Meta: Llama 3 8B Instruct (nitro)

Description

Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.

It has demonstrated strong performance compared to leading closed-source models in human evaluations.

To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.

Note: this is a higher-throughput version of Llama 3 8B Instruct. It may have higher prices and slightly different outputs.

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)

You can use the marshmallow library to define a schema for your JSON data. Here's an example of how you can do it:

from marshmallow import Schema, fields, post_load
from marshmallow.utils import datetime
from datetime import datetime as dt

class OrderSchema(Schema):
    created = fields.DateTime(format="%Y-%m-%dT%H:%M:%S")
    amount = fields.Decimal(as_string=True)

class UserSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(format="%m/%d/%Y")
    orders = fields.Nested(OrderSchema, default=[], missing=[])

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

class User:
    def __init__(self, first_name, last_name, date_of_birth, orders=None):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = dt.strptime(date_of_birth, "%m/%d/%Y")
        self.orders = orders if orders is not None else []

# Load the data
user_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.load(user_data)

print(user)

This will create a User class with first_name, last_name, date_of_birth and orders properties. The date_of_birth is loaded as a datetime object and the orders is a list of Order objects.

The UserSchema is defined with the fields class from marshmallow, which defines the fields for the schema. The date_of_birth is loaded as a string in the format "%m/%d/%Y" and converted to a datetime object. The orders is a list of Order objects defined by the OrderSchema.

The post_load function is used to create the User object from the loaded data.

Finally, the user is loaded from the user_data using the load method of the UserSchema.

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) (0.99¢)

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

The solution defines two classes: User and OrderSchema. However, it doesn't use dataclasses as requested. (1/2 marks)
The solution defines two schema classes: UserSchema and OrderSchema. (2/2 marks)
The solution correctly specifies the date format for the date_of_birth field as format="%m/%d/%Y". (1/1 mark)
The decimal field (amount) is correctly defined with as_string=True. (1/1 mark)
The schema and classes are linked with a @post_load method on the UserSchema class. However, it's missing for the OrderSchema. (1/2 marks)
Additional points:
The solution imports necessary modules.
It provides an example of how to use the schema to load data.
The User class is defined, although not as a dataclass.
(2/2 marks)

Here's the breakdown of marks:

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

Total score:

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

Comments:
The solution is generally good and addresses most of the requirements. However, it doesn't use dataclasses as requested, which is a significant omission. The Order class is also missing, and the @post_load method is only implemented for the UserSchema. Despite these issues, the solution demonstrates a good understanding of using marshmallow for schema definition and data loading.

openai/gpt-4-0125-preview says: (7.0) ()

The evaluation of the provided solution based on the given marking scheme is as follows:

Definition of two dataclasses: The solution correctly defines one dataclass (User), but there is no explicit definition for an Order dataclass. Instead, order data appears to be dealt with directly within the lists handled in the User class instantiation.
Score: 1 point
Definition of two schema classes: The solution includes definitions for both UserSchema and OrderSchema.
Score: 2 points
Specification of the date format for the date_of_birth field: The date format is specified appropriately in the UserSchema as fields.Date(format="%m/%d/%Y").
Score: 1 point
Decimal field with as_string=True: The amount in the OrderSchema correctly uses as_string=True.
Score: 1 point
Link schema and dataclasses with a @post_load method: The solution implements a @post_load method for the UserSchema, which appropriately constructs the User object from the schema data. However, there is no similar setup for Order, partly because an explicit Order class is missing.
Score: 1 point
Additional considerations: The solution misses a direct dataclass for the Order. Furthermore, the User class constructor should ideally utilize date and datetime objects rather than handling string conversions internally. This aspect could fall outside the specific marking scheme but indicates possible improvements regarding separation of concerns and overall code clarity.
Score: 1 point

Total Score:

{
  "score": 7
}

The solution provides essential functionalities, correctly handles JSON data through the marshmallow schemas, and effectively loads and processes nested data. However, an improvement could be made in better structuring the code by defining an Order dataclass, which would enhance the separation of concerns and maintain consistent data handling using both schema and dataclass paradigms together. This misalignment resulted in the deduction in the evaluation.

