Datasets:
jblitzar
/
github-python-corpus

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"value if set."
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
},
)
train_file: Optional[str] = field(
default=None, metadata={"help": "A csv or a json file containing the training data."}
)
validation_file: Optional[str] = field(
default=None, metadata={"help": "A csv or a json file containing the validation data."}
)
test_file: Optional[str] = field(
default=None,
metadata={"help": "A csv or a json file containing the test data."}
)
template_id: Optional[int] = field(
default=0,
metadata={
"help": "The specific prompt string to use"
}
)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
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)."
},
)
prefix: bool = field(
default=False,
metadata={
"help": "Will use P-tuning v2 during training"
}
)
prompt: bool = field(
default=False,
metadata={
"help": "Will use prompt tuning during training"
}
)
pre_seq_len: int = field(
default=4,
metadata={
"help": "The length of prompt"
}
)
prefix_projection: bool = field(
default=False,
metadata={
"help": "Apply a two-layer MLP head over the prefix embeddings"
}
)
prefix_hidden_size: int = field(
default=512,
metadata={
"help": "The hidden size of the MLP projection head in Prefix Encoder if prefix projection is used"
}
)
hidden_dropout_prob: float = field(
default=0.1,
metadata={