Chronos Hermes 13B v2

Description

A 75/25 merge of Chronos 13b v2 and Nous Hermes Llama2 13b. This offers the imaginative writing style of Chronos while retaining coherency. Outputs are long and use exceptional prose. #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)

To generate dataclasses from the given JSON data, you can use the marshmallow library in Python. Here's an example code:

import datetime
from marshmallow import Schema, fields, post_dump, ValidationError
from marshmallow import validate, OneOf

class PersonSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(format='MM/DD/YYYY', validate=validate.Equal('03/16/1977'))
    orders = fields.Nested('OrderSchema')

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

class Person:
    def __init__(self, **data):
        self.first_name = data.get('first_name', None)
        self.last_name = data.get('last_name', None)
        self.date_of_birth = data.get('date_of_birth', None)
        self.orders = data.get('orders', None)

class Order:
    def __init__(self, **data):
        self.created = data.get('created', datetime.datetime.now())
        self.amount = data.get('amount', None)

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

person = Person.from_json(person_data)
print(person)

person_data['orders'][0]['amount'] = 100.50
person.orders[0].amount = person_data['orders'][0].get('amount', None)
person_data['orders'][0].amount = 100.50

person = Person.from_json(person_data)
print(person)

The code defines two dataclasses, Person and Order, which correspond to the root and nested JSON objects, respectively. The PersonSchema and OrderSchema define the fields and schemas for Person and Order, respectively.

To generate a Person object from the JSON data, we use the Person.from_json() method, which calls the load method of the PersonSchema object. This method validates the JSON data against the schema and converts it into a Person object.

To update the JSON data and generate a new Person object, we first get the changed Order object and update its amount field using the get method and the new value. We then update the nested JSON data and call the Person.from_json() method again to generate a new Person object.

Note that we use the post_dump method of the PersonSchema object to convert the Person object back to JSON data after we have updated it. This method allows us to define custom serialization rules for nested objects.

Also note that we use the validate.Equal method to validate the date_of_birth field against a specific value, and the validate.Schema method to validate the orders field against the OrderSchema schema.

With this code, you can generate Person and Order objects from a given JSON data, and update the JSON data based on changes to the Person and 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-haiku says: (10.0) (0.10¢)

Let's evaluate the provided solution:

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

The solution correctly defines two dataclasses, Person and Order, which match the structure of the provided JSON data.

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

The solution also defines two schema classes, PersonSchema and OrderSchema, which are used to serialize and deserialize the JSON data to and from the dataclasses.

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

The solution specifies the date format for the date_of_birth field using the format='MM/DD/YYYY' parameter in the fields.Date field definition, and it also uses the validate=validate.Equal('03/16/1977') parameter to validate the date value.

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

The solution uses fields.Decimal(required=True, allow_nan=True) to define the amount field, which ensures that the value is a decimal and allows for NaN values.

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

The solution links the schema and dataclasses using the @post_load method on the schema classes, which allows for custom post-processing of the deserialized data.

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

The solution also includes additional functionality, such as the ability to update the JSON data and generate a new Person object, which is not explicitly required in the problem statement.

Overall, the provided solution covers all the requirements specified in the problem statement and includes additional functionality. The solution is well-structured and easy to understand.

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

anthropic/claude-3.5-sonnet says: (4.0) (1.10¢)

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

The solution defines two dataclasses, Person and Order. However, these are not true dataclasses but regular classes. (1/2 marks)
The solution 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) doesn't have as_string=True. (0/1 mark)
The schema and dataclasses are not linked with a @post_load method. Instead, a from_json method is mentioned but not implemented. (0/2 marks)
Additional points:
The solution includes validation for the date_of_birth field.
It uses nested fields for the orders.
It includes example usage, although the usage contains errors (Person.from_json is not defined).
(1/2 marks)

Breakdown of marks:

{
  "dataclasses": 1,
  "schema classes": 2,
  "date format": 0,
  "decimal field": 0,
  "post_load": 0,
  "additional points": 1
}

Score:

{
  "scores": [1, 2, 0, 0, 0, 1],
  "total_score": 4
}

Comments:
The solution shows a good understanding of using marshmallow for data serialization, but it misses some key requirements. The classes defined are not true dataclasses, and the crucial @post_load decorator is missing. The date format is incorrectly specified, and the decimal field lacks the required as_string=True parameter. The example usage contains errors, as the from_json method is not defined. Despite these issues, the solution does demonstrate knowledge of nested schemas and field validation.

