Update README.md

README.md

@@ -1,3 +1,44 @@
 ---
-license: unknown
+license: mit
+language:
+- en
+widget:
+- text: >
+    In a busy city street, an autonomous car navigates around pedestrians and cyclists, making a series of complex decisions to ensure safety.
+  example_title: Complex VQA Scenario
+- text: >
+    On a clear highway, an autonomous vehicle adjusts its speed based on the flow of traffic and the presence of a slower-moving truck ahead.
+  example_title: Simple VQA Scenario
+tags:
+- vision-language
+- autonomous-driving
 ---
+
+### What is this?
+Lingo-Judge, a novel evaluation metric that aligns closely with human judgment on the LingoQA evaluation suits.
+
+### How to use
+```python
+# Import necessary libraries
+from transformers import pipeline
+
+# Define the model name to be used in the pipeline
+model_name = 'wayveai/Lingo-Judge'
+
+# Define the question and its corresponding answer and prediction
+question = "Are there any pedestrians crossing the road? If yes, how many?"
+answer = "1"
+prediction = "Yes, there is one"
+
+# Initialize the pipeline with the specified model, device, and other parameters
+pipe = pipeline("text-classification", model=model_name)
+# Format the input string with the question, answer, and prediction
+input = f"[CLS]\nQuestion: {question}\nAnswer: {answer}\nStudent: {prediction}"
+
+# Pass the input through the pipeline to get the result
+result = pipe(input)
+
+# Print the result and score
+score = result[0]['score']
+print(score > 0.5, score)
+