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b813321 | 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 60 61 62 63 64 65 66 67 68 69 70 71 | """Data preparation utilities for training."""
import logging
from typing import Dict, List, Optional, Any
from torch.utils.data import Dataset
from transformers import PreTrainedTokenizer
from src.config import DataConfig
logger = logging.getLogger(__name__)
class SummarizationDataset(Dataset):
"""PyTorch Dataset for summarization training."""
def __init__(
self,
data: List[Dict[str, str]],
tokenizer: PreTrainedTokenizer,
config: Optional[DataConfig] = None,
):
self.data = data
self.tokenizer = tokenizer
self.config = config or DataConfig()
def __len__(self) -> int:
return len(self.data)
def __getitem__(self, idx: int) -> Dict[str, Any]:
item = self.data[idx]
prompt = self.config.format_prompt(item[self.config.input_column])
full_text = prompt + item[self.config.target_column] + self.tokenizer.eos_token
encoding = self.tokenizer(
full_text,
truncation=True,
max_length=self.config.max_input_length + self.config.max_target_length,
padding="max_length",
return_tensors="pt",
)
return {
"input_ids": encoding["input_ids"].squeeze(),
"attention_mask": encoding["attention_mask"].squeeze(),
"labels": encoding["input_ids"].squeeze(),
}
def create_dummy_data(num_samples: int = 10) -> List[Dict[str, str]]:
"""Generate dummy data for testing the training pipeline."""
samples = []
for i in range(num_samples):
samples.append({
"text": f"This is sample document {i}. It contains information about topic {i % 3}. "
f"The document discusses various aspects and provides detailed analysis. "
f"Key points include methodology, results, and conclusions for study {i}.",
"summary": f"Document {i} analyzes topic {i % 3}, covering methodology, results, and conclusions.",
})
logger.info(f"Created {num_samples} dummy samples")
return samples
def prepare_dataset(
data: List[Dict[str, str]],
tokenizer: PreTrainedTokenizer,
config: Optional[DataConfig] = None,
) -> SummarizationDataset:
"""Prepare dataset for training."""
return SummarizationDataset(data, tokenizer, config)
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