Update README.md

README.md

@@ -1,6 +1,20 @@
-This is the IndicBART model. For detailed documentation look here: https://indicnlp.ai4bharat.org/indic-bart/ and https://github.com/AI4Bharat/indic-bart/
+IndicBART is a multilingual, sequence-to-sequence pre-trained model focusing on Indic languages and English. It currently supports 11 Indian languages and is based on the mBART architecture. You can use IndicBART model to build natural language generation applications for Indian languages by finetuning the model with supervised training data for tasks like machine translation, summarization, question generation, etc. Some salient features of the IndicBART are:
 
-Usage:
+<ul>
+<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>
+</ul>
+
+You can read more about IndicBART in this paper: https://arxiv.org/abs/2109.02903
+
+For detailed documentation, look here: https://github.com/AI4Bharat/indic-bart/ and https://indicnlp.ai4bharat.org/indic-bart/
+
+# Pre-training corpus
+
+We used the IndicCorp data spanning 12 languages with 452 million sentences (9 billion tokens). The model was trained using the text-infilling objective used in mBART.
+
+# Usage:
 
 ```
 from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
@@ -57,8 +71,34 @@ decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean
 print(decoded_output) # I am happy
 ```
 
-Notes:
+# Notes:
 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 let 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. If you wish to fine-tune this model, then you can do so using the toolkit YANMTT following the instructions here: https://github.com/AI4Bharat/indic-bart
-4. 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. 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.
+
+# Contributors
+<ul>
+<li> Raj Dabre </li>
+<li> Himani Shrotriya </li>
+<li> Anoop Kunchukuttan </li>
+<li> Ratish Puduppully </li>
+<li> Mitesh M. Khapra </li>
+<li> Pratyush Kumar </li>
+</ul>
+
+# Paper
+If you use IndicBART, please cite the following paper:
+```
+@misc{dabre2021indicbart,
+      title={IndicBART: A Pre-trained Model for Natural Language Generation of Indic Languages}, 
+      author={Raj Dabre and Himani Shrotriya and Anoop Kunchukuttan and Ratish Puduppully and Mitesh M. Khapra and Pratyush Kumar},
+      year={2021},
+      eprint={2109.02903},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+    }    
+```
+
+#License
+The model is available under the MIT License.