Response 2 (5.5)

You can use the marshmallow library in Python to generate dataclasses from your JSON schema. Here's an example of how you can do this:

import datetime
from marshmallow import Schema, fields, validate

class DateOfBirthField(fields.Field):
    def _deserialize(self, value, data, **kwargs):
        return datetime.datetime.strptime(value, '%m/%d/%Y').date()

class OrderSchema(Schema):
    created = fields.DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount = fields.Decimal(as_string=True)

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Nested(DateOfBirthField)
    orders = fields.List(fields.Nested(OrderSchema))

person_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()
data = person_schema.load(person_data)

This code defines three dataclasses: PersonSchema, OrderSchema, and DateOfBirthField. The DateOfBirthField is a custom field that converts the date_of_birth string to a datetime.date object.

The PersonSchema has fields for first_name, last_name, date_of_birth, and orders. The orders field is a list of OrderSchema objects.

The OrderSchema has fields for created (a datetime object) and amount (a decimal string).

The code then uses the load() method of the PersonSchema to deserialize the JSON data into a Python object.

You can then access the deserialized data using dot notation, for example:

print(data.first_name)  # Output: Jane
print(data.date_of_birth)  # Output: 1977-03-16
print(data.orders[0].created)  # Output: 2024-01-25 15:00:04
print(data.orders[0].amount)  # Output: 100.00

Note that the DateOfBirthField is a custom field, and you may need to adjust it to fit your specific use case. Additionally, you can customize the marshmallow schema to fit your needs, for example, by adding additional validation or converting 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: (5.5) (0.95¢)

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

The solution does not explicitly define two dataclasses for the person and order. It defines schemas instead. (0 marks)
The solution correctly defines two schema classes: PersonSchema and OrderSchema. (2 marks)
The solution specifies the date format for the date_of_birth field, but uses a custom field instead of the required format. (0.5 marks)
The decimal field correctly uses as_string=True. (1 mark)
The solution does not include a @post_load method to link the schemas and dataclasses. (0 marks)
Additional points:
The solution includes a custom DateOfBirthField for handling date_of_birth. (1 mark)
The solution demonstrates how to use the schema to deserialize data. (1 mark)

Comments:
The solution provides a good starting point using marshmallow schemas but falls short in some key areas. It doesn't use dataclasses as requested, and doesn't link schemas to dataclasses using @post_load. The date_of_birth handling is creative but doesn't meet the exact requirements. The solution does well in defining schemas and handling the decimal field correctly.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 0.5,
  "Decimal field handling": 1,
  "Schema-dataclass linking": 0,
  "Additional points": 2
}

Final score:

{
  "scores": [0, 2, 0.5, 1, 0, 2],
  "total_score": 5.5
}

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

The provided solution attempts to use the marshmallow library for converting JSON data into Python objects. Here's the evaluation based on the marking scheme criteria:

Definition of Two Dataclasses:
The solution does not explicitly define dataclasses using dataclasses.dataclass from Python's standard library. Instead, it creates Schema classes using marshmallow. However, marshmallow schemas are not dataclasses.
Score: 0/2
Definition of Two Schema Classes:
The solution provides appropriate schema definitions using marshmallow for both the person and the orders.
Score: 2/2
Specification of Date Format for date_of_birth:
The solution incorrectly uses a custom Field (DateOfBirthField) instead of just specifying the format directly in a fields.Date(format="%m/%d/%Y").
Score: 0/1
Use of as_string=True for Decimal Field:
The solution appropriately uses as_string=True for the amount field, ensuring it is serialized back to a string.
Score: 1/1
Link via @post_load Method:
The solution does not use any @post_load decarator to transform loaded data into relevant classes, thereby not linking schema and dataclasses properly. Marshmallow's typical use would involve this to create actual instance of a dataclass or another type after loading.
Score: 0/2
Additional Considerations:
While the use of a custom field for date_of_birth is innovative, it does not directly address the requirement to define a clear date format according to the task ("format should be %m/%d/%Y"). Furthermore, there is no proper dataclass definition and missing utilization of @post_load for object instantiation, which might be critical for reusability and clarity in practical applications.
Score: 1/2

Overall, the solution shows an understanding of basic marshmallow functionalities but lacks complete adherence to the task specifications and proper use of marshmallow and dataclasses together. It misses some critical points like the structure and supposed usage of dataclasses alongside marshmallow schemas properly.

