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This repository contains the IndicBARTSS checkpoint finetuned on the 11 languages of IndicSentenceSummarization dataset. For finetuning details, see the paper.

  • Supported languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Odiya, Punjabi, Kannada, Malayalam, Tamil, and Telugu. Not all of these languages are supported by mBART50 and mT5.
  • The model is much smaller than the mBART and mT5(-base) models, so less computationally expensive for decoding.
  • Trained on large Indic language corpora (5.53 million sentences).
  • Unlike MultiIndicSentenceSummarization each language is written in its own script, so you do not need to perform any script mapping to/from Devanagari.

Using this model in transformers

from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
from transformers import AlbertTokenizer, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS", do_lower_case=False, use_fast=False, keep_accents=True)
# Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS", do_lower_case=False, use_fast=False, keep_accents=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS")
# Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS")

# Some initial mapping
bos_id = tokenizer._convert_token_to_id_with_added_voc("<s>")
eos_id = tokenizer._convert_token_to_id_with_added_voc("</s>")
pad_id = tokenizer._convert_token_to_id_with_added_voc("<pad>")

# To get lang_id use any of ['<2as>', '<2bn>', '<2en>', '<2gu>', '<2hi>', '<2kn>', '<2ml>', '<2mr>', '<2or>', '<2pa>', '<2ta>', '<2te>']
# First tokenize the input. The format below is how IndicBART was trained so the input should be "Sentence </s> <2xx>" where xx is the language code. Similarly, the output should be "<2yy> Sentence </s>".
inp = tokenizer("जम्मू एवं कश्मीर के अनंतनाग जिले में शनिवार को सुरक्षाबलों के साथ मुठभेड़ में दो आतंकवादियों को मार गिराया गया। </s> <2hi>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids 

# For generation. Pardon the messiness. Note the decoder_start_token_id.

model_output=model.generate(inp, use_cache=True,no_repeat_ngram_size=3, num_beams=5, length_penalty=0.8, max_length=20, min_length=1, early_stopping=True, pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id, decoder_start_token_id=tokenizer._convert_token_to_id_with_added_voc("<2hi>"))

# Decode to get output strings
decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(decoded_output) # अनंतनाग में सुरक्षाबलों के साथ मुठभेड़ में दो आतंकवादी ढेर


Scores on the IndicSentenceSummarization test sets are as follows:

Language Rouge-1 / Rouge-2 / Rouge-L
as 63.56 / 49.90 / 62.57
bn 52.52 / 36.15 / 50.60
gu 47.69 / 29.77 / 45.61
hi 50.43 / 28.13 / 45.15
kn 77.06 / 69.36 / 76.33
ml 65.00 / 51.99 / 63.76
mr 47.05 / 25.97 / 45.52
or 50.96 / 30.32 / 49.23
pa 54.95 / 36.26 / 51.26
ta 58.52 / 38.36 / 56.49
te 53.75 / 35.17 / 52.66


If you use this model, please cite the following paper:

  title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
  author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
  url = "https://arxiv.org/abs/2203.05437"
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Dataset used to train ai4bharat/MultiIndicSentenceSummarizationSS