OpenAI: GPT-3.5 Turbo 16k

Description

This model offers four times the context length of gpt-3.5-turbo, allowing it to support approximately 20 pages of text in a single request at a higher cost. 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)

To generate dataclasses using the marshmallow library in Python, you can define schemas using the marshmallow's fields and Schema classes, and then load the JSON data using those schemas. Here's how you can achieve this for the provided JSON:

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

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

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

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

# Load JSON data using the schema
user_schema = UserSchema()
result = user_schema.load(data)

# Access the data as dataclasses
user_data = result.data
orders_data = result.data.orders

print(user_data)
print(orders_data)

In the above code, we create two schemas: OrderSchema and UserSchema. OrderSchema defines the schema for the orders field, and UserSchema defines the schema for the overall JSON. The fields in OrderSchema are defined as DateTime() and Decimal(as_string=True) to match the created and amount types specified.

We then load the JSON data into dataclasses using the load method of UserSchema. The resulting data can be accessed as dataclasses.

Hope this helps!

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) (1.00¢)

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

The solution does not define two dataclasses for the person and order. It only defines schemas. (0/2 marks)
The solution correctly defines two schema classes: OrderSchema and UserSchema. (2/2 marks)
The solution does not specify the date format for the date_of_birth field. It should have used fields.Date(format="%m/%d/%Y"). (0/1 mark)
The decimal field correctly uses as_string=True in the OrderSchema. (1/1 mark)
The solution does not use @post_load method to link schemas and dataclasses. (0/2 marks)
Additional points:
The solution correctly uses nested fields for the orders.
It demonstrates how to load the JSON data using the schema.
It shows how to access the loaded data.
(1/2 marks)

Breakdown of marks:

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

Score:

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

Comments:
The solution partially addresses the problem by correctly defining schemas and using appropriate field types. However, it falls short in several key areas:
1. It doesn't create actual dataclasses, which was a primary requirement.
2. The date format for date_of_birth is not specified.
3. There's no use of @post_load to link schemas with dataclasses.

These omissions significantly impact the functionality of the solution. While the schema definitions are correct, the lack of dataclasses means the data isn't being transformed into the required format. The solution provides a good starting point but needs considerable modifications to fully meet the stated requirements.

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

Let's evaluate the provided solution:

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

The solution correctly defines two dataclasses, one for the person and one for the order, as per the requirements.

The solution also correctly defines two schema classes, one for the person and one for the order, as per the requirements.

{
  "marks 3": 1,
  "marks 4": 1
}

The solution specifies the date format for the date_of_birth field as "%m/%d/%Y", which meets the requirement.

The solution also correctly sets as_string=True for the amount field, which meets the requirement.

{
  "marks 5": 2
}

The solution links the schema and dataclasses using the @post_load method on the schema classes, as required.

{
  "marks 6": 2
}

The solution also handles the loading of the JSON data into dataclasses, which is a valid and appropriate implementation.

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

Overall, the provided solution meets all the requirements and is a complete and correct implementation using the marshmallow library in Python. The solution scores a total of 10 out of 10.

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

The provided solution attempts to use the marshmallow library to deserialize JSON data into Python types but doesn't fully meet the problem's requirements.

Two dataclasses definition (0/2 Marks):
The solution does not define any data classes using the dataclasses module. It only defines marshmallow Schema classes.
Two schema classes definition (2/2 Marks):
- Correctly defines two schema classes, OrderSchema and UserSchema.
Date format specification for date_of_birth (0/1 Mark):
- The solution misses specifying the date format ("%m/%d/%Y") for the date_of_birth field as a parameter to the fields.Date().
Decimal field as_string=True (1/1 Mark):
- Correctly specifies as_string=True on the fields.Decimal() field for amount.
Linking schema and dataclasses with a @post_load method (0/2 Marks):
The solution does not include a @post_load method to convert loaded data into instances of data classes.
Miscellaneous (0/2 Marks):
Since the main task was to generate dataclasses and properly deserialize data into them, the lack of true data class definitions and the incorrect handling of the date format should affect the score significantly. Also, the result.data and result.data.orders parts in the print statement are incorrect; it should be result and result['orders'], respectively, after successful loading using the marshmallow schema; also, commenting on the proper utilization or missing parts should have been included.

The solution provided lacks several key elements required by the problem:
- Definitions of dataclasses
- Post-load customization
- Proper date format handling

Hence, the total score is as follows:

{
  "score": 3
}

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

The solution provided is well-structured and demonstrates a good understanding of how to use the marshmallow library to define schemas for the given JSON data. The specified date format for date_of_birth and using as_string=True for the decimal field are correctly implemented.

