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f030d3a fd05733 f030d3a fd05733 f030d3a 5da4724 f030d3a fd05733 f030d3a 65db57d fd05733 65db57d 1d2f1ad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | from dataclasses import dataclass
from pathlib import Path
@dataclass(frozen=True)
class DataIngestionConfig:
root_dir : Path
source_URL : str
local_data_file : Path
unzip_dir : Path
@dataclass(frozen=True)
class DataValidationConfig:
root_dir : Path
STATUS_FILE : str
ALL_REQUIRED_FILES : list
data_dir : Path
@dataclass(frozen=True)
class DataTransformationConfig:
root_dir: Path
data_path: Path
tokenizer_name: str
# dev quick-run options
dev_run: bool = False
dev_model: str | None = None
@dataclass(frozen=True)
class ModelTrainerConfig:
root_dir: Path
data_path: Path
model_ckpt: str
num_train_epochs: int
warmup_steps: int
per_device_train_batch_size: int
weight_decay: float
logging_steps: int
eval_strategy: str
eval_steps: int
save_steps: float
gradient_accumulation_steps: int
# dev / quick-train options
dev_run: bool = False
dev_model: str | None = None
dev_subset: int = 0
@dataclass(frozen=True)
class ModelEvaluationConfig:
root_dir: Path
data_path: Path
model_path: Path
tokenizer_path: Path
metric_file_name: Path
# Hub model ID used as fallback when the local fine-tuned model is absent
hub_model_id: str = "google/pegasus-cnn_dailymail" |