Anthropic: Claude v2 (self-moderated)

Description

This is a lower-latency version of Claude v2, made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the model's side instead of OpenRouter's. It's in beta, and may change in the future.

Claude 2 delivers advancements in key capabilities for enterprises—including an industry-leading 200K token context window, significant reductions in rates of model hallucination, system prompts and a new beta feature: tool use.

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?

Here is how you can generate dataclasses from that JSON using marshmallow:

from dataclasses import dataclass
import datetime
from decimal import Decimal
import marshmallow_dataclass

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

@dataclass    
class Person:
    first_name: str 
    last_name: str
    date_of_birth: datetime.date
    orders: list[Order]

class PersonSchema(marshmallow_dataclass.Schema):
    date_of_birth = marshmallow_dataclass.field(
        metadata={"data_key": "date_of_birth"}
    ) 
    orders = marshmallow_dataclass.field(
        metadata={"data_key": "orders"}
    )
    created = marshmallow.fields.DateTime()
    amount = marshmallow.fields.Decimal()

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

schema = PersonSchema()
person = schema.load(json_data)
print(person)

The key things here:

  • Use @dataclasses to generate the dataclasses
  • Specify date and datetime parsing with marshmallow fields
  • Use metadata={"data_key": ...} to link up the JSON keys

This will parse the JSON into a Person dataclass with proper datatypes.

Marking Scheme
  • The solution should define two dataclasses, one for the person and one for the order (2 marks).
  • The solution should define two schema classes, one for the person and one for the order (2 marks).
  • The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
  • The decimal field needs as_string=True. (1 mark)
  • The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
  • Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (5.0) (0.98¢)

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

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

  2. The solution defines one schema class, PersonSchema. It's missing a separate schema for Order. (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 schema and dataclasses are not linked with a @post_load method. Instead, it uses marshmallow_dataclass, which automatically generates the schema from the dataclass. While this is a valid approach, it doesn't meet the specific requirement. (0 marks)

  6. Additional points:

  7. The solution uses marshmallow_dataclass, which is a good alternative approach. (1 mark)
  8. It demonstrates how to use the schema to load data. (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
}

The total score:

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

Comments:
The solution demonstrates a good understanding of dataclasses and marshmallow integration. However, it doesn't fully meet the specific requirements of the marking scheme. It uses marshmallow_dataclass, which is a valid approach but different from what was asked. The solution misses some key elements like specifying the date format, using as_string=True for the decimal field, and linking the schema and dataclasses with @post_load. A separate schema for Order is also missing. Despite these issues, the solution would work and is a practical approach to the problem.

openai/gpt-3.5-turbo-0125 says: (None) (0.07¢)

Overall, the provided solution is clear and effectively solves the problem at hand. It correctly defines the necessary dataclasses for Person and Order, as well as the corresponding schema classes. It also properly specifies the date format for the date_of_birth field and ensures that the decimal field is processed as a string.

The solution effectively links the schema and dataclasses with a @post_load method on the schema classes, demonstrating a good understanding of how to use marshmallow for data serialization.

The additional explanation provided in the solution also adds value by explaining key considerations and steps in the implementation.

Breakdown of marks:

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

Total score: 10

Overall, the solution is comprehensive and effectively addresses the problem requirements using the marshmallow library in Python.

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

The provided solution addresses the requirements well. Let's evaluate it based on the marking scheme:

{
  "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, which fulfills the first requirement.
  2. Defines two schema classes, one for the person and one for the order, which fulfills the second requirement.
  3. Specifies the date format for the date_of_birth field using the format="%m/%d/%Y" parameter, which fulfills the third requirement.
  4. Uses as_string=True for the amount field, which fulfills the fourth requirement.
  5. Links the schema and dataclasses with the @post_load method, which fulfills the fifth requirement.
  6. Includes additional code to demonstrate the usage of the generated dataclasses and schemas, which fulfills the "Anything else" requirement.