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

The provided solution outlines an approach using the marshmallow library to convert JSON data into Python objects through the use of dataclass and schema definitions. Here is the evaluation based on the given marking scheme:

Defining two dataclasses for the person and the order: The solution defines classes for Person and Order but does not use the dataclasses.dataclass decorator from the Python standard library; instead, they are regular classes.
Score: 1/2
Defining two schema classes, one for the person and one for the order: The solution successfully includes both PersonSchema and OrderSchema.
Score: 2/2
Specifying the date format for the date_of_birth field: The solution uses a date format in PersonSchema for date_of_birth but incorrectly uses 'MM/DD/YYYY' instead of "%m/%d/%Y".
Score: 0/1
The decimal field amount should have as_string=True: The solution does not use the as_string=True parameter for the Decimal field.
Score: 0/1
Linking the schema and dataclasses using a @post_load method: The solution provided does not implement a @post_load method to connect schema loading directly to dataclass instantiation.
Score: 0/2
Additional tasks (Error checks, methods, proper validations): The use of validate.Equal on the date_of_birth is incorrect here as it binds the schema to accept only one specific date, limiting its utility. Additionally, there are flaws like use of a wrong decorator (@post_dump instead of @post_load) and a nonexistent method Person.from_json() which has not been defined.
Score: 0/2

Overall, the submitted solution does not utilize the features of the marshmallow library to their full extent, especially overlooking the crucial post_load mechanism and dataclasses module features, along with incorrect handling of data types and validations.

Final Score:

{
  "score": 3
}

openai/gpt-3.5-turbo-0125 says: (10.0) (0.09¢)

This solution provides a clear and structured approach to generating dataclasses from the given JSON data using the marshmallow library in Python. The code defines the necessary dataclasses, schema classes, and includes the proper formatting for date, decimal, and datetime fields.

The use of post_dump method for custom serialization rules, validation for specific values using validate.Equal, and linking schema and dataclasses with @post_load method demonstrate a good understanding of the marshmallow library.

Overall, the solution is well-written and provides a comprehensive approach to solving the problem.

Break down of marks:

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

Score:

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

openai/gpt-4-1106-preview says: (2.0) (2.47¢)

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

Comments on the solution:

Marks 1 (2 marks): The solution defines two classes for Person and Order (mentioned as dataclasses, but they are not actually using the dataclasses module). Given only one mark as the classes are not defined using the dataclass decorator from the dataclasses module, which is expected when referring to 'dataclasses' in Python.
Marks 2 (2 marks): Two schema classes are defined for Person and Order. Given only one mark because it has some flaws, such as the 'orders' field in PersonSchema should be a list of nested OrderSchema and not just a single nested order.
Marks 3 (1 mark): The date format for date_of_birth uses 'MM/DD/YYYY' which is incorrect – it should be '%m/%d/%Y'. No marks awarded.
Marks 4 (1 mark): There is no use of as_string=True for the decimal field in OrderSchema. No marks awarded.
Marks 5 (2 marks): There is no @post_load method to link the schemas and classes. This method is essential to create instances of the dataclasses from the deserialized data. No marks awarded.
Marks 6 (2 marks): There are several issues with the provided solution that are not addressed or marked in the previous sections. No from_json method is defined on the Person class to handle deserialization; post_dump is mentioned but not defined; validation on date_of_birth does not seem relevant to the task; improper handling of Decimal class; there is no proper linking between the schemas and the model classes to actually serialize and deserialize between objects and JSON. No marks awarded.

Total score: 2 out of 10.

This solution has a foundational structure for the problem but contains several significant errors and omissions that prevent it from being a valid or complete solution.

Response 2 (4.0)

To generate dataclasses from the given JSON data, you can use the marshmallow library in Python. Here's how you can do it:

import marshmallow

class Person(marshmallow.Schema):
    first_name = marshmallow.fields.String(required=True)
    last_name = marshmallow.fields.String(required=True)
    date_of_birth = marshmallow.fields.Date(format='%m/%d/%Y')
    orders = marshmallow.fields.Nested(Order, many=True)

class Order(marshmallow.Schema):
    created = marshmallow.fields.DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount = marshmallow.fields.Number(allow_nan=True, allow_none=True, always_decode=True)

data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}
person_data = Person().load(data)
print(person_data)

Output:

{'date_of_birth': datetime.date(2022, 3, 16), 'first_name': 'Jane', 'last_name': 'Smith', 'orders': [{'amount': 100.0, 'created': datetime.datetime(2024, 1, 25, 15, 0, 4, 22053, tzinfo=tzutc()), 'name': None}]}

In this example, we define two dataclasses - Person and Order. The Person dataclass has fields for first_name, last_name, date_of_birth, and orders. The Order dataclass has fields for created and amount.

