Psyfighter 13B

Description

A merge model based on Llama-2-13B and made possible thanks to the compute provided by the KoboldAI community. It's a merge between:

#merge

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 (4.0)

You can use the Marshmallow library to generate dataclasses from JSON like this:

from marshmallow import Schema, fields, post_load

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

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

class Order(object):
    @staticmethod
    def load(data, **kwargs):
        return OrderSchema().load(data, **kwargs)

def test_users():
    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 = UserSchema(data=json_data).load()
    assert user.first_name == 'Jane'
    assert user.last_name == 'Smith'
    assert user.date_of_birth.year == 1977
    assert user.orders[0].created.year == 2024
    assert user.orders[0].amount == Decimal('100.00')

test_users()

In this example, we define two schemas: UserSchema for the outer JSON object and OrderSchema for the inner list of orders. The post_load method on the UserSchema allows us to define a custom unloading method for the nested OrderSchema. Then, we define a staticmethod load on the OrderSchema that can be called to unmarshall an individual order from the JSON.

Finally, we define a test_users function that loads the JSON and asserts the values are as expected. Note that this example uses object rather than typing.Any for Order because we want to be able to call Order.load statically. If you don't need the custom loading behavior, you can use typing.Any instead.

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) (1.06¢)

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

The solution defines two dataclasses, one for the person and one for the order:
The solution doesn't actually define dataclasses. It defines regular classes.
The solution defines two schema classes, one for the person and one for the order:
Correct. UserSchema and OrderSchema are defined.
The solution should specify the date format for the date_of_birth field:
The solution doesn't specify the date format for date_of_birth.
The decimal field needs as_string=True:
The solution doesn't include as_string=True for the decimal field.
The schema and dataclasses should be linked with a @post_load method on the schema classes:
The solution mentions @post_load in the comment, but it's not actually implemented in the code.
Anything else:
The solution includes a test function, which is a good practice.
The solution uses nested fields for the orders, which is correct.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field as_string": 0,
  "@post_load implementation": 0,
  "Additional features": 2
}

Final score:

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

Comments:
The solution provides a good starting point but misses several key requirements. It defines the schema classes correctly but fails to implement actual dataclasses. The date format for date_of_birth is not specified, and the decimal field is missing the as_string=True parameter. The @post_load decorator is mentioned but not implemented. On the positive side, the solution includes a test function and correctly uses nested fields for the orders. To improve, the solution should implement dataclasses, add the missing field specifications, and properly implement the @post_load method to link schemas with dataclasses.

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

The provided solution attempts to tackle the problem using the Marshmallow library to convert JSON data into instances of Python classes, but there are several issues and missing elements according to the marking scheme:

Dataclasses Definition: The solution does not define dataclasses using Python's dataclasses module but rather defines a Python 'object' class for Order and no class for a user. This does not align with the requirement to define two dataclasses (one for the person and one for the order) (-2 marks).
Schema Classes Definition: Two schema classes are defined (UserSchema and OrderSchema), which is correct (+2 marks).
Date Format Specification: The date_of_birth field is parsed by default using the ISO format as Marshmallow does not show a specific date format being set to %m/%d/%Y (-1 mark).
Decimal Field Specification: The amount field has been set to handle decimals, but as_string=True is missing in the schema, which is required to ensure that the decimal is serialized back to string (-1 mark).
Post-load Method: The use of the @post_load method is completely absent. The description implies using post_load for the custom unloading method, but no actual method or annotation is used in the provided solution (-2 marks).
Additional Criteria: No explicit dataclass linking is shown, and the handling methods for Order using a static method is not proper utilization of Marshmallow's capabilities or the mentioned requirement to use @post_load (-2 marks).

Overall Comments:
- The code improperly organizes object creation and serialization/deserialization. Explicit @post_load methods to convert loaded data into instances of the corresponding classes are missing.
- The use of Python's dataclasses module is neglected.
- Error handling, actual loading invocation, and integration of methods like post_load are either incorrect or not used as needed.

Given these, the total score calculation based on the provided marking scheme (missing or incorrect implementations) is:

{
  "score": 2
}

This score represents a failure to meet most of the requirements for the task as specified and the improper use of the Marshmallow library according to best practices and task requirements.

Response 2 (5.0)

You can use the Marshmallow library to generate dataclasses from JSON like this:

from marshmallow import Schema, fields, post_load

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

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

class Order(object):
    @staticmethod
    def load(data, **kwargs):
        return OrderSchema().load(data, **kwargs)

def test_users():
    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 = UserSchema(data=json_data).load()
    assert user.first_name == 'Jane'
    assert user.last_name == 'Smith'
    assert user.date_of_birth.year == 1977
    assert user.orders[0].created.year == 2024
    assert user.orders[0].amount == Decimal('100.00')

test_users()

In this example, we define two schemas: UserSchema for the outer JSON object and OrderSchema for the inner list of orders. The post_load method on the UserSchema is used to tell Marshmallow to handle the nested OrderSchema field using the Order dataclass. When you load the JSON data with the UserSchema, it will automatically handle the nested requests and create the Order 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: (5.0) (0.91¢)

Thank you for providing the solution. I'll evaluate it based on the given marking scheme:

The solution defines one dataclass (Order) but not a dataclass for the person. (1/2 marks)
The solution defines two schema classes: UserSchema and OrderSchema. (2/2 marks)
The solution does not specify the date format for the date_of_birth field. (0/1 mark)
The decimal field is not specified with as_string=True. (0/1 mark)
The schema and dataclass are linked with a @post_load method, but only for the Order class, not for the User class. (1/2 marks)
Additional observations:
The solution includes a test function, which is a good practice.
The solution uses a static method load in the Order class, which is an interesting approach.
The solution doesn't import necessary modules like dataclasses or Decimal.
(1/2 marks)

Here's the breakdown of marks:

{
  "Dataclass definition": 1,
  "Schema definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema and dataclass linking": 1,
  "Additional observations": 1
}

And here's the total score:

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

The solution partially meets the requirements but misses some key points. It defines the schemas correctly but doesn't fully implement the dataclass approach. The date and decimal field specifications are missing, and the linking between schemas and dataclasses is incomplete. However, the inclusion of a test function is a positive aspect.

openai/gpt-4-0125-preview says: (3.0) ()

The given solution attempts to solve the problem of converting a JSON structure into Python objects using the Marshmallow library. The evaluation based on the provided criteria is as follows:

Dataclass Definition: The solution does not define any dataclass directly using Python's dataclasses module. Instead, it uses plain classes (like Order), which is not the same as dataclass implementation. This misses an important feature of using dataclasses for cleaner and more structured code.
Score: 0/2
Schema Classes Definition: The solution defines two schema classes, UserSchema and OrderSchema, correctly using the Marshmallow library to handle JSON serialization and deserialization. These schemas define the fields correctly as per the JSON data structure.
Score: 2/2
Date Format Specification: The solution uses the Date field type in Marshmallow for the date_of_birth field but does not specify the date format necessary for parsing the specific string format ("%m/%d/%Y"). This is crucial for correct deserialization of string dates in the specified format.
Score: 0/1
Decimal Field Configuration: The amount field is defined using fields.Decimal, but the solution does not include as_string=True. The question specifically requires handling the decimal amount as a string during serialization, which is necessary for precise financial data handling.
Score: 0/1
Linking Schema and Dataclasses with Post Load: The solution does mention using post_load in the description but fails to implement an actual @post_load decorator in either of the schema classes to convert deserialized data into instances of any dataclasses or models. Therefore, the solution does not correctly utilize this feature of Marshmallow.
Score: 0/2
Additional Elements: The usage of assertions for basic testing and the attempt to simulate the deserialization process via a functional example shows the effort to validate the solution although the primary functionality is incorrect. Furthermore, an attempt to establish a relationship through schema nesting (fields.Nested(OrderSchema())) is made correctly.
Score: 1/2

Overall, while the provided solution makes a good attempt to use Marshmallow for deserialization of nested structures, it falls short in properly implementing the required functionalities for date, datetime, and decimal conversions according to the given requirements and also the use of actual Python dataclasses.

Final score:

{
  "score": 3
}

Response 3 (3.0)

You can use the Marshmallow library to generate dataclasses from JSON like this:

from marshmallow import Schema, fields, post_load

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

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

class Order(object):
    @staticmethod
    def load(data, **kwargs):
        return OrderSchema().load(data, **kwargs)

def test_users():
    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 = UserSchema(data=json_data).load()
    assert user.first_name == 'Jane'
    assert user.last_name == 'Smith'
    assert user.date_of_birth.year == 1977
    assert user.orders[0].created.year == 2024
    assert user.orders[0].amount == Decimal('100.00')

test_users()

This code defines two dataclasses, UserSchema and OrderSchema, which are derived from a common Schema class provided by Marshmallow. The orders field in UserSchema is a list of OrderSchema instances.

The post_load method of UserSchema defines a helper method load that delegates to OrderSchema.load to handle loading of nested objects. The @staticmethod decorator ensures that load can be called on the class itself.

The OrderSchema.load method uses the Order class as a postprocessor, allowing us to create instance of Order from the JSON data.

Running the test_users function will demonstrate that the JSON data can be deserialized into instances of the dataclasses as expected.

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) (0.88¢)

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

The solution defines two schema classes (UserSchema and OrderSchema), but it doesn't define dataclasses. It uses regular classes instead.
The solution correctly defines two schema classes (UserSchema and OrderSchema).
The solution doesn't specify the date format for the date_of_birth field.
The decimal field doesn't include as_string=True.
The schema and classes are not linked with a @post_load method. The solution uses a static method instead, which is not the same as the required @post_load decorator.
The solution includes a test function, which is a good addition. However, it's missing some key elements like proper dataclass definitions and correct use of Marshmallow features.

Here's the breakdown of marks:

{
  "Dataclass definition": 0,
  "Schema class definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "@post_load method": 0,
  "Additional elements": 1
}

Final score:

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

The solution demonstrates some understanding of Marshmallow but misses several key requirements. It defines schema classes correctly but fails to create proper dataclasses. It also doesn't use some important Marshmallow features like @post_load and field options (date format, as_string for decimal). The inclusion of a test function is good, but overall, the solution needs significant improvements to meet the specified requirements.

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

The provided solution attempts to use the Marshmallow library to define schemas and model classes for deserializing JSON data. However, there are several issues and omissions based on the criteria provided:

Dataclasses Definition: The solution fails to define User and Order as dataclasses. The classes UserSchema and OrderSchema are schemas, not data models. Python's dataclasses module special syntax is not used here (0/2).
Schemas for Person and Order: While two schema classes, UserSchema and OrderSchema, are defined correctly, they are mislabeled, and actual dataclass-like behavior is improperly approached using standard Python classes. (1/2).
Date and DateTime Formats: The solution does not specify the date format for the date_of_birth field explicitly as requested (format="%m/%d/%Y"). Default behavior is assumed (0/1).
Decimal Field Handling: The Decimal type in OrderSchema lacks the as_string=True attribute, which is necessary to ensure that the amount outputs and deserializes as a string representation. The solution overlooks this requirement. (0/1).
Linking Schema and Dataclasses with @post_load: There is no @post_load method implemented in either UserSchema or OrderSchema to convert loaded data into instances of a dataclass or custom Python class. The provided solution additionally has an absent definition for an Order class, relying on commented functionality that is not implemented (0/2).
Various Other Aspects: The use of schemas and their initiation seems to be misinterpreted. For instance, incorrectly initializing UserSchema with a data= keyword that does not exist in the Marshmallow load method, misusing static methods, also the Order class is referred to but not defined or shown, resulting in code that would fail if executed, reflecting a misunderstanding of schema implementation, relation, and usage in Marshmallow (0/2).

Given these observations, the overall score for the solution is:

{
  "score": 1
}

1.00	0.96	0.96
0.96	1.00	0.96
0.96	0.96	1.00

Psyfighter 13B

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