ def main():
|
|||
# download the dataset.
|
||||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
|
||||
datasets = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
if data_args.train_file is not None:
|
||||
|
@ -291,7 +296,13 @@ def main():
|
|||
if data_args.test_file is not None:
|
||||
data_files["test"] = data_args.test_file
|
||||
extension = data_args.test_file.split(".")[-1]
|
||||
datasets = load_dataset(extension, data_files=data_files, field="data", cache_dir=model_args.cache_dir)
|
||||
datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
field="data",
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
# endregion
|
||||
|
|
|
@ -391,7 +391,10 @@ def main():
|
|||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
|
@ -404,7 +407,12 @@ def main():
|
|||
if data_args.test_file is not None:
|
||||
data_files["test"] = data_args.test_file
|
||||
extension = data_args.test_file.split(".")[-1]
|
||||
raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
# endregion
|
||||
|
|
|
@ -236,7 +236,12 @@ def main():
|
|||
|
||||
# Downloading and loading a dataset from the hub. In distributed training, the load_dataset function guarantee
|
||||
# that only one local process can concurrently download the dataset.
|
||||
datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir)
|
||||
datasets = load_dataset(
|
||||
"glue",
|
||||
data_args.task_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
|
|
|
@ -236,7 +236,12 @@ def main():
|
|||
|
||||
if data_args.input_file_extension == "csv":
|
||||
# Loading a dataset from local csv files
|
||||
datasets = load_dataset("csv", data_files=data_files, cache_dir=model_args.cache_dir)
|
||||
datasets = load_dataset(
|
||||
"csv",
|
||||
data_files=data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
# Loading a dataset from local json files
|
||||
datasets = load_dataset("json", data_files=data_files, cache_dir=model_args.cache_dir)
|
||||
|
|
|
@ -266,7 +266,11 @@ def main():
|
|||
# download the dataset.
|
||||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
if data_args.train_file is not None:
|
||||
|
@ -274,7 +278,11 @@ def main():
|
|||
if data_args.validation_file is not None:
|
||||
data_files["validation"] = data_args.validation_file
|
||||
extension = data_args.train_file.split(".")[-1]
|
||||
raw_datasets = load_dataset(extension, data_files=data_files)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
|
|
|
@ -347,7 +347,10 @@ def main():
|
|||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
raw_datasets = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
|
@ -357,7 +360,12 @@ def main():
|
|||
if data_args.validation_file is not None:
|
||||
data_files["validation"] = data_args.validation_file
|
||||
extension = data_args.validation_file.split(".")[-1]
|
||||
raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
|
||||
raw_datasets = load_dataset(
|
||||
extension,
|
||||
data_files=data_files,
|
||||
cache_dir=model_args.cache_dir,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
# endregion
|
||||
|
|
Loading…
Reference in New Issue