README.md

metadata

license: mit
language:
  - en
metrics:
  - accuracy
  - precision
  - recall
  - f1
pipeline_tag: token-classification
tags:
  - ner
  - spacika_spacy
  - english
  - token classification

🛰️ Spacika — Custom Named Entity Recognition Model

Spacika is a powerful and lightweight Named Entity Recognition (NER) model, fine-tuned to extract meaningful entities like names, organizations, locations, and more from natural language text.

Created with precision and passion by Varnika, Spacika blends the power of transformer-backed models with production-friendly NER pipeline.

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✨ Features

• ✅ Fast and efficient NER tagging
• 🧠 Transformer-based backbone
• 📚 Trained on domain-specific and/or general English data
• 🔖 Identifies entities like PERSON, ORG, GPE, DATE, MONEY, and more
• 🌐 Easy to load, test, and integrate into any Python NLP workflow

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🤝 Collaborate with Me

I'm open to collaborations, research projects, and ideas to extend this model or build similar applications.

📬 Email: varnikas753@gmail.com