OpenAI: GPT-3.5 Turbo 16k

Description

The latest GPT-3.5 Turbo model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Training data: up to Sep 2021.

This version has a higher accuracy at responding in requested formats and a fix for a bug which caused a text encoding issue for non-English language function calls.

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 (6.0)

You can achieve this by creating dataclasses using the marshmallow library in python. Here is an example code snippet for creating dataclasses with the specified data types:

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

@dataclass
class Order:
    created: datetime
    amount: Decimal

@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()

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

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

customer_schema = CustomerSchema()
result = customer_schema.load(data)
customer = Customer(**result)

print(customer)

This code snippet defines the dataclasses Order and Customer with the specified data types for the fields. It also creates schemas OrderSchema and CustomerSchema using the marshmallow library to load the data into the defined dataclasses. Finally, the data from the JSON is loaded into the Customer dataclass instance and printed out.

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: (5.0) ()

Solution Evaluation:

Definition of Data Classes:
Correctly defines Order and Customer data classes, matching the problem's structure and data types (datetime, decimal).
Score: 2/2
Definition of Schema Classes:
Correctly defines OrderSchema and CustomerSchema.
Score: 2/2
Specification of Data Format for date_of_birth:
The schema does not specify the required format ("%m/%d/%Y") for the date_of_birth field.
Score: 0/1
Decimal Field Specification:
The field amount in OrderSchema lacks the specified as_string=True attribute. This attribute ensures that the decimal is loaded/serialized as a string, maintaining exact precision.
Score: 0/1
Linking Schema and Dataclasses using @post_load Method:
The solution lacks any @post_load method in the schema classes, which is necessary to transform loaded data into an instance of the respective data class.
Score: 0/2
Other Aspects:
The overall example does load JSON and tries to parse it using marshmallow, which partly addresses the task.
However, it doesn't handle type conversions correctly for decimal and date due to missing specific declarations.
Score: 1/2 (for attempting to parse and use marshmallow)

Final Score:

{
  "score": 5
}

This score reflects the correct setup of data classes and basic schema, but misses essential features for data parsing and conversion specific to the problem's requirements.

anthropic/claude-3.5-sonnet says: (6.0) (0.98¢)

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

The solution defines two dataclasses, Order and Customer. (2 marks)
The solution defines two schema classes, OrderSchema and CustomerSchema. (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 marks)
The decimal field does not have as_string=True. It should have been fields.Decimal(as_string=True). (0 marks)
The schema and dataclasses are not linked with a @post_load method on the schema classes. (0 marks)
Additional points:
The solution imports necessary modules. (1 mark)
The solution provides an example of how to use the schemas to load data. (1 mark)

Here's the breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema and dataclass linking": 0,
  "Additional points": 2
}

Total score:

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

Comments:
The solution provides a good starting point by defining the dataclasses and schema classes correctly. However, it misses some important details:

The date format for date_of_birth is not specified, which may lead to parsing errors with the given date format.
The decimal field is not set to use strings, which could cause issues with precision.
The schemas and dataclasses are not linked using @post_load, which means the loaded data won't be automatically converted to dataclass instances.

These issues should be addressed to make the solution more robust and to fully meet the requirements of the problem.

openai/gpt-3.5-turbo-0125 says: (8.0) (0.06¢)

Overall, the solution provided is clear and concise. It defines the necessary dataclasses and schema classes using the marshmallow library to handle the conversion of JSON data to Python dataclasses with the correct data types.

Breakdown of Marks:
- Defines two dataclasses: 2 marks
- Defines two schema classes: 2 marks
- Specifies the date format for the date_of_birth field: 1 mark
- Specifies as_string=True for the decimal field: 1 mark
- Links schema and dataclasses using a @post_load method: 2 marks

Additional Comments:
- The solution effectively utilizes marshmallow to handle the conversion of JSON to dataclasses.
- The code snippet provided is well-structured and easy to follow.

Final Score:

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

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

The provided solution is generally good and meets the requirements of the problem. 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
}

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. (format="%m/%d/%Y") (1 mark)
4. The decimal field needs as_string=True. (1 mark)
5. The schema and dataclasses are linked with a @post_load method on the schema classes. (2 marks)
6. The solution handles the conversion of the JSON data to the defined dataclasses, which is a good implementation. (2 marks)

Total Score: 10/10

The provided solution is complete and correctly implements the requirements of the problem using the marshmallow library in Python.

