In today’s globalized world, languages often borrow terms from one another. Spanish is no exception, with numerous English words making their way into everyday usage. This blog will guide you through using the mBERT model to identify these lexical borrowings, known as anglicisms, in Spanish news. Let’s dive into how to harness this powerful tool in a user-friendly manner!

What is mBERT?

mBERT, or multilingual BERT, is a language model created to process multiple languages, making it particularly suited for tasks like detecting unassimilated English lexical borrowings in Spanish texts. This pretrained model identifies foreign words (primarily from English) that have not been fully integrated into the Spanish language.

Setting Up the Environment

Before you start detecting anglicisms, you’ll need to set up your coding environment. Here’s how you can do that:

• Ensure you have Python and pip installed on your system.
• Install the necessary libraries:
  

  pip install transformers

Using the mBERT Model

Now that your environment is ready, follow these steps to utilize the model for detecting anglicisms:

• Import the required libraries:

from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer

• Load the tokenizer and model:

tokenizer = AutoTokenizer.from_pretrained("lirondos/anglicisms-spanish-mbert")

model = AutoModelForTokenClassification.from_pretrained("lirondos/anglicisms-spanish-mbert")

• Create the NLP pipeline:

nlp = pipeline("ner", model=model, tokenizer=tokenizer)

• Run the model on a sample input:

example = "Buscamos data scientist para proyecto de machine learning."

borrowings = nlp(example)

print(borrowings)

Understanding the Code through an Analogy

Imagine you’re a security guard at a party, responsible for identifying guests who are dressed inappropriately for the event. Similarly, the mBERT model acts as your security team, identifying borrowed English words that haven’t been adapted to Spanish. Each word is analyzed, and when the guard (the model) spots someone who doesn’t fit in (an anglicism), it flags them for further examination (classifies them as ENG or OTHER borrowings).

Metrics for Performance Evaluation

To evaluate the effectiveness of your model, consider the following metrics obtained from testing:

• Overall Precision: 88.09%
• Overall Recall: 79.46%
• F1 Score: 83.55%

Troubleshooting Tips

If you encounter issues during your implementation, consider the following troubleshooting steps:

• Ensure all libraries are updated to their latest versions.
• Double-check your internet connection; model downloads may fail otherwise.
• If you receive an error concerning missing files, re-install the necessary models with the correct paths.

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

Conclusion

By following the steps outlined in this blog, you should be well-equipped to identify anglicisms in Spanish texts using the mBERT model. 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.