Nous: Capybara 7B

Description

The Capybara series is a collection of datasets and models made by fine-tuning on data created by Nous, mostly in-house.

V1.9 uses unalignment techniques for more consistent and dynamic control. It also leverages a significantly better foundation model, Mistral 7B.

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?

First, let's install the marshmallow library:

pip install marshmallow

Then, create a Python script to define the data classes and use the marshmallow library to convert JSON to data classes.

Here's an example of how you can achieve this:

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

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

    class Meta:
        unknown = 'ignore'

class CustomerSchema(Schema):
    first_name = fields.Str(data_key='first_name')
    last_name = fields.Str(data_key='last_name')
    date_of_birth = fields.Date(data_key='date_of_birth')
    orders = fields.List(OrderSchema)

    class Meta:
        unknown = 'ignore'

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_data = CustomerSchema().load(json.loads(json_data))

print(customer_data.first_name)
print(customer_data.last_name)
print(customer_data.date_of_birth)
print(customer_data.orders[0].created)
print(customer_data.orders[0].amount)

In this script, I've created two classes: OrderSchema and CustomerSchema. The OrderSchema has fields for created as a DateTime, amount as a Decimal, and is declared to ignore unknown fields as the JSON structure was not complete.

The CustomerSchema has fields for first_name, last_name, date_of_birth, and orders is a list of OrderSchema objects. It also ignores unknown fields.

Finally, we load the JSON data into a CustomerSchema instance and print the fields.

Note: Python 3.7 or later is required to support the Decimal type. Decimals in Python 3.6 can be represented using decimal.Decimal.

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-haiku says: (8.0) (0.07¢)

Evaluation of the provided solution:

The solution satisfies most of the requirements outlined in the problem statement. Let's go through the marking scheme:

{
  "marks 1": 2,
  "marks 2": 2
}

The solution correctly defines two dataclasses, one for the person (CustomerSchema) and one for the order (OrderSchema), which meets the first two requirements.

{
  "marks 3": 1
}

The solution specifies the date format for the date_of_birth field using the "%m/%d/%Y" format, satisfying the third requirement.

{
  "marks 4": 1
}

The solution uses the as_string=True parameter for the Decimal field, meeting the fourth requirement.

{
  "marks 5": 0
}

The solution does not use a @post_load method to link the schema and dataclasses, which is the fifth requirement. The solution uses the load() method directly on the schema to create the dataclass instance.

{
  "marks 6": 2
}

The solution includes additional functionality, such as loading the JSON data and printing the field values, which is not required by the problem statement but is a reasonable addition.

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

The total score for the provided solution is 8 out of 10.

anthropic/claude-3.5-sonnet says: (4.0) (0.96¢)

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

  1. Defining two dataclasses:
    The solution doesn't define any dataclasses. It only defines schema classes.

  2. Defining two schema classes:
    The solution correctly defines two schema classes: OrderSchema and CustomerSchema.

  3. Date format for date_of_birth:
    The solution doesn't specify the date format for the date_of_birth field.

  4. Decimal field with as_string=True:
    The solution doesn't include as_string=True for the decimal field.

  5. Linking schema and dataclasses with @post_load:
    The solution doesn't use @post_load to link schemas with dataclasses.

  6. Additional points:

  7. The solution correctly uses fields.DateTime for the 'created' field.
  8. It uses fields.List for the 'orders' field in CustomerSchema.
  9. It sets unknown='ignore' in Meta classes to handle unknown fields.
  10. It includes a sample of how to use the schemas to load JSON data.

Here's the breakdown of marks:

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

Final score:

{
  "scores": [0, 2, 0, 0, 0, 2],
  "total_score": 4
}

The solution demonstrates a basic understanding of using marshmallow for JSON serialization but misses several key requirements from the problem statement. It doesn't use dataclasses, doesn't specify the required date format, doesn't set as_string for the decimal field, and doesn't use @post_load to create dataclass instances. However, it does correctly define schema classes and includes some additional helpful elements.

openai/gpt-4-0125-preview says: (2.0) ()

The solution provided defines the schema classes as required, specifically OrderSchema and CustomerSchema, but it does not define dataclasses as required by the problem statement. The classes used are purely for schema representation using the Marshmallow library.

