|
---
|
|
tags:
|
|
- paraphrase-generation
|
|
- multilingual
|
|
- nlp
|
|
- indicnlp
|
|
datasets:
|
|
- ai4bharat/IndicParaphrase
|
|
language:
|
|
- as
|
|
- bn
|
|
- gu
|
|
- hi
|
|
- kn
|
|
- ml
|
|
- mr
|
|
- or
|
|
- pa
|
|
- ta
|
|
- te
|
|
licenses:
|
|
- cc-by-nc-4.0
|
|
|
|
|
|
---
|
|
|
|
# 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("<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 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("I am a boy </s> <2en>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids # tensor([[ 466, 1981, 80, 25573, 64001, 64004]])
|
|
|
|
# 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("<2en>"))
|
|
# Decode to get output strings
|
|
decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
|
|
print(decoded_output) # I am a boy
|
|
# 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.
|
|
# What if we mask?
|
|
inp = tokenizer("I am [MASK] </s> <2en>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
|
|
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("<2en>"))
|
|
decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
|
|
print(decoded_output) # I am happy
|
|
inp = tokenizer("मैं [MASK] हूँ </s> <2hi>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
|
|
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("<2en>"))
|
|
decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
|
|
print(decoded_output) # मैं जानता हूँ
|
|
inp = tokenizer("मला [MASK] पाहिजे </s> <2mr>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
|
|
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("<2en>"))
|
|
decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
|
|
print(decoded_output) # मला ओळखलं पाहिजे
|
|
```
|
|
# Note:
|
|
If you wish to use any language written in a non-Devanagari script (except English), 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 `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"
|
|
}
|
|
```
|
|
|