readme: ---
  tags:
  - paraphrase-generation
  - multilingual
  - nlp
  - indicnlp
  datasets:
  - ai4bharat/IndicParaphrase
  language:
  - as
  - bn
  - gu
  - hi
  - kn
  - ml
  - mr
  - or
  - pa
  - ta
  - te
  license:
  - mit
  
  
  ---
  
  # MultiIndicParaphraseGenerationSS
  
  This repository contains the [IndicBARTSS](https://huggingface.co/ai4bharat/IndicBARTSS) checkpoint finetuned on the 11 languages of [IndicParaphrase](https://huggingface.co/datasets/ai4bharat/IndicParaphrase) 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/MultiIndicParaphraseGeneration">MultiIndicParaphraseGeneration</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/MultiIndicParaphraseGenerationSS", do_lower_case=False, use_fast=False, keep_accents=True)
  # Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicParaphraseGenerationSS", do_lower_case=False, use_fast=False, keep_accents=True)
  model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicParaphraseGenerationSS")
  # Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/MultiIndicParaphraseGenerationSS")
  
  # 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("दिल्ली यूनिवर्सिटी देश की प्रसिद्ध यूनिवर्सिटी में से एक है. </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,encoder_no_repeat_ngram_size=3, num_beams=4, 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) #दिल्ली यूनिवर्सिटी भारत की सबसे बड़ी यूनिवर्सिटी है।
  ```
  
  ## Benchmarks
  
  Scores on the `IndicParaphrase` test sets are as follows:
  
  Language | BLEU / Self-BLEU / iBLEU
  ---------|----------------------------
  as | 1.19 / 1.64 / 0.34
  bn | 10.04 / 1.08 / 6.70
  gu | 18.69 / 1.62 / 12.60
  hi | 25.05 / 1.75 / 17.01
  kn | 13.14 / 1.89 / 8.63
  ml | 8.71 / 1.36 / 5.69
  mr | 18.50 / 1.49 / 12.50
  or | 23.02 / 2.68 / 15.31
  pa | 17.61 / 1.37 / 11.92
  ta | 16.25 / 2.13 / 10.74
  te | 14.16 / 2.29 / 9.23
  
  
  
  ## 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"
    }
  ```
  