We then use the Person dataclass to load the JSON data into a Python object. The marshmallow library automatically converts the JSON data into Python objects based on the defined dataclasses.

The output shows the resulting Python object, which contains the expected values for the Person and Order fields, as defined 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: (4.0) (0.92¢)

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

Dataclasses:
The solution doesn't actually define dataclasses. It defines schema classes using marshmallow.Schema.
Schema classes:
The solution correctly defines two schema classes: Person and Order.
Date format for date_of_birth:
The solution correctly specifies the date format for the date_of_birth field as '%m/%d/%Y'.
Decimal field:
The solution doesn't use a DecimalField for the amount, and doesn't include 'as_string=True'.
@post_load method:
The solution doesn't include @post_load methods to link schemas and dataclasses.
Other observations:
The solution uses marshmallow.fields.Number for the amount field instead of DecimalField.
The Order schema is nested within the Person schema, which is correct.
The solution includes a working example of how to use the schemas to deserialize data.

Mark breakdown:

{
  "Dataclasses": 0,
  "Schema classes": 2,
  "Date format": 1,
  "Decimal field": 0,
  "@post_load method": 0,
  "Other observations": 1
}

Final score:

{
  "scores": [0, 2, 1, 0, 0, 1],
  "total_score": 4
}

The solution demonstrates a basic understanding of using marshmallow for data deserialization but misses several key requirements, particularly the use of dataclasses and proper handling of the decimal field. It also lacks the @post_load method to create actual dataclass instances. However, it does correctly define schema classes and handles the date format properly.

openai/gpt-3.5-turbo-0125 says: (8.0) (0.08¢)

Overall, the solution provided successfully defines the required dataclasses and schema classes using the marshmallow library in Python. It correctly specifies the formats for the date_of_birth and created fields, as well as including as_string=True for the amount field.

Additionally, the solution correctly links the schema and dataclasses with a @post_load method on the schema classes, ensuring proper data conversion.

One minor improvement could be providing additional comments or explanations within the code for better clarity.

Breakdown of marks:
- The solution defines two dataclasses and schema classes: 2 marks
- Date format specified for the date_of_birth field: 1 mark
- Decimal field with as_string=True: 1 mark
- Schema and dataclasses linked with a @post_load method: 2 marks
- Additional improvements/comments: 2 marks

Total score: 8 out of 10

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "total_score": 8
}

anthropic/claude-3-haiku says: (9.0) (0.07¢)

The provided solution meets most of the requirements outlined in the marking scheme. Here's a breakdown of the scores:

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

Breakdown:

The solution defines two dataclasses, one for the person and one for the order. (2 marks)
The solution defines two schema classes, one for the person and one for the order. (2 marks)
The solution specifies 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 are linked with a @post_load method on the schema classes. (2 marks)
The solution could be improved by adding more error handling, type annotations, and docstrings. (1 mark)

Total score: 9/10

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

The provided solution is a good starting point, but it could be improved by adding more error handling, type annotations, and docstrings to make the code more robust and maintainable.

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

The provided solution uses the marshmallow library to parse JSON data into Python objects. Here's the evaluation based on the provided marking scheme:

Definition of two dataclasses, one for the person and one for the order (2 marks):
The solution defines Person and Order classes, but incorrectly labels them as marshmallow.Schema instead of using dataclasses.dataclass. This is a departure from the correct implementation, since they should indeed be separate data classes rather than schema classes for JSON serialization and deserialization.
Score: 0/2
Definition of two schema classes, one for the person and one for the order (2 marks):
The Person and Order classes are defined as subclasses of marshmallow.Schema, which correctly makes them schemas per the problem's intent, but not as per dataclass standards.
Score: 2/2
Specification of the date format for the date_of_birth field (format="%m/%d/%Y") (1 mark):
The date_of_birth field correctly uses the marshmallow.fields.Date with the specified format.
Score: 1/1
The decimal field should have as_string=True (1 mark):
The solution uses marshmallow.fields.Number for the amount field without specifying as_string=True. The output is therefore a float, not a string as specified.
Score: 0/1
Link schema and dataclasses with a @post_load method on the schema classes (2 marks):
The provided solution lacks the @post_load decorator and method entirely, which is necessary for creating dataclass instances from loaded data.
Score: 0/2
Anything else (2 marks):
Handling of nested Order within Person is set up but it uses the direct nested field. There is an issue with the creation in the instantiation of Order in the Person class due to forward reference 'Order' being used before it's defined.
Error handling, validation, or further manipulation post data retrieval aren't considered.
There's a mismatch between actual usage of dataclasses and schemas.
Score: 0/2

In summation, the solution attempts to employ the marshmallow library but contains several critical errors in correctly applying Python dataclasses and proper schema mappings.

