--- tags: - paraphrase-generation - multilingual - nlp - indicnlp datasets: - ai4bharat/IndicParaphrase language: - as - bn - gu - hi - kn - ml - mr - or - pa - ta - te license: - mit --- # MultiIndicParaphraseGeneration This repository contains the [IndicBART](https://huggingface.co/ai4bharat/IndicBART) 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). ## Using this model in `transformers` ``` from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM from transformers import AlbertTokenizer, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("ai4bharat/MultiIndicParaphraseGeneration", do_lower_case=False, use_fast=False, keep_accents=True) # Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicParaphraseGeneration", do_lower_case=False, use_fast=False, keep_accents=True) model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicParaphraseGeneration") # Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/MultiIndicParaphraseGeneration") # Some initial mapping bos_id = tokenizer._convert_token_to_id_with_added_voc("") eos_id = tokenizer._convert_token_to_id_with_added_voc("") pad_id = tokenizer._convert_token_to_id_with_added_voc("") # 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 <2xx>" where xx is the language code. Similarly, the output should be "<2yy> Sentence ". inp = tokenizer("दिल्ली यूनिवर्सिटी देश की प्रसिद्ध यूनिवर्सिटी में से एक है. <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 विश्वविद्यालय देश की प्रमुख विश्वविद्यालयों में शामिल है। # Note that if your output language is not Hindi or Marathi, you should convert its script from Devanagari to the desired language using the Indic NLP Library. ``` # Note: If you wish to use any language written in a non-Devanagari script, then you should first convert it to Devanagari using the Indic NLP Library. After you get the output, you should convert it back into the original script. ## Benchmarks Scores on the `IndicParaphrase` test sets are as follows: Language | BLEU / Self-BLEU / iBLEU ---------|---------------------------- as | 1.66 / 2.06 / 0.54 bn | 11.57 / 1.69 / 7.59 gu | 22.10 / 2.76 / 14.64 hi | 27.29 / 2.87 / 18.24 kn | 15.40 / 2.98 / 9.89 ml | 10.57 / 1.70 / 6.89 mr | 20.38 / 2.20 / 13.61 or | 19.26 / 2.10 / 12.85 pa | 14.87 / 1.35 / 10.00 ta | 18.52 / 2.88 / 12.10 te | 16.70 / 3.34 / 10.69 ## 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" } ```