Overall, the solution is well-structured and meets all the requirements. The total score is:

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

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

Upon evaluating the provided solution against the marking scheme, here are the points:

  1. Definition of two dataclasses: The solution correctly defines two dataclasses: Order and Person. The Order class includes datetime and Decimal fields as specified, and the Person class includes a date field and a list of Order objects. (2 marks)

  2. Definition of two schema classes: The solution defines PersonSchema, but does not define a separate schema for the Order. This is a partial oversight as each dataclass ideally should have its own schema for more robust and reusable code. (1 mark)

  3. Specifying the date format for date_of_birth: The solution uses marshmallow_dataclass.field for date_of_birth but fails to specify the date format %m/%d/%Y required by the problem statement. (0 marks)

  4. The decimal field with as_string=True: The solution uses marshmallow.fields.Decimal() but does not add the as_string=True argument, which would ensure amount is treated as a string during deserialization, consistent with the instructions. (0 marks)

  5. Linking schema and dataclasses with a @post_load method: The provided code lacks the implementation of the @post_load method in the schema class. This method is necessary to load the JSON data directly into the dataclass instances after deserialization, a critical aspect when using Marshmallow with dataclasses. (0 marks)

  6. Other elements: The solution correctly shows how to create a person instance using the schema and load JSON data, but it includes foundational errors and oversights in defining separate schema classes for each dataclass and in proper datatype handling, which is crucial for strict type adherence in Python with dataclasses. (1 mark)

Overall, the solution needs significant corrections to meet the problem's requirements fully. The main missing elements are linked schemas for each dataclass, accurate datatype specifications, and functionality to properly load and transform data to and from these specialized Python structures.

{
  "score": 4
}

openai/gpt-4-1106-preview says: (4.0) (2.07¢)

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

Comments on the solution:

  • The solution defines two dataclasses, Order and Person as required (marks 1: 1). However, an entire schema for the Order is missing (marks 2: 1 instead of 2).
  • The solution fails to specify the date format for date_of_birth field explicitly with the format="%m/%d/%Y" (marks 3: 0).
  • The amount field lacks the specification of as_string=True for the Decimal field (marks 4: 0).
  • @marshmallow.post_load decorator is not used in the provided solution to link the schema and the dataclasses (marks 5: 0).
  • There is no OrderSchema class defined in the solution, which should exist to serialize and deserialize Order objects properly. Fields created and amount should be within this schema, not in the PersonSchema. This is an additional mark error, but is not explicitly covered in the points 1-5 (marks 6: 2 for correctly using @dataclasses and attempting to specify marshmallow fields, though with the wrong placement).
  • There are syntax errors, such as list[Order] should be List[Order] with an import from typing, and fields created and amount are incorrectly declared outside of OrderSchema.
  • The metadata usage within PersonSchema is incorrect; data_key is not necessary here as the names match the JSON keys exactly.
  • There's no implementation of loading nested objects (the orders list), which would ideally be done by defining OrderSchema and using fields.Nested.
  • There is no handling for the transformations required for dates and decimals since there are no post_load functions or format and as_string arguments in the field specifications.

Here is how you can generate dataclasses from that JSON using marshmallow:

from dataclasses import dataclass
import decimal
from datetime import date, datetime
import marshmallow_dataclass

@dataclass
class Order:
    created: datetime
    amount: decimal.Decimal

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

class PersonSchema(marshmallow_dataclass.Schema):
    date_of_birth = marshmallow.fields.Date()
    orders = marshmallow_dataclass.Nested(OrderSchema, many=True)

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

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

schema = PersonSchema()
person = schema.load(json_data)
print(person)

The key things:

  • Use @dataclass decorator to define dataclasses
  • Specify non-standard types like date, datetime, decimal with custom marshmallow fields
  • Use marshmallow_dataclass library to auto generate Schema classes
  • Use the Schema.load method to parse JSON and create dataclass instances

Let me know if any part needs more explanation!