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

openai/gpt-4-1106-preview says: (3.0) (1.81¢)

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

Comments on the solution:

The solution defined dataclasses one for the person and one for the order (1 mark). However, the data types are not explicitly cooperating with the marshmallow library. The Order class amount field should be initialized to ensure it's a Decimal and the Customer class date_of_birth should ensure it's a date.
The solution also defined the corresponding schema classes (1 mark).
The solution did not specify the date format for the date_of_birth field using format="%m/%d/%Y" (0 marks).
It did not specify that the decimal field should be a string using as_string=True (0 marks).
The @post_load method is not present. The dataclass instances are being created manually using unpacking (**result) which is unnecessary when using @post_load (0 marks).
In the "Anything else" category, the score is (1 mark) for understanding the concept of the marshmallow library and how to create Schemas that define fields and how to serialize JSON data to a Python object. However, the sample is missing the actual data conversion within a @post_load method and it doesn’t ensure proper data types linkage (e.g., date formatting) between the marshmallow fields and the dataclasses.

The resulting score, based on the above criteria, is 3 out of 10.

Response 2 (7.0)

You can achieve this by defining the dataclasses using the marshmallow library in Python. Here is an example code snippet that demonstrates how to achieve this:

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

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

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

class CustomerSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.Nested(OrderSchema)

# Load the 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'
    }
  ]
}

# Deserialize the JSON data using the schema
customer_schema = CustomerSchema()
customer_data = customer_schema.load(data)

# Print the deserialized data
print(customer_data)

In this code snippet, we define the Order dataclass to represent the order data, and the Customer dataclass to represent the customer data. We then define schema classes OrderSchema and CustomerSchema that map to the corresponding dataclasses. We use the fields.DateTime(), fields.Decimal(as_string=True), and fields.Date() to specify the data types for the fields in the schema.

Finally, we load the JSON data using the CustomerSchema and print the deserialized data.

Marking Scheme

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

anthropic/claude-3.5-sonnet says: (7.0) (1.01¢)

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

The solution defines two dataclasses, Order and Customer. (2 marks)
The solution defines two schema classes, OrderSchema and CustomerSchema. (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() definition. (0 marks)
The decimal field correctly uses as_string=True. (1 mark)
The schema and dataclasses are not linked with a @post_load method on the schema classes. (0 marks)
Additional points:
The solution imports necessary modules.
It provides a sample JSON data and demonstrates how to use the schema to deserialize the data.
(2 marks)

Breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 1,
  "Schema-dataclass linking": 0,
  "Additional features": 2
}

Score:

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

Comments:
The solution provides a good foundation for using marshmallow with dataclasses. However, it misses a few key points:

The date format for date_of_birth is not specified, which is necessary for proper parsing of the input string.
The schema classes are not linked to the dataclasses using @post_load decorators, which means the deserialized data will be dictionaries rather than dataclass instances.
The orders field in the Customer dataclass is defined as a single Order, but in the schema and sample data, it's a list of orders.

These issues should be addressed to fully meet the requirements and to ensure the deserialized data is in the correct format.

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

The provided solution demonstrates a basic implementation of dataclasses and marshmallow schemas for the given JSON data structure. Here's the evaluation based on the provided marking scheme:

Dataclasses Definition: Two dataclasses (Order and Customer) are defined correctly to represent the structure and types of the data in question.
Score: 2 marks
Schema Classes Definition: Corresponding schema classes (OrderSchema and CustomerSchema) are correctly defined to describe the serialization and deserialization process for the dataclasses.
Score: 2 marks
Date Format Specification: While the solution implements the use of fields.Date(), there is no specification for the date format (format="%m/%d/%Y") for the date_of_birth field as required.
Score: 0 marks
Decimal Field Handling: The solution correctly specifies as_string=True for the decimal field, ensuring correct serialization of the amount field.
Score: 1 mark
@post_load Method: There is no @post_load method defined in either of the schema classes to link the schema deserialization directly to dataclass instantiation. The schema directly deserializes the data into a dictionary structure instead.
Score: 0 marks
Other considerations:
The solution correctly uses fields such as fields.DateTime() and fields.Decimal(as_string=True) to match the respective types.
Error handling, validation, or explanation of the deserialization errors and their resolutions is not considered or addressed.
Score: 0.5 marks for matching field types but not addressing other details or enhancements such as nested handling or advanced marshmallow features.

Total Score:
2 + 2 + 0 + 1 + 0 + 0.5 = 5.5

{
  "score": 5.5
}

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

The provided solution is a good implementation of the given problem using the Marshmallow library. 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:
1. The solution should define two dataclasses, one for the person and one for the order (2 marks). The solution correctly defines the Order and Customer dataclasses.
2. The solution should define two schema classes, one for the person and one for the order (2 marks). The solution correctly defines the OrderSchema and CustomerSchema classes.
3. The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark) The solution uses the fields.Date() field in the CustomerSchema to handle the date_of_birth field, but it doesn't explicitly specify the date format.
4. The decimal field needs as_string=True. (1 mark) The solution correctly uses fields.Decimal(as_string=True) to handle the amount field.
5. The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks) The solution doesn't use the @post_load method to link the schema and dataclasses. Instead, it directly creates the Customer and Order instances from the deserialized data.
6. Anything else: (2 marks) The solution is well-structured and easy to understand. It demonstrates a good understanding of the Marshmallow library and its usage.

