Welcome to the world of AI language models! Today, we will delve into the fascinating SanBERTa, a RoBERTa model specifically trained on Sanskrit. We will walk you through its features, configuration, usage examples, and tips for troubleshooting. Let’s embark on this linguistic journey!

Understanding SanBERTa

SanBERTa leverages the capabilities of RoBERTa but is tailored for the ancient language of Sanskrit. Think of SanBERTa as a skilled translator who not only understands Sanskrit but can also provide context, emotion, and meaning to its expressions.

Model Size and Configuration

The SanBERTa model is compact yet powerful, with a size of 340MB. The model configuration includes:

• Number of Attention Heads: 12
• Number of Hidden Layers: 6
• Hidden Size: 768
• Vocabulary Size: 29,407

Dataset Details

SanBERTa has been trained on:

• Wikipedia articles (used in iNLTK)
• Sanskrit scraps from CLTK

Training and Evaluation

SanBERTa was trained using the TPU, focusing on language modeling with iterative adjustments to the block size from 128 to 256 over epochs. After training, the model achieved a perplexity score of 4.04 with a block size of 256, signifying good performance.

Example Usage of SanBERTa

Now, let’s see how to implement SanBERTa in your projects.

For Embeddings

You can easily extract embeddings from SanBERTa with the following code:

tokenizer = AutoTokenizer.from_pretrained('surajp/SanBERTa')
model = RobertaModel.from_pretrained('surajp/SanBERTa')
op = tokenizer.encode('इयं भाषा न केवलं भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।', return_tensors='pt')
ps = model(op)

The output will yield:

torch.Size([1, 47, 768])

For Mask Prediction

To predict masked tokens in a sentence, use the following code:

from transformers import pipeline
fill_mask = pipeline('fill-mask', model='surajp/SanBERTa', tokenizer='surajp/SanBERTa')

fill_mask('इयं भाषा न केवलmask भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।')

This will give you predictions for the masked word and can look something like this:

[score: 0.7516744136810303,  sequence: 'इयं भाषा न केवलं भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।', token: 280,  token_str: 'à¤Ĥ', ...]

Troubleshooting Tips

If you encounter issues while working with SanBERTa, consider the following troubleshooting ideas:

• Ensure that all required libraries (Transformers, PyTorch, etc.) are up to date.
• Check your internet connection while downloading models and datasets.
• If you receive memory errors, consider reducing the batch size during inference.
• Verify the integrity of the input data; malformed data can lead to errors.
• For further assistance and community support, explore resources or reach out to collaborators.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

SanBERTa stands as a significant advancement in understanding and utilizing Sanskrit within the realm of natural language processing. Its training on a variety of datasets, along with its user-friendly implementation, makes it a fantastic tool for developers and scholars alike. 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.