OpenAI: GPT-4 32k (older v0314)

Description

GPT-4-32k is an extended version of GPT-4, with the same capabilities but quadrupled context length, allowing for processing up to 40 pages of text in a single pass. This is particularly beneficial for handling longer content like interacting with PDFs without an external vector database. 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 (8.0)

To achieve this using the marshmallow library in Python, you can follow the steps below:

Install the marshmallow library if you haven't already: pip install marshmallow
Import the necessary modules and create dataclasses for the given JSON structure.
Define Marshmallow schemas for your dataclasses and specify how the fields should be deserialized.

Here's an example of how to do this:

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


@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()

    @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.List(fields.Nested(OrderSchema))

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


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

loaded_json = json.loads(json_str)
person_schema = PersonSchema()
person = person_schema.load(loaded_json)

print(person)

In this example, Order and Person dataclasses are defined, along with their respective schemas using the marshmallow library. The post_load decorators are used to create instances of the dataclasses after deserializing the input data.

Finally, the JSON string is deserialized into the Person dataclass instance (person), with the specified field types correctly set (date, datetime, and decimal).

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) (0.93¢)

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

Two dataclasses defined (Person and Order):

{
  "marks 1": 2
}

Two schema classes defined (PersonSchema and OrderSchema):

{
  "marks 2": 2
}

Date format for date_of_birth:
The solution doesn't specify the date format for the date_of_birth field.

{
  "marks 3": 0
}

Decimal field with as_string=True:
The solution doesn't include as_string=True for the decimal field.

{
  "marks 4": 0
}

Schema and dataclasses linked with @post_load:
The solution correctly uses @post_load decorators to link schemas and dataclasses.

{
  "marks 5": 2
}

Additional points:
Proper imports and use of marshmallow library
Correct handling of nested structures (orders list)
Example usage of the schema to deserialize JSON data

{
  "marks 6": 2
}

Comments:
The solution correctly defines dataclasses and schema classes, and uses @post_load decorators to link them. It also demonstrates how to use the schema to deserialize JSON data. However, it misses two important details: specifying the date format for date_of_birth and using as_string=True for the decimal field. These omissions might lead to incorrect parsing of the input data.

Score breakdown:

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

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

The solution provided does a commendable job of demonstrating how to utilize the marshmallow library in Python to deserialize JSON data into Python data classes. However, there are some shortcomings relative to the marking scheme provided. Here's how it stands:

Two dataclasses defined: Both Order and Person dataclasses are defined according to the requirements, correctly handling complex data types such as datetime and decimal.
Score: 2/2
Two schema classes defined: Corresponding schema classes OrderSchema and PersonSchema are defined.
Score: 2/2
Specify the date format for the date_of_birth field: The solution has included the fields for date and datetime conversion (date_of_birth = fields.Date() and created = fields.DateTime()) but has missed specifying the date format for date_of_birth as %m/%d/%Y.
Score: 0/1
Decimal field with as_string=True: The schema correctly defines the decimal type for amount but misses the as_string=True parameter. Consequently, this may lead to it being processed as a floating point number rather than a string which can introduce floating point errors.
Score: 0/1
Linking schema and dataclasses with a @post_load method: Designer utilizes the @post_load decorator correctly in both schemas, ensuring the creation of dataclass instances post deserialization.
Score: 2/2
Additional attributes:
Correct handling of lists and nested schema with fields.List(fields.Nested(OrderSchema)).
Proper import of required modules and use of JSON parsing.
Detailed comments and clear code structure, aiding in readability and maintainability.
Score: 2/2

Final Evaluation:

{
  "score": 8
}

The solution effectively demonstrates the use of the marshmallow library for deserialization into dataclasses, ensuring type correctness for datetime and lists, yet it slightly misses solving precisely as per the specified formatting and string representation for decimal types.

Response 2 (8.0)

First, you need to install the marshmallow library if you haven't already:

pip install marshmallow

Next, you can create a Python script with the following code to define the data classes and use the marshmallow library for deserialization:

import datetime
import decimal
from typing import List
from dataclasses import dataclass
from marshmallow import Schema, fields, post_load


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


@dataclass
class User:
    first_name: str
    last_name: str
    date_of_birth: datetime.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 UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.List(fields.Nested(OrderSchema))

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


json_string = """
{
  "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_string)
print(user)

Replace the json_string variable with the JSON string you want to deserialize.

This script defines data classes for User and Order, and creates schema classes to handle the deserialization with marshmallow. The @post_load decorator is used to create the data class instances after loading the data.

Once you run the script, it will deserialize the JSON string into a User object with the specified attributes.

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: (7.0) ()

Evaluation of the Provided Solution

Dataclass Definition (2 marks)
The provided solution defines two dataclasses, Order and User, which includes all necessary attributes, including the collections and proper types (datetime and decimal).
Schema Class Definition (2 marks)
Two schema classes (OrderSchema and UserSchema) are correctly defined. They match the dataclasses in structure and provide fields matching the properties of each dataclass.
Date Format Specification (0.5 marks)
The date format for date_of_birth should be specifically defined as format="%m/%d/%Y", which is missing. The solution uses fields.Date() without specifying serialization and deserialization formats.
Decimal Field as String (0.5 marks)
The decimal field should be serialized back to a string using as_string=True, but this is missing in the provided solution. It only states fields.Decimal(). Including as_string=True would make it more faithful to the JSON structure for amounts which are in string format.
Linking Schema and Dataclasses with @post_load (2 marks)
The @post_load decorators make_order and make_user are implemented correctly. They effectively convert the dictionary loaded from JSON back into instances of the dataclasses Order and User.
Additional Elements (2 marks)
Proper handling and conversion of JSON data through dataclasses and schema.
The correct use of marshmallow for loading data from JSON and creating instances.
Overall completeness and functionality of the code provided it is run in an appropriate environment.