Final score:

{
  "score": 3
}

openai/gpt-4-1106-preview says: (2.0) (2.14¢)

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

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

Evaluation and Comments:
- The given solution fails to define actual dataclasses; instead, it defines schema classes using the Marshmallow Schema class directly. This is incorrect given the requirement. (0 marks on marks 1)
- The defined Person and Order classes are not dataclasses but schemas. This means they don't fulfill the request to have dataclasses separately. (0 marks on marks 2)
- The date format for the date_of_birth field is specified correctly. However, since it is not in a @post_load method and the classes are schemas instead of dataclasses, this part of the solution is not valid. (0 marks on marks 3)
- The decimal field does not have as_string=True. Instead, the field is defined as a Number with incorrect parameters. (0 marks on marks 4)
- There are no @post_load methods defined in the schema classes to create dataclass instances upon deserializing the data. (0 marks on marks 5)
- The extra marks could be interpreted as an attempt at data conversion, correctly displaying the intended type conversions (dates and decimals). However, as a correct solution has not been provided based on the problem definition, full marks cannot be given. A partial score is provided considering the conversion in the expected output and for providing a load functionality with a sample JSON input. (2 marks on marks 6)

Overall, the solution does not solve the problem as stated, and therefore, the total score is low. There are critical misunderstandings about the difference between dataclasses and Marshmallow schemas, the @post_load decorator, and field specifications necessary for proper deserialization.

Response 3 (3.0)

You can use the Marshmallow library to define dataclasses based on the given JSON schema. Here's an example code snippet that defines the dataclasses:

from marshmallow import fields, validate, Shape

class Person:
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(format='%m/%d/%Y')
    orders = fields.Nested(Order, many=True)

class Order:
    created = fields.DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount = fields.Float(required=True, validate=validate.Range(min=0))

schema = Shape(map_rin=Person)

You can then use Marshmallow's make_autodict() function to convert the JSON data into a Python dictionary:

import json
data = 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 = schema.load(data)
person_dict = schema.make_autodict(person)

This will create a Person object and convert it to a Python dictionary using the make_autodict() function. The date_of_birth field is automatically converted to a Python date object, created field is converted to a Python datetime object, and amount field is converted to a 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-3.5-sonnet says: (3.0) (0.95¢)

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

The solution defines two classes, Person and Order, but they are not dataclasses. (0/2 marks)
The solution defines two schema-like classes, but they are not proper Marshmallow Schema classes. (1/2 marks)
The solution correctly specifies the date format for the date_of_birth field (format='%m/%d/%Y'). (1/1 mark)
The solution does not use as_string=True for the decimal field. (0/1 mark)
The solution does not implement @post_load methods to link schemas and dataclasses. (0/2 marks)
Other observations:
The solution uses fields.Float instead of fields.Decimal for the amount field.
The solution incorrectly uses Shape instead of Schema.
The solution attempts to use make_autodict(), which is not a standard Marshmallow function.
The solution doesn't properly separate schema definitions from data loading.
(1/2 marks)

Breakdown of marks:

{
  "Dataclass definitions": 0,
  "Schema definitions": 1,
  "Date format specification": 1,
  "Decimal field specification": 0,
  "Schema and dataclass linking": 0,
  "Other observations": 1
}

Score:

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

The solution demonstrates some understanding of Marshmallow concepts but falls short in several key areas. It doesn't properly implement dataclasses or Marshmallow schemas, and misses important details like the correct handling of the decimal field. The attempt to use non-standard Marshmallow functions also indicates a misunderstanding of the library's API. Overall, the solution needs significant improvements to meet the requirements of the problem.

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

Overall, the solution provides a clear and concise way to define dataclasses and schema classes using the Marshmallow library in Python. It correctly addresses the requirements of converting the date_of_birth field to a date, created field to a datetime, and amount field to a decimal. The provided code snippets demonstrate how to achieve this transformation effectively.

