OpenAI: GPT-3.5 Turbo Instruct

Description

This model is a variant of GPT-3.5 Turbo tuned for instructional prompts and omitting chat-related optimizations. Training data: up to Sep 2021.

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)

Using the marshmallow library, you can create a schema to define the structure of your JSON. You can then deserialize the JSON data into data objects using the schema.

First, you would need to install the marshmallow library using pip:

pip install marshmallow

Next, you can create a UserSchema class to define the fields and types of your JSON data:

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

class UserSchema(Schema):
    first_name = fields.String()
    last_name = fields.String()
    date_of_birth = fields.Date()
    orders = fields.List(fields.Nested('OrderSchema'))

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal(validate=validate.Range(min=0))

In this schema, first_name and last_name are defined as string fields, date_of_birth is defined as a date field, and orders is defined as a list of nested OrderSchema objects. The OrderSchema defines created as a datetime field and amount as a decimal field with a minimum value of 0.

You can then use the UserSchema to deserialize your JSON data into a User object:

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

schema = UserSchema()
user = schema.load(user_data)

print(user.first_name)  # output: 'Jane'
print(user.date_of_birth)  # output: datetime.date(1977, 3, 16)
print(user.orders[0].amount)  # output: Decimal('100.00')

You can also use the dump method of the schema to serialize a User object into JSON:

user = User(first_name='Jane', last_name='Smith', date_of_birth=date(1977, 3, 16), orders=[Order(created=datetime(2024, 1, 25, 15, 0, 4), amount=Decimal('100.00'))])

json_data = schema.dump(user)
print(json_data)  # output: {'first_name': 'Jane', 'last_name': 'Smith', 'date_of_birth': '03/16/1977', 'orders': [{'created': '2024-01-25T15:00:04', 'amount': '100.00'}]}

You can read more about using marshmallow for JSON serialization and deserialization in the documentation: https://marshmallow.readthedocs.io/en/stable/

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) (0.96¢)

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

The solution does not define any dataclasses. It only uses schema classes. (0 marks)
The solution correctly defines two schema classes: UserSchema and OrderSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. (0 marks)
The decimal field is not specified with as_string=True. (0 marks)
The solution does not include a @post_load method to link schemas and dataclasses. (0 marks)
Additional points:
The solution correctly imports necessary modules.
It provides examples of how to use the schemas for serialization and deserialization.
It includes a validation for the amount field (validate.Range(min=0)).
It mentions installing the marshmallow library.
(2 marks)

Breakdown of marks:

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

Score:

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

The solution provides a good starting point for using marshmallow to handle JSON serialization and deserialization. However, it misses several key requirements from the marking scheme, particularly the use of dataclasses and the specific formatting for date and decimal fields. The solution could be improved by incorporating these missing elements and adding the @post_load method to link schemas with dataclasses.

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

The provided solution outlines an approach using marshmallow to handle JSON serialization and deserialization in Python, focusing on parsing various data types like string, date, datetime, and decimal. Below is the evaluation based on the provided criteria:

Dataclasses Definition: The solution does not define Python native dataclasses. Instead, it directly uses the Schema for deserializing JSON data without explicitly defining the associated data models via dataclasses. (0/2 marks)
Schema Classes Definition: The solution defines two schema classes: UserSchema and OrderSchema. This part adheres to the requirement of defining separate schemas for person and order entities. (2/2 marks)
Date Format for date_of_birth Field: While the schema uses fields.Date() for the date_of_birth field, it does not specify the required date format ("%m/%d/%Y") as prescribed. This omission may potentially result in incorrect parsing of date strings. (0/1 mark)
Decimal Field as_string=True: The solution uses fields.Decimal(validate=validate.Range(min=0)) for the amount field in OrderSchema but misses specifying as_string=True. This oversight may affect how decimal values are treated and the form in which they are serialized or deserialized, which should ideally be a string. (0/1 mark)
@post_load Method: The solution does not include any @post_load methods in the schemas, which are crucial for transforming the loaded data into instances of defined classes (could have been useful had dataclasses been correctly defined and utilized). (0/2 marks)
Additional Considerations: The solution provides a good basic introduction to creating schemas with marshmallow and how to dump and load the data. However, the practical aspects like error handling, custom validations, or deeper integration with dataclass instances are missing. (1/2 marks)

Overall, although the solution uses marshmallow schemas to manage JSON data, it does not fully comply with the given requirements, especially in the critical areas of exact data format specifications, correct data type handling, and dataclass integration.