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

The provided solution is a good implementation of the problem, with a total score of 10 out of 10.

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

The solution provides a clear and concise implementation using the marshmallow library in Python to define the required dataclasses and schema classes for the given JSON structure. It correctly handles defining the classes, specifying the correct data types and formats for date, datetime, and decimal fields, as well as linking the schema and dataclasses with a @post_load method on the schema classes.

However, the solution could be improved by providing more detailed explanations or comments within the code snippet to enhance clarity for readers who may not be familiar with the marshmallow library.

Overall, the solution effectively addresses the problem requirements and demonstrates a solid understanding of using marshmallow for data serialization tasks.

Score: 9/10

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

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

openai/gpt-4-1106-preview says: (5.0) (1.68¢)

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

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

Comments on the solution:

The solution correctly defines two dataclasses for the Order and Customer, fulfilling the requirements for the first item in the marking scheme.
Two schema classes, OrderSchema and CustomerSchema, are also correctly defined, fulfilling the second item in the marking scheme.
However, the solution does not specify the date format for the date_of_birth field using the format "%m/%d/%Y" as required, thus missing marks for the third item.
The solution correctly uses as_string=True for the decimal field, fulfilling the fourth item in the marking scheme.
The solution fails to link the schemas and dataclasses with a @post_load method. This step is crucial to return an instance of the dataclass when loading the JSON data. Therefore, no marks are given for the fifth and the "anything else" items in the marking scheme.

The total score, therefore, is 5 out of 10, as detailed in the score breakdown.

Response 3 (6.0)

To achieve this using the marshmallow library in Python, you can create dataclasses for the JSON data and use marshmallow schema to define the data structure and data types. Here's an example code snippet to demonstrate this:

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

# Define dataclass for orders
@dataclass
class Order:
    created: datetime
    amount: Decimal

# Define dataclass for user
@dataclass
class User:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: [Order]

# Define schema for Order dataclass
class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal(places=2)

# Define schema for User dataclass
class UserSchema(Schema):
    first_name = fields.String()
    last_name = fields.String()
    date_of_birth = fields.Date()
    orders = fields.Nested(OrderSchema, many=True)

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

# Initialize UserSchema
user_schema = UserSchema()

# Deserialize JSON data to User dataclass object
user_object = user_schema.load(data)

# Print User dataclass object
print(user_object)

In this code snippet, we first define the dataclasses for Order and User, where Order contains information about the order, and User contains information about the user along with a list of orders. We then define schemas for Order and User using marshmallow, specifying the data types for each attribute.

We load the JSON data into the UserSchema and use the load method to deserialize the JSON data into a User dataclass object. Finally, we print the User dataclass object to verify the deserialization process.

Make sure to have the marshmallow library installed in your Python environment by running pip install marshmallow.

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) (1.03¢)

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

The solution correctly defines two dataclasses, Order and User. (2 marks)
The solution correctly defines two schema classes, OrderSchema and UserSchema. (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 marks)
The decimal field does not include as_string=True. It should have been fields.Decimal(places=2, as_string=True). (0 marks)
The solution does not include @post_load methods to link the schema and dataclasses. (0 marks)
Additional points:
The solution correctly imports necessary modules.
It provides a sample JSON data and demonstrates how to use the schema to deserialize the data.
The code is well-structured and easy to read.
(2 marks)

Breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema and dataclass linking": 0,
  "Additional points": 2
}

Score:

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

The solution provides a good foundation but misses some important details required in the marking scheme. It correctly defines the dataclasses and schema classes, which is a significant part of the task. However, it doesn't specify the correct date format for date_of_birth, doesn't include as_string=True for the decimal field, and doesn't use @post_load methods to link the schemas and dataclasses. These omissions result in a loss of points. The additional points are awarded for the overall structure and demonstration of usage. The total score is 6 out of 10.

