Update README.md

README.md

@@ -1,50 +1,30 @@
 ---
-{}
+license: apache-2.0
+language:
+- en
+base_model:
+- HuggingFaceTB/SmolLM2-135M-Instruct
+pipeline_tag: text-classification
 ---
----
-    language: en
-    tags:
-    - text-classification
-    - eou-detection
-    - conversation
-    pipeline_tag: text-classification
-    inference: true
-    ---
-
-    # Turnsense: Turn-Detector Model
-
-    A lightweight end-of-utterance (EOU) detection model fine-tuned on SmolLM2-135M, optimized for Raspberry Pi and low-power devices.
-
-    ## Model Details
-    - **Task:** Text Classification (End-of-User-Turn Detection)
-    - **Type:** llama
-    - **Last Updated:** 2025-03-13 04:24:17
-    - **Updated By:** latishab
+# Turnsense: Turn-Detector Model
 
-    ## Usage
+A lightweight end-of-utterance (EOU) detection model fine-tuned on SmolLM2-135M, optimized for Raspberry Pi and low-power devices.
 
-    ### With Transformers Pipeline
-    ```python
-    from transformers import pipeline
+## Model Details
+  - **Task:** Text Classification (End-of-User-Turn Detection)
+  - **Type:** llama
+  - **Last Updated:** 2025-03-13 04:24:17
+  - **Updated By:** latishab
 
-    # Example 1: Basic usage
-    classifier = pipeline("text-classification", model="latishab/turnsense")
-    text = "Hello, how are you?"
-    result = classifier(text)
-    print(result)
+## Usage
 
-    # Example 2: Using with messages format
-    messages = [
-        {'role': 'user', 'content': 'Who are you?'},
-    ]
-    result = classifier(messages)
-    print(result)
+### With Transformers Pipeline
+  ```python
+  from transformers import pipeline
 
-    # Example 3: Batch processing
-    texts = [
-        "Hello, how are you?",
-        "I'm doing great, thanks for asking!",
-    ]
-    results = classifier(texts)
-    print(results)
-    
+  # Example 1: Basic usage
+  classifier = pipeline("text-classification", model="latishab/turnsense")
+  text = "Hello, how are you?"
+  result = classifier(text)
+  print(result)
+  ```