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--- |
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tags: |
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- sentence-summarization |
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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/IndicSentenceSummarization |
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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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license: |
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- mit |
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widget: |
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- जम्मू एवं कश्मीर के अनंतनाग जिले में शनिवार को सुरक्षाबलों के साथ मुठभेड़ में दो आतंकवादियों को मार गिराया गया। <s> <2hi> |
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--- |
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# MultiIndicSentenceSummarizationSS |
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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, |
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see the [paper](https://arxiv.org/abs/2203.05437). |
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<ul> |
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<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> |
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<li >The model is much smaller than the mBART and mT5(-base) models, so less computationally expensive for decoding. </li> |
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<li> Trained on large Indic language corpora (5.53 million sentences). </li> |
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<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> |
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</ul> |
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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/MultiIndicSentenceSummarizationSS", do_lower_case=False, use_fast=False, keep_accents=True) |
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# Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS", do_lower_case=False, use_fast=False, keep_accents=True) |
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model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS") |
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# Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS") |
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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>', '<2en>', '<2gu>', '<2hi>', '<2kn>', '<2ml>', '<2mr>', '<2or>', '<2pa>', '<2ta>', '<2te>'] |
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# 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>". |
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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 |
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# For generation. Pardon the messiness. Note the decoder_start_token_id. |
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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>")) |
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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) # अनंतनाग में सुरक्षाबलों के साथ मुठभेड़ में दो आतंकवादी ढेर |
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``` |
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## Benchmarks |
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Scores on the `IndicSentenceSummarization` test sets are as follows: |
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Language | Rouge-1 / Rouge-2 / Rouge-L |
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---------|---------------------------- |
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as | 63.56 / 49.90 / 62.57 |
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bn | 52.52 / 36.15 / 50.60 |
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gu | 47.69 / 29.77 / 45.61 |
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hi | 50.43 / 28.13 / 45.15 |
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kn | 77.06 / 69.36 / 76.33 |
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ml | 65.00 / 51.99 / 63.76 |
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mr | 47.05 / 25.97 / 45.52 |
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or | 50.96 / 30.32 / 49.23 |
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pa | 54.95 / 36.26 / 51.26 |
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ta | 58.52 / 38.36 / 56.49 |
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te | 53.75 / 35.17 / 52.66 |
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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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