Update README.md

README.md

@@ -1,3 +1,98 @@
 ---
-license: mit
+tags:
+- sentence-summarization
+- multilingual
+- nlp
+- indicnlp
+datasets:
+- ai4bharat/IndicSentenceSummarization
+language:
+- as
+- bn
+- gu
+- hi
+- kn
+- ml
+- mr
+- or
+- pa
+- ta
+- te
+license:
+- mit
+
+
 ---
+
+# MultiIndicSentenceSummarizationSS
+
+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,
+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/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>
+</ul>
+
+
+
+## Using this model in `transformers`
+
+```
+from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
+from transformers import AlbertTokenizer, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS", do_lower_case=False, use_fast=False, keep_accents=True)
+# Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS", do_lower_case=False, use_fast=False, keep_accents=True)
+model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS")
+# Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS")
+
+# 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("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 
+
+# For generation. Pardon the messiness. Note the decoder_start_token_id.
+
+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>"))
+
+# Decode to get output strings
+decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
+print(decoded_output) # अनंतनाग में सुरक्षाबलों के साथ मुठभेड़ में दो आतंकवादी ढेर
+```
+
+## Benchmarks
+
+Scores on the `IndicSentenceSummarization` test sets are as follows:
+
+Language | Rouge-1 / Rouge-2 / Rouge-L
+---------|----------------------------
+as	|	63.56	/	49.90	/	62.57
+bn	|	52.52	/	36.15	/	50.60
+gu	|	47.69	/	29.77	/	45.61
+hi	|	50.43	/	28.13	/	45.15
+kn	|	77.06	/	69.36	/	76.33
+ml	|	65.00	/	51.99	/	63.76
+mr	|	47.05	/	25.97	/	45.52
+or	|	50.96	/	30.32	/	49.23
+pa	|	54.95	/	36.26	/	51.26
+ta	|	58.52	/	38.36	/	56.49
+te	|	53.75	/	35.17	/	52.66
+
+
+
+## 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"
+  }
+```