readme: ---
  tags:
  - sentence-summarization
  - multilingual
  - nlp
  - indicnlp
  datasets:
  - ai4bharat/IndicSentenceSummarization
  language:
  - as
  - bn
  - gu
  - hi
  - kn
  - ml
  - mr
  - or
  - pa
  - ta
  - te
  license:
  - mit
  widget:
  - जम्मू एवं कश्मीर के अनंतनाग जिले में शनिवार को सुरक्षाबलों के साथ मुठभेड़ में दो आतंकवादियों को मार गिराया गया। <s> <2hi>
  
  
  ---
  
  # MultiIndicSentenceSummarizationSS
  
  This repository contains the [IndicBARTSS](https://huggingface.co/ai4bharat/IndicBARTSS) checkpoint finetuned on the 11 languages of [IndicSentenceSummarization](https://huggingface.co/datasets/ai4bharat/IndicSentenceSummarization) dataset. For finetuning details,
  see the [paper](https://arxiv.org/abs/2203.05437). 
  <ul>
  <li >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. </li>
  <li >The model is much smaller than the mBART and mT5(-base) models, so less computationally expensive for decoding. </li>
  <li> Trained on large Indic language corpora (5.53 million sentences). </li>
  <li> Unlike <a href="https://huggingface.co/ai4bharat/MultiIndicSentenceSummarization">MultiIndicSentenceSummarization</a> each language is written in its own script, so you do not need to perform any script mapping to/from Devanagari. </li>
  </ul>
  
  
  
  ## 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
  ```
  
  ## Benchmarks
  
  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
  
  
  
  ## Citation
  
  If you use this model, please cite the following paper:
  ```
  @inproceedings{Kumar2022IndicNLGSM,
    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},
    year={2022},
    url = "https://arxiv.org/abs/2203.05437"
    }
  ```
  