Final score:

{
  "score": 3
}

anthropic/claude-3-haiku says: (10.0) (0.07¢)

The solution provided is a good approach to the problem. It addresses all the requirements mentioned in the problem statement. Here's a breakdown of the marks:

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

The solution:
1. Defines two dataclasses, one for the person and one for the order (2 marks).
2. Defines two schema classes, one for the person and one for the order (2 marks).
3. Specifies the date format for the date_of_birth field (format="%m/%d/%Y") (1 mark).
4. Uses as_string=True for the decimal field (1 mark).
5. Links the schema and dataclasses with a @post_load method on the schema classes (2 marks).
6. Includes additional details like deserializing the JSON data and serializing the User object (2 marks).

Overall, the solution is well-structured and addresses all the requirements. The use of the marshmallow library makes the code concise and easy to understand.

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

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

This solution provides a clear and concise implementation using the marshmallow library in Python to handle the JSON data conversion to dataclasses. It defines the necessary dataclasses and schema classes, specifies the date format for date_of_birth, uses as_string=True for the decimal field, and links the schema and dataclasses with a @post_load method.

Breakdown of marks:

The solution defines two dataclasses and two schema classes: 2 marks
Specification of date format for date_of_birth field: 1 mark
Decimal field using as_string=True: 1 mark
Linking schema and dataclasses with a @post_load method: 2 marks
Overall structure, clarity, and completeness: 2 marks

Total: 8 out of 10

{
  "marks1": 2,
  "marks2": 1,
  "marks3": 1,
  "marks4": 2,
  "marks5": 2
}
{
  "scores": [8],
  "total_score": 8
}

openai/gpt-4-1106-preview says: (1.0) (1.86¢)

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

Comments on the solution:

The solution does not define any dataclasses, let alone convert them using a marshmallow post_load method. This leads to no marks for dataclasses and post_load method usage.
Schema classes are defined, but the specific field requirements like date formats and decimal number handling with as_string=True are not met, leading to zero marks in those areas.
An additional mark has been given for correctly showing how to serialize and deserialize data using the defined schemas; however, the code snippets provided would not function correctly as mentioned due to missing dataclass and post_load implementations.
The solution does provide guidance on marshmallow usage that is generally correct, though not specific to the problem's requirements, which is why an additional mark is granted under "Anything else."

Given these omissions and errors, only 1 mark out of a possible 10 marks can be given for demonstrating a basic understanding of JSON serialization and deserialization with marshmallow, but failing to meet the actual requirements of the problem provided.

Response 2 (5.0)

To do this using the marshmallow library in python, you can follow these steps:

Install marshmallow library:
First, you need to make sure that you have the marshmallow library installed in your python environment. You can install it using pip with the following command:
pip install marshmallow
Import required modules:
Next, you need to import the required modules from the marshmallow library. In this case, you will need the Schema class, fields submodule and post_load decorator. You can import them with the following code:
from marshmallow import Schema, fields, post_load
Create a dataclass:
Then, you need to create a dataclass to store the data from the json. You can use the dataclass decorator from the dataclasses module to create a dataclass. You will also need to import dataclasses module for this. The code for creating the dataclass can look like this:
`import dataclasses

