How to Use BERT-base-hi-cased for Multilingual NLP

May 20, 2021

In this blog, we will explore the implementation of the bert-base-hi-cased model, a smaller version of the BERT (Bidirectional Encoder Representations from Transformers) specifically designed for Hindi language processing. This model aims to preserve accuracy while providing a more efficient alternative to larger multilingual models.

What is BERT-base-hi-cased?

The bert-base-hi-cased model is an innovative creation that handles a custom number of languages, offering the same high-quality representations as the original bert-base-multilingual-cased model. This model is particularly useful for users who prioritize both performance and resource efficiency.

How to Use BERT-base-hi-cased

Here’s a step-by-step guide on how to implement the bert-base-hi-cased in your Python project using the Transformers library:

First, ensure you have the Transformers library installed. You can do this by running the following command:

pip install transformers

Next, import the necessary packages from the Transformers library.

from transformers import AutoTokenizer, AutoModel

Load the tokenizer and the model using the below code:

tokenizer = AutoTokenizer.from_pretrained('Geotrend/bert-base-hi-cased')

model = AutoModel.from_pretrained('Geotrend/bert-base-hi-cased')

That’s it! You are now ready to generate representations using the bert-base-hi-cased model!

Understanding the Code: An Analogy

Imagine you are a librarian dealing with a vast collection of books (language data). You need a systematic way to categorize these books for readers (NLP applications). The original BERT library is like a huge mainframe database that can handle any request, but it is resource-heavy and cumbersome. The bert-base-hi-cased is a specialized smaller bookshelf that still holds the essential titles but is easier to navigate and more efficient for specific user needs. By using this model, you are simplifying your task, ensuring that you can quickly access the same quality of information without the overhead of a large system.

Troubleshooting

If you run into any issues while implementing the model, here are a few suggestions:

Ensure you have internet connectivity as the model will be downloaded from the Transformers library.
The name Geotrend/bert-base-hi-cased should be typed accurately, or else it might throw errors.
If you encounter ImportError, double-check the installation of the Transformers library.
For any additional assistance, feel free to reach out via email or visit our paper titled Load What You Need: Smaller Versions of Multilingual BERT.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

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.