Building Your Own Custom Named Entity Recognition (NER) Model with spaCy V3: A Step-by-Step Guide
In this blog post, I’ll take you on a journey into the world of custom NER using spaCy v3. We’ll explore why custom NER is essential, how it outperforms ready-made NER libraries, and guide you through building your own NER model from annotated data.
In the world of Natural Language Processing (NLP), extracting valuable information from text data is a fundamental task. Named Entity Recognition (NER) is the secret sauce behind many NLP applications, helping to identify and categorize entities like names, dates, and locations within text.
While there are pre-built NER libraries available, check out my other blogs. Creating a custom NER model offers unparalleled flexibility and accuracy for your NLP projects.
In this blog post, I’ll take you through the process of building your own custom NER model using spaCy, one of the most powerful NLP libraries out there. We’ll go step by step, from loading data to training the model. By the end, you’ll have the skills to harness the full potential of custom NER for your specific needs.
Custom NER vs Pre-trained Models:
- Domain Specificity: Custom NER excels at domain-specific data for better accuracy.
