Update README.md

README.md

@@ -16,11 +16,28 @@ pipeline_tag: text-classification
 library_name: transformers
 tags:
 - text-classification
-- trip
 - multi-text-classification
 - classification
+- intent-classification
+- intent-detection
+- nlp
+- natural-language-processing
+- transformers
+- edge-ai
+- iot
+- smart-home
+- location-intelligence
+- voice-assistant
+- conversational-ai
+- real-time
+- boltuix
+- neurobert
 ---
 
+
+![Banner](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg0DZmojOOjUG9wZGTDZ4xIkVO6ifyCF06eO4N4x3cKIDPRZlym5tlCP3qfJJSK7UVFix2TUvzvrJDPFyuylmzJrPiCWxwWou74YXhSwMhRVohlG34w9Ln29MbhzU_NSc1rRzfZgJE8i7BViLiuSR7hyphenhyphenEBYg12C26HCyLne5VLkWzfClbMRKAGzMJSxv-4/s6250/NeuroLocale.jpg)
+
+
 # 🌍 NeuroLocale — Your Smarter Nearby Assistant! 🗺️
 
 [![License: Open Source](https://img.shields.io/badge/License-Open%20Source-green.svg)](https://opensource.org/licenses)
@@ -91,6 +108,7 @@ print(result)  # 🖨️ Example Output: [{'label': 'game', 'score': 0.9999}]
 
 ---
 
+
 ## Supported Categories 🏪
 
 NeuroLocale supports **120+ local business categories**, each paired with an emoji for clarity:
@@ -228,7 +246,7 @@ Get started with NeuroLocale:
 pip install transformers torch pandas scikit-learn tqdm
 ```
 
-- **Requirements** 📋: Python 3.8+, ~500MB storage for model and dependencies.
+- **Requirements** 📋: Python 3.8+, ~50MB storage for model and dependencies.
 - **Optional** 🔧: CUDA-enabled GPU for faster training/inference.
 - **Model Download** 📥: Grab the pre-trained model from [Hugging Face](https://huggingface.co/boltuix/NeuroLocale).
 
@@ -247,6 +265,37 @@ NeuroLocale is trained using **NeuroBERT** for multi-class text classification.
   ...
   ```
 
+
+# 🤖 Supported Categories from `boltuix/NeuroLocale`
+
+This file shows how to extract the full list of intent labels supported by the `boltuix/NeuroLocale` model using Hugging Face Transformers.
+
+---
+
+## 🔧 How to List All Supported Categories
+
+```python
+from transformers import AutoModelForSequenceClassification
+
+# 📥 Load the fine-tuned intent classification model
+model = AutoModelForSequenceClassification.from_pretrained("boltuix/NeuroLocale")
+
+# 🏷️ Extract the ID-to-label mapping dictionary
+label_mapping = model.config.id2label
+
+# 📋 Convert and sort all labels to a clean list
+supported_labels = sorted(label_mapping.values())
+
+# ✅ Print the supported categories
+print("✅ Supported Categories:", supported_labels)
+
+#✅ Output
+#✅ Supported Categories: ['accounting firm', 'airport', 'amusement park', ',...
+
+  ```
+---
+
+
 ### Training Code
 ```python
 import pandas as pd
@@ -491,7 +540,7 @@ NeuroLocale was tested on **122 test cases**, achieving **94.26% accuracy** (115
 - **Source**: Open-source datasets, augmented with custom queries (e.g., ChatGPT, Grok, or proprietary data).
 - **Format**: CSV with `text` (query) and `label` (category) columns.
 - **Categories**: 120+ (see [Supported Categories](#supported-categories)).
-- **Size**: Varies based on dataset; model footprint ~500MB.
+- **Size**: Varies based on dataset; model footprint ~50MB.
 - **Preprocessing**: Handled via tokenization and label encoding (see [Training the Model](#training-the-model)).
 
 ---