Update chess_ground-targz.py

chess_ground-targz.py

@@ -2,27 +2,11 @@ import os
 import json
 import tarfile
 import datasets
-
-# Description of the dataset
-_DESCRIPTION = """\
-Dataset for extracting notations from chess scoresheets, integrating both image and text data.
-"""
-
-# BibTeX citation for the dataset
-_CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {A great new dataset},
-author={huggingface, Inc.},
-year={2020}
-}
-"""
-
-# License of the dataset
-_LICENSE = "Creative Commons Attribution 3.0"
+from collections import defaultdict
 
 class ChessImageTextDataset(datasets.GeneratorBasedBuilder):
-    """Dataset for linking chess scoresheet images with ground truth text."""
-
+    """Dataset for linking chess scoresheet images with multiple ground truth texts."""
+    
     def _info(self):
         # Define the features of your dataset (images + text)
         features = datasets.Features(
@@ -44,12 +28,9 @@ class ChessImageTextDataset(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Define the splits of the dataset."""
-
-        # Load the image dataset (tar.gz file)
         image_dataset_url = "https://huggingface.co/datasets/Chesscorner/chess-images/resolve/main/flat_images.tar.gz"
         extracted_image_path = dl_manager.download(image_dataset_url)
 
-        # Load the text dataset (ground truths)
         text_dataset_url = "https://huggingface.co/datasets/Chesscorner/jsonl-chess-dataset/resolve/main/train.jsonl/train.jsonl"
         text_filepath = dl_manager.download(text_dataset_url)
 
@@ -64,12 +45,14 @@ class ChessImageTextDataset(datasets.GeneratorBasedBuilder):
         ]
 
     def _generate_examples(self, image_tar_path, text_filepath):
-        """Generate examples by linking images and text."""
+        """Generate examples by linking images with multiple related texts."""
         idx = 0
-        
         # Extract and map text IDs to their corresponding images
         image_mapping = self._extract_images_from_tar(image_tar_path)
 
+        # Dictionary to hold multiple texts for each image ID
+        grouped_texts = defaultdict(list)
+
         # Load the text dataset (ground truths) from the JSONL file
         with open(text_filepath, encoding="utf-8") as fp:
             for line in fp:
@@ -79,19 +62,24 @@ class ChessImageTextDataset(datasets.GeneratorBasedBuilder):
                 # Extract the text ID (assuming text ID matches image filename)
                 text_id = text[:5]  # Adjust this based on the actual pattern of text IDs
                 
-                # Find the corresponding image file
-                image_file = image_mapping.get(f"{text_id}.png")  # Adjust file extension if necessary
-
-                # Ensure the image exists and yield the example
-                if image_file:
-                    yield idx, {
-                        "image": image_file,
-                        "text": text,
-                    }
-                else:
-                    print(f"Image not found for ID: {text_id}")
-                
+                # Group texts by their text_id (which corresponds to the image)
+                grouped_texts[text_id].append(text)
+
+        # Now generate examples, linking each image to its grouped texts
+        for text_id, texts in grouped_texts.items():
+            image_file = image_mapping.get(f"{text_id}.png")  # Adjust file extension if necessary
+            
+            # Ensure the image exists and yield the example
+            if image_file:
+                # Join the texts related to the same image
+                combined_text = " ".join(texts)
+                yield idx, {
+                    "image": image_file,
+                    "text": combined_text,  # Link all related texts together
+                }
                 idx += 1
+            else:
+                print(f"Image not found for ID: {text_id}")
 
     def _extract_images_from_tar(self, tar_path):
         """Extracts the images from the tar.gz archive and returns a mapping of image filenames to file paths."""
@@ -116,4 +104,3 @@ class ChessImageTextDataset(datasets.GeneratorBasedBuilder):
                     image_mapping[image_filename] = extracted_image_path
         
         return image_mapping
-