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

The provided solution does implement a basic structure using dataclasses and marshmallow schemas to serialize and deserialize JSON data but falls short on some of the required specifications:

Dataclasses Definition (2 marks):
The solution defines two dataclasses, Order and User, properly matching the structure needed for serialization and deserialization of the given JSON. This achieves the requirement completely.
Score: 2/2
Schema Classes Definition (2 marks):
The solution includes OrderSchema and UserSchema aligning with the required marshmallow Schema for handling the specified data types for the various JSON fields.
Score: 2/2
Specifying Date Format (date_of_birth field) (1 mark):
The solution specifies the fields.Date() for the date_of_birth but does not explicitly mention the format="%m/%d/%Y" as required in the problem to correctly parse the date format given in the JSON example (03/16/1977). This could lead to deserialization errors.
Score: 0/1
Decimal Field Specifications (as_string=True) (1 mark):
The solution does not include the as_string=True parameter in the fields.Decimal(). This parameter is crucial for ensuring the amount is treated as a string during serialization, as specified in the task.
Score: 0/1
Linking schema and dataclasses with @post_load Method (2 marks):
The provided solution does not implement the @post_load method in the schemas to ensure that the loaded data is transformed into instances of the respective dataclasses (User and Order). This is a critical part for integrating the schema with the dataclasses.
Score: 0/2
Anything else (2 marks):
The solution includes proper dataclasses and schema structure for handling nested data and types.
Demonstrates how to load and print the deserialized object, which indeed shows a practical application.
However, there’s incorrect element (date format error and missing @post_load) in the demonstration, and these issues could have been noted or corrected.
Score: 1/2

In summary, the solution outlines a decent approach using marshmallow but misses on some key specific requirements leading to potential functional discrepancies. As a result, the score totals to:

{
  "score": 5
}

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

The provided solution is a good approach to solving the problem using the marshmallow library in Python. Let's evaluate the solution 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 correctly defines two dataclasses, one for the order and one for the user. (2 marks)
The solution correctly defines two schema classes, one for the order and one for the user. (2 marks)
The solution specifies the date format for the date_of_birth field using the %m/%d/%Y format. (1 mark)
The solution correctly sets as_string=True for the amount field, which ensures that the decimal value is stored as a string. (1 mark)
The solution uses the @post_load method on the schema classes to link the schema and dataclasses. (2 marks)
The overall solution is clean, well-organized, and includes comments to explain the purpose of each part of the code. (2 marks)

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

The provided solution is a complete and accurate implementation of the problem using the marshmallow library in Python. It fully meets the requirements specified in the marking scheme.

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

The solution provided effectively defines the necessary dataclasses and schema classes using the marshmallow library in Python. It correctly specifies the formats for the date_of_birth field, decimal field, and establishes the relationship between the schema and dataclasses with the @post_load method.

Breakdown of marks:

Two dataclasses defined: 2 marks
Two schema classes defined: 2 marks
Date format specified for date_of_birth field: 1 mark
Decimal field configured with as_string=True: 1 mark
Schema and dataclasses linked with @post_load method: 2 marks
Additional considerations and completeness of the solution: 2 marks

Total Score: 10/10

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

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

openai/gpt-4-1106-preview says: (1.0) (2.07¢)

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

Comments on the solution:
- No @post_load method is provided to link the schema and dataclasses; thus, the user_object created would not be an instance of the User dataclass. This is a critical part of using Marshmallow with dataclasses (-2 marks).
- The dataclasses have been defined, but without a @post_load method, the schema does not properly deserialize into these dataclasses (-2 marks).
- The schema classes for both the person (User) and the order (Order) have been defined, but they do not use the correct formats for date_of_birth, created, and amount fields (-4 marks).
- The amount field is missing the as_string=True attribute, which is required to ensure that the decimal is treated as a string and not a float (-1 marks).
- The date format for the date_of_birth field is not specified (format should be "%m/%d/%Y") (-1 marks).
- While the solution does create dataclasses and schema classes, and attempts to deserialize JSON data, it incorrectly assumes the date format in the JSON (format should be "%m/%d/%Y" for the date_of_birth field and should deserialize the created field as a datetime object) and does not implement appropriate formats or post-load transformations (-2 marks).

Given the marking scheme and the mistakes present in the solution provided, I awarded 1 point for correctly creating dataclasses and schema classes (even though they are incomplete and lack integration) under "Anything else", which captures some intrinsic value of the code written.

The total score awarded is 1 out of 10.

1.00	0.95	0.89
0.95	1.00	0.89
0.89	0.89	1.00

OpenAI: GPT-3.5 Turbo 16k

Description

Most Similar

Also by openai

Model Consistency

Model Evaluations

claude-3-haiku	10.0	10.0	10.0
claude-3.5-sonnet	6.0	7.0	6.0
gpt-3.5-turbo-0125	8.0	9.0	10.0
gpt-4-0125-preview	5.0	5.5	5.0
gpt-4-1106-preview	3.0	5.0	1.0