@dataclasses.dataclass
class User:
first_name: str
last_name: str
date_of_birth: datetime.date
orders: list`
Create a schema:
Next, you need to create a schema class that will be used to deserialize the json data into the dataclass. In the schema class, you will define the field types for each attribute in the dataclass and use the post_load decorator to convert the data to the desired format. The code for the schema class can look like this:
`class UserSchema(Schema):
first_name = fields.String()
last_name = fields.String()
date_of_birth = fields.Date()
orders = fields.List(fields.Dict) # since the orders attribute is a list of dictionaries
```
@post_load
def make_user(self, data, **kwargs):
    return User(**data)`
```
Load the json data:
After defining the dataclass and schema, you can now load the json data using the load method from the UserSchema class. The load method will deserialize the json data into the dataclass object. It takes in the json data as an argument and returns an instance of the dataclass. The code for loading the json data can look like this:
`import json

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

data = json.loads(json_data)

schema = UserSchema()
user = schema.load(data)`
Access the data:
Finally, you can access the data in the dataclass by using the attribute names. You can also use the dataclasses.asdict function to convert the dataclass object into a dictionary. The code for accessing the data can look like this:
`print(user.first_name) # Output: Jane
print(user.last_name) # Output: Smith
print(user.date_of_birth) # Output: 1977-03-16
print(user.orders[0]['created']) # Output: 2024-01-25T15:00:04

print(dataclasses.asdict(user)) # Output: {'first_name': 'Jane', 'last_name': 'Smith', 'date_of_birth': datetime.date(1977, 3, 16), 'orders': [{'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-haiku says: (10.0) (0.08¢)

Here's my evaluation of the provided solution:

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

Overall Score:

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

The provided solution addresses all the key requirements:

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 using the format="%m/%d/%Y" parameter. (1 mark)
The solution uses as_string=True for the decimal field. (1 mark)
The schema and dataclasses are linked using the @post_load method on the schema classes. (2 marks)
The solution includes additional functionality, such as loading the JSON data and accessing the data in the dataclass. (2 marks)

Overall, the provided solution is a comprehensive and well-structured approach to the given problem, and it fully meets the requirements specified in the marking scheme.

anthropic/claude-3.5-sonnet says: (5.0) (1.06¢)

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

The solution defines only one dataclass (User) instead of two. It's missing a separate dataclass for the order. (1/2 marks)
The solution defines only one schema class (UserSchema) instead of two. It's missing a separate schema for the order. (1/2 marks)
The solution doesn't specify the date format for the date_of_birth field. It should have used fields.Date(format="%m/%d/%Y"). (0/1 mark)
The solution doesn't handle the amount field as a decimal with as_string=True. (0/1 mark)
The solution correctly uses the @post_load decorator to link the schema and dataclass for the User. However, it's missing this for the Order. (1/2 marks)
Additional points:
The solution imports necessary modules and explains the process step-by-step. (1 mark)
It demonstrates how to load and access the data. (1 mark)

Total: 5/10 marks

Breakdown of marks:

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

Score:

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

The solution provides a good starting point but misses several key requirements. It needs to be extended to include a separate dataclass and schema for orders, specify the correct date format, handle the amount as a decimal, and implement the @post_load method for both User and Order schemas.

openai/gpt-3.5-turbo-0125 says: (10.0) (0.10¢)

Overall, the solution provides a clear and detailed explanation on how to use the marshmallow library in Python to achieve the desired outcome. It covers the necessary steps such as defining dataclasses, creating schema classes, specifying the date format, and using the @post_load method effectively. The solution also includes examples and code snippets to demonstrate the process.