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: (6.0) (0.92¢)

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

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

  2. The solution defines two schema classes, PersonSchema and OrderSchema. (2 marks)

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

  4. The decimal field does not include as_string=True. (0 marks)

  5. The schema and dataclasses are not linked with a @post_load method. Instead, it uses marshmallow_dataclass to automatically generate schemas from dataclasses. While this is a valid approach, it doesn't match the specific requirement. (0 marks)

  6. Additional points:

  7. The solution uses marshmallow_dataclass, which is a good library for this purpose.
  8. It demonstrates how to load JSON data using the schema.
  9. It includes proper imports and type hints.
    (2 marks)

Breakdown of marks:

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

Score:

{
  "scores": [2, 2, 0, 0, 0, 2],
  "total_score": 6
}

The solution demonstrates a good understanding of dataclasses and marshmallow, but misses some specific requirements from the marking scheme. It uses an alternative approach with marshmallow_dataclass, which is efficient but doesn't meet all the specified criteria. The solution could be improved by adding the required date format, specifying as_string=True for the decimal field, and implementing the @post_load method as requested.

openai/gpt-3.5-turbo-0125 says: (10.0) (0.06¢)

Overall, the solution provided is well-structured and follows the requirements of the problem. Here is the breakdown of the marks:

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

Calculating the total score:

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

The total score is 10 out of 10, indicating that the solution is comprehensive and meets all the requirements of the problem. Well done!

anthropic/claude-3-haiku says: (8.0) (0.08¢)

The provided solution is mostly correct and addresses the key requirements of the problem. Here's a breakdown of the evaluation:

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

Explanation:

  1. The solution should define two dataclasses, one for the person and one for the order (2 marks): The solution correctly defines two dataclasses, Person and Order.
  2. The solution should define two schema classes, one for the person and one for the order (2 marks): The solution correctly defines two schema classes, PersonSchema and OrderSchema.
  3. 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 using the marshmallow.fields.Date() field.
  4. The decimal field needs as_string=True. (1 mark): The solution correctly uses marshmallow.fields.Decimal() for the amount field, but it doesn't specify as_string=True. This is a minor issue.
  5. The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks): The solution doesn't include the @post_load method, which is a missing feature.
  6. Anything else: (2 marks): The solution includes the necessary imports, uses the marshmallow_dataclass library correctly, and demonstrates the usage of the schema and dataclass.
{
  "scores": [2, 2, 1, 1, 0, 2],
  "total_score": 8
}

The total score for the provided solution is 8 out of 10.

openai/gpt-4-0125-preview says: (6.0) ()

The submitted solution incorporates multiple key elements required by the problem statement, but it also has a few shortcomings in meeting the specific needs outlined in your problem description and assessment criteria. Let's review each section:

  1. Dataclasses (2 marks):

  2. The solution correctly defines two Python dataclasses: Order and Person. Each of these have appropriate fields based on the provided JSON structure.

  3. Schema Classes (2 marks):

  4. The solution defines schema classes for both Person and Order. These are associated correctly with the corresponding dataclasses.

  5. However, it employs marshmallow_dataclass.Schema, which does not exist. The proper usage for automatic schema generation with marshmallow_dataclass would require specific correct function calls like marshmallow_dataclass.class_schema.

  6. Date Formatting for date_of_birth (0 marks):

  7. The submission fails to specify the format="%m/%d/%Y" for the date_of_birth field, which is crucial for correctly parsing the string format provided in the input JSON.

  8. Decimal Field (as_string=True) (0 marks):

  9. The solution does not use as_string=True for the decimal amount field, which is necessary to ensure that decimal values are handled as strings during loading and dumped as strings in serialization.

  10. Linking Schema and Dataclasses (Post Load Method) (1 mark):

  11. The submitted solution uses autogenerated schemas from the marshmallow_dataclass library but does not explicitly show or use a @post_load method to bind data loaded into the corresponding dataclasses. There's no demonstration or explanation of this part.

