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--- |
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tags: |
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- question-generation |
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- multilingual |
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- nlp |
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- indicnlp |
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datasets: |
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- ai4bharat/IndicQuestionGeneration |
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- squad |
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language: |
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- as |
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- bn |
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- gu |
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- hi |
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- kn |
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- ml |
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- mr |
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- or |
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- pa |
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- ta |
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- te |
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licenses: |
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- cc-by-nc-4.0 |
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--- |
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# MultiIndicQuestionGenerationSS |
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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, |
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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: |
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<ul> |
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<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> |
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<li >The model is much smaller than the mBART and mT5(-base) models, so less computationally expensive for finetuning and decoding. </li> |
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<li> Fine-tuned on large Indic language corpora (770 K examples). </li> |
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<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> |
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</ul> |
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You can read more about MultiIndicQuestionGenerationSS in this <a href="https://arxiv.org/abs/2203.05437">paper</a>. |
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## Using this model in `transformers` |
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``` |
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from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM |
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from transformers import AlbertTokenizer, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("ai4bharat/MultiIndicQuestionGenerationSS", do_lower_case=False, use_fast=False, keep_accents=True) |
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# Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicQuestionGenerationSS", do_lower_case=False, use_fast=False, keep_accents=True) |
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model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicQuestionGenerationSS") |
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# Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/MultiIndicQuestionGenerationSS") |
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# Some initial mapping |
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bos_id = tokenizer._convert_token_to_id_with_added_voc("<s>") |
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eos_id = tokenizer._convert_token_to_id_with_added_voc("</s>") |
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pad_id = tokenizer._convert_token_to_id_with_added_voc("<pad>") |
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# To get lang_id use any of ['<2as>', '<2bn>', '<2gu>', '<2hi>', '<2kn>', '<2ml>', '<2mr>', '<2or>', '<2pa>', '<2ta>', '<2te>'] |
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# 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>". |
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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 |
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out = tokenizer("<2hi> सुपर बाउल किस दिन खेला गया? </s>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids |
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model_outputs=model(input_ids=inp, decoder_input_ids=out[:,0:-1], labels=out[:,1:]) |
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# For loss |
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model_outputs.loss ## This is not label smoothed. |
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# For logits |
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model_outputs.logits |
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# For generation. Pardon the messiness. Note the decoder_start_token_id. |
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model.eval() # Set dropouts to zero |
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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>")) |
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# Decode to get output strings |
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decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False) |
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print(decoded_output) # कब होगा पहला एएफएल गेम? |
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``` |
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## Benchmarks |
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Scores on the `IndicQuestionGeneration` test sets are as follows: |
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Language | RougeL |
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---------|---------------------------- |
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as | 20.73 |
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bn | 30.38 |
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gu | 28.13 |
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hi | 34.42 |
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kn | 23.77 |
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ml | 22.24 |
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mr | 23.62 |
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or | 27.53 |
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pa | 32.53 |
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ta | 23.49 |
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te | 25.81 |
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## Citation |
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If you use this model, please cite the following paper: |
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``` |
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@inproceedings{Kumar2022IndicNLGSM, |
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title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages}, |
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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}, |
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year={2022}, |
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url = "https://arxiv.org/abs/2203.05437" |
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} |
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``` |
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# License |
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The model is available under the MIT License. |
