Update README.md

README.md

@@ -4,6 +4,7 @@ IndicBART is a multilingual, sequence-to-sequence pre-trained model focusing on
 <li >Supported languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Odiya, Punjabi, Kannada, Malayalam, Tamil, Telugu and English. Not all of these languages are supported by mBART50 and mT5. </li>
 <li >The model is much smaller than the mBART and mT5(-base) models, so less computationally expensive for finetuning and decoding. </li>
 <li> Trained on large Indic language corpora (452 million sentences and 9 billion tokens) which also includes Indian English content. </li>
+<li> All languages, except English, have been represented in Devanagari script to encourage transfer learning among the related languages. </li>
 </ul>
 
 You can read more about IndicBART in this <a href="https://arxiv.org/abs/2109.02903">paper</a>.
@@ -92,6 +93,7 @@ print(decoded_output) # मला ओळखलं पाहिजे
 1. This is compatible with the latest version of transformers but was developed with version 4.3.2 so consider using 4.3.2 if possible.
 2. While I have only shown how to get logits and loss and how to generate outputs, you can do pretty much everything the MBartForConditionalGeneration class can do as in https://huggingface.co/docs/transformers/model_doc/mbart#transformers.MBartForConditionalGeneration
 3. Note that the tokenizer I have used is based on sentencepiece and not BPE. Therefore, I used the AlbertTokenizer class and not the MBartTokenizer class.
+4. If you wish to use any language written in a non-Devanagari script (except English), then you should first convert it to Devanagari using the <a href="https://github.com/anoopkunchukuttan/indic_nlp_library">Indic NLP Library</a>. After you get the output, you should convert it back into the original script.
 
 # Fine-tuning on a downstream task