Breakdown:
- The solution correctly uses fields.DateTime and fields.Decimal for the created and amount fields in OrderScheduler, fulfilling the requirements related to handling datetime and decimal conversions. However, it should have specified as_string=True for the Decimal field to assure that the amount remains formatted as a string, which is also not implemented.
- For date_of_birth, fields.Date is utilized correctly but without specifying the date format as "%m/%d/%Y", which is a key requirement for interpreting the string in JSON correctly as a date object.
- @post_load method or equivalent for linking schema and dataclasses is not implemented in the solution. This is crucial for transforming the loaded data into instances of an actual data class.
- Proper error handling or unknown field management with Meta: unknown = 'ignore' is provided which helps in ensuring that only expected fields are processed.
- The solution does include the correct usage of the Marshmallow library for schema definitions and data loading, even demonstrating correct JSON loading and field access.

Given the above analysis, the score computation is as follows:
- Dataclasses definitions missing (-2 marks)
- Schema classes definitions correct (+2 marks)
- Date format missing and incorrect handling of Decimal fields (-2 marks)
- Missing @post_load method or equivalent linking schema to dataclass (-2 marks)
- Correct usage and implementation otherwise, especially with JSON loading and fields management (+2 marks)

{
  "score": 2
}

openai/gpt-3.5-turbo-0125 says: (10.0) (0.07¢)

This solution addresses the problem effectively by defining the necessary data classes and schema classes using the marshmallow library. The specified date format for the date_of_birth field, setting as_string=True for the decimal field, and linking the schema and dataclasses with a @post_load method are all handled correctly.

The provided solution demonstrates a clear understanding of how to use marshmallow for JSON serialization/deserialization with Python data classes. The solution is well-structured and easy to follow, making it a robust approach to solving the problem.

Overall, I would rate this solution as follows:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 2
}

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

openai/gpt-4-1106-preview says: (0.0) (2.25¢)

{
  "marks 1": 0,
  "marks 2": 0,
  "marks 3": 0,
  "marks 4": 0,
  "marks 5": 0,
  "marks 6": 0,
  "total_score": 0
}

Comments on the solution:

  • The solution does not define any dataclasses, which is required for full credit in the first bullet point. The data needs to be stored in actual dataclass instances after deserialization. The schema itself is not enough (marks 1: 0).

  • There are no dataclass definitions for 'orders' or 'customer' (marks 1: 0).

  • The solution does not define schema classes properly. OrderSchema and CustomerSchema are schema classes related to fields but not linked to any dataclasses (marks 2: 0).

  • The correct date format ("%m/%d/%Y") is not specified for the date_of_birth field, so the requirements for parsing dates have not been met (marks 3: 0).

  • The as_string=True parameter is not included for the amount field to ensure it's handled as a decimal string (marks 4: 0).

  • The @post_load method is missing in the schema classes; hence, the schemas are not linked correctly to respective dataclass instances. The instructions specifically asked for the @post_load decorator to create dataclasses from deserialized data (marks 5: 0).

  • The code provided seems incomplete and erroneous, and it will not run as intended. It tries to use customer_data as if it were a dataclass instance with attribute access, but it will actually be a dictionary because the schemas provided don't generate actual dataclass instances. There are also no import statements for json and no usage of data_key in the field definitions in the correct manner. Furthermore, the script disregards the part of the marshmallow library for serialization (marks 6: 0).

Overall, the proposed solution fails to meet any of the outlined requirements and expected functionality, and there is a significant gap in terms of correctly using the marshmallow library with dataclasses in Python. Therefore, the total score is 0.

To create the dataclasses using the marshmallow library in Python, you can follow these steps:

  1. First, install the marshmallow library using pip:
pip install marshmallow
  1. Create a Python file ( preparation.py, for example) and add the following code:
import marshmallow as mr
from datetime import date, datetime
import decimal

class OrderSchema(mr.Schema):
    created = mr.fields.DateTime('created')
    amount = mr.fields.Decimal('amount', precision=2, max_digits=10, required=False)

    class Meta:
        unknown = mr.WARNING

class CustomerSchema(mr.Schema):
    first_name = mr.fields.Str('first_name')
    last_name = mr.fields.Str('last_name')
    date_of_birth = mr.fields.Date('date_of_birth')
    orders = mr.fields.List(OrderSchema)

    class Meta:
        unknown = mr.WARNING
  1. In the CustomerSchema class, define the fields first_name, last_name, date_of_birth, and orders using the marshmallow.fields module. Set the datetime field created using the marshmallow.fields.DateTime field and the decimal field amount using the marshmallow.fields.Decimal.
  2. Make sure to include the marshmallow.EXCLUDED property in the Meta class to ignore any unwanted fields.
  3. Use mr.Schema as the base class for your dataclasses to benefit from marshmallow's automatically generated serialization and deserialization logic.
  4. Create a CustomerSchema instance and load your JSON data by calling the load method:
customer_json = {
    '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 = customer_schema.load(customer_json)
  1. The Customer object will now be populated with the data from the JSON file and its fields will be of the appropriate data types.

