WizardLM-2 7B

Description

WizardLM-2 7B is the smaller variant of Microsoft AI's latest Wizard model. It is the fastest and achieves comparable performance with existing 10x larger opensource leading models

It is a finetune of Mistral 7B Instruct, using the same technique as WizardLM-2 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 (6.5)

To convert the provided JSON to Python data classes using the marshmallow library, you'll first need to define your data classes and then create schemas for serialization and deserialization. You'll also need to use the date and datetime fields provided by marshmallow for date_of_birth and created, and fields.Float or fields.Decimal for amount.

Here's how you can do it:

Install marshmallow if you haven't already:

pip install marshmallow

Import the necessary modules and create your data classes and schemas:

from marshmallow import Schema, fields, validate
from marshmallow.validate import Range
from datetime import datetime
from dateutil.parser import parse
from decimal import Decimal

# Data classes
class Customer:
    first_name: str
    last_name: str
    date_of_birth: datetime
    orders: list

class Order:
    created: datetime
    amount: Decimal

class User(Customer):
    orders: list[Order]

# Marshmallow schemas
class CustomerSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(format="%m/%d/%Y", validate=validate.Range(min=18000101, max=datetime.now().date()))
    orders = fields.List(fields.Nested(OrderSchema, many=True))

class OrderSchema(Schema):
    created = fields.DateTime(format="%Y-%m-%dT%H:%M:%S")
    amount = fields.Float(validate=Range(min=0.01))

# Convert the JSON to a User object using the schemas
json_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}

# Load the JSON data into the User schema
user_schema = CustomerSchema()
user = user_schema.load(json_data)

# Now `user` is an instance of the User data class with a date of birth as a datetime object,
# order dates as datetime objects, and amounts as decimals.
print(user.date_of_birth)
print(user.orders[0].created)
print(user.orders[0].amount)

In this example, I've used validate.Range to ensure the date_of_birth is within a reasonable valid range and Range(min=0.01) for the amount to ensure it's not zero or negative. You might adjust these validations according to your needs.

Please note that the date_of_birth is validated to be a date in the format %m/%d/%Y, and the created field is a timestamp in the format %Y-%m-%dT%H:%M:%S. The amount field is validated to be a positive float with a minimum of 0.01 to avoid very small or negative values.

The OrderSchema is nested within the CustomerSchema to handle the list of orders. The CustomerSchema expects a list of Order objects, which are created using the OrderSchema.

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)

openai/gpt-4-0125-preview says: (4.5) ()

Here's an evaluation based on the given marking scheme:

Data Classes Definition: The solution defines two main classes, Customer and Order but doesn't use Python's dataclasses module, instead, defining them as regular classes. This does not fully meet the requirement specified in the problem statement, which explicitly asks for "dataclasses." Additionally, an extra class User is defined which is not necessary or relevant to the problem. (0.5/2 marks)
Schema Classes Definition: Two schema classes are defined: CustomerSchema and Order Gardenia. These correctly match the data structures in the problem, handling serialization and deserialization of user details and the orders respectively. (2/2 marks)
Date Formatting: The date_of_birth in the CustomerSchema is indeed specified to use the format %m/%d/%Y as required. (1/1 marks)
Handling Decimal with as_string=True: The OrderSchema uses fields.Float instead of fields.Decimal for the amount, and it does not implement the required as_string=True to ensure the amount is handled as a string representing a decimal. This is incorrect. (0/1 marks)
Post-Load Method Implementation: There is no @post_load method implemented in either the CustomerSchema or OrderSchema. This omission means the loaded JSON will not convert directly into instances of the defined Python classes but will remain dictionaries. This is a critical missing feature as per the requirements. (0/2 marks)
Additional Aspects: Adding validations like Range is a good practice and enhances the robustness of the schema, although it was not a requirement. The solution also details the installation and basic usage of marshmallow, which aligns with good practices but deviates from specifics like proper use of decimal and date types matching with the definitions. (1/2 marks)

Overall score as per the requirements and mark scheme:

{
  "score": 4.5
}

anthropic/claude-3.5-sonnet says: (6.5) (1.07¢)

