Update README.md

README.md

@@ -1,22 +1,39 @@
 ---
-license: mit
 tags:
-- text-classification
-- pytorch
+- spaCy
+- ner
+- named-entity-recognition
+- custom-model
+- transformer
+- spacy-pipeline
+license: mit
+language: en
+library_name: spacy
+pipeline_tag: ner
 ---
 
+# 🛰️ Spacika — Custom Named Entity Recognition Model
+
+**Spacika** is a powerful and lightweight Named Entity Recognition (NER) model built with [spaCy](https://spacy.io/), fine-tuned to extract meaningful entities like names, organizations, locations, and more from natural language text.
 
-This model is fine-tuned for sentiment analysis wgiach predicts various human emotions.
+Created with precision and passion by **[Varnika](https://huggingface.co/Varnikasiva)**, Spacika blends the power of transformer-backed models with spaCy’s fast and production-friendly NER pipeline.
 
+---
+
+## ✨ Features
 
-## Usage
+- ✅ Fast and efficient NER tagging
+- 🧠 Transformer-based backbone (custom-trained with spaCy v3)
+- 📚 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
 
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+---
 
-tokenizer = AutoTokenizer.from_pretrained("Varnikasiva/Spacika")
-model = AutoModelForSequenceClassification.from_pretrained("Varnikasiva/Spacika")
+## 🚀 How to Use
 
-inputs = tokenizer("This movie was great!", return_tensors="pt")
-outputs = model(**inputs)
+### 🔧 Installation
 
+```bash
+pip install spacy
+python -m spacy download Varnikasiva/Spacika