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

![Banner](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi-yQzJW_E0WLjBtsYo0uOYY5HftkAWrBMM1vrM0bf_i4rZLGKBvunILf6dp61jaOLjwfNgcqZ_TuamidRQnKWZljk4MsyGnv71-E_-0RSvnb7LpivdvJ8s6rLzGNNHmlsVXepkH2t4Jv4elclD0P90zE7ge3t6fJka8HwBWcJ0_mE433Rj7uoYhXWG-D4/s2000/ml%20(2).png)

# 🛰️ 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)