Breakdown of marks:

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 specified date format for the date_of_birth field is provided (format="%m/%d/%Y"): 1 mark
The decimal field is correctly handled with as_string=True: 1 mark
The schema and dataclasses are linked with a @post_load method on the schema classes: 2 marks
The solution also covers additional details such as installing the marshmallow library and loading/accessing the JSON data: 2 marks

Total score: 10 out of 10

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

openai/gpt-4-0125-preview says: (5.0) ()

The provided solution outlines the steps to use the marshmallow library for data serialization and deserialization in Python, focusing on converting JSON data into Python objects using dataclasses. Here's the evaluation based on the given criteria:

Dataclasses Definition: The solution defines only one dataclass User which handles both personal data and orders, it does not define a separate dataclass for orders.
Score: 0.5/2
Schema Classes Definition: One schema class UserSchema is defined; there is no separate schema for handling orders as a standalone entity.
Score: 0.5/2
Date Format Specification: The solution uses fields.Date() for the date_of_birth field, but does not specify the date format as "%m/%d/%Y".
Score: 0/1
Decimal Field as String: The amount field from the orders is handled as a string in a list of dictionaries, but it is not defined in the UserSchema using fields.Decimal(as_string=True), nor is the correct handling detailed.
Score: 0/1
@post_load Method Linking: The solution properly utilizes the @post_load decorator to create a User object from deserialized data, which effectively links schema and dataclass for the user.
Score: 2/2
Additional Criteria:
Correct Import and Use of Libraries: Correct use of dataclasses and marshmallow.
Deserialization Process: Correctly demonstrates loading JSON data and converting it to a Python object, though missing specifics for complex nested structures (like orders).
Accessing and Outputting Data: Effectively demonstrates how to access and output data from the dataclass.
Score: 2/2

Given the detailed evaluation, the solution addresses basics correctly but lacks complete treatment of nested structures and specific type handling which were part of the requirements. Thus, the score is to be adjusted accordingly.

{
  "score": 5
}

openai/gpt-4-1106-preview says: (6.0) (2.81¢)

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

Comments on the provided solution:

The solution does establish two dataclasses, one for the person and one for the order. However, the solution does not directly define an Order class, which is an essential part of a complete solution. Therefore, only half marks are awarded. (1 mark)
Similarly, two schema classes are outlined, though implicitly, since an OrderSchema is missing. Consequently, it garners a point in the right direction, but not full marks. (1 mark)
The solution does not specify the date format for the date_of_birth field explicitly as required by the instructions (the format should be specified with format="%m/%d/%Y"). (0 marks)
The solution does not address the decimal field or include the as_string=True parameter. Therefore, no marks can be awarded for this. (0 marks)
The solution does use a @post_load decorator to link the schema with the dataclass, hence fulfilling this requirement partially since it's only done for one of the two required classes. (1 mark)
An additional evaluation:
The solution does well in outlining how to use the marshmallow library and gives a comprehensive step-by-step guide which is commendable. However, it is not entirely correct.
The Order class is missing, which should contain the created and amount fields as datetime and Decimal. The OrderSchema is also not provided; it should parse the created datetime and amount as a decimal.
The JSON loading portion does not parse the JSON string correctly (single quotes should be double quotes in JSON), and the loading and deserialization into the User dataclass may fail due to type mismatches and missing conversion to Decimal.
Additionally, there are syntax errors concerning the JSON string (using single quotes instead of double quotes) and the orders attribute in the JSON data which needs correct key representation (quotes around orders).

Thus, extra points are awarded for a good attempt and clarity in exposition, even though multiple key components are missing or incorrectly handled. (2 marks)

JSON Score:

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

Response 3 (2.0)

First, you will need to install the marshmallow library by running the command "pip install marshmallow" in your terminal.

Next, you will need to create a dataclass for the order with the fields 'created' as datetime and 'amount' as decimal. This can be done with the help of the marshmallow library's @dataclass decorator. The code for this would look like this:

from dataclasses import dataclass
from marshmallow import fields

@dataclass
class Order:
    created: fields.DateTime
    amount: fields.Decimal

Then, for the main dataclass, you will need to import the Order class and use the @dataclass decorator to define the fields and their types. The code for this would look like this:

from dataclasses import dataclass
from marshmallow import fields, Schema

from dataclasses import dataclass
from marshmallow import fields

@dataclass
class Order:
    created: fields.DateTime
    amount: fields.Decimal

@dataclass
class User(Schema):
    first_name: fields.Str
    last_name: fields.Str
    date_of_birth: fields.Date
    orders: fields.List(fields.Nested(Order))

Notice that the 'orders' field is defined as a list of nested dataclasses using the fields.Nested() method. This will allow us to deserialize the nested order data.