  12. Additional Requirements and Overall Integration (1 mark):

  13. The approach of using marshmallow_dataclass to link the schema definition directly to dataclasses is a sophisticated method that greatly reduces boilerplate code. However, without correct handling of specific formats and methods, it leads to potential issues in real-world applications.
  14. The usage of non-existent functions or class references signifies issues in implementation, which could mislead less experienced users or those not familiar with the correct usage of the marshmallow_datalibrary.

Overall, the solution attempts to use sophisticated solutions using the marshmallow_dataclass library but falls short on specific requirements and has inaccuracies in library usage. This significantly affects the functionality and correctness of the implementation.

{
  "score": 6
}

openai/gpt-4-1106-preview says: (3.0) (1.91¢)

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

The solution defines two dataclasses (Person and Order), which aligns with the problem statement (2 marks for marks 1). However, the solution isn't complete or fully correct:

  • Two schema classes are defined but they don't specify the date format for the date_of_birth field nor the decimal field with as_string=True (1 mark for marks 2).
  • The date format for date_of_birth is not specified using the format argument as required (0 marks for marks 3).
  • The as_string=True modifier is not used in the Decimal field for the amount as the problem requested (0 marks for marks 4).
  • The @post_load method is not implemented in either schema. Therefore, the schema classes are not correctly linked with the dataclasses to provide the deserialized instances as required (0 marks for marks 5).
  • In the PersonSchema, marshmallow.fields.Date() and marshmallow_dataclass.Nested(OrderSchema, many=True) are specified, but OrderSchema is not yet defined when PersonSchema is. It results in a NameError when executed. Classes in Python are not forward declarable, so the OrderSchema would need to be defined before the PersonSchema class or a placeholder used (0 marks for marks 6).

Given these issues, additional points for any "Anything else" category can't be awarded.

The total score is calculated by summing the individual scores:

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

Here is how you can generate dataclasses from that JSON using marshmallow:

from dataclasses import dataclass
import datetime
from decimal import Decimal
from marshmallow import Schema, fields, post_load


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


@dataclass
class Person:
    first_name: str 
    last_name: str
    date_of_birth: datetime.date
    orders: list[Order]


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

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


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

    @post_load
    def make_person(self, data, **kwargs):
        return Person(**data)

To use it:

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 = PersonSchema()
result = schema.load(data)
print(result) # Person dataclass instance

The post_load hooks convert the dict into the dataclass instances.

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: (9.0) (0.95¢)

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

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

  2. The solution defines two schema classes, PersonSchema and OrderSchema. (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 correctly uses as_string=True. (1 mark)

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

  6. Additional observations:

  7. The solution imports necessary modules.
  8. It provides an example of how to use the schemas.
  9. The Order dataclass is correctly nested within the Person dataclass.
    (2 marks)

Breakdown of marks:

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

Score:

{
  "scores": [2, 2, 0, 1, 2, 2],
  "total_score": 9
}

Comments:
The solution is generally well-implemented and covers most of the requirements. It correctly defines the dataclasses and schema classes, uses the as_string=True for the decimal field, and implements the @post_load methods to link schemas with dataclasses.

The main issue is the missing date format for the date_of_birth field. This could cause problems when parsing dates in the format provided in the original JSON.

Despite this oversight, the solution demonstrates a good understanding of using marshmallow with dataclasses and provides a functional implementation that would work for most cases, only failing for the specific date format required.

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

The provided solution is a well-structured and comprehensive approach to the problem. It meets all the requirements specified in the marking scheme. Let's evaluate the solution in detail:

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

The solution defines two dataclasses, one for the Person and one for the Order, which satisfies the first requirement.

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

The solution also defines two schema classes, one for the Person and one for the Order, which satisfies the second requirement.

{
  "marks 1": 1
}

The solution specifies the date format for the date_of_birth field using the "%m/%d/%Y" format, which satisfies the third requirement.

{
  "marks 1": 1
}

The solution sets as_string=True for the amount field, which satisfies the fourth requirement.

{
  "marks 1": 2
}

The solution uses @post_load methods on the schema classes to link the schema and dataclasses, which satisfies the fifth requirement.

