How to Use the BERTurk Model for Turkish NLP

May 20, 2021

In this guide, we will explore how to effectively utilize the BERTurk model, an innovative uncased BERT model intended for the Turkish language. The BERTurk model has been developed by the talented team at the MDZ Digital Library in collaboration with the vibrant Turkish NLP community.

What is BERTurk?

BERTurk is a community-driven BERT model specifically designed for Turkish, making it a valuable asset for complex NLP tasks in this language. With a training corpus of 35GB constructed from a variety of sources including OSACR, Wikipedia, and OPUS datasets, this model is equipped with the ability to handle a range of tasks, such as part-of-speech tagging and named entity recognition.

Getting Started

To use the BERTurk model in your projects, follow these simple steps:

Install Transformers: To get started with BERTurk, first, ensure that you have the Transformers library installed in your environment. You can do this using pip:

pip install transformers

Load the Model: You can load the BERTurk model with just a few lines of code:

from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-turkish-128k-uncased")
model = AutoModel.from_pretrained("dbmdz/bert-base-turkish-128k-uncased")

Run Your NLP Tasks: Once the model is loaded, you can start using it for various natural language processing tasks!

Understanding the Code

To help you better understand the code snippet above, let’s use an analogy. Think of the BERTurk model as a cooking recipe. Loading the model is akin to gathering all your ingredients (the tokenizer and model) before you start cooking (performing NLP tasks). The function from_pretrained() acts as your delivery service, bringing all necessary items directly to your kitchen.

Troubleshooting

If you encounter any issues while working with the BERTurk model, consider the following troubleshooting tips:

Ensure that you are using an updated version of the Transformers library (version 2.3 or higher).
Check your Python environment for any package conflicts.
If you need TensorFlow checkpoints, raise an issue directly on their GitHub page as mentioned in the original documentation.
Verify your internet connection, as loading pretrained models requires downloading data.

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Conclusion

The BERTurk model is an exciting development for the Turkish NLP community, allowing you to leverage the power of Transformers in your projects. By following this guide, you can effortlessly load and utilize this model to enhance your natural language processing tasks.

Acknowledgments

A special thanks goes to the contributors and supporters who made this project possible, including Kemal Oflazer for supplying vast corpora and the Hugging Face team for their generous support.

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.