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

Defining two dataclasses:
The solution defines three dataclasses: Customer, Order, and User. While it's not exactly two as specified, it does cover the required structure.
Defining two schema classes:
The solution correctly defines two schema classes: CustomerSchema and OrderSchema.
Date format for date_of_birth:
The solution correctly specifies the date format for date_of_birth as format="%m/%d/%Y".
Decimal field with as_string=True:
The solution uses fields.Float for the amount field but doesn't include as_string=True. This is a minor oversight.
Linking schema and dataclasses with @post_load:
The solution doesn't include @post_load methods to link the schemas with the dataclasses.
Additional points:
The solution includes input validation for date_of_birth and amount.
It uses fields.List(fields.Nested(OrderSchema, many=True)) for handling nested orders.
The solution provides an example of how to use the schemas to load JSON data.

Here's the breakdown of marks:

{
  "Dataclasses": 1.5,
  "Schema classes": 2,
  "Date format": 1,
  "Decimal field": 0,
  "Post_load method": 0,
  "Additional points": 2
}

Final score:

{
  "scores": [1.5, 2, 1, 0, 0, 2],
  "total_score": 6.5
}

The solution covers many aspects well but misses a few key points from the marking scheme. It provides a good foundation but could be improved by adding @post_load methods and using as_string=True for the decimal field.

Response 2 (9.0)

To create dataclasses from the provided JSON using the marshmallow library in Python, you'll first define your dataclasses and then create corresponding Schema classes for validation and serialization. Here's how you can do it step by step:

Define your dataclasses with the appropriate types for date_of_birth, created, and amount.
Create Schema classes for each dataclass.
Use the DateTimeField, DateField, and DecimalField for the respective fields in the Schema classes to handle the serialization and deserialization of datetime and decimal types.

Here's an example implementation:

from dataclasses import dataclass, field
from marshmallow import Schema, fields, validate, ValidationError
from marshmallow.exc import ValidationError as MarshmallowValidationError
from marshmallow.utils import isoformat
from datetime import datetime
from decimal import Decimal

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

class OrderSchema(Schema):
    created = fields.DateTime(format="iso", dump_only=True)
    amount = fields.Decimal(precision=2, parse_as=Decimal, allow_none=True)

class CustomerSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(format="%m/%d/%Y", error_messages={'invalid': 'Date format is incorrect'})
    orders = fields.List(fields.Nested(OrderSchema, many=True))

    @post_load
    def make_customer(self, data):
        return Customer(
            first_name=data.get('first_name'),
            last_name=data.get('last_name'),
            date_of_birth=datetime.strptime(data.get('date_of_birth'), '%m/%d/%Y').date(),
            orders=data.get('orders')
        )

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

try:
    customer_schema = CustomerSchema()
    customer = customer_schema.load(json_data)
    print(customer)  # This will be an instance of the Customer dataclass
except MarshmallowValidationError as err:
    print(err)

In this example, the CustomerSchema class is responsible for converting JSON data to the Customer dataclass, and the OrderSchema class is responsible for converting JSON data to the Order dataclass. The make_customer method in the CustomerSchema class instantiates the Customer dataclass from the loaded data.

Note that the date_of_birth in the JSON is expected to be a string in the format mm/dd/yyyy. The datetime.strptime function is used to convert it to a date object. If the date_of_birth is already provided as a date object (e.g., from a database), you can skip the conversion and directly use it in the dataclass.

The created field in the OrderSchema is marked as dump_only=True because we don't need to load it from JSON, only serialize it.

The amount field in both Customer and Order dataclasses is represented by Decimal to handle precision and avoid floating-point issues. The precision=2 in the DecimalField specifies that the decimal will have two digits after the decimal point. The parse_as=Decimal tells marshmallow to parse the incoming string as a Decimal object.