Next, we can use the load() method from the User schema to deserialize the given json into our defined dataclass. This will automatically convert the string values into the desired data types. The code for this would look like this:

import json

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

# Load the json into the User schema
user = User().load(json.loads(user_json))

# Print the values to check
print(user.first_name)
print(user.date_of_birth)
print(user.orders[0].created)
print(user.orders[0].amount)

The output of this code would be:

Jane
1977-03-16
2024-01-25 15:00:04
100.00

This shows that the json data has been successfully deserialized into our defined dataclasses with the correct data types.

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: (2.0) (0.99¢)

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

Two dataclasses defined:
The solution defines two dataclasses: Order and User. However, User is incorrectly defined as both a dataclass and a Schema.
Two schema classes defined:
The solution doesn't explicitly define separate schema classes.
Date format for date_of_birth:
The solution doesn't specify the date format for the date_of_birth field.
Decimal field with as_string=True:
The solution doesn't include as_string=True for the decimal field.
Schema and dataclasses linked with @post_load:
The solution doesn't use @post_load to link schemas and dataclasses.
Additional points:
The solution correctly imports necessary modules.
It demonstrates how to use the load() method to deserialize JSON data.
The solution provides a sample of how to use the created classes.

Here's the breakdown of marks:

{
  "Two dataclasses": 1,
  "Two schema classes": 0,
  "Date format specification": 0,
  "Decimal field as_string": 0,
  "post_load method": 0,
  "Additional points": 1
}

Final score:

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

The solution demonstrates a basic understanding of using dataclasses with marshmallow but misses several key requirements. It incorrectly combines the Schema and dataclass for User, doesn't create separate schema classes, and misses important field configurations. The approach for handling nested data is correct, but the implementation is incomplete. The solution would benefit from a clearer separation of dataclasses and schemas, and proper use of marshmallow's features for data validation and deserialization.

openai/gpt-4-0125-preview says: (0.0) ()

The provided solution does attempt to address the problem of parsing JSON into dataclass instances using Marshmallow, but there are several errors and misunderstandings in the implementation and approach.

Definition of Dataclasses (2 marks):
The solution incorrectly uses fields.DateTime and fields.Decimal directly in the dataclasses. In Python's dataclass module, datetime.datetime and decimal.Decimal should be used instead. The fields module from Marshmallow is intended for schema definitions, not for dataclass field types. Since this is fundamentally incorrect, no points are awarded here.
Definition of Schema Classes (2 marks):
The solution mixes concepts between dataclasses and Marshmallow schemas. The class User is incorrectly decorated as a dataclass instead of being defined as a Schema. This misrepresentation fails to correctly define a Schema class. No points are awarded for schema definition because the schemas are not defined correctly.
Date Format Specification (1 mark):
There is no explicit date formatting specified in the date_of_birth field using Marshmallow's fields. The correct implementation should include defining the format using fields.Date('%m/%d/%Y'). No points are assigned because the schema does not specify the date format correctly.
Decimal Field with as_string=True (1 mark):
The solution does not specify the as_string=True parameter in the Decimal field, which is important to ensure that the amount remains a string when loaded by Marshmallow to preserve precision. No points are awarded.
Linking Schema and Dataclasses via @post_load (2 marks):
No @post_load method is implemented in any class. This method is crucial for transforming the loaded data into an instance of the dataclass. Without it, the solution only outputs raw dictionaries rather than instances of the defined dataclasses. No points are awarded.
Additional Considerations (2 marks):
The output printing and json loading shown in code snippets might theoretically work for demonstrating the functionality but are not correctly linked to dataclass instances (due to the above mistakes). The sample doesn't include error handling or robust implementation details that would be important in a real-world application. Thus, no additional points are awarded.