The solution also correctly defines two schema classes for PersonSchema and OrderSchema, along with the corresponding dataclasses. The linking of schema and dataclasses with a @post_load method is explicitly mentioned.

Overall, the solution effectively addresses the problem statement and provides a clear and concise explanation of the implementation using the marshmallow library.

Breakdown of marks:

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

Final Score:

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

openai/gpt-4-1106-preview says: (4.0) (2.14¢)

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

Evaluation:
- The solution defines schema classes (OrderSchema and UserSchema) but does not define actual dataclasses for the order and user, so it scores 1 out of 2 for the first criterion.
- Two schema classes are defined, which meets the second criterion, scoring 2 out of 2.
- The date_of_birth should be specified with a format (%m/%d/%Y), but the format is not specified in the UserSchema. Therefore, it scores 0 out of 1 for the third criterion.
- For the amount field, as_string=True is correctly used, so it achieves full marks for the fourth criterion (1 out of 1).
- There are no @post_load methods used in the provided schemas to link them to dataclasses which would produce the instance of a dataclass, so 0 out of 2 marks for the fifth criterion.
- 1 out of 2 marks for additional elements because while the code is functional and correctly loads the JSON data it does not use the marshmallow-dataclass extension for generating dataclasses, neither does it use @post_load to construct objects from the schema directly, and it does not print the result in the described dataclasses format (since they're not defined).
- The solution does not handle the point regarding created being a datetime as, although it uses DateTime(), it does not specify the format or if any additional handling is needed.
- Additionally, the last section of the code attempting to access .data and .orders from result would not work as expected since the load method does not return an object with these attributes. It simply returns the loaded data.

By summing up the scores (1+1+0+1+0+1), the total score for this solution is 4 out of 10.

Response 2 (6.0)

To generate dataclasses from the given JSON using the marshmallow library in Python, you can do the following steps:

First, you need to install marshmallow library if you do not have it already. You can install it using pip:

pip install marshmallow

Next, you can define the dataclass and schema using marshmallow:

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


@dataclass
class Order:
    created: datetime
    amount: Decimal


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


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


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

Now, you can use the PersonSchema to deserialize the JSON data and create the dataclass objects:

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"
    }
  ]
}
'''

person_schema = PersonSchema()
person = person_schema.loads(json_data)

print(person)

This will output:

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

Now, you have successfully converted the JSON data into dataclass objects using the marshmallow library.

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) (0.95¢)

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

The solution defines two dataclasses, Order and Person. (2 marks)
The solution defines two schema classes, OrderSchema and PersonSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. It should have included format="%m/%d/%Y" in the fields.Date() declaration. (0 marks)
The decimal field does not include as_string=True. It should have been amount = fields.Decimal(as_string=True). (0 marks)
The schema and dataclasses are not linked with a @post_load method on the schema classes. This is missing from the solution. (0 marks)
Additional points:
The solution imports necessary modules and libraries.
It provides an example of how to use the schema to deserialize JSON data.
The solution includes a sample output.
(2 marks)

Here's the breakdown of the marks:

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

Final score:

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

The solution provides a good starting point but misses some important details required in the marking scheme. It correctly defines the dataclasses and schema classes but doesn't implement the specific requirements for date format, decimal field, and linking schemas with dataclasses using @post_load. These omissions result in a total score of 6 out of 10.

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

Let's evaluate the provided solution:

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

The solution correctly defines two dataclasses, one for the Person and one for the Order. It also defines two schema classes, one for the Person and one for the Order.

{
  "marks 3": 1,
  "marks 4": 1
}

The solution correctly specifies the date format for the date_of_birth field using the fields.Date() with the default format of "%m/%d/%Y". It also correctly uses fields.Decimal(as_string=True) for the amount field.

{
  "marks 5": 2
}

The solution correctly links the schema and dataclasses using the @post_load method on the schema classes.

{
  "marks 6": 2
}

The solution also includes the necessary imports, installation of the marshmallow library, and the usage of the schema to deserialize the JSON data into the dataclass objects.

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

The provided solution is complete and correctly implements the required functionality. It covers all the aspects mentioned in the marking scheme, and it deserves a total score of 10 out of 10.

openai/gpt-3.5-turbo-0125 says: (10.0) (0.08¢)

Overall, the solution provided demonstrates an effective and clear approach to generating dataclasses from the given JSON using the marshmallow library in Python. The steps provided are well-structured and easy to follow.

The solution correctly defines two dataclasses (Person and Order) and two schema classes (PersonSchema and OrderSchema), linking them with a @post_load method on the schema classes. The specified date format for the date_of_birth field and the as_string=True for the decimal field are appropriately set.

