Update README.md

README.md

@@ -105,7 +105,13 @@ File size and page rendering time are used to set thresholds in the final datase
 We get to 48 million pages kept as valid samples.
 
 As a last step, we use XLM-Roberta to restrict the dataset to an english subset, specifically `papluca/xlm-roberta-base-language-detection` , on the first 512 words of the first page of each document. 
-Be aware that some documents may have several languages embedded in them, or that some predictions might be inaccurate. 
+Be aware that some documents may have several languages embedded in them, or that some predictions might be inaccurate. A majority of documents from the original corpus are in English language.
+
+ 
+<center>
+    <img src="https://huggingface.co/datasets/pixparse/pdfa-english-train/resolve/main/doc_images/languages_pdfa_xlmroberta.png" alt="A histogram of languages count in the PDFA dataset." width="600" height="300">
+    <p><em>A histogram of language distribution taken on a fraction of the original -non-filtered on language- PDFA dataset. </em></p>
+</center>
 
 At the end, each document exists as a pairing of a pdf and a json file containing extensive OCR annotation as well as metadata information about rendering times. The filterings and packaging in 
 webdataset format are tailored towards multimodal machine learning at scale, specifically image-to-text tasks.