How to Use IndicBERT for Multilingual NLP Tasks

April 12, 2021

IndicBERT is an incredibly powerful multilingual AI model that has been fine-tuned for major Indian languages. This blog post will guide you on how to get started with IndicBERT, whether you’re a seasoned NLP expert or just someone with a curious mind.

What is IndicBERT?

IndicBERT is a bilingual language model based on the ALBERT architecture. It has been pretrained exclusively on 12 major Indian languages: Assamese, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, and Telugu. Interestingly, this model is trained on a whopping 9 billion tokens contained in AI4Bharat’s monolingual corpus, making it unique compared to other models like mBERT and XLM-R.

Understanding the Code: An Analogy

Imagine you are a chef preparing a lavish feast involving everything from starters to desserts. Each dish represents a language, and the ingredients are akin to the tokens that go into each dish. The recipe you follow is IndicBERT. Just as a chef masters each dish individually while creating a harmonious meal, IndicBERT has learned from a vast pool of tokens in different languages to serve an impressive variety. When you invoke the model, you are asking the chef to whip up precisely the dish (language task) you need.

Steps to Get Started with IndicBERT

Clone the Repository: Begin by cloning the IndicBERT repository from GitHub. You can find the code here.
Download the Model: You can download the pre-trained model here. The archive contains both TensorFlow checkpoints and PyTorch binaries.
Set Up Your Environment: Make sure your machine has the required libraries and dependencies for running IndicBERT. This includes TensorFlow or PyTorch based on your choice of framework.
Load the Model: Using the appropriate library, load the IndicBERT model into your environment. This will allow you to perform various NLP tasks.
Perform NLP Tasks: With the model loaded, you can now tackle tasks like Named Entity Recognition, sentiment analysis, and more!

Troubleshooting Common Issues

Here are some common issues you might encounter, along with solutions:

Issue: Model not loading.
Solution: Ensure that all dependencies are installed and that your TensorFlow or PyTorch environment is set up correctly.
Issue: Insufficient memory for computations.
Solution: Try using a smaller batch size or ensure you have adequate computational resources available.
Model performance seems poor.
Solution: Check if you are using the correct input format and tokens. You might also want to review the language settings inherent to indicative tasks.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.