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import csv
from torch.utils.data import IterableDataset
from huggingface_hub import hf_hub_download
class ParsedMultiCharDataset(IterableDataset):
def __init__(self,
repo_id: str,
delimiter: str = ".,|,.",
start_file: int = 0,
num_files: int = 190,
guess_total_count = True):
self.repo_id = repo_id
self.files = [f"captions/caption_{i+start_file:03d}.csv" for i in range(num_files)]
self.delimiter = ".,|,."
self.total_rows = -1
if guess_total_count:
self.total_rows = self.guess_total_rows()
print(f"Total rows: {self.total_rows} totaling {len(self.files) * self.total_rows}")
def guess_total_rows(self):
# count the rows in the first file
path = hf_hub_download(self.repo_id, self.files[0], repo_type="dataset")
with open(path, encoding="utf-8") as f:
reader = csv.DictReader(f)
return sum(1 for _ in reader)
def __iter__(self):
for rel_path in self.files:
path = hf_hub_download(self.repo_id, rel_path, repo_type="dataset")
with open(path, encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
id_ = row.get("id", "").strip()
text_field = row.get("text", "")
for caption in text_field.split(self.delimiter):
caption = caption.strip()
if caption:
yield (id_, caption)
#ds = ParsedMultiCharDataset(
# repo_id="AbstractPhil/human-templated-captions-1b",
# start_file=0,
# num_files=10
#)
#for i, ex in enumerate(ds):
# print(ex)
# if i > 10:
# break
# |