Create dataset.py

dataset.py

@@ -0,0 +1,50 @@
+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
+#