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---
license: mit
language:
- en
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: token-classification
tags:
- ner
- spacika_spacy
- english
- token classification
---
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# 🛰️ 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](https://huggingface.co/Varnikasiva)**, Spacika blends the power of transformer-backed models with production-friendly NER pipeline.
---
## ✨ 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
---
## 🤝 Collaborate with Me
I'm open to collaborations, research projects, and ideas to extend this model or build similar applications.
📬 **Email:** [varnikas753@gmail.com](mailto:varnikas753@gmail.com)
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