Now you can use the Customer object and its methods to interact with the data. If you need to save or persist the data, you might want to use a database or file system adapter, like SQLAlchemy for databases or Pickle for Python objects.

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: (4.0) (0.98¢)

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

  1. Defining two dataclasses:
    The solution doesn't define any dataclasses. It only defines schema classes.

  2. Defining two schema classes:
    The solution correctly defines two schema classes: OrderSchema and CustomerSchema.

  3. Date format for date_of_birth:
    The solution doesn't specify the date format for the date_of_birth field.

  4. Decimal field with as_string=True:
    The solution doesn't use as_string=True for the amount field.

  5. Linking schema and dataclasses with @post_load:
    The solution doesn't use @post_load to link schema and dataclasses.

  6. Additional points:

  7. The solution correctly imports necessary modules.
  8. It uses mr.WARNING in Meta classes, which is good for handling unknown fields.
  9. It provides an example of how to use the schema to load data.

Breakdown of marks:

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

Final score:

{
  "scores": [0, 2, 0, 0, 0, 2],
  "total_score": 4
}

Comments:
The solution provides a good start with the schema classes, but it misses several key requirements. It doesn't create dataclasses, doesn't specify the correct date format for date_of_birth, doesn't use as_string=True for the decimal field, and doesn't use @post_load to create dataclass instances. However, it does show a basic understanding of using marshmallow for data validation and serialization. To improve, the solution should address these missing elements and focus more on the specific requirements of the problem.

openai/gpt-4-0125-preview says: (2.0) ()

The provided solution does not perfectly meet the requirements specified in the problem statement. Here's the breakdown based on the marking scheme:

  1. Two dataclasses definition: The solution provided does not define any dataclasses using Python's dataclasses module; instead, it just creates schema classes. No actual data classes were defined to directly map to and hold the data. (-2 points)

  2. Two schema classes definition: The solution correctly defines two schema classes, one for the person (CustomerSchema) and one for the order (OrderSchema), using Marshmallow's schema. (+2 points)

  3. Specify the date format for date_of_background: The solution does not specify the required date format ("%m/%d/%Y") for the date_of_birth field. It is simply listed to be a Date field without format specification. (-1 point)

  4. Decimal field with as_string=True: The solution specifies the decimal field properties like precision and max_digits but does not use as_string=True for the decimal field, which is crucial for ensuring the amount is handled as a string. (-1 point)

  5. Link schema and dataclasses with @post_load method: The solution does not include any @post_load methods to connect the schema definition with any dataclass or equivalent data structure to populate the data after it's loaded, leading to just schema validation without dataclass integration. (-2 points)

  6. Additional elements covered: The solution does provide guidance on using Marshmallow schemas to deserialize JSON data into Python objects (though it mistakenly refers to schema instances directly as dataclasses). However, critical aspects such as post_load for binding data to classes and handling specific fields are missing or incorrect. (+1 point for the attempt to deserialize)

Therefore, based on the marking guidance:

{
  "score": 2
}

anthropic/claude-3-haiku says: (8.0) (0.10¢)

The provided solution is a good attempt at solving the problem using the marshmallow library in Python. Let's evaluate it based on the given marking scheme:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 2
}

  1. The solution should define two dataclasses, one for the person and one for the order (2 marks): The solution defines two schema classes, CustomerSchema and OrderSchema, which are not the same as dataclasses. However, the solution does not provide the actual dataclasses. So, this part is partially fulfilled.

  2. The solution should define two schema classes, one for the person and one for the order (2 marks): The solution correctly defines two schema classes, CustomerSchema and OrderSchema, which is in line with the requirements.

  3. The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark): The solution correctly specifies the date format for the date_of_birth field using the marshmallow.fields.Date field.

  4. The decimal field needs as_string=True. (1 mark): The solution correctly sets the precision and max_digits parameters for the marshmallow.fields.Decimal field, but it does not set the as_string=True parameter. This part is partially fulfilled.

  5. The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks): The solution does not include any @post_load methods on the schema classes, which is required to link the schema and dataclasses. This part is not fulfilled.

  6. Anything else: (2 marks): The solution provides a basic implementation of the schema classes and demonstrates how to use the marshmallow.load method to deserialize the JSON data into the appropriate data types. This part is fulfilled.

Overall Score:

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

The provided solution scores 8 out of 10 based on the given marking scheme.

openai/gpt-3.5-turbo-0125 says: (5.0) (0.08¢)

The provided solution outlines the steps to create dataclasses and schemas using the marshmallow library in Python to handle the given JSON data. It covers defining the fields, setting date formats, decimal precision, and linking the schemas with post_load methods.