Remember to install the marshmallow library if you haven't already:

pip install marshmallow

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) (1.22¢)

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

The solution defines two dataclasses: Order and Customer. (2 marks)
The solution defines two schema classes: OrderSchema and CustomerSchema. (2 marks)
The solution specifies the date format for the date_of_birth field correctly:
fields.Date(format="%m/%d/%Y", ...) (1 mark)
The decimal field (amount) is defined, but it doesn't include as_string=True. Instead, it uses parse_as=Decimal. While this achieves a similar result, it doesn't match the exact requirement. (0 marks)
The schema and dataclasses are linked with a @post_load method on the CustomerSchema class. (2 marks)
Additional points:
The solution includes error handling with try/except block. (1 mark)
The solution provides an example of how to use the schema. (1 mark)

Breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes definition": 2,
  "Date format specification": 1,
  "Decimal field specification": 0,
  "Schema and dataclasses linking": 2,
  "Additional features": 2
}

Total score:

{
  "scores": [2, 2, 1, 0, 2, 2],
  "total_score": 9
}

Comments:
The solution is generally well-implemented and covers most of the requirements. It correctly defines the dataclasses and schema classes, specifies the date format, and links the schema and dataclasses using the @post_load decorator.

The main issue is that the decimal field doesn't use as_string=True as specified in the requirements. While the provided solution (parse_as=Decimal) achieves a similar result, it doesn't exactly match the given specification.

The solution also includes some additional helpful features, such as error handling and an example of how to use the schema, which adds value to the implementation.

Overall, it's a strong solution that meets most of the requirements, with room for a minor improvement in the decimal field specification.

openai/gpt-4-0125-preview says: (9.0) ()

The provided solution covers several aspects of the requirements effectively, but also overlooks some key details specified in the original question:

Dataclasses Definition: The solution properly defines two dataclasses: Order and Customer. This matches the requirement to create one dataclass for the person and another for orders. (2 marks)
Schema Classes Definition: Two schema classes, OrderSchema and CustomerSchema, are defined which correspond to the defined dataclasses. (2 marks)
Date Format Specification in Schema: The solution specifies the date format ("%m/%d/%Y") for the date_of_name field in the CustomerSchema. This is in line with the requirement. (1 mark)
Decimal as String: The solution does use Decimal fields, but it fails to meet the specific requirement of setting as_string=True for the amount field. This configuration would ensure that the decimal is serialized back to string format. (0 marks)
Link Schema and Dataclasses Using @post_load: The @post_load method is used in CustomerSchema to create an instance of Customer from the loaded data, satisfying this requirement. (2 marks)
Additional Requirements:
The solution includes error handling using a try-except block to catch and print any validation errors, which enhances robustness.
The use of appropriate fields like DateTime for created and Date for date_of_birth is done correctly.
Use of a custom error message for date_of_birth validation improves user feedback.
These elements show good practical handling beyond the basic requirements. (2 marks)

Total Score

Based on the criteria provided and assessed functionality, the score for this solution would be:

{
  "score": 9
}

The solution provided only missed the requirement concerning the verification of decimal fields as strings, but otherwise addressed all points competently and included additional robust practices.

Response 3 (8.0)

To create data classes from JSON using the marshmallow library in Python, you can define schema classes that correspond to the JSON structure and then use the In and out methods to instantiate them from a dictionary (which can be generated from JSON). The marshmallow.fields such as marshmallow.DateTimeField, marshmallow.DateField, and marshmallow.DecimalField are specifically designed for different data types like date, datetime, and decimal, respectively.

Here's how you can define the necessary data classes using marshmallow:

from marshmallow import Schema, fields, validate, post_load
from dateutil import parser
from marshmallow.exceptions import ValidateError,ValidationError
from typing import Dict, Optional
from datetime import datetime
from decimal import Decimal

class DateTimeField(fields.Field):
    """Custom DateTimeField that attempts to parse datetime strings as ISO8601 formatted."""
    def _deserialize(self, value: Optional[str]) -> datetime:
        if value is None:
            return
        try:
            return parser.isoparse(value)
        except ValueError as err:
            raise ValueError(f"Date {value!r} failed to parse.") from err

class OrderSchema(Schema):
    created = DateTimeField(data_type='datetime', required', bson_type='date_time')
    amount = Decimal(precision=2, coerce=True)

class CustomerSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(serialize=lambda d: d.strftime('%m/%d/%Y'),
                                            deserialize=lambda d: datetime.strptime(d, '%m/%d/%Y').date())
    orders = fields.List(fields.Nested(OrderSchema), required=False)

    @post_load
    def make_customer(self, data: Dict, **kwargs):
        return Customer(**data)

class Customer:
    def __init__(self, first_name, last_name, date_of_birth, orders=None):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = date_of_birth
        self.orders = orders or []


