IndicTransToolkit

The goal of this repository is to provide a simple, modular, and extendable toolkit for IndicTrans2 and be compatible with the HuggingFace models released.

Minor Update (v1.0.2)

• The repository has been renamed to IndicTransToolkit.
• The custom tokenizer is now removed from the repository. Please revert to a previous commit (v1.0.1) to use it (strongly discouraged). The official (and only tokenizer) is available on HF along with the models.

Major Update (v1.0.0)

• The PreTrainedTokenizer for IndicTrans2 is now available on HF 🎉🎉 Note that, you still need the IndicProcessor to pre-process the sentences before tokenization.
• In favor of the standard PreTrainedTokenizer, we deprecated the custom tokenizer. However, this custom tokenizer will still be available here for backward compatibility, but no further updates/bug-fixes will be provided.
• The indic_evaluate function is now consolidated into a concrete IndicEvaluator class.
• The data collation function for training is consolidated into a concrete IndicDataCollator class.
• A simple batching method is now available in the IndicProcessor.

Pre-requisites

• Python 3.8+
• Indic NLP Library
• Other requirements as listed in requirements.txt

Configuration

• Editable installation (Note, this may take a while):

git clone https://github.com/VarunGumma/IndicTransToolkit
cd IndicTransToolkit

pip install --editable ./

Examples

For the training usecase, please refer here.

PreTainedTokenizer

import torch
from IndicTransToolkit import IndicProcessor
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

ip = IndicProcessor(inference=True)
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/indictrans2-en-indic-dist-200M", trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/indictrans2-en-indic-dist-200M", trust_remote_code=True)

sentences = [
    "This is a test sentence.",
    "This is another longer different test sentence.",
    "Please send an SMS to 9876543210 and an email on newemail123@xyz.com by 15th October, 2023.",
]

batch = ip.preprocess_batch(sentences, src_lang="eng_Latn", tgt_lang="hin_Deva")
batch = tokenizer(batch, padding="longest", truncation=True, max_length=256, return_tensors="pt")

with torch.inference_mode():
    outputs = model.generate(**batch, num_beams=5, num_return_sequences=1, max_length=256)

with tokenizer.as_target_tokenizer():
    # This scoping is absolutely necessary, as it will instruct the tokenizer to tokenize using the target vocabulary.
    # Failure to use this scoping will result in gibberish/unexpected predictions as the output will be de-tokenized with the source vocabulary instead.
    outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True, clean_up_tokenization_spaces=True)

outputs = ip.postprocess_batch(outputs, lang="hin_Deva")
print(outputs)

>>> ['यह एक परीक्षण वाक्य है।', 'यह एक और लंबा अलग परीक्षण वाक्य है।', 'कृपया 9876543210 पर एक एस. एम. एस. भेजें और 15 अक्टूबर, 2023 तक newemail123@xyz.com पर एक ईमेल भेजें।']

Evaluation

• IndicEvaluator is a python implementation of compute_metrics.sh.
• We have found that this python implementation gives slightly lower scores than the original compute_metrics.sh. So, please use this function cautiously, and feel free to raise a PR if you have found the bug/fix.

from IndicTransToolkit import IndicEvaluator

# this method returns a dictionary with BLEU and ChrF2++ scores with appropriate signatures
evaluator = IndicEvaluator()
scores = evaluator.evaluate(tgt_lang=tgt_lang, preds=pred_file, refs=ref_file) 

# alternatively, you can pass the list of predictions and references instead of files 
# scores = evaluator.evaluate(tgt_lang=tgt_lang, preds=preds, refs=refs)

Batching

ip = IndicProcessor(inference=True)

for batch in ip.get_batches(source_sentences, batch_size=32):
    # perform necessary operations on the batch
    # ... pre-processing
    # ... tokenization 
    # ... generation 
    # ... decoding

• For Python >= 3.12, you can use the inbuilt batching function,itertools.batched, instead of the get_batches method. (docs)

Authors

• Varun Gumma (varun230999@gmail.com)
• Jay Gala (jaygala24@gmail.com)
• Pranjal Agadh Chitale (pranjalchitale@gmail.com)
• Raj Dabre (prajdabre@gmail.com)

Bugs and Contribution

Since this a bleeding-edge module, you may encounter broken stuff and import issues once in a while. In case you encounter any bugs or want additional functionalities, please feel free to raise Issues/Pull Requests or contact the authors.

Citation

If you use our codebase, or models, please do cite the following paper:

@article{
    gala2023indictrans,
    title={IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages},
    author={Jay Gala and Pranjal A Chitale and A K Raghavan and Varun Gumma and Sumanth Doddapaneni and Aswanth Kumar M and Janki Atul Nawale and Anupama Sujatha and Ratish Puduppully and Vivek Raghavan and Pratyush Kumar and Mitesh M Khapra and Raj Dabre and Anoop Kunchukuttan},
    journal={Transactions on Machine Learning Research},
    issn={2835-8856},
    year={2023},
    url={https://openreview.net/forum?id=vfT4YuzAYA},
    note={}
}