update our README.md file

README.md

@@ -1,19 +1,53 @@
 ---
-## This project uses a fine-tuned BERT model that is an encoder only model without a decoder used to detect user intent from text inputs (e.g., chatbot queries).
-
-
-
 license: apache-2.0
+base_model: google-bert/bert-base-uncased
 datasets:
 - clinc/clinc_oos
 metrics:
 - accuracy
-base_model:
-- google-bert/bert-base-uncased
 library_name: transformers
 tags:
+- nlp
+- intent-classification
 - finance
 - travel
 - banking
-- nlp
----
+
+---
+
+## 🤖 Intent Detection using Fine-Tuned BERT
+
+This project utilizes a fine-tuned BERT model (`bert-base-uncased`) for **intent classification** tasks. It is an **encoder-only transformer** designed to detect user intents from text inputs (e.g., chatbot queries) and classify them into predefined categories such as `banking`, `travel`, `finance`, and more.
+
+The model is trained on the [CLINC150 (clinc_oos)](https://huggingface.co/datasets/clinc/clinc_oos) dataset and evaluated using accuracy as the primary metric.
+
+---
+## 📊 Dataset --> CLINC150
+
+The project uses the **CLINC150 dataset**, a benchmark dataset for intent classification in task-oriented dialogue systems.
+
+---
+### 🧾 Dataset Overview
+
+- **Total intents**: 150 unique user intents
+- **Domains**: 10 real-world domains (e.g., banking, travel, weather, small talk)
+- **Examples**: ~22,500 utterances
+- **Language**: English
+- **Out-of-scope (OOS)**: Includes OOS examples to test robustness
+
+---
+
+### 📦 Source
+
+- Official repo: [clinc/oos-eval](https://github.com/clinc/oos-eval)
+- Hugging Face: [`clinc_oos`](https://huggingface.co/datasets/clinc_oos)
+
+---
+
+## 🚀 Example
+
+### Request: "I want to book a flight"
+
+### Response: "book_flight"
+```
+