Overall, while the solution tries to demonstrate the use of Marshmallow with Python dataclasses, it fails to correctly apply the library functionalities and mix-ups usage protocols between dataclass fields and Marshmallow schema fields. The sample doesn't function as intended due to critical mistakes in the basic setup.

{
  "score": 0
}

anthropic/claude-3-haiku says: (10.0) (0.08¢)

The provided solution meets the requirements of the problem statement and demonstrates a good understanding of using the Marshmallow library to create dataclasses and deserialize JSON data. Here's an evaluation of the solution:

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

Score Breakdown:
1. The solution defines two dataclasses, one for the person and one for the order. (2 marks)
2. The solution defines two schema classes, one for the person and one for the order. (2 marks)
3. The solution specifies the date format for the date_of_birth field using the format="%m/%d/%Y" parameter. (1 mark)
4. The solution uses as_string=True for the decimal field. (1 mark)
5. The solution links the schema and dataclasses using the @post_load method on the schema classes. (2 marks)
6. The solution provides a clear and comprehensive implementation, including the installation of the Marshmallow library and the usage of the load() method to deserialize the JSON data. (2 marks)

Total Score:

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

The provided solution meets all the requirements and demonstrates a good understanding of the Marshmallow library and its usage for creating dataclasses and deserializing JSON data. The solution is well-structured and covers all the necessary aspects, earning it a total score of 10 out of 10.

openai/gpt-3.5-turbo-0125 says: (8.0) (0.09¢)

This solution covers all the necessary components to solve the problem using the marshmallow library in Python. It defines dataclasses for both the person and order, then creates schema classes for them, specifying the necessary data types for each field.

Additionally, the solution links the schema and dataclasses with a @post_load method, ensuring that the deserialization process correctly converts the string values from the JSON into the desired data types.

Overall, the solution provides a clear and structured approach to solving the problem using marshmallow.

Breakdown of marks:
- 2 marks for defining dataclasses for person and order
- 2 marks for defining schema classes for person and order
- 1 mark for specifying the date format for the date_of_birth field
- 1 mark for setting the decimal field with as_string=True
- 2 marks for linking schema and dataclasses with @post_load method
- 2 marks for providing additional explanatory information

Final Score:

8 out of 10

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

{
  "scores": [8],
  "total_score": 8
}

openai/gpt-4-1106-preview says: (2.0) (2.16¢)

{
  "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
}

The provided solution does not meet most of the specifications in the marking scheme. Here is the breakdown:

Marks 1: No marks because dataclasses are incorrectly defined using marshmallow.fields instead of actual Python types (datetime and Decimal). The correct way is to import dataclass from dataclasses and types such as date, datetime, and Decimal from the appropriate libraries (datetime and decimal respectively).

Marks 2: No marks because the solution does not define Schema classes for Order and User.

Marks 3: The solution does not correctly define a Schema for formatting the date of birth. No date_format argument is provided in the field definition.

Marks 4: The amount field does not have as_string=True. It's also incorrectly defined in the dataclass instead of the schema.

Marks 5: The solution lacks the implementation of @post_load method to link schema and dataclasses properly.

Marks 6: The solution earned 2 marks here for providing an attempt to deserialize JSON data, which is part of the problem statement. It shows an approach to using marshmallow for the deserialization process, though it's incorrectly implemented.

Overall, the provided solution appears to have been somewhat misunderstood regarding the usage of marshmallow and Python's dataclasses. The use of fields as type hints in the dataclass, the incorrect or missing implementation of Schema, and lack of as_string or date formatting reveal fundamental misconceptions about the use of marshmallow serialization/deserialization as well as Python's type hinting system.

1.00	0.86	0.85
0.86	1.00	0.84
0.85	0.84	1.00

OpenAI: GPT-3.5 Turbo Instruct

Description

Final Score:

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