indictranstoolkit 1.1.1
A simple, consistent, and extendable module for IndicTrans2 compatible with HF models
License: MIT
Requires Python: >=3.10

# IndicTransToolkit

## About
- The goal of this repository is to provide a simple, modular, and extendable toolkit for [Rotary-IndicTrans2](https://huggingface.co/collections/prajdabre/indictrans2-rope-6742ddac669a05db0804db35) and [IndicTrans2](https://huggingface.co/collections/ai4bharat/indictrans2-664ccb91d23bbae0d681c3ca) series of models and be compatible with the HuggingFace models released. 
- Please refer to the `CHANGELOG.md` for latest developments.

## Pre-requisites
 - `Python 3.10+`
 - A `Linux/MacOS` based environment (This toolkit is not meant/built/tested for `Windows` as of now).

## Configuration
 - Direct installation:
```bash
pip install indictranstoolkit
```

 - Editable installation (Note, this may take a while):
```bash 
git clone https://github.com/VarunGumma/IndicTransToolkit
cd IndicTransToolkit

pip install --editable ./
```

 - Common Installation Failures:
    - _Version incompatibilty_: We highly recommend using the latest versions of `numpy>=2.1`, `torch>=2.5` and `transformers>=4.51` for using this toolkit. We cannot guarantee the stability of the module below these requirements. We try our best to uphold backward compatibility, but prioritize any major releases of the dependencies. 


## Examples
For the training usecase, please refer [here](https://github.com/AI4Bharat/IndicTrans2/tree/main/huggingface_interface).

### PreTainedTokenizer 
```python
import torch
from IndicTransToolkit import IndicProcessor # NOW IMPLEMENTED IN CYTHON !!
## BUG: If the above does not work, try:
# from IndicTransToolkit.IndicTransToolkit import IndicProcessor
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
device = "cuda" is torch.cuda.is_available() else "cpu"

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).to(device)

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", visualize=False) # set it to visualize=True to print a progress bar
batch = tokenizer(batch, padding="longest", truncation=True, max_length=256, return_tensors="pt").to(device)

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

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।']
```

- In case `num_return_sequences` > 1 in the generation config, please set the corresponding value `num_return_sequences` argument in the `postprocess_batch` method as well. By default, the value is 1. 
- For `batch_size=N` and `num_return_sequences=M`, the processoer output is then similar to the original decoded tokenizer output, i.e. a list of $N \times M$ sentences, where the $T_i$ ... $T_{i+M}$ are translations of $S_i$.

### Evaluation
- `IndicEvaluator` is a python implementation of [compute_metrics.sh](https://github.com/AI4Bharat/IndicTrans2/blob/main/compute_metrics.sh). 
- We have found that this python implementation gives slightly lower scores than the original `compute_metrics.sh`. See [here](http://github.com/mjpost/sacrebleu/issues/259) for more information. 
```python
from IndicTransToolkit.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)
```

## 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:
```bibtex
@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={}
}
```