Update README.md

README.md

@@ -93,16 +93,14 @@ Here is an example using a custom collator to bundle batches in a trainable way
 
 from datasets import load_dataset
 
-
-
-docvqa_dataset = load_dataset("pixparse/docvqa-single-page", split="train"
+docvqa_dataset = load_dataset("pixparse/docvqa-single-page-questions", split="train"
 )
-
-collator_class = Collator()
-loader = DataLoader(docvqa_dataset, batch_size=8, collate_fn=collator_class.collate_fn)
+next(iter(dataset["train"])).keys()
+>>> dict_keys(['image', 'question_id', 'question', 'answers', 'data_split', 'ocr_results', 'other_metadata'])
 ```
+`image` will be a byte string containing the image contents. `answers` is a list of possible answers, aligned with the expected inputs to the [ANLS metric](https://arxiv.org/abs/1905.13648).
 
-The loader can then be iterated on normally and yields image + question and answer samples. 
+The loader can then be iterated on normally and yields questions. Many questions rely on the same image, so there is some amount of data duplication.
 
 ### Data Splits