Update README.md

README.md

@@ -9,10 +9,11 @@ base_model: sentence-transformers/all-MiniLM-L6-v2
 metrics:
 - accuracy
 widget:
-- text: 'Xác suất để trúng giải thưởng khi bạn mua một tờ vé số là 0.05%. Giả sử mỗi
+- text: >-
+    Xác suất để trúng giải thưởng khi bạn mua một tờ vé số là 0.05%. Giả sử mỗi
     ngày bạn mua 1 tờ vé số, vậy
 
-    chúng ta cần bao nhiêu ngày (trung bình) để có 98% cơ hội trúng?'
+    chúng ta cần bao nhiêu ngày (trung bình) để có 98% cơ hội trúng?
 - text: Briefly describe the concept of photosynthesis.
 - text: What are the benefits of using cloud storage?
 - text: Write a Python function that checks if a given number is prime.
@@ -32,10 +33,22 @@ model-index:
     - type: accuracy
       value: 0.25
       name: Accuracy
+license: mit
+datasets:
+- chibao24/gpt_routing
+language:
+- vi
+- en
 ---
 
 # SetFit with sentence-transformers/all-MiniLM-L6-v2
 
+This model is gpt routing between gpt.5 and gpt-4o based on my prompt (to reduce cost). You can take a look at the dataset for more information.
+I got the idea from this [LLM classifier](https://github.com/lamini-ai/llm-classifier) 
+
+The model utilizes Few-Shot Learning techniques through SetFit, requiring only 8 examples per class. It can be trained in less than 1 minute on an RTX 3060 graphics card. 
+This method provides an efficient solution for developing lightweight models suitable for real-world applications.
+
 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
 
 The model has been trained using an efficient few-shot learning technique that involves: