MythoMax 13B

Description

One of the highest performing and most popular fine-tunes of Llama 2 13B, with rich descriptions and roleplay. #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 (3.0)

Here are the dataclasses generated using Marshmallow:

from marshmallow import Schema, fields

class PersonSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(format="MM/DD/YYYY", required=True)

    orders = fields.Nested('OrderSchema', many=True, required=True)

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

To load the json data into these classes, you can use the loads() method:

import json

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

person_data = PersonSchema().load(person_json)
print(person_data)

for order in person_data['orders']:
    print(order)

This will output:

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

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) (0.93¢)

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

The solution does not define dataclasses. It only defines schema classes. (0/2 marks)
The solution correctly defines two schema classes: PersonSchema and OrderSchema. (2/2 marks)
The date format for the date_of_birth field is specified, but it's incorrect. It should be "%m/%d/%Y" instead of "MM/DD/YYYY". (0/1 mark)
The decimal field (amount) is not specified with as_string=True. (0/1 mark)
The solution does not include @post_load methods to link the schemas to dataclasses. (0/2 marks)
Additional points:
The solution correctly uses nested fields for the orders.
It provides an example of how to use the schemas to load JSON data.
(1/2 marks)

Comments:
The solution provides a good start with Marshmallow schemas, but it misses several key requirements. It doesn't use dataclasses, which was a core part of the task. The date format for date_of_birth is close but not exact. The decimal field is missing the as_string=True parameter. There's no implementation of @post_load methods to create actual dataclass instances. While the schema definitions are mostly correct, the solution doesn't fully meet the requirements of the task.

Mark breakdown:

{
  "Dataclass definitions": 0,
  "Schema definitions": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Post_load methods": 0,
  "Additional points": 1
}

Final score:

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

openai/gpt-4-0125-preview says: (4.0) ()

The provided solution meets some parts of the assignment requirements and has a few shortcomings:

Dataclasses definition:
The solution does not create explicit dataclass objects for Person and Order. Thus, it misses the dataclass definition and defers directly to the schema-defined structure (0/2).
Schema classes definition:
The solution successfully defines the PersonSchema and OrderSchema classes using the marshmallow library (2/2).
Date format specification:
date_of_birth should be formatted as %m/%d/%Y. However, the provided solution uses 'MM/DD/YYYY', which is incorrect syntax with Marshmallow that ultimately leads to incorrect parsing of dates (0/1).
Decimal field handling:
The amount field is specified as a Decimal, but the specification as_string=True is missing, which is necessary to ensure the amount is dealt with properly as a string representation. This was specified in the problem but omitted in the solution (0/1).
Linking schema and dataclasses with @post_load method:
The solution does not define or use an @post_load method in any schema. This is crucial for converting loaded data into instances of dataclasses, as required in the problem (0/2).
Additional Considerations:
The solution correctly shows how to load JSON data using the Marshmallow schemas and demonstrates the output of the data loading. It also correctly nested OrderSchema inside PersonSchema for representing the list of orders (2/2).

Overall, the solution shows basic understanding of using Marshmallow for schema definitions and JSON data processing, but it misses critical aspects like dataclass integration and proper type handling as per the problem requirements.

{
  "score": 4
}

Response 2 (5.0)

You can use the marshmallow library in Python to convert the JSON data into Python dataclasses. Here's an example of how you can do it:

from marshmallow import fields, Schema, post_load
from marshmallow_datetime import Date, DateTime

class PersonSchema(Schema):
    class Meta:
        unknown = 'ignore'

    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = DateTime(date_format='%m/%d/%Y', required=True)

    @post_load
    def make_person(self, data):
        return data

class OrderSchema(Schema):
    class Meta:
        unknown = 'ignore'

    created = DateTime(required=True)
    amount = fields.Float(required=True)

    @post_load
    def make_order(self, data):
        return data

# Load JSON data into dataclass objects
json_data = '{ "first_name": "Jane", "last_name": "Smith", "date_of_birth": "03/16/1977", "orders": [ { "created": "2024-01-25T15:00:04", "amount": "100.00" } ] }'
persons = PersonSchema().dump(json.loads(json_data))
print(persons)

# Loop through objects and print their details
for person in persons:
    print('First Name:', person['first_name'])
    print('Last Name:', person['last_name'])
    print('Date of Birth:', person['date_of_birth'])
    for order in person['orders']:
        print('Order Created:', order['created'])
        print('Order Amount:', order['amount'])

This code defines two Schema classes, PersonSchema and OrderSchema, based on the JSON data you provided. The PersonSchema class has fields for first_name, last_name, and date_of_birth, and the OrderSchema class has fields for created and amount.

The make_person() and make_order() methods are used for post-processing the loaded data. The make_person() method simply returns the loaded data, and the make_order() method returns the loaded data.

Finally, the JSON data is loaded into a list of Person objects using the PersonSchema().dump() method. The dump() method converts Python dataclasses to JSON format. The resulting list of Person objects is then printed to the console.

Note that this code also formats the date_of_birth field as a date object using the marshmallow_datetime module. The date_format argument is used to specify the format of the date string in the JSON data.

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) (0.98¢)

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

The solution does not define dataclasses. It only defines Schema classes. (0 marks)
The solution correctly defines two schema classes: PersonSchema and OrderSchema. (2 marks)
The solution correctly specifies the date format for the date_of_birth field using date_format='%m/%d/%Y'. (1 mark)
The solution does not use as_string=True for the decimal field. It uses fields.Float() instead of fields.Decimal(as_string=True). (0 marks)
The solution includes @post_load decorators on both schema classes, but they don't create dataclass instances. They just return the data. (1 mark)
Additional observations:
The solution uses DateTime for date_of_birth instead of Date.
The solution includes code to load and print the data, which wasn't required.
The solution uses dump() instead of load() to process the JSON data.
(1 mark)

Here's the breakdown of marks:

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

Final score:

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

The solution demonstrates a good understanding of using marshmallow for JSON processing but misses some key requirements, particularly the use of dataclasses and proper decimal field handling. The total score is 5 out of 10.

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

The provided solution aims to solve the problem of converting JSON to Python dataclasses using marshmallow, but it has several critical issues based on the evaluation criteria:

Dataclasses Definition: The solution did not define dataclasses for Person and Order. It only defines schema classes using marshmallow.
Score: 0/2
Schema Classes Definition: The solution defines schema classes (PersonSchema and OrderSchema) correctly.
Score: 2/2
Date Format Specification: The date_of_birth uses a DateTime field with the specified format "%m/%d/%Y". However, the field should be a Date not DateTime to correctly map to a date.
Score: 0/1
Decimal Field Configuration: The solution uses fields.Float, and it does not set as_string=True for the amount field which should be a Decimal.
Score: 0/1
Linking Schema and Dataclasses with Post Load: Although @post_load methods (make_person and make_order) are defined, they do not create or return any dataclass instances but just return the input data. Moreover, actual dataclasses are not defined to link with.
Score: 0/2
Additional Points: The use of marshmallow_datetime is incorrect as the correct module for date and datetime fields is directly from marshmallow.fields. Managing unknown = 'ignore' correctly prevents issues with unexpected data, though this is not a specifically requested feature.
Misuse of dump (should use load for parsing) and no handling of the orders field in relation to schema nesting.
Score: 0/2

Overall, the solution misconstrues several key aspects, particularly around field type handling and the fundamental requirement of using dataclasses. The misimplementation of dumping instead of loading data and the direct mapping of JSON to schema without converting to the appropriate data types further mark down the quality of the solution.

Final score:

{
  "score": 2
}

Response 3 (5.0)

You can use the marshmallow library in Python to create dataclasses from the given JSON data. Here's an example code:

from marshmallow import Shape, fields, validate, EXCLUDE, Schema, dataclass

class PersonSchema(Shape):
    class Meta:
        unknown = EXCLUDE

    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(validate=validate.Length(min=10, max=10), resolve=lambda x: datetime.strptime(x, '%m/%d/%Y'))
    orders = fields.List(fields.Nested(OrderSchema()))


@dataclass
class Person(PersonSchema):
    pass

class OrderSchema(Shape):
    class Meta:
        unknown = EXCLUDE

    created = fields.DateTime(validate=validate.Length(min=19, max=25), resolve=lambda x: datetime.strptime(x, '%Y-%m-%dT%H:%M:%S'))
    amount = fields.Float()


@dataclass
class Order(OrderSchema):
    pass

person = Person(**json_data)
print(person)

In the above code, we have created two schemas: PersonSchema and OrderSchema. The PersonSchema defines the structure of a person object, including their first_name, last_name, date_of_birth, and a list of orders. The OrderSchema defines the structure of an order object, including its created date and time, and the amount spent.

We then define a Person dataclass that inherits from the PersonSchema, and a Order dataclass that inherits from the OrderSchema. Finally, we create a person object using the given JSON data and print it out.

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) (0.95¢)

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

The solution defines two dataclasses, Person and Order. (2 marks)
The solution defines two schema classes, PersonSchema and OrderSchema. (2 marks)
The date format for the date_of_birth field is not explicitly specified as "format='%m/%d/%Y'". Instead, it uses a custom resolver function. (0 marks)
The decimal field (amount) is not specified with as_string=True. It's defined as a Float instead of Decimal. (0 marks)
The schema and dataclasses are not linked with a @post_load method. They are linked by inheritance, which is not the requested method. (0 marks)
Additional observations (1 mark):
The solution uses Shape instead of Schema, which is not standard in marshmallow.
It includes validation for field lengths, which wasn't required but can be useful.
It uses EXCLUDE for unknown fields, which is a good practice.

Here's the breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema and dataclass linking": 0,
  "Additional observations": 1
}

Final score:

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

The solution demonstrates a basic understanding of using marshmallow with dataclasses, but it misses several key requirements from the marking scheme. It doesn't properly handle the date format specification, the decimal field, or the linking of schemas and dataclasses using @post_load. These are important aspects for correctly processing the given JSON data. The solution could be improved by addressing these points and adhering more closely to standard marshmallow practices.

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

The provided solution attempts to use the marshmallow library in conjunction with Python's dataclass to convert JSON data to Python objects while preserving specified types for certain fields such as dates and decimals. However, there are numerous problems and inaccuracies in the implementation, which require attention.

Detailed Review:

Definition of Dataclasses:
Marked usage of @dataclass for both Person and Order. However, inheriting directly from schemas defined as Shape (which seems to be a typographical error, possibly meant Schema) is not correct or typical in Marshmallow usage.
Correct approach would use dataclasses.dataclass for actual data structures and separately defined schemas for serialization/deserialization.
Score: 1/2
Definition of Schema Classes:
Schemas are defined, but use Shape instead of Schema, which appears to be incorrect (Shape is not a recognized class in the Marshmallow library). Correct class should be Schema.
Score: 1/2
Date Format Specification for date_of_birth:
The schema incorrectly attempts to validate length and use a resolve method which is not a standard marshmallow approach. Instead, it should directly specify the format in the Date field.
Score: 0/1
Decimal Field Specification:
The amount field is defined using fields.Float(), totally missing the requirement to treat it as a Decimal and ensuring it remains a string representation in JSON.
Score: 0/1
@post_load Method Linking:
There is no @post_load method used in any schema to link the loaded data to a dataclass, which is critical for converting loaded data into a dataclass instance.
Score: 0/2
Miscellaneous:
The use of inheritance from what are designated as 'schema' classes directly into data classes is conceptually incorrect in the usage of dataclasses and Marshmallow.
Error in usage of Shape instead of Schema.
Misuse of the resolve method and lack of actual formatting specification where needed.
No handling of JSON loading or proper testing of string to Decimal conversion.
Score: 0/2

Additional Observations:

The implementation lacks appropriate error handling and examples of actual JSON parsing.
Python import errors (e.g., datetime is used but not imported).
Misunderstanding of Marshmallow's capabilities and mechanisms (like resolve, which isn't recognized by Marshmallow).

{
  "score": 2
}

This score reflects both the attempt made to integrate complex data types using Marshmallow and dataclasses, but with significant misunderstanding and incorrect implementation details.

1.00	0.86	0.86
0.86	1.00	0.87
0.86	0.87	1.00

MythoMax 13B

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