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

customer_schema = CustomerSchema()
customer_instance = customer_schema.load(json_data)

# Now `customer_instance` is an instance of `Customer` populated with the JSON data
print(customer_instance.first_name, customer_instance.last_name, customer_instance.date_of_birth)
print(customer_instance.orders[0].created, customer_instance.orders[0].amount)

In this code, we define two schema classes: OrderSchema and CustomerSchema. The OrderSchema has fields for created (which uses the custom DateTimeField for proper datetime parsing) and amount as a Decimal. The CustomerSchema has fields for first_name, last_name, and date_of_birth, where date_of_birth also uses custom serialization and deserialization functions to match the mm/dd/yyyy format.

The post_load decorator in CustomerSchema is used to transform the loaded dictionary data into a Customer instance after validation and deserialization have succeeded.

Finally, we load the JSON data into a Customer instance using CustomerSchema. The customer_instance will have the first_name, last_name, date_of_birth, and orders properly populated.

Remember to install marshmallow and python-dateutil for this code to work:

pip install marshmallow python-dateutil

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: (8.0) (1.09¢)

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

The solution defines two classes: Customer and OrderSchema. While Customer is a regular class, not a dataclass, OrderSchema is a schema class. Partial marks.
The solution defines two schema classes: OrderSchema and CustomerSchema. Full marks.
The solution specifies the date format for the date_of_birth field using custom serialization and deserialization functions. While it's not exactly format="%m/%d/%Y", it achieves the same result. Full marks.
The solution doesn't use as_string=True for the decimal field. No marks.
The solution uses a @post_load decorator on the CustomerSchema class to link the schema and the Customer class. Full marks.
Additional points:
Custom DateTimeField for ISO8601 parsing.
Use of python-dateutil for enhanced date parsing.
Proper error handling in custom field.
Demonstration of usage with sample JSON data.

Here's the breakdown of marks:

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

Final score:

{
  "scores": [1, 2, 1, 0, 2, 2],
  "total_score": 8
}

The solution demonstrates a good understanding of marshmallow and implements most of the required features. However, it doesn't use dataclasses as requested, and misses the as_string=True for the decimal field. Despite these minor issues, it provides additional useful features and error handling, resulting in a strong overall implementation.

openai/gpt-4-0125-preview says: (3.5) ()

The provided solution attempts to use the marshmallow library to parse JSON data into Python objects using data classes and schemas. However, there are multiple issues and missing criteria based on the marking scheme provided:

Dataclasses Definition (0/2 marks):
The solution does not make use of Python's dataclasses module, which typically involves using the @dataclass decorator. Instead, plain Python classes are used.
Schema Classes Definition (1/2 marks):
Two schema classes (OrderSchema and CustomerSchema) are defined, which is mostly in line with the requirements. However, not all properties (like 'amount' in OrderSchema) are handled correctly or conventionally per marshmallow standards.
Date Format Specification for date_of_birth (0.5/1 mark):
While there's an attempt to handle date formatting in the date_of_birth through a custom lambda function, it does not completely align with the standard practice in marshmallow. The correct approach would involve more typical usage of marshmallow date field specifications.
Decimal Field with as_string=True (0/1 mark):
The provided solution doesn't properly use marshmallow.DecimalField for the amount field and hasn’t set as_string=True. Instead, it defines amount in a non-standard way which does not align with marshmallow field constructions.
Linkage with @post_load Method (1/2 marks):
There is a @post_load method used in the CustomerSchema class intended to link the schema with the corresponding user-defined class. This part aligns with the assignment requirement, but due to the incorrect usage of the plain class instead of a dataclass, this doesn’t fully satisfy the requirement.
Additional Criteria (1/2 marks):
The additional implementation for custom field handling like DateTimeField is a good practice for more complex parsing cases. However, the solution should also have handled errors more appropriately and adhere to marshmallow best practices.

Given these points, the evaluation for the provided solution is as follows:

{
  "score": 3.5
}

The solution needs improvement especially in correctly using marshmallow fields, proper dataclass definitions, and overall adherence to typical marshmallow usage patterns.

1.00	0.88	0.83
0.88	1.00	0.91
0.83	0.91	1.00

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