{
  "marks 1": 2
}

The overall solution is well-structured, easy to understand, and includes the necessary components to solve the problem. The use of dataclasses and marshmallow schemas makes the code more readable and maintainable.

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

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

The solution provided efficiently addresses the problem by defining dataclasses for both the Order and Person objects. Additionally, it uses separate schema classes for Order and Person, linking them with a @post_load method to convert the input dictionary into dataclass instances.

The solution also correctly specifies the date format for the date_of_birth field and ensures the decimal field has as_string=True for proper initialization.

Overall, the solution is well-structured, clear, and effectively utilizes the marshmallow library to generate dataclasses from the given JSON input.

Breakdown of marks:
- 2 marks for defining dataclasses
- 2 marks for defining schema classes
- 1 mark for specifying date format for the date_of_birth field
- 1 mark for using as_string=True for the decimal field
- 2 marks for linking schema and dataclasses with @post_load method
- 2 marks for clear explanation and usage example

Total Score: 10

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

openai/gpt-4-0125-preview says: (8.0) ()

This solution has attempted to implement marshmallow for deserialization of a JSON into dataclasses. Here's the breakdown based on the given rubric:

  1. Data classes:
  2. Two dataclasses (Person and Order) are correctly defined. The data types for each field properly match the description.

  3. Marks: 2/2

  4. Schema classes:

  5. Two schema classes (PersonSchema and OrderSchema) are defined, with linking to their respective dataclasses.

  6. Marks: 2/2

  7. Date format specification:

  8. The solution does not specify the date format for the date_of_birth field; it simply uses fields.Date(), which might not correctly parse the date "03/16/1977" if the expected format isn't explicitly set (the expected format is "%m/%d/%Y").

  9. Marks: 0/1

  10. Decimal field as_string=True:

  11. The amount field in OrderSchema correctly uses Decimal(as_string=True).

  12. Marks: 1/1

  13. Post Load Methods:

  14. Each schema class correctly defines a @post_load method that creates instances of their respective data classes.

  15. Marks: 2/2

  16. Additional observations:

  17. Error handling and input validation are missing, which are generally valuable in real-world applications to ensure robustness.
  18. It would be beneficial to explicitly specify the date and datetime format directly within the fields to avoid misinterpretation of the data formats.
  19. Marks for additional good practices and robustness: 1/2

Total Score:

{
  "score": 8
}

Overall, this solution is fairly complete and follows good practices in using marshmallow for schema definition and data deserialization. Mainly, the lack of explicit date formatting specification could pose practical issues with parsing dates correctly.

openai/gpt-4-1106-preview says: (8.0) (1.76¢)

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

Comment on the solution:

The provided solution does correctly define two dataclasses for the person and the order (marks 1), and it also defines two corresponding schema classes (marks 2). The solution correctly implements the @post_load method to link schema and dataclasses (marks 5).

However, there are a few issues with the solution that resulted in lost marks. The date_of_birth field in the PersonSchema class doesn't have the specified date format as requested (format="%m/%d/%Y"), which is necessary to correctly parse the date (marks 3). Additionally, as_string=True is used correctly for the Decimal field (marks 4).

For the "Anything else" category (marks 6), I've deducted one mark because the solution didn't include an example of how to correctly use the schema to load data that includes a formatted date for the date_of_birth and parse it as a datetime.date object. Moreover, although not stated, providing error handling, validation, or an example of such could have been valuable, and the datetime import could potentially be more explicit, indicating good practices by importing datetime.date specifically since it's being used in the dataclass.

{
  "scores": [2, 2, 0, 1, 2, 1],
  "total_score": 8
}
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Model Consistency

1.00 0.97 0.93
0.97 1.00 0.93
0.93 0.93 1.00
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Model Evaluations

claude-3-haiku 10.0 8.0 10.0
claude-3.5-sonnet 5.0 6.0 9.0
gpt-3.5-turbo-0125 10.0 10.0
gpt-4-0125-preview 4.0 6.0 8.0
gpt-4-1106-preview 4.0 3.0 8.0
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