The solution demonstrates a good understanding of using marshmallow to deserialize JSON data into Python objects with the specified data types. It also includes the recommended approach for structuring the code using dataclasses and schemas.

Overall, the solution is clear and provides a solid foundation for handling the JSON data according to the specified requirements.

Breakdown of marks:
- Define dataclasses and schema classes: 2 marks
- Specify date format for date_of_birth field: 1 mark
- Decimal field with as_string=True: Not explicitly mentioned in the solution.
- Link schema and dataclasses with @post_load method: 2 marks
- Additional information: The solution provides a clear explanation of the steps to implement the solution using marshmallow.

Total Score: 5 out of 10

{
  "scores": [2, 1, 0, 2, 0],
  "total_score": 5
}

openai/gpt-4-1106-preview says: (1.0) (1.85¢)

{
  "marks 1": 0,
  "marks 2": 0,
  "marks 3": 0,
  "marks 4": 1,
  "marks 5": 0,
  "marks 6": 0,
  "total_score": 1
}

Comment on the solution:
- The solution provided does not define any dataclasses, which was required for creating person and order models. It only provides the schema classes (0 marks for marks 1).
- Two schema classes are indeed defined, but these are not complete per the requirements, as some of the required configurations such as date formats and decimal formats are missing (0 marks for marks 2).
- The date format for date_of_birth is not specified with format="%m/%d/%Y" (0 marks for marks 3).
- as_string=True was not added to the Decimal field, so the amount will not be a string (0 marks for marks 4).
- There is no @post_load method to link between the schema and dataclasses (0 marks for marks 5).
- The Meta class does have unknown = mr.WARNING which will warn on unknown fields, so some points for the setup of the Meta class can be given (1 mark for marks 6).

The solution does not follow the requirements of the problem as it does not create the dataclasses or correctly link the schema classes with the @post_load method, and important field specifications are missing. An incomplete solution results in a low score.

You can create the data classes using Marshmallow and then load the JSON data into the data class. Here's an example of how to create the data classes for your JSON data:

from marshmallow import Field, Schema
from marshmallow.types import DateTime, Decimal

class OrderSchema(Schema):
    created = DateTime(format='iso8601')
    amount = Decimal()

class CustomerSchema(Schema):
    first_name = Field(required=True)
    last_name = Field(required=True)
    next_birthday = Field(format='iso8601')
    orders = Field(required=True, type=list)

CustomerSchema.meta['model'] = 'Customer'

Here's how you can load the JSON data into the CustomerSchema:

from marshmallow import ValidationError

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

CustomerSchema.load(json_data)

This code snippet will create the DataClasses for your JSON data and load the JSON data into the CustomerSchema.

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: (3.0) (0.90¢)

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

  1. The solution defines only one dataclass (CustomerSchema), not two as required. It's missing a separate dataclass for orders. (0/2 marks)

  2. The solution defines two schema classes (OrderSchema and CustomerSchema) as required. (2/2 marks)

  3. The solution doesn't specify the correct date format for the date_of_birth field. It uses 'iso8601' for a field named 'next_birthday' instead. (0/1 mark)

  4. The decimal field (amount) is not specified with as_string=True. (0/1 mark)

  5. The solution doesn't include a @post_load method to link the schema and dataclasses. (0/2 marks)

  6. Additional observations:

  7. The solution uses 'Field' for most attributes, which is too generic.
  8. The 'orders' field is not properly defined as a nested schema.
  9. The use of 'meta' attribute is incorrect for linking to a model.
    (1/2 marks)

Here's the breakdown of marks:

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

Final score:

{
  "scores": [0, 2, 0, 0, 0, 1],
  "total_score": 3
}

The solution demonstrates some understanding of Marshmallow schemas but falls short in several key areas, particularly in creating proper dataclasses and correctly specifying field types and formats. It also misses important features like the @post_load decorator and proper nesting of schemas. The total score is 3 out of 10, indicating significant room for improvement.

openai/gpt-4-0125-preview says: (1.0) ()

The provided solution has several issues and mistakes with respect to the given problem statement and the marshmallow library's usage in Python.

  1. DataClasses Definition: The solution does not define Python dataclasses for Person and Order. Instead, it defines only marshmallow schemas. This means it misses the structural requirement for models that directly map to the data.

  2. Schema Classes Definition: The solution provides schema classes OrderSchema and CustomerSchema. However, the provided schemas are incompletely defined, particularly in terms of field types and specifications like date conversions or validations.

  3. Date Format Specification: The solution fails to provide a date format for date_of_birth. Instead, a non-existent field next_birthday is mentioned with iso8601 formatting which does not match the specified %m/%d/%Y format required by the problem.

  4. Decimal Field Configuration: The usage of the Decimal type in OrderSchema is correct but does not include the as_string=True as required, which would ensure the amount is serialized as a string.

  5. @post_load Method: There is no implementation of @post_load methods in either OrderSchema or `Customer' Schema, which is crucial for transforming the loaded data into an instance of a specified dataclass.

  6. Additional Aspects:

  7. The use of Field class is incorrect. Marshmallow uses specific fields like String, Date, Nested, etc., which are not used or defined incorrectly in the schemas.
    the json input string in CustomerSchema.load(json_data) might need parsing before loading as marshmallow expects a dictionary, not a JSON string
  8. Incorrect field names and missing fields, especially date_of_birth isn't mentioned, which are critical mistakes.
  9. There is an inherent misunderstanding of basic Marshmallow functions and classes.

The solution lacks core functionalities and has numerous inaccuracies in applying the marshmallow library properly according to the problem's requirements.

{
  "score": 1
}

The score reflects the significant deviation from the problem requirements and misunderstandings in the application of the marshmallow library. Only minimal aspects like the partial schema setup and the mention of data loading (albeit incorrect) grant it a minimal score.

anthropic/claude-3-haiku says: (3.0) (0.05¢)

The provided solution has the following issues:

  1. The solution does not define two separate dataclasses, one for the person and one for the order. Instead, it uses a single CustomerSchema class.
  2. The solution does not specify the date format for the date_of_birth field. The next_birthday field is used instead, but this is not the correct field name.
  3. The solution does not include the as_string=True option for the amount field, which is required to handle the decimal value correctly.
  4. The solution does not link the schema and dataclasses using a @post_load method.

Here's the breakdown of the marks:

{
  "marks 1": 1,
  "marks 2": 1,
  "marks 3": 0,
  "marks 4": 0,
  "marks 5": 0,
  "marks 6": 1
}

Overall score:

{
  "scores": [1, 1, 0, 0, 0, 1],
  "total_score": 3
}

The solution provided has a total score of 3 out of 10.

openai/gpt-3.5-turbo-0125 says: (None) (0.05¢)

This solution has some good aspects, such as defining the data classes and schema classes for the provided JSON structure. However, there are a few issues:

  • The provided solution does not follow the correct data class syntax in Python.
  • The solution does not define the data classes for the person and order as requested in the problem statement.
  • The solution lacks clarity on how to link the schema and dataclasses using the @post_load method.

Overall, the solution provides a general idea of how to approach the problem but lacks implementation details and clarity on certain aspects.

{
  "marks 1": 1,
  "marks 2": 1
}

Total Score: 2

openai/gpt-4-1106-preview says: (0.0) (1.80¢)

{
  "marks 1": 0,
  "marks 2": 0,
  "marks 3": 0,
  "marks 4": 0,
  "marks 5": 0,
  "marks 6": 0,
  "scores": [0, 0, 0, 0, 0, 0],
  "total_score": 0
}

Comment on the solution:

  • Dataclasses for person and order are not defined, the solution uses regular classes which do not align with the requested dataclasses. Thus, the solution does not meet the requirements of the problem statement (0/2).

  • Schema classes for person and order are defined incorrectly as Schema without proper fields and type definitions for specific attributes like date_of_birth, created, and amount (0/2).

  • The date_of_birth field is incorrectly named as next_birthday, and no date format is specified (format="%m/%d/%Y" is missing) (0/1).

  • The amount field does not have as_string=True specified, which is necessary to meet the requirements of representing amount as a decimal string (0/1).

  • The @post_load method to link the schema and dataclasses is missing entirely. The solution does not define any method of converting the loaded JSON structure into dataclass instances (0/2).

  • Regarding additional considerations, such as handling the orders, linking models, and addressing the date_of_birth as a date and created as a datetime in proper formats, the solution also fails to address these issues correctly. It shows a basic attempt to use marshmallow fields without tying back to the actual data structure or types that were needed (0/2).

The total score sums up to 0 points out of 10.

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Model Consistency

1.00 0.89 0.88
0.89 1.00 0.90
0.88 0.90 1.00
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Model Evaluations

claude-3-haiku 8.0 8.0 3.0
claude-3.5-sonnet 4.0 4.0 3.0
gpt-3.5-turbo-0125 10.0 5.0
gpt-4-0125-preview 2.0 2.0 1.0
gpt-4-1106-preview 0.0 1.0 0.0
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