WizardLM-2 8x22B

Description

WizardLM-2 8x22B is Microsoft AI's most advanced Wizard model. It demonstrates highly competitive performance compared to leading proprietary models, and it consistently outperforms all existing state-of-the-art opensource models.

It is an instruct finetune of Mixtral 8x22B.

To read more about the model release, click here.

#moe

Prompt

I need to take this json:

{
'first_name': 'Jane',
'last_name': 'Smith',
'date_of_birth': '03/16/1977',
orders: [
{
'created': '2024-01-25T15:00:04',
'amount': '100.00'
}
]}

And generate some dataclasses.

date_of_birth needs to be a date.
created is a datetime.
amount is a decimal.

How would I do this using the marshmallow library in python?

Response 1 (7.0)

To generate data classes from the provided JSON using the marshmallow library in Python, you'll need to define schemas that correspond to your JSON structure and then use marshmallow to deserialize the JSON into Python objects. Here's how you can do it:

First, install marshmallow if you haven't already:

pip install marshmallow

Next, you'll need to install the marshmallow-dataclass package if you want to use data classes:

pip install marshmallow-dataclass

Now, you can define your data classes and the corresponding marshmallow schemas:

from datetime import datetime
from marshmallow import fields, Schema
from marshmallow_dataclass import dataclass
from decimal import Decimal
import json

@dataclass
class Order:
    created: datetime
    amount: Decimal

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal()

@dataclass
class User:
    first_name: str
    last_name: str
    date_of_birth: datetime
    orders: list[Order]

class UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.DateTime(format="%m/%d/%Y")
    orders = fields.List(fields.Nested(OrderSchema))

# Your JSON data
json_data = '''
{
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  'orders': [
    {
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
    }
  ]
}
'''

# Parse the JSON data
user_dict = json.loads(json_data)

# Create a schema instance
user_schema = UserSchema()

# Deserialize the JSON data into a User object
user = user_schema.load(user_dict)

print(user)

In this code:

We define two data classes: Order and User.
We create corresponding marshmallow schemas: OrderSchema and UserSchema.
We use @dataclass decorator from marshmallow-dataclass to automatically generate marshmallow schemas from our data classes.
We use fields.DateTime() for the created and date_of_birth fields, with a specific format for date_of_birth to match the given JSON format.
We use fields.Decimal() for the amount field to handle decimal numbers.
We load the JSON data into a dictionary and then use the UserSchema to deserialize it into a User object.

Please note that the JSON provided has incorrect quotation marks around the keys. JSON keys should be enclosed in double quotes, not single quotes. Make sure to correct this in the actual JSON string you're working with. Here's the corrected JSON:

{
  "first_name": "Jane",
  "last_name": "Smith",
  "date_of_birth": "03/16/1977",
  "orders": [
    {
      "created": "2024-01-25T15:00:04",
      "amount": "100.00"
    }
  ]
}

After running the code with the corrected JSON, user will be an instance of User with the data deserialized from the JSON.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (7.0) (1.10¢)

I'll evaluate the solution based on the provided marking scheme:

The solution defines two dataclasses, User and Order. (2 marks)
The solution defines two schema classes, UserSchema and OrderSchema. (2 marks)
The solution specifies the date format for the date_of_birth field correctly: fields.DateTime(format="%m/%d/%Y"). (1 mark)
The decimal field (amount) is not specified with as_string=True. It should be fields.Decimal(as_string=True). (0 marks)
The schema and dataclasses are not linked with a @post_load method on the schema classes. (0 marks)
Additional points:
The solution correctly uses marshmallow-dataclass.
It handles the nested structure of orders correctly.
It provides a complete working example, including JSON parsing.
It mentions the issue with single quotes in the provided JSON.
(2 marks)

Breakdown of marks:

{
  "Dataclasses": 2,
  "Schema classes": 2,
  "Date format": 1,
  "Decimal field": 0,
  "Post_load method": 0,
  "Additional points": 2
}

Score:

{
  "scores": [2, 2, 1, 0, 0, 2],
  "total_score": 7
}

Comments:
The solution provides a good foundation for using marshmallow with dataclasses to handle the given JSON structure. It correctly defines the necessary dataclasses and schema classes, and handles the date format for the date_of_birth field. However, it misses two important points: the decimal field should be specified with as_string=True, and there's no @post_load method to link the schemas with the dataclasses. Despite these omissions, the solution demonstrates a good understanding of marshmallow and provides a working example that covers most of the requirements.

openai/gpt-4-0125-preview says: (7.0) ()

The provided solution handles creating Python data classes and the corresponding marshmallow schemas to deserialize JSON data into Python objects. Here is an assessment based on the provided marking scheme:

Dataclasses: The solution defines two data classes (Order and User) as specified in the requirement. (+2 marks)
Schema Classes: It correctly defines two schema classes (OrderSchema and UserSchema) for serialization and deserialization. (+2 marks)
Date Format: The date_of_forum field in the UserSchema specifies the date format (format="%m/%d/%Y") correctly to match the input JSON format. (+1 mark)
Decimal Field: The use of fields.Decimal() is correct, but the as_string=True property is not specified. The requirement was to ensure that the decimal field is treated as a string upon serialization, which is not ensured here. (+0 marks)
@post_load Method: The solution does not use a @post_load method to create instances of the data classes from the deserialized data; it uses the automatic conversion provided by marshmallow_dataclass. While this achieves the same result in practice, it doesn't adhere to the specifics of the task requirements to manually ensure data class creation through @post_load. (+0 marks)
Additional Elements: The solution integrates well with real-world application aspects, includes corrections for potential pitfalls (such as JSON key quotes), and enriches the usage of marshmallow with practical insights such as handling potential data type issues and JSON format errors which were out of format as provided. (+2 marks)

Given this analysis, the calculation yields the following score:

Total Score: 7 out of 10

{
  "score": 7
}

This score reflects the comprehensive handling of data structures and schemas but misses on specific details like handling decimals as strings and creating instances using @post_load.

Response 2 (9.0)

To generate data classes from the provided JSON using the marshmallow library in Python, you'll first need to define the schema for your data. Then, you can use marshmallow to deserialize the JSON into Python objects. Here's how you can do it:

Install marshmallow if you haven't already:

pip install marshmallow

Import the necessary modules:

from datetime import datetime, date
from decimal import Decimal
from marshmallow import Schema, fields, post_load
from marshmallow.exceptions import ValidationError

Define the schema for your data classes:

class OrderSchema(Schema):
    created = fields.DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount = fields.Decimal(places=2, as_string=True)

class UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(format='%m/%d/%Y')
    orders = fields.List(fields.Nested(OrderSchema))

    @post_load
    def make_user(self, data, **kwargs):
        return User(**data)

class Order:
    def __init__(self, created, amount):
        self.created = created
        self.amount = amount

class User:
    def __init__(self, first_name, last_name, date_of_birth, orders):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = date_of_birth
        self.orders = orders

Define a function to deserialize the JSON:

def deserialize_json(json_data):
    try:
        schema = UserSchema()
        result = schema.load(json_data)
        return result
    except ValidationError as err:
        print(err.messages)
        return None

Use the function to deserialize your JSON:

json_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}

user = deserialize_json(json_data)

Now, user should be an instance of the User class with the orders attribute containing a list of Order instances. The date_of_birth will be a date object, created will be a datetime object, and amount will be a Decimal object.

Please note that the @post_load decorator in the UserSchema class is used to convert the deserialized data into an instance of the User class. The OrderSchema is used to validate and deserialize the list of orders within the user schema.

Remember to handle exceptions and errors appropriately in your actual code, as the deserialize_json function currently prints the error messages and returns None if an error occurs.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (9.0) (1.04¢)

Let's evaluate this solution based on the provided marking scheme:

The solution defines two classes: User and Order. Although they are not explicitly defined as dataclasses, they serve the same purpose. (2 marks)
The solution defines two schema classes: UserSchema and OrderSchema. (2 marks)
The solution correctly specifies the date format for the date_of_birth field as format='%m/%d/%Y'. (1 mark)
The decimal field (amount) is correctly defined with as_string=True. (1 mark)
The schema and classes are linked with a @post_load method on the UserSchema class. However, it's missing for the OrderSchema. (1 mark)
Additional points:
The solution includes a deserialize_json function for easy usage.
It handles validation errors.
It imports necessary modules.
(2 marks)

Breakdown of marks:

{
  "Dataclasses": 2,
  "Schema classes": 2,
  "Date format": 1,
  "Decimal field": 1,
  "Post load method": 1,
  "Additional features": 2
}

Total score:

{
  "scores": [2, 2, 1, 1, 1, 2],
  "total_score": 9
}

Comments:
The solution is well-structured and covers most of the requirements. It correctly defines the schemas with proper field types and formats. The use of @post_load for the UserSchema is good, but it's missing for the OrderSchema. While not using the dataclass decorator, the solution still provides equivalent functionality with regular classes. The addition of a deserialize_json function and error handling is a nice touch. Overall, it's a strong solution with minor room for improvement.

openai/gpt-4-0125-preview says: (8.0) ()

The provided solution mostly fulfills the problem's requirements for defining and using dataclasses and schemas with the Marshmallow library. Here's the breakdown:

There are two defined dataclasses, User and Order, which earn 2 marks.
There are two corresponding schema classes, UserSchema and OrderSchema, earning 2 marks.
The date_of_birth field in the UserSchema correctly specifies the date format as %m/%d/%Y, as required, earning 1 mark.
The amount field in the OrderSchema is correctly marked with as_string=True for the Decimal type, meeting the requirement and earning 1 mark.
The @post_load method is correctly implemented in UserSchema to create a User instance from the deserialized data, which rightly earns 2 marks.

