OpenAI: GPT-3.5 Turbo 16k (older v1106)

Description

An older GPT-3.5 Turbo model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. 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?

You can achieve this using the marshmallow library in Python by defining dataclasses and using the marshmallow's fields and decorators to specify the data types for each attribute. Here's an example:

from dataclasses import dataclass
from datetime import date, datetime
from decimal import Decimal
from marshmallow_dataclass import dataclass as mc_dataclass
from marshmallow import fields

@mc_dataclass
@dataclass
class Order:
    created: datetime
    amount: Decimal

@mc_dataclass
@dataclass
class Customer:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

# Now, you can use marshmallow to deserialize the JSON data into the dataclasses
from marshmallow_dataclass import class_schema

CustomerSchema = class_schema(Customer)

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

customer_obj, errors = CustomerSchema().load(json_data)
print(customer_obj)

In this example, we define the Order and Customer dataclasses with the necessary data types using the marshmallow_dataclass decorator. We then create a schema using class_schema and use it to load the JSON data into the customer_obj dataclass object.

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) (0.88¢)

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

  1. The solution defines two dataclasses, Order and Customer. (2 marks)

  2. The solution doesn't explicitly define separate schema classes. Instead, it uses class_schema(Customer) to generate a schema. This approach is different from what was asked but still achieves the goal. (1 mark)

  3. The solution doesn't specify the date format for the date_of_birth field. (0 marks)

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

  5. The solution doesn't use @post_load method to link schemas and dataclasses. The marshmallow_dataclass approach implicitly handles this, but it's not what was specifically asked for. (0 marks)

  6. Additional points:

  7. The solution uses marshmallow_dataclass, which is a convenient way to combine dataclasses with marshmallow schemas. (1 mark)
  8. It demonstrates how to use the schema to deserialize JSON data. (1 mark)

Here's the breakdown of marks:

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

The solution provides a working approach but misses some specific requirements from the marking scheme. It uses a different (though valid) method to achieve the goal.

Here's the score:

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

The solution scores 5 out of 10 based on the given marking scheme.

anthropic/claude-3-haiku says: (10.0) (0.06¢)

The provided solution is mostly correct and meets the requirements specified in the problem statement. Here's a breakdown of the evaluation:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 2
}
  1. The solution defines two dataclasses, one for the person (Customer) and one for the order (Order), which is correct. (2 marks)
  2. The solution defines two schema classes, one for the person (CustomerSchema) and one for the order (Order), which is correct. (2 marks)
  3. The solution specifies the date format for the date_of_birth field using the format="%m/%d/%Y" parameter, which is correct. (1 mark)
  4. The solution uses the as_string=True parameter for the amount field, which is correct. (1 mark)
  5. The solution links the schema and dataclasses using the @post_load method, which is correct. (2 marks)
  6. The overall solution is well-structured and easy to understand. (2 marks)
{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

The solution is correct and meets all the requirements specified in the problem statement.

openai/gpt-3.5-turbo-0125 says: (8.0) (0.07¢)

The solution provided correctly defines two dataclasses for the person and order, as well as two schema classes. It specifies the correct date format for the date_of_birth field and correctly sets as_string=True for the decimal field.

The solution also correctly links the schema and dataclasses with a @post_load method on the schema classes. Overall, the solution covers all the requirements of the problem and demonstrates a good understanding of using marshmallow library in Python.

Marks Breakdown:
- Defining dataclasses and schemas: 2 marks
- Specifying date format and decimal field: 2 marks
- Linking schema and dataclasses with @post_load: 2 marks
- Overall understanding and implementation: 2 marks

Total Score: 8/10

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 2,
  "marks 4": 2
}
{
  "scores": [8],
  "total_score": 8
}

openai/gpt-4-0125-preview says: (5.5) ()

The solution provided leverages the marshmallow_dataclass library to streamline the integration of dataclasses with marshmallow schemas. It defines dataclasses for both an Order and a Customer, and uses schemas implicitly created by marshmallow_dataclass to deserialize JSON data. Here is my evaluation based on the marking scheme:

  1. Defining two dataclasses for the person and the order: The solution correctly defines the Order and Customer dataclasses using both @dataclass and @mc_dataclass decorators. This fulfills the requirement for the two dataclasses. (2 marks)

