Update README.md

README.md

@@ -32,16 +32,38 @@ PDFA dataset is a document dataset filtered from the SafeDocs corpus, aka CC-MAI
 
 ### Usage
 
-This instance of PDFA is in [webdataset](https://github.com/webdataset/webdataset/commits/main) .tar format.
-It can be used with webdataset library or current releases of Hugging Face `datasets` library. It can also be streamed directly from the hub that way.
+This instance of PDFA is in [webdataset](https://github.com/webdataset/webdataset/commits/main) .tar format and can be used with derived forms of the `webdataset` library.
+We recommend to use it with the [chug](https://github.com/huggingface/chug) library, an optimized library for distributed data loading.
+
+# TODO The following example uses fitz which is AGPL. We should also recommend the same with pypdf.
 
 ```python
-from datasets import load_dataset
+from chug import create_wds_loader, create_doc_anno_pipe
+
+# TODO import image transforms and text transforms from pixparse.data
+
+
+
 
-pdfa_english = load_dataset('pixparse/pdfa-english-train', streaming=True)
+decoder = create_decoder_pipe(
+    image_preprocess=image_preprocess, # callable of transforms to image tensors
+    anno_preprocess=anno_preprocess, # callable of transforms to text into tensors of labels and targets
+    image_key="pdf",
+    image_fmt="RGB",
+)
 
-print(next(iter(dataset['train'])).keys())
->> dict_keys(['__key__', '__url__', 'json', 'pdf'])
+loader = create_wds_loader(
+    "/my_data/pdfa-eng-train-*.tar",
+    decoder,
+    is_train=True,
+    resampled=False,
+    start_interval=0,
+    num_samples=2159432,
+    workers=8,
+    batch_size=32, # adjust to your architecture capacity
+    seed=seed, # set a seed
+    world_size=world_size, # get world_size from your training environment
+)
 
 ```