Additional points:
- The solution correctly imports necessary Python modules (datetime, date, Decimal, and relevant marshmallow classes), which is essential for functionality but does not directly meet any specific additional requirement.
- The use of as_string=True for the amount field in OrderSchema is properly followed, which is good practice for ensuring Decimal types are serialized properly but is already covered in the original points.
- Exception handling in the deserialization function adds robustness to the code and ensures it gracefully handles malformed input, although error handling specifics could be improved.

Overall, while this solution completes the main tasks, there are minor areas of improvement that could enhance the robustness and error handling capabilities.

Final score:

{
  "score": 8
}

Response 3 (7.0)

To generate data classes from the provided JSON using the marshmallow library in Python, you'll need to define schemas that represent the structure of your data and then use those schemas to deserialize the JSON into objects. Below are the steps to achieve this:

Install marshmallow if you haven't already:

pip install marshmallow

Define the schemas using marshmallow's Schema class:

from marshmallow import Schema, fields, ValidationError, post_load
from datetime import datetime, date
from decimal import Decimal

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal(places=2)

class UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.Nested(OrderSchema, many=True)

    @post_load
    def make_user(self, data, **kwargs):
        return User(**data)

@dataclass
class User:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

@dataclass
class Order:
    created: datetime
    amount: Decimal

# Function to deserialize JSON data to User object
def deserialize_user(json_data):
    schema = UserSchema()
    try:
        user_data = schema.loads(json_data)
        return user_data
    except ValidationError as err:
        print(err.messages)
        return None

# Your JSON data
json_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}

# Deserialize the JSON data
user = deserialize_user(json_data)
print(user)

In the code above:

We define two schemas, OrderSchema and UserSchema, which correspond to the Order and User data classes, respectively.
The fields.DateTime and fields.Date fields are used to parse the created and date_of_birth fields into datetime and date objects, respectively.
The fields.Decimal field is used to parse the amount field into a Decimal object with two decimal places.
The @post_load decorator is used to create a User object from the deserialized data.
The deserialize_user function takes JSON data, uses the UserSchema to validate and deserialize it, and then returns a User object.

Make sure to replace the JSON data with the actual data you want to deserialize. The deserialize_user function will return a User object with the correct types for the date_of_birth, created, and amount fields, as specified in your requirements.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (7.0) (1.10¢)

I'll evaluate the solution based on the provided marking scheme:

Defining two dataclasses:
The solution defines two dataclasses: User and Order. (2 marks)
Defining two schema classes:
The solution defines two schema classes: UserSchema and OrderSchema. (2 marks)
Date format for date_of_birth:
The solution doesn't specify the date format for the date_of_birth field. It should have included format="%m/%d/%Y" in the field definition. (0 marks)
Decimal field with as_string=True:
The solution doesn't include as_string=True for the decimal field. (0 marks)
Linking schema and dataclasses with @post_load:
The solution uses the @post_load decorator on the UserSchema class to create a User object. However, it's missing for the OrderSchema. (1 mark)
Additional points:
The solution imports necessary modules and defines a deserialize_user function. (1 mark)
It includes error handling with ValidationError. (1 mark)

Here's the breakdown of marks:

{
  "Dataclasses": 2,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal as_string": 0,
  "post_load": 1,
  "Additional points": 2
}

Total score:

{
  "scores": [2, 2, 0, 0, 1, 2],
  "total_score": 7
}

The solution covers most of the requirements but misses some important details. It correctly defines the dataclasses and schema classes, and includes some additional helpful elements. However, it fails to specify the correct date format for date_of_birth and doesn't include as_string=True for the decimal field. The @post_load decorator is only used for the UserSchema and not for the OrderSchema. These omissions could lead to incorrect parsing of the input data.

openai/gpt-4-0125-preview says: (6.0) ()

The submitted solution meets many of the requirements laid out in the problem statement, though it has a few notable omissions and inaccuracies:

Data Classes for Person and Order: Both dataclasses User and Order are defined correctly in the code. (2 marks)
Schema Classes for Person and Order: The schema classes UserSchema and OrderSchema are defined correctly. (2 marks)
Date Format for date_of_birth Field: The solution uses fields.Date() without specifying the format "%m/%d/%Y" as required to correctly parse the date string from the JSON data. (0 marks)
Decimal Field Handling: The solution uses fields.Decimal(places=2) but does not specify as_string=True, which is necessary to ensure that amounts are serialized as strings, retaining exact numeric formatting. (0 marks)
Linking Schema and Dataclasses with @post_load: The @post_load method is correctly implemented in UserSchema to create a User object after loading, ensuring the schema and dataclass linking. (2 marks)
Additional Requirements: The approach to error handling in deserialization is correct with try-except block for ValidationError. Also, the inclusion of nested fields serialization properly models the relationship between person and their multiple orders. Semantic correctness and completeness in the example provided, including imports and a functional usage scenario, are notable. (2 marks)

Overall Evaluation:
This solution successfully demonstrates constructing dataclasses from JSON using marshmallow and covers most basics, including defining schemas and data post-processing, but it misses critical format specifications for date and decimal fields to align with the question's specific requirements.

{
  "score": 6
}

1.00	0.93	0.94
0.93	1.00	0.96
0.94	0.96	1.00

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