
# MultiIndicWikiBioUnified

MultiIndicWikiBioUnified is a multilingual, sequence-to-sequence pre-trained model, a [IndicBART](https://huggingface.co/ai4bharat/IndicBART) checkpoint fine-tuned on the 9 languages of [IndicWikiBio](https://huggingface.co/datasets/ai4bharat/IndicWikiBio) dataset. For fine-tuning details,
see the [paper](https://arxiv.org/abs/2203.05437). You can use MultiIndicWikiBio to build biography generation applications for Indian languages by fine-tuning the model with supervised training data. Some salient features of the MultiIndicWikiBio are:

<ul>
<li >Supported languages: Assamese, Bengali, Hindi, 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 fine-tuning and decoding. </li>
<li> Fine-tuned on an Indic language corpora (34,653 examples). </li>
<li> All languages have been represented in Devanagari script to encourage transfer learning among the related languages. </li>
</ul>

You can read more about MultiIndicWikiBioUnified 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/MultiIndicWikiBioUnified", do_lower_case=False, use_fast=False, keep_accents=True)
# Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicWikiBioUnified", do_lower_case=False, use_fast=False, keep_accents=True)

model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicWikiBioUnified")
# Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/MultiIndicWikiBioUnified")

# 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>', '<2hi>', '<2kn>', '<2ml>', '<2or>', '<2pa>', '<2ta>', '<2te>']

# First tokenize the input and outputs. 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("<TAG> name </TAG> भीखा लाल <TAG> office </TAG> विधायक - 318 - हसनगंज विधान सभा निर्वाचन क्षेत्र , उत्तर प्रदेश <TAG> term </TAG> 1957 से 1962 <TAG> nationality </TAG> भारतीय</s><2hi>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids 

out = tokenizer("<2hi> भीखा लाल ,भारत के उत्तर प्रदेश की दूसरी विधानसभा सभा में विधायक रहे। </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) # भीखा लाल ,भारत के उत्तर प्रदेश की दूसरी विधानसभा सभा में विधायक रहे।

# Disclaimer
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](https://github.com/AI4Bharat/indic-bart/blob/main/indic_scriptmap.py).
```
# Note:
If you wish to use any language written in a non-Devanagari script, then you should first convert it to Devanagari using the <a href="https://github.com/anoopkunchukuttan/indic_nlp_library">Indic NLP Library</a>. After you get the output, you should convert it back into the original script.

## Benchmarks

Scores on the `IndicWikiBio` test sets are as follows:

Language | RougeL
---------|----------------------------
as | 56.28
bn | 57.42
hi | 67.48
kn | 40.01
ml | 38.84
or | 67.13
pa | 52.88
ta | 51.82
te | 51.43



## 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.
{"language": ["as", "bn", "hi", "kn", "ml", "or", "pa", "ta", "te"], "tags": ["wikibio", "multilingual", "nlp", "indicnlp"], "datasets": ["ai4bharat/IndicWikiBio"], "licenses": ["cc-by-nc-4.0"], "widget": ["<TAG> name </TAG> \u0928\u0935\u0924\u0947\u091c \u092d\u093e\u0930\u0924\u0940 <TAG> image </TAG> NavtejBharati . jpg <TAG> birth name </TAG> \u0928\u0935\u0924\u0947\u091c <TAG> birth date </TAG> 1938 <TAG> birth place </TAG> \u0930\u094b\u0921\u0947 , \u092d\u093e\u0930\u0924\u0940\u092f \u092a\u0902\u091c\u093e\u092c , \u092d\u093e\u0930\u0924 \u0964 \u092a\u0902\u091c\u093e\u092c <TAG> occupation </TAG> \u0932\u0947\u0916\u0915 , \u0915\u0935\u093f <TAG> nationality </TAG> \u0915\u0948\u0928\u0947\u0921\u093e \u0964 \u0915\u0948\u0928\u0947\u0921\u093f\u092f\u0928 <TAG> ethnicity </TAG> \u092a\u0902\u091c\u093e\u092c\u0940 \u0932\u094b\u0915 \u0964 \u092a\u0902\u091c\u093e\u092c\u0940 </s> <2hi>"]}
transformers pytorch mbart text2text-generation wikibio multilingual nlp indicnlp as bn hi kn ml or pa ta te dataset:ai4bharat/IndicWikiBio arxiv:2203.05437 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mbart #text2text-generation #wikibio #multilingual #nlp #indicnlp #as #bn #hi #kn #ml #or #pa #ta #te #dataset-ai4bharat/IndicWikiBio #arxiv-2203.05437 #autotrain_compatible #endpoints_compatible #region-us 

MultiIndicWikiBioUnified
========================


MultiIndicWikiBioUnified is a multilingual, sequence-to-sequence pre-trained model, a IndicBART checkpoint fine-tuned on the 9 languages of IndicWikiBio dataset. For fine-tuning details,
see the paper. You can use MultiIndicWikiBio to build biography generation applications for Indian languages by fine-tuning the model with supervised training data. Some salient features of the MultiIndicWikiBio are:


* Supported languages: Assamese, Bengali, Hindi, Oriya, Punjabi, Kannada, Malayalam, Tamil, and Telugu. Not all of these languages are supported by mBART50 and mT5.
* The model is much smaller than the mBART and mT5(-base) models, so less computationally expensive for fine-tuning and decoding.
* Fine-tuned on an Indic language corpora (34,653 examples).
* All languages have been represented in Devanagari script to encourage transfer learning among the related languages.


You can read more about MultiIndicWikiBioUnified in this <a href="URL


Using this model in 'transformers'
----------------------------------


Note:
=====


If you wish to use any language written in a non-Devanagari script, then you should first convert it to Devanagari using the <a href="URL NLP Library. After you get the output, you should convert it back into the original script.


Benchmarks
----------


Scores on the 'IndicWikiBio' test sets are as follows:



If you use this model, please cite the following paper:


License
=======


The model is available under the MIT License.



TAGS
#transformers #pytorch #mbart #text2text-generation #wikibio #multilingual #nlp #indicnlp #as #bn #hi #kn #ml #or #pa #ta #te #dataset-ai4bharat/IndicWikiBio #arxiv-2203.05437 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #mbart #text2text-generation #wikibio #multilingual #nlp #indicnlp #as #bn #hi #kn #ml #or #pa #ta #te #dataset-ai4bharat/IndicWikiBio #arxiv-2203.05437 #autotrain_compatible #endpoints_compatible #region-us 