{
  "score": 4
}

Response 3 (7.0)

You can use the marshmallow library in Python to convert JSON data to a Python dataclass. Here's how you can do it:

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

pip install marshmallow

Then, create the following Python code:

import datetime
from marshmallow import fields, Schema
from marshmallow_dataclass import dataclass

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

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

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(format='%m/%d/%Y')
    orders = fields.Nested('OrderSchema', default=[])
    class Meta:
        fields = ('first_name', 'last_name', 'date_of_birth', 'orders')

class OrderSchema(Schema):
    created = fields.DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount = fields.Decimal(as_string=True)

person = Person('Jane', 'Smith', '03/16/1977', [Order(datetime.datetime.fromisoformat('2024-01-25T15:00:04'), 100.0)])
person_schema = PersonSchema()
person_json = person_schema.dumps(person)

print(person_json)

This code will generate a JSON string from the given Python data. The PersonSchema class defines the fields and their types for the person data, and the OrderSchema class defines the fields and their types for the orders list.

Please note that the date_of_birth field is a string in the JSON data, so it needs to be converted to a datetime.date object before it can be used in the Python code. The created field in the Order class is also a string in the JSON data, so it needs to be converted to a datetime.datetime object before it can be used in the Python code.

Also, the amount field is a string in the JSON data, so it needs to be converted to a float object before it can be used in the Python code.

You can use the dumps method to generate a JSON string from the person data, and the loads method to convert a JSON string back to a person object.

Marking Scheme

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

anthropic/claude-3.5-sonnet says: (7.0) (1.00¢)

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, PersonSchema and OrderSchema. (2 marks)
The solution specifies the date format for the date_of_birth field correctly: fields.Date(format='%m/%d/%Y'). (1 mark)
The decimal field (amount) is correctly defined with as_string=True. (1 mark)
The solution does not use the @post_load decorator to link the schema and dataclasses. (0 marks)
Additional observations:
The solution uses marshmallow_dataclass, which simplifies the creation of dataclasses.
The Order class uses float instead of decimal.Decimal for the amount field.
The solution includes an example of creating a Person object and serializing it to JSON.
(1 mark)

Breakdown of marks:

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

Score:

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

The solution covers most of the requirements but misses the important @post_load decorator to link the schema and dataclasses. It also uses float instead of decimal.Decimal for the amount field in the Order dataclass, which might lead to precision issues with monetary values. Despite these shortcomings, the solution demonstrates a good understanding of using marshmallow with dataclasses and provides a working example of serialization.

openai/gpt-4-0125-preview says: (7.0) ()

The provided solution demonstrates a fundamental approach to using the marshmallow library and dataclasses for JSON serialization and deserialization in Python. Below is a detailed evaluation based on the specified marking scheme:

Dataclass Definition (2 marks):
Two @dataclass decorated classes, Order and Person, are defined which properly correspond to the JSON structure. This is correct.
However, the type specification for amount in the Order dataclass is float, which does not meet the requirement of it being a Decimal. Also, orders in Person should be typed as List[Order] for clarity.
Schema Class Definition (2 marks):
Two schema classes, PersonSchema and OrderSchema, are correctly defined.
Fields are mapped with appropriate marshmallow field types.
Date and DateTime Format Specification (1 mark):
The date_of_birth in PersonSchema correctly specifies the format as %m/%d/%Y.
The created in OrderSchema also correctly uses a datetime format.
Decimal Field Representation (1 mark):
The amount in OrderSchema correctly uses fields.Decimal(as_string=True). This ensures the decimal amount is serialized as a string.
Linking Schema and Dataclasses with @post_load (0 marks):
The solution lacks any @post_load methods. These are essential for converting loaded data back into instances of the dataclasses.
Additional Observations (1 mark):
The example creation and serialization of a Person object with nested Order shows practical usage, although the date fields should be converted to the appropriate types (datetime.date and datetime.datetime) before instantiation, not left as strings.
The solution includes beneficial comments and proper installation guidance for the library, but critical linkages for automatic deserialization into dataclasses (needing @post_load or direct marshmallow_dataclass usage) are missing.

Summing up the given scores from the checklist yields a total score of 7 out of 10, based on the outlined criteria. Some critical functionality such as the proper @post_load implementation and correct initial type handling in the instantiation of the dataclasses are missing, affecting the completeness of the solution.

{
  "score": 7
}

1.00	0.80	0.76
0.80	1.00	0.84
0.76	0.84	1.00

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