Total Score

Considering the evaluation points:

{
  "score": 7
}

While the solution comprehensively applies marshmallow and dataclasses to deserialize JSON and transform values into specified Python types, it does miss correct specification of date formatting and the proper serialization of decimal amounts to string format in JSON, which are crucial for complete compliance with given specifications.

anthropic/claude-3.5-sonnet says: (8.0) (0.92¢)

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

The solution correctly defines two dataclasses, User and Order. (2 marks)
The solution correctly defines two schema classes, UserSchema and OrderSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. 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 correctly linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution imports necessary modules.
It includes a sample JSON string and demonstrates how to use the schema to deserialize the data.
The solution uses type hints in the dataclasses.
(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 usage": 2,
  "Additional points": 2
}

And here's the total score:

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

The solution demonstrates a good understanding of using marshmallow with dataclasses, but misses two important details in the field specifications. Overall, it's a solid implementation that would work for most cases, but could be improved for more precise handling of date and decimal fields.

Response 3 (8.0)

To use the Marshmallow library, you should first install it using pip:

pip install marshmallow

Then, you can create dataclasses and use Marshmallow schemas to deserialize the JSON string and validate the data types. Here's an example:

import datetime as dt
from decimal import Decimal
from dataclasses import dataclass
from typing import List
from marshmallow import Schema, fields, post_load


# Dataclasses
@dataclass
class Order:
    created: dt.datetime
    amount: Decimal


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


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

    @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.List(fields.Nested(OrderSchema))

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


# Deserialize 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'
        }
    ]
}

# Load data into dataclasses
person_schema = PersonSchema()
person = person_schema.load(json_data)
print(person)

# Deserialize JSON to a dictionary with validated data types
validated_data = person_schema.load(json_data)
print(validated_data)

In this example, we first define the dataclasses Order and Person. Then, we create Marshmallow schemas OrderSchema and PersonSchema to specify the validation rules and deserialization methods (post_load methods used to create dataclasses).

Finally, we load the JSON data into the PersonSchema using the load method, which generates the Person and Order dataclass instances with the correct data types.

Marking Scheme

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

anthropic/claude-3.5-sonnet says: (8.0) (1.05¢)

Let's evaluate this 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 used 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 imports necessary modules. (1 mark)
It provides an example of how to use the schemas to deserialize JSON data. (1 mark)

Here's the breakdown of the marks:

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

Final score:

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

Comments:
The solution provides a good overall structure and demonstrates the use of Marshmallow with dataclasses. It correctly defines the dataclasses and schema classes, and uses the @post_load decorator to link them.

However, it misses two important details:
1. The date_of_birth field should have specified the date format to match the input JSON.
2. The amount field in OrderSchema should have used as_string=True to properly handle the decimal input.

These omissions could lead to errors when parsing the actual JSON data. Despite these issues, the solution provides a solid foundation and demonstrates a good understanding of using Marshmallow with dataclasses.

openai/gpt-4-0125-preview says: (7.5) ()

The solution provided meets most of the requirements for using marshmallow with Python dataclasses to process and validate JSON data. Let's evaluate it based on the given criteria:

Dataclasses Definition:
The solution defines two dataclasses, Order and Person, as required. This adheres to the specified requirements.
(2 marks)
Schema Classes Definition:
The solution also defines two schema classes, OrderSchema and PersonSchema, corresponding to the dataclasses.
(2 marks)
Date Format Specification:
The solution does not specify the format for the date_of_birth field. The requirement to have the date format as "%m/%d/%Y" is missing.
(0 marks)
Decimal Field Specification:
The solution uses the fields.Decimal() for the amount attribute in OrderSchema, but it does not specify the as_string=True property to ensure the amount is treated as a string when loaded and not as a float.
(0 marks)
Linking Schemas and Dataclasses via @post_load:
The solution correctly uses the @post_load decorator for linking schemas to their respective dataclasses. The make_order and make_person methods efficiently transform the parsed data into respective dataclass instances.
(2 marks)
Additional Considerations:
Correct import and use of necessary libraries and modules.
Proper handling and demonstration of data loading and dataclass instance creation.
Demonstrates printing of loaded and validated data.
However, some additional error handling, explanation or demonstration of precise date handling and missing specification details are areas of improvement.
(1.5 marks)

Overall Evaluation:
Overall, the solution demonstrates a solid understanding of using dataclasses with Marshmallow in Python. However, it fails to meet all the requirements specified in the problem concerning the date and decimal formats, which are crucial for accurately preserving the consistency and type integrity of imported data.

{
  "score": 7.5
}

1.00	0.86	0.91
0.86	1.00	0.86
0.91	0.86	1.00

OpenAI: GPT-4 32k (older v0314)

Description

Evaluation of the Provided Solution

Total Score

Most Similar

Also by openai

Model Consistency

Model Evaluations

claude-3-haiku
claude-3.5-sonnet	8.0	8.0	8.0
gpt-3.5-turbo-0125
gpt-4-0125-preview	8.0	7.0	7.5
gpt-4-1106-preview