Breakdown of marks:
- The solution defines two dataclasses and schema classes - 2 marks
- Specifies the date format for the date_of_birth field - 1 mark
- The decimal field is set with as_string=True - 1 mark
- Links the schema and dataclasses with a @post_load method - 2 marks
- Overall, the solution covers all the key aspects of the problem and provides a complete implementation - 2 marks

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

Score:

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

anthropic/claude-3-haiku says: (9.0) (0.09¢)

The provided solution is a good approach to the problem, and it covers most of the requirements. Here's the evaluation of the solution:

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

Breakdown:

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution correctly defines the Person and Order dataclasses.
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution correctly defines the Person schema and the Order schema.
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 as "%m/%d/%Y".
The decimal field needs as_string=True. (1 mark)
The solution uses the fields.Float field for the amount field, which does not directly handle the decimal format. The solution should have used the fields.Decimal field with as_string=True to handle the decimal format correctly.
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
The solution does not include the @post_load method to link the schema and dataclasses. It uses the make_autodict() function instead, which is a valid approach but does not directly link the schema and dataclasses.
Anything else: (2 marks)
The overall solution is good, and it covers the main requirements. The only minor issue is the handling of the decimal field.

Total Score:

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

The total score for the provided solution is 9 out of 10.

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

The solution provided incorrectly uses the Marshmallow library and contains multiple fundamental errors that deviate from the required implementation. Here’s a detailed analysis based on the marking scheme:

Defining Dataclasses:
The solution does not define proper Python dataclasses; instead, it uses Marshmallow fields directly within a class. This is inconsistent with the requirement to use dataclasses. Hence, no marks can be awarded for defining dataclasses.
Score: 0/2
Defining Schema Classes:
The solution does not define any specific schema classes using Marshmallow. It incorrectly uses a Shape class that does not exist in the library, manifesting a fundamental misunderstanding of how Marshmallow works. Marshmallow requires the use of Schema with field matching, and explicit schema classes were expected.
Score: 0/2
Specifying Date Format:
The date_of_birth correctly sets the format to %m/%d/%Y. This aligns with the requirements.
Score: 1/1
Decimal Conversion with as_string=True:
The field for the amount is declared as Float, and there is no usage or mention of as_string=True which was a requirement for handling decimals in the schema. Additionally, the datatype should have been Decimal, not Float.
Score: 0/1
Link with @post_load Method:
The solution is lacking any use or definition of the @post_load decorator. This decorator is crucial for transforming the loaded data into an instance of the defined dataclasses, which was a critical requirement for this task.
Score: 0/2
Miscellaneous and Overall Approach:
The overall implementation suggests a misunderstanding of the Marshmallow library, shown by the introduction of nonexistent features like Shape and make_autodict(). It demonstrates a lack of core concepts necessary for the task. While the JSON parsing with json.loads is correctly demonstrated, it isn’t enough to gain points here due to the overarching errors.
Score: 0/2

In summary, most of the core criteria are not met, primarily due to incorrect implementation and usage of the Marshmallow library, incorrect data handling, and misuse of Python class structures. The only correct aspect is the date formatting.

Final Score:

{
  "score": 1
}

openai/gpt-4-1106-preview says: (0.0) (1.62¢)

{
  "evaluation": {
    "marks 1": 0,
    "marks 2": 0,
    "marks 3": 0,
    "marks 4": 0,
    "marks 5": 0,
    "marks 6": 0
  },
  "comments": {
    "marks 1": "The solution did not define dataclasses using Python's dataclass functionality.",
    "marks 2": "No schema classes were defined with marshmallow.",
    "marks 3": "The date format was mentioned but not in the context of a schema class or a field that is automatically converted.",
    "marks 4": "Did not use Decimal field nor specify as_string=True.",
    "marks 5": "@post_load method not used or implemented in the proposed solution.",
    "marks 6": "Used fields in a non-schema class; incorrect use of marshmallow's fields and classes; 'Shape' is not a valid marshmallow class and 'make_autodict()' function does not exist."
  },
  "scores": [
    0,
    0,
    0,
    0,
    0,
    0
  ],
  "total_score": 0
}

The provided solution is incorrect and does not follow the correct usage of marshmallow for data serialization and schema definition. It appears that the solution confused the functionality provided by marshmallow with standard Python classes and syntax, and additional non-existent functions were mentioned.

1.00	0.89	0.82
0.89	1.00	0.85
0.82	0.85	1.00

Chronos Hermes 13B v2

Description

Score:

Most Similar

Also by austism

Model Consistency

Model Evaluations

claude-3-haiku	10.0	9.0	9.0
claude-3.5-sonnet	4.0	4.0	3.0
gpt-3.5-turbo-0125	10.0	8.0	10.0
gpt-4-0125-preview	3.0	3.0	1.0
gpt-4-1106-preview	2.0	2.0	0.0