MultiIndicQuestionGenerationSS is a multilingual, sequence-to-sequence pre-trained model, a
IndicBARTSS
checkpoint fine-tuned on the 11 languages of
IndicQuestionGeneration
dataset. For fine-tuning details,
see the
paper
. 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:
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.
The model is much smaller than the mBART and mT5(-base) models, so less computationally expensive for finetuning and decoding.
Fine-tuned on large Indic language corpora (770 K examples).
Unlike ai4bharat/MultiIndicQuestionGenerationUnified, each language is written in its own script, so you do not need to perform any script mapping to/from Devanagari.
You can read more about MultiIndicQuestionGenerationSS in this
paper
.
Using this model in
transformers
from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
from transformers import AlbertTokenizer, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/MultiIndicQuestionGenerationSS", do_lower_case=False, use_fast=False, keep_accents=True)
# Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicQuestionGenerationSS", do_lower_case=False, use_fast=False, keep_accents=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicQuestionGenerationSS")
# Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/MultiIndicQuestionGenerationSS")
# 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>', '<2gu>', '<2hi>', '<2kn>', '<2ml>', '<2mr>', '<2or>', '<2pa>', '<2ta>', '<2te>']
# 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>".
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
out = tokenizer("<2hi> सुपर बाउल किस दिन खेला गया? </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) # कब होगा पहला एएफएल गेम?
Benchmarks
Scores on the
IndicQuestionGeneration
test sets are as follows:
Language
RougeL
as
20.73
bn
30.38
gu
28.13
hi
34.42
kn
23.77
ml
22.24
mr
23.62
or
27.53
pa
32.53
ta
23.49
te
25.81
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.
Runs of ai4bharat MultiIndicQuestionGenerationSS on huggingface.co
9
Total runs
0
24-hour runs
0
3-day runs
1
7-day runs
1
30-day runs
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