Update README.md

README.md

@@ -18,7 +18,8 @@ size_categories:
 ### Dataset Summary
 
 Industry Documents Library (IDL) is a document dataset filtered from [UCSF documents library](https://www.industrydocuments.ucsf.edu/) with 19 million pages kept as valid samples.
-Each document exists as a collection of a pdf, a tiff image with the same contents rendered, a json file containing extensive Textract OCR annotations from the [idl_data](https://github.com/furkanbiten/idl_data) project, and a .ocr file with the original, older OCR annotation.
+Each document exists as a collection of a pdf, a tiff image with the same contents rendered, a json file containing extensive Textract OCR annotations from the [idl_data](https://github.com/furkanbiten/idl_data) project, and a .ocr file with the original, older OCR annotation. In each pdf, there may be from 1 to up to 3000 pages.
+
 <center>
     <img src="https://huggingface.co/datasets/pixparse/IDL-wds/resolve/main/doc_images/idl_page_example.png" alt="An addendum from an internal legal document" width="600" height="300">
     <p><em>An example page of one pdf document from the Industry Documents Library. </em></p>
@@ -26,8 +27,15 @@ Each document exists as a collection of a pdf, a tiff image with the same conten
 
 
 ### Usage
+This instance of IDL is in [webdataset](https://github.com/webdataset/webdataset) .tar format and can be used with derived forms of the webdataset library. For dataloading, the `datasets` library can readily be used, and we also recommend to use it with the chug library, an optimized library for distributed data loading.
+```python
+from datasets import load_dataset
+dataset = load_dataset('pixparse/IDL-wds', streaming=True)
+print(next(iter(dataset['train'])).keys())
+>> dict_keys(['__key__', '__url__', 'json', 'ocr', 'pdf', 'tif'])
+```
 
-For faster download, you can use directly the `huggingface_hub` library. Make sure `hf_transfer` is installed prior to downloading and mind that you have enough space locally. 
+For faster download, you can directly use the `huggingface_hub` library. Make sure `hf_transfer` is installed prior to downloading and mind that you have enough space locally. 
 
 ```python
  import os
@@ -128,13 +136,19 @@ def get_columnar_separators(page, min_prominence=0.3, num_bins=10, kernel_width=
 
 That way, columnar documents can be better separated. This is a basic heuristic but it should improve overall the readability of the documents.
 
+
 <div style="text-align: center;">
-    <img src="https://huggingface.co/datasets/pixparse/IDL-wds/resolve/main/doc_images/arrows_plot_straight.png" alt="Numbered bounding boxes on a document" style="width: 600px; height: 300px; display: inline-block;">
-    <img src="https://huggingface.co/datasets/pixparse/IDL-wds/resolve/main/doc_images/bounding_boxes_straight.png" alt="A simple representation of reading order" style="width: 600px; height: 300px; display: inline-block;">
+    <img src="https://huggingface.co/datasets/pixparse/IDL-wds/resolve/main/doc_images/bounding_boxes_straight.png" alt="Numbered bounding boxes on a document" style="width: 600px; height: 800px; object-fit: cover; display: inline-block;">
+    <img src="https://huggingface.co/datasets/pixparse/IDL-wds/resolve/main/doc_images/arrows_plot_straight.png" alt="A simple representation of reading order" style="width: 600px; height: 800px; object-fit: cover; display: inline-block;">
 </div>
-<p style="text-align: center;"><em>Standard reading order for a single-column document.</em></p>
+<p style="text-align: center;"><em>Standard reading order for a single-column document. On the left, bounding boxes are ordered, and on the right a rendition of the corresponding reading order is given.</em></p>
 
 
+<div style="text-align: center;">
+    <img src="https://huggingface.co/datasets/pixparse/IDL-wds/resolve/main/doc_images/bounding_boxes.png" alt="Numbered bounding boxes on a document" style="width: 600px; height: 800px; object-fit: cover; display: inline-block;">
+    <img src="https://huggingface.co/datasets/pixparse/IDL-wds/resolve/main/doc_images/arrows_plot.png" alt="A simple representation of reading order" style="width: 600px; height: 800px; object-fit: cover; display: inline-block;">
+</div>
+<p style="text-align: center;"><em>Heuristic-driven columnar reading order for a two-columns document. On the left, bounding boxes are ordered, and on the right a rendition of the corresponding reading order is given. Some inaccuracies remain but the overall reading order is preserved.</em></p>
 
 For each pdf document, we store statistics on number of pages per shard, number of valid samples per shard. A valid sample is a sample that can be encoded then decoded, which we did for each sample.
 
@@ -161,7 +175,10 @@ For faster download, you can use directly the `huggingface_hub` library. Make su
  hf.snapshot_download("pixparse/pdfa-english-train", repo_type="dataset", local_dir_use_symlinks=False)                               
 ```
 ### Data, metadata and statistics.
-Add example image here
+<center>
+    <img src="https://huggingface.co/datasets/pixparse/IDL-wds/resolve/main/doc_images/idl_page_example.png" alt="An addendum from an internal legal document" width="600" height="300">
+    <p><em>An example page of one pdf document from the Industry Documents Library. </em></p>
+</center>
 
 The metadata for each document has been formatted in this way. Each `pdf` is paired with a `json` file with the following structure. Entries have been shortened for readability.