update examples

docs/functions/cast.mdx

@@ -16,19 +16,17 @@ For complex transformations, consider creating a custom task. The `cast` functio
 
 Convert natural language descriptions of numbers into their numeric representation:
 
-<CodeGroup>
-```python Code
+```python
 import marvin
 
 # Convert text to a number
 price = marvin.cast("three dollars and fifty cents", float)
 print(price)
 ```
 
-```python Result
+```python
 3.5
 ```
-</CodeGroup>
 
 ## Parameters
 
@@ -63,42 +61,37 @@ asyncio.run(main())
 
 Convert natural language descriptions of quantities into numeric values:
 
-<CodeGroup>
-```python Code
+```python
 import marvin
 
 price = marvin.cast("three dollars and fifty cents", float)
 print(price)
 ```
 
-```python Result
+```python
 3.5
 ```
-</CodeGroup>
 
 ### Boolean Conversion
 
 Interpret natural language expressions as boolean values:
 
-<CodeGroup>
-```python Code
+```python
 import marvin
 
 answer = marvin.cast("that sounds great!", bool)
 print(answer)
 ```
 
-```python Result
+```python
 True
 ```
-</CodeGroup>
 
 ### Structured Data
 
 Extract structured information from natural language descriptions:
 
-<CodeGroup>
-```python Code
+```python
 import marvin
 from dataclasses import dataclass
 
@@ -115,17 +108,15 @@ address = marvin.cast(
 print(f"{address.street}, {address.city}, {address.country}")
 ```
 
-```python Result
+```python
 "123 Main St", "New York", "USA"
 ```
-</CodeGroup>
 
 ### Custom Instructions
 
 Use instructions to control how text is interpreted and formatted:
 
-<CodeGroup>
-```python Code
+```python
 import marvin
 
 phone = marvin.cast(
@@ -136,7 +127,6 @@ phone = marvin.cast(
 print(phone)
 ```
 
-```python Result
+```python
 "555-0123"
-```
-</CodeGroup> 
+``` 

docs/functions/classify.mdx

@@ -16,8 +16,7 @@ For complex classification needs, consider creating a custom task. The `classify
 
 Classify text into predefined categories:
 
-<CodeGroup>
-```python Code
+```python
 import marvin
 
 sentiment = marvin.classify(
@@ -27,10 +26,9 @@ sentiment = marvin.classify(
 print(sentiment)
 ```
 
-```python Result
+```python
 "positive"
 ```
-</CodeGroup>
 
 ## Parameters
 
@@ -66,8 +64,7 @@ asyncio.run(main())
 
 Use simple string labels to categorize content:
 
-<CodeGroup>
-```python Code
+```python
 import marvin
 
 priority = marvin.classify(
@@ -77,17 +74,15 @@ priority = marvin.classify(
 print(priority)
 ```
 
-```python Result
+```python
 "critical"
 ```
-</CodeGroup>
 
 ### Using Enums
 
 Add type safety with Python enums:
 
-<CodeGroup>
-```python Code
+```python
 import marvin
 from enum import Enum
 
@@ -105,18 +100,16 @@ print(priority)
 print(priority.value)
 ```
 
-```python Result
+```python
 Priority.LOW
 "low"
 ```
-</CodeGroup>
 
 ### Multi-label Classification
 
 Identify multiple applicable categories:
 
-<CodeGroup>
-```python Code
+```python
 import marvin
 
 genres = marvin.classify(
@@ -127,17 +120,15 @@ genres = marvin.classify(
 print(genres)
 ```
 
-```python Result
+```python
 ["comedy", "romance", "sci-fi"]
 ```
-</CodeGroup>
 
 ### Custom Instructions
 
 Guide the classification with specific criteria:
 
-<CodeGroup>
-```python Code
+```python
 import marvin
 
 sentiment = marvin.classify(
@@ -148,7 +139,6 @@ sentiment = marvin.classify(
 print(sentiment)
 ```
 
-```python Result
+```python
 "negative"
-```
-</CodeGroup> 
+``` 

docs/functions/extract.mdx

@@ -16,8 +16,7 @@ For complex extraction patterns, consider creating a custom task. The `extract`
 
 Pull email addresses from text:
 
-<CodeGroup>
-```python Code
+```python
 import marvin
 
 emails = marvin.extract(
@@ -28,10 +27,9 @@ emails = marvin.extract(
 print(emails)
 ```
 
-```python Result
+```python
 ["[email protected]", "[email protected]"]
 ```
-</CodeGroup>
 
 ## Parameters
 
@@ -66,8 +64,7 @@ asyncio.run(main())
 
 Find all numbers in text:
 
-<CodeGroup>
-```python Code
+```python
 import marvin
 
 temperatures = marvin.extract(
@@ -77,17 +74,15 @@ temperatures = marvin.extract(
 print(temperatures)
 ```
 
-```python Result
+```python
 [75.0, 62.0]
 ```
-</CodeGroup>
 
 ### Named Entities
 
 Extract specific types of information:
 
-<CodeGroup>
-```python Code
+```python
 import marvin
 
 cities = marvin.extract(
@@ -98,17 +93,15 @@ cities = marvin.extract(
 print(cities)
 ```
 
-```python Result
+```python
 ["New York", "London"]
 ```
-</CodeGroup>
 
 ### Dates and Times
 
 Find temporal information in text:
 
-<CodeGroup>
-```python Code
+```python
 import marvin
 from datetime import datetime
 
@@ -120,17 +113,15 @@ dates = marvin.extract(
 print(dates)
 ```
 
-```python Result
+```python
 [datetime(2024, 3, 15, 14, 30)]
 ```
-</CodeGroup>
 
 ### Structured Data
 
 Pull complex information into structured types:
 
-<CodeGroup>
-```python Code
+```python
 import marvin
 from dataclasses import dataclass
 
@@ -151,8 +142,7 @@ print(f"{contacts[0].name}: {contacts[0].phone}")
 print(f"{contacts[1].name}: {contacts[1].phone}")
 ```
 
-```python Result
-"John Smith: 555-0123"
-"Mary Johnson: None"
+```python
+John Smith: 555-0123
+Mary Johnson: None
 ```
-</CodeGroup>