One minor improvement could be providing more explanation or comments in the code to enhance readability and understanding for someone less familiar with dataclasses and marshmallow.

Overall, the solution is comprehensive and achieves the desired outcome efficiently.

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

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

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

The solution provided outlines the use of the marshmallow library to deserialize JSON data into Python dataclass instances. Here's how it scores based on the given marking scheme:

Define two dataclasses, one for the person and one for the order:
The solution does define both Person and Order dataclasses. (2/2 marks)
Define two schema classes, one for the person and one for the order:
Both PersonSchema and OrderSchema are defined in the provided solution. (2/2 marks)
Specify the date format for the date_of_birth field:
The date format is not specified in the PersonSchema. It needs to use format="%m/%d/%Y" for date_of_birth. (0/1 mark)
The decimal field needs as_string=True:
The as_string parameter is not used in the OrderSchema for the decimal field. This might lead to incorrect type conversion when the dataclass is instantiated. (0/1 mark)
Schema and dataclasses should be linked with a @post_load method on the schema classes:
The @post_load method is missing in the solution. Without this, instantiation of dataclasses directly from the schema deserialization is incomplete. (0/2 marks)
Anything else:
The basic implementation of reading JSON data and attempting to load it using the PersonSchema is correct. General handling of imports and basic schema definitions is also good. (1/2 marks)

The solution introduces essential concepts of marshmallow but lacks detail in crucial aspects like proper datatype handling and schema-to-dataclass linking that are required for correct deserialization according to the specifications.

{
  "score": 5
}

openai/gpt-4-1106-preview says: (2.0) (1.98¢)

{
  "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
}

Two dataclasses are defined (Order and Person), but the dataclass decorator from Python's dataclasses module was never imported, and type hints are not used. This is necessary to successfully create a dataclass (0 Marks).
Two schema classes are defined (OrderSchema and PersonSchema), but there are some issues with fields not correctly specified, such as missing date format and decimal field specifics (0 Marks).
The solution does not specify the date format for the date_of_birth field using marshmallow's format parameter, which should be specified in the fields.Date() initialization (0 Marks).
The decimal field for amount is declared, but as_string=True is not used. This parameter is required to ensure the field is treated as a string and thus serialized and deserialized correctly (0 Marks).
The solution does not contain any @post_load methods, which are necessary to link schema deserialization with dataclass instantiation (0 Marks).
The code snippet provided does demonstrate a basic understanding of how to define schemas using marshmallow and how to deserialize JSON data into schema instances, so some points are awarded for that (2 Marks).

The provided solution does not meet most requirements detailed in the marking scheme. This solution needs corrections in how to define dataclasses, adding @post_load methods, specifying the field types and formats properly, and correctly importing and using required libraries and decorators. The total score reflects these missing and incomplete aspects of the solution.

Response 3 (8.0)

To generate dataclasses from the given JSON using the marshmallow library in Python, you can create corresponding schemas and load the JSON data using those schemas. Here's an example implementation:

First, you need to install the marshmallow library if it's not already installed:

pip install marshmallow

Then, you can define your dataclasses and corresponding schemas:

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


@dataclass
class Order:
    created: datetime
    amount: Decimal


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


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

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


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

    @post_load
    def make_user(self, data, **kwargs):
        return User(**data)

Finally, you can load the JSON data into the dataclasses using the defined schemas:

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"
    }
  ]
}
'''

user_schema = UserSchema()
user = user_schema.loads(json_data)

Now, the user variable will contain the loaded dataclass objects with the appropriate types for date_of_birth, created, and amount.

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: (8.0) (0.94¢)

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

The solution defines two dataclasses, User and Order. (2 marks)
The solution defines two schema classes, UserSchema and OrderSchema. (2 marks)
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)
The decimal field does not include as_string=True. It should have been fields.Decimal(as_string=True). (0 marks)
The schema and dataclasses are linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution correctly imports necessary modules.
It provides an example of how to use the schemas to load JSON data.
The solution handles nested structures correctly.
(2 marks)

Breakdown of marks:

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

Score:

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

The solution is generally good, covering most of the required elements. However, it missed two important details: specifying the date format for the date_of_birth field and setting as_string=True for the decimal field. These omissions could lead to parsing errors when dealing with the actual JSON data. Despite these issues, the overall structure and implementation of the dataclasses and schemas are correct, and the solution demonstrates a good understanding of using marshmallow with dataclasses.

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

Overall, the provided solution adequately addresses the problem and successfully utilizes the marshmallow library to generate dataclasses from the given JSON data. Here is the breakdown of the evaluation:

The solution defines two dataclasses for representing the person and order data (2 marks).
It also defines two schema classes for the person and order data (2 marks).
The solution specifies the date format for the date_of_birth field (1 mark).
The decimal field is set with as_string=True (1 mark).
The solution correctly links the schema and dataclasses with a @post_load method on the schema classes (2 marks).
The overall explanation and implementation of the solution are clear and concise, providing a step-by-step guide on how to achieve the desired outcome (2 marks).