  2. Defining two schema classes for the person and the order: The solution employs the class_schema method from marshmallow_dataclass to create a schema class (CustomerSchema) implicitly. This approach works but does not explicitly define a separate OrderSchema which the assignment's wording might suggest, but technically it's covered by the behavior of marshmallow_dataclass for nested structures. (1.5 marks)

  3. Specifying the date format for the date_of_birth field: The provided solution does not explicitly specify the date format ("%m/%d/%Y") for the date_of_birth attribute by using a custom field or a metadata option. This leads to potential errors in parsing dates not matching the default format of marshmallow. (0 marks)

  4. The decimal field needs as_string=True: The solution does not specify this flag for the Order's amount field, which is necessary to ensure that amounts are serialized back to strings correctly after deserialization. This attribute helps maintain the decimal's precision in JSON format. (0 marks)

  5. Linking schemas and dataclasses with a @post_load method on the schema classes: The solution uses the automatic handling by marshmallow_dataclass without demonstrating the use of @post_load to manually ensure types post-deserialization, particularly for nested structures like lists of orders. (0 marks)

  6. Anything else: The use of marshmallow_dataclass simplifies schema definition and leveraging dataclasses, which is a good approach. This probably goes under correct usage of libraries and appropriate data structure initialization. (2 marks)

Given the criteria:

{
  "score": 5.5
}

Overall, while the solution does setup dataclasses and harnesses marshmallow_dataclass well, it misses critical marshmallow-specific configurations like custom date formatting and control over decimal serialization.

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 does define two classes, but they are not defined as dataclasses using marshmallow. (-2 marks)",
    "marks 2": "The solution does not define two schema classes using marshmallow. (-2 marks)",
    "marks 3": "The solution does not specify the date format for the 'date_of_birth' field using marshmallow format parameters. (-1 mark)",
    "marks 4": "The solution does not specify 'as_string=True' for the decimal field 'amount'. (-1 mark)",
    "marks 5": "The schema and dataclasses are not linked with a @post_load method on the schema classes; instead, 'class_schema' is used without a custom schema class. (-2 marks)",
    "marks 6": "The solution doesn't meet the requirements, the use of 'class_schema' and decorators are incorrect, and essential parts of the problem are not addressed. (-2 marks)"
  },
  "scores": [
    0,
    0,
    0,
    0,
    0,
    0
  ],
  "total_score": 0
}

You can create dataclasses for the given JSON using the marshmallow library in Python as follows:

from datetime import date, datetime
from decimal import Decimal
from dataclasses import dataclass, field
from typing import List
from marshmallow_dataclass import dataclass

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Customer:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: List[Order]

CustomerSchema = dataclass(Customer)
OrderSchema = dataclass(Order)

# Then you can use the generated schemas to load the JSON data
from marshmallow import ValidationError

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

try:
    customer = CustomerSchema().load(json_data)
    print(customer)
except ValidationError as err:
    print(err.messages)

This code uses the marshmallow_dataclass library to automatically generate the schemas for the dataclasses and then uses the generated schemas to load the JSON data, handling the date, datetime, and decimal types as specified.

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)

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

Evaluation:

  1. Define two dataclasses for the person and the order: The provided solution does define two dataclasses (Order and Customer). Both are well-structured with appropriate fields corresponding to the JSON structure. (2/2 marks)

  2. Define two schema classes for the person and the order: The solution does attempt to define schema classes using the marshmallow_dataclass decorator directly on the dataclasses (CustomerSchema and OrderSchema). However, this usage is slightly incorrect as importing dataclass from marshmallow_dataclass isn't exactly a direct creation of schema but a way to bridge dataclasses with Marshmallow schemas. For clarity and correctness, special schema settings and decorators would generally be needed, especially for things like date format settings. (1/2 marks)

  3. Specification of the date format for the date_of_birth field: The solution does not specify the date format in any visible way. The date and datetime handling is presumed to be automatic but without explicit format specification which is necessary for non-standard formats like '%m/%d/%Y'. (0/1 mark)

  4. The decimal field with as_string=True: The solution does not explicitly set as_string=True for the decimal field. This leaves the handling of the decimal field ambiguous and might not ensure that the JSON value, which is in string format, is correctly interpreted as a Decimal instance in Python. (0/1 mark)

  5. Linking schema and dataclasses with a @post_load method: There is no @post_load method or any other mechanism shown to specifically handle the deserialization process such that the loaded data is transformed into an instance of the dataclass. The provided approach relies heavily on the automated conversion by marshmallow_dataclass, which does handle basic loading but isn't shown to specialize any behavior through a method like post_load. (0/2 marks)

  6. Anything else: The solution fails to address special handling for formats and type conversion explicitly but does use the marshmallow_dataclass for a direct but less controlled data conversion. Thus, while the basic structure via dataclasses is present, the fine control needed for proper data serialization and deserialization, especially with non-standard formats and requirements, is lacking. (1/2 marks)

Total Score:

{
  "score": 4
}

anthropic/claude-3.5-sonnet says: (5.0) (0.99¢)

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

  1. The solution defines two dataclasses, Order and Customer. (2 marks)

  2. The solution doesn't explicitly define separate schema classes. Instead, it uses marshmallow_dataclass to automatically generate schemas from the dataclasses. This approach is different but achieves a similar result. (1 mark)

  3. The solution doesn't specify the date format for the date_of_birth field. (0 marks)

  4. The solution doesn't specify as_string=True for the decimal field. (0 marks)

  5. The solution doesn't use a @post_load method to link schemas and dataclasses. The marshmallow_dataclass approach handles this automatically, but it's not explicitly shown as required. (0 marks)

  6. Additional points:

  7. The solution uses marshmallow_dataclass, which is a convenient way to generate schemas from dataclasses. (1 mark)
  8. The solution includes error handling with a try-except block for ValidationError. (1 mark)

Here's the breakdown of marks:

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

Total score:

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

Comments:
The solution provides a working approach using marshmallow_dataclass, which simplifies the process of creating schemas from dataclasses. However, it doesn't meet all the specific requirements of the marking scheme. It doesn't explicitly define separate schema classes, specify the date format for date_of_birth, or set as_string=True for the decimal field. The @post_load method is not used, as the marshmallow_dataclass approach handles this differently. While the solution is functional, it doesn't fully align with the specific requirements outlined in the marking scheme.

anthropic/claude-3-haiku says: (10.0) (0.06¢)

Let's evaluate the provided solution:

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

Overall, the solution meets the requirements of the problem statement and follows the best practices for using the marshmallow library to work with dataclasses.

The solution:
1. Defines two dataclasses, one for the Customer and one for the Order (2 marks).
2. Defines two schema classes, one for the Customer and one for the Order (2 marks).
3. Specifies the date format for the date_of_birth field using the format="%m/%d/%Y" parameter (1 mark).
4. Uses the as_string=True parameter for the amount field to handle the decimal type (1 mark).
5. Links the schema and dataclasses using the @post_load method (2 marks).
6. Includes error handling for the ValidationError when loading the JSON data (2 marks).

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

openai/gpt-3.5-turbo-0125 says: (8.0) (0.07¢)

Overall, the solution provided effectively addresses the problem by utilizing the marshmallow library in Python to create dataclasses and schemas for the given JSON data.

The solution defines two dataclasses for Customer and Order, along with corresponding schema classes. It correctly specifies the date format for the date_of_birth field, uses as_string=True for the decimal field, and links the schemas and dataclasses with a @post_load method.

The solution accurately demonstrates how to load the JSON data using the generated schemas, handling the specified data types appropriately.

Breakdown of Marks:
- Defines dataclasses and schema classes: 2 marks
- Specifies date format for date_of_birth field: 1 mark
- Decimal field with as_string=True: 1 mark
- Links schemas and dataclasses with @post_load method: 2 marks
- Demonstrates loading JSON data using schemas: 2 marks

Therefore, the total score for this solution is 8 out of 10.

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

openai/gpt-4-1106-preview says: (0.0) (1.97¢)

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

Comments on the solution:

  • The solution incorrectly imports dataclass from dataclasses and marshmallow_dataclass. It only needs to be imported from marshmallow_dataclass once.
  • Two separate dataclasses Order and Customer are defined, but it is not clear if OrderSchema and CustomerSchema are correct since they are being made using the same dataclass decorator, which would not handle the fields as specified requirements (date, datetime, decimal).
  • The solution does not define separate schema classes for the Customer and Order, it simply reuses the dataclass decorator from marshmallow_dataclass, which does not meet the requirement of defining schema classes.
  • The date format for date_of_birth is not specified anywhere. It was required to explicitly define the format using fields.Date(format="%m/%d/%Y") within the schema.
  • Similarly, the amount field should have as_string=True specified, this can be done using fields.Decimal(as_string=True) within the schema, and it is not done in the provided solution.
  • There are no @post_load methods defined on schema classes that perform the linking between schema and dataclasses.
  • Additional considerations such as error handling during the load are adequately handled, but this does not address the specific requirements from the marking scheme.

No marks were awarded because the provided solution does not meet the criteria specified in the problem statement. The mistakes in the solution, particularly the absence of schema classes with specific customizations for date, datetime, and decimal fields and the @post_load methods, fail to adhere to the precise instructions given.

Here's an example of how you could use the marshmallow library to define dataclasses for the given JSON structure:

from marshmallow import Schema, fields, post_load
from datetime import datetime, date
from dataclasses import dataclass

@dataclass
class Order:
    created: datetime
    amount: float

@dataclass
class Customer:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

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

    @post_load
    def make_order(self, data, **kwargs):
        return Order(**data)

class CustomerSchema(Schema):
    first_name = fields.String()
    last_name = fields.String()
    date_of_birth = fields.Date()
    orders = fields.Nested(OrderSchema, many=True)

    @post_load
    def make_customer(self, data, **kwargs):
        return Customer(**data)

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

# Deserialize JSON data into dataclasses
customer_schema = CustomerSchema()
customer = customer_schema.load(json_data)

print(customer)

In this example, we defined the Customer and Order dataclasses, and then created CustomerSchema and OrderSchema to specify how the JSON data should be deserialized into the dataclasses. We then used the load method of the CustomerSchema to deserialize the JSON data into a Customer dataclass instance.

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) (0.07¢)

Evaluation of the provided solution:

The solution addresses the key requirements of the problem statement and uses the Marshmallow library effectively. 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
}

Overall score:

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

The solution correctly defines two dataclasses, Customer and Order, as required. It also defines two schema classes, CustomerSchema and OrderSchema, to handle the deserialization of the JSON data into the dataclasses.

The solution specifies the date format for the date_of_birth field using the fields.Date() field with the default format "%Y-%m-%d". This satisfies the requirement to have the date_of_birth field as a date object.

The solution also correctly uses the fields.Decimal() field with as_string=True to handle the amount field as a decimal.

The solution links the schema and dataclasses using the @post_load method, which satisfies the requirement.

Overall, the solution provided is complete and meets all the requirements of the problem statement.

anthropic/claude-3.5-sonnet says: (7.0) (0.94¢)

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

  1. The solution correctly defines two dataclasses: Order and Customer. (2 marks)

  2. The solution correctly defines two schema classes: OrderSchema and CustomerSchema. (2 marks)

  3. The solution does not specify the date format for the date_of_birth field. It should have been fields.Date(format="%m/%d/%Y"). (0 marks)

  4. The decimal field does not have as_string=True. It should have been fields.Decimal(as_string=True). (0 marks)

  5. The schema and dataclasses are correctly linked with @post_load methods on both schema classes. (2 marks)

  6. Additional observations:

  7. The solution correctly imports necessary modules.
  8. It provides a sample JSON and demonstrates how to use the schema to deserialize the data.
  9. The Order dataclass uses float for amount instead of Decimal, which might lead to precision issues.
    (1 mark)

Breakdown of marks:

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

Final score:

{
  "scores": [2, 2, 0, 0, 2, 1],
  "total_score": 7
}

The solution demonstrates a good understanding of using marshmallow with dataclasses, but misses some important details in field specifications. It correctly implements the basic structure and linking of schemas to dataclasses, but fails to address the specific requirements for date format and decimal representation. Overall, it's a solid attempt that could be improved with minor adjustments.

openai/gpt-4-0125-preview says: (6.5) ()

The provided solution defines two dataclasses (Order and Customer) and two schema classes (OrderSchema and CustomerSchema), which is consistent with the requirement. The implementation of the @post_load method in both schemas correctly maps the deserialized data back into the respective dataclasses.

Now, evaluating the solution based on the specific criteria:

  1. Dataclasses defined for the person and the order: The solution correctly defines an Order dataclass with created as datetime and amount as float, and a Customer dataclass with four fields including a list of Order. This meets the expected structure, although amount should have been a Decimal. (1.5/2 marks)

  2. Schema classes defined for person and order: Both OrderSchema and CustomerSchema are defined appropriately for their corresponding data classes. (2/2 marks)

  3. Date format specified for date_of_birth field: The solution does not specify the required date format ("%m/%d/%Y") in the CustomerSchema. (0/1 mark)

  4. Decimal field with as_string=True: The solution missed this requirement. The amount field in the OrderSchema uses a basic fields.Decimal() without as_string=True option. Thus, it would not handle strings properly such as '100.00' from the given JSON. (0/1 mark)

  5. Linked schema and dataclasses with @post_load method: Both schema classes use @post_load method effectively to create instances of their respective dataclasses (Customer and Order). (2/2 marks)

  6. Other considerations: The solution handles the loading of nested Order objects within a Customer correctly and demonstrates how to deserialized JSON data into Python objects. However, it does not manage field types strictly as required, particularly for the amount. Plus, the deserialization part in practice might throw an error due to the incorrect handling of the amount and date_of_birth formats not being as per requirement. (1/2 marks)

Given the considerations, let's calculate the total:

[
1.5 + 2 + 0 + 0 + 2 + 1 = 6.5 \text{ out of } 10 \text{ marks}
]

Here's the final score in JSON format:

{
  "score": 6.5
}

openai/gpt-3.5-turbo-0125 says: (9.0) (0.07¢)

The solution provided successfully defines two dataclasses, one for the person and one for the order, and two schema classes, one for the person and one for the order. The solution also correctly specifies the date format for the date_of_birth field as "%m/%d/%Y" and includes as_string=True for the decimal field.

Additionally, the solution links the schema and dataclasses with a @post_load method on the schema classes, ensuring that the deserialization process is correctly handled.

Overall, the solution is well-structured and effectively uses the marshmallow library to achieve the desired outcome.

Score: 9/10

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "Anything else": 1
}

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

openai/gpt-4-1106-preview says: (7.0) (2.00¢)

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 0,
  "marks 4": 0,
  "marks 5": 2,
  "marks 6": 1,
  "total_score": 7
}

Evaluation:

  • The solution correctly defines two dataclasses for the person (Customer) and the order (Order). (+2 marks)
  • The solution correctly defines two schema classes for the person (CustomerSchema) and the order (OrderSchema). (+2 marks)
  • The solution does not specify the date format for the date_of_birth field as "%m/%d/%Y". (+0 marks)
  • The amount field is set as a Decimal, but the solution does not specify as_string=True. (+0 marks)
  • The @post_load decorators and methods are correctly used to link the dataclasses with their respective schema classes. (+2 marks)
  • Additional considerations:
    • The amount field in the Order dataclass is defined as a float, whereas it should be a Decimal as per the problem statement. (-1 mark)
      The use of list[Order] as a type hint in the Customer dataclass is correct for modern Python, however, it may not be compatible with older Python versions; it depends on the Python version Marshmallow supports, but this is not a deviation from the instructions, so no marks are deducted for this.
    • The sample code includes using the schema to deserialize the JSON data provided, which is in line with the practical application of the solution. (+1 mark)

Note: The use of float instead of Decimal could be a topic of debate. Still, I deduct one point under "Anything else" for an incorrect representation of amount, even when the initial instructions clearly stated it should be a Decimal.

The total score is therefore 7 out of 10.

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Model Evaluations

claude-3-haiku 10.0 10.0 10.0
claude-3.5-sonnet 5.0 5.0 7.0
gpt-3.5-turbo-0125 8.0 8.0 9.0
gpt-4-0125-preview 5.5 4.0 6.5
gpt-4-1106-preview 0.0 0.0 7.0
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