readme: ---
  tags:
  - question-generation
  - multilingual
  - nlp
  - indicnlp
  datasets:
  - ai4bharat/IndicQuestionGeneration
  - squad
  language:
  - as
  - bn
  - gu
  - hi
  - kn
  - ml
  - mr
  - or
  - pa
  - ta
  - te
  licenses:
  - cc-by-nc-4.0
  
  
  ---
  
  # MultiIndicQuestionGenerationSS
  
  MultiIndicQuestionGenerationSS is a multilingual, sequence-to-sequence pre-trained model, a [IndicBARTSS](https://huggingface.co/ai4bharat/IndicBARTSS) checkpoint fine-tuned on the 11 languages of [IndicQuestionGeneration](https://huggingface.co/datasets/ai4bharat/IndicQuestionGeneration) dataset. For fine-tuning details,
  see the [paper](https://arxiv.org/abs/2203.05437). You can use MultiIndicQuestionGenerationSS to build question generation applications for Indian languages by fine-tuning the model with supervised training data for the question generation task. Some salient features of the MultiIndicQuestionGenerationSS are:
  
  <ul>
  <li >Supported languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Oriya, 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 finetuning and decoding. </li>
  <li> Fine-tuned on large Indic language corpora (770 K examples). </li>
  <li> Unlike ai4bharat/MultiIndicQuestionGenerationUnified, each language is written in its own script, so you do not need to perform any script mapping to/from Devanagari. </li>
  </ul>
  
  You can read more about MultiIndicQuestionGenerationSS in this <a href="https://arxiv.org/abs/2203.05437">paper</a>.
  
  
  ## Using this model in `transformers`
  
  ```
  from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
  from transformers import AlbertTokenizer, AutoTokenizer
  
  tokenizer = AutoTokenizer.from_pretrained("ai4bharat/MultiIndicQuestionGenerationSS", do_lower_case=False, use_fast=False, keep_accents=True)
  
  # Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicQuestionGenerationSS", do_lower_case=False, use_fast=False, keep_accents=True)
  
  model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicQuestionGenerationSS")
  
  # Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/MultiIndicQuestionGenerationSS")
  
  # 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>', '<2gu>', '<2hi>', '<2kn>', '<2ml>', '<2mr>', '<2or>', '<2pa>', '<2ta>', '<2te>']
  
  # First tokenize the input and outputs. The format below is how IndicBARTSS 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("7 फरवरी, 2016 [SEP] खेल 7 फरवरी, 2016 को कैलिफोर्निया के सांता क्लारा में सैन फ्रांसिस्को खाड़ी क्षेत्र में लेवी स्टेडियम में खेला गया था। </s> <2hi>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids 
  
  out = tokenizer("<2hi> सुपर बाउल किस दिन खेला गया? </s>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids 
  
  model_outputs=model(input_ids=inp, decoder_input_ids=out[:,0:-1], labels=out[:,1:])
  
  # For loss
  model_outputs.loss ## This is not label smoothed.
  
  # For logits
  model_outputs.logits
  
  # For generation. Pardon the messiness. Note the decoder_start_token_id.
  
  model.eval() # Set dropouts to zero
  
  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) # कब होगा पहला एएफएल गेम?
  
  ```
  
  ## Benchmarks
  
  Scores on the `IndicQuestionGeneration` test sets are as follows:
  
  Language | RougeL
  ---------|----------------------------
  as | 20.73
  bn | 30.38
  gu | 28.13
  hi | 34.42
  kn | 23.77
  ml | 22.24
  mr | 23.62
  or | 27.53
  pa | 32.53
  ta | 23.49
  te | 25.81
  
  
  ## 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"
    }
  ```
  # License
  The model is available under the MIT License.