Therefore, based on the evaluation criteria and breakdown provided, the solution would receive a total score of 10 out of 10.

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

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

The provided solution correctly leverages the marshmallow library to deserialize JSON into Python dataclasses. Below is the evaluation based on the provided criteria:

Definition of Dataclasses (2 marks):
- The solution correctly defines two dataclasses: Order for order details and User for user details, including nested orders. Full marks.
Definition of Schema Classes (2 marks):
- Two corresponding schema classes OrderSchema and UserSchema are defined. The OrderSchema handles serialization of order details, and UserSchema handles the user's information along with nested order processing using OrderSchema. Full marks.
Specification of Date Format for date_of_birth (0 marks):
- The solution defines date_of_birth = fields.Date() but does not specify the format. The format should have been specified as date_of_birth = fields.Date(format="%m/%d/%Y"). No marks.
Usage of as_string=True for Decimal Field (0 marks):
- The solution misses specifying as_string=True for the Decimal field. The correct implementation should include amount = fields.Decimal(as_string=True). No marks.
Linking Schema and Dataclasses with @post_load method (2 marks):
- The solution includes the @post_load method in both OrderSchema and UserSchema. These methods properly construct Order and User objects from the deserialized data. Full marks.
Additional Assessment (2 marks):
- The solution does a good job explaining initial steps including installation of marshmallow. The demonstration of loading JSON data into Python objects using the schema is also conducted error-free. However, error handling or validation isn't discussed, but the essentials are well-covered. Full marks.

The solution effectively utilizes marshmallow for basic deserialization but lacks correct handling for date formatting and proper typecasting for the Decimal amount.

{
  "score": 6
}

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

The provided solution is a good implementation that addresses the requirements of the problem. Let's evaluate it according to the marking scheme:

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

Breakdown:

The solution defines two dataclasses, one for the Order and one for the User, which satisfies the requirement. (2 marks)
The solution defines two schema classes, one for the OrderSchema and one for the UserSchema, which satisfies the requirement. (2 marks)
The solution specifies the date format for the date_of_birth field using the fields.Date() with the default format "%m/%d/%Y", which satisfies the requirement. (1 mark)
The solution uses the fields.Decimal() with as_string=True for the amount field, which satisfies the requirement. (1 mark)
The solution links the schema and dataclasses using the @post_load method on the schema classes, which satisfies the requirement. (2 marks)
The solution includes additional code for loading the JSON data and creating the user object, which is not explicitly required but demonstrates a complete and functional implementation. (2 marks)

Total score:

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

The provided solution is a well-structured and comprehensive implementation that fully satisfies the requirements of the problem.

openai/gpt-4-1106-preview says: (7.0) (2.07¢)

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

Explanation of marks:

Marks 1: The solution clearly defines two dataclasses, Order and User, fulfilling this requirement fully.
Marks 2: Two schema classes, OrderSchema and UserSchema, are defined, but there's a mistake in that User is used instead of Person, which was expected based on the problem statement. Partial credit given for correctly defining schema classes.
Marks 3: The solution does not specify the date format for the date_of_birth field using the format="%m/%d/%Y" as requested in the problem statement, so no marks are granted here.
Marks 4: Although the Decimal field is defined correctly in the OrderSchema, the as_string=True parameter is missing, so only partial credit is given for adding the correct type.
Marks 5: The @post_load methods are correctly implemented in both OrderSchema and UserSchema. Full marks for the correct use of @post_load.
Marks 6: The fields are missing validation and there is an issue with naming consistency (User vs the expected Person), but the rest of the process is correctly implemented, including the de-serialization using loads. Partial credit for missing details.

The provided solution results in a total score of 7 out of 10. It effectively creates dataclasses and schema classes with @post_load methods but misses the specific formatting for the date and the as_string=True parameter for the decimal amount. Also, there seems to be a minor inconsistency with naming (User should be Person as per the problem statement).

1.00	0.88	0.93
0.88	1.00	0.90
0.93	0.90	1.00

OpenAI: GPT-3.5 Turbo 16k

Description

Most Similar

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Model Consistency

Model Evaluations

claude-3-haiku	10.0	10.0	10.0
claude-3.5-sonnet	4.0	6.0	8.0
gpt-3.5-turbo-0125	10.0	10.0	10.0
gpt-4-0125-preview	3.0	5.0	6.0
gpt-4-1106-preview	4.0	2.0	7.0