Meta: Llama 3 70B Instruct (nitro)

Description

Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 70B 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 70B 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)

Here's an example of how you can use the marshmallow library to generate dataclasses from your JSON data:

import marshmallow as ma
from dataclasses import dataclass
from datetime import datetime
from decimal import Decimal

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Customer:
    first_name: str
    last_name: str
    date_of_birth: datetime

class CustomerSchema(ma.Schema):
    class Meta:
        dataclass = Customer

    date_of_birth = ma.fields.DateTime(format='%m/%d/%Y')
    orders = ma.fields.List(ma.fields.Nested(OrderSchema))

class OrderSchema(ma.Schema):
    class Meta:
        dataclass = Order

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

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

customer_json = json.loads(customer_data)
schema = CustomerSchema()
customer = schema.load(customer_json)
print(customer)

This code defines two dataclasses: Customer and Order. The Customer dataclass has a date_of_birth field that is a datetime object, and an orders field that is a list of Order objects.

The CustomerSchema and OrderSchema classes are used to define the serialization and deserialization rules for the dataclasses. The fields attribute is used to specify the types of the fields, and the format attribute is used to specify the format of the date and datetime fields.

The load method is used to deserialize the JSON data into a Customer object.

Note that you need to use the as_string=True argument when defining the amount field in the OrderSchema class to convert the decimal value to a string.

Also, you need to install marshmallow-dataclass library to use the marshmallow with dataclasses.

You can install it using pip:

pip install marshmallow-dataclass

This will generate the following output:

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

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) (0.91¢)

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

The solution defines two dataclasses, Customer and Order. (2 marks)
The solution defines two schema classes, CustomerSchema and OrderSchema. (2 marks)
The date format for the date_of_birth field is correctly specified as format='%m/%d/%Y'. (1 mark)
The decimal field (amount) 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 libraries.
It provides an example of how to use the schemas to deserialize JSON data.
It mentions the need to install marshmallow-dataclass.
(2 marks)

Breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 2,
  "Date format specified": 1,
  "Decimal field as_string": 1,
  "@post_load method": 0,
  "Additional points": 2
}

Total score:

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

The solution covers most of the requirements well, but it misses the @post_load method to link the schema and dataclasses. Otherwise, it's a solid implementation that demonstrates good understanding of using marshmallow with dataclasses for JSON deserialization.

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

The provided solution does a good job in several aspects for using the marshmallow library in Python to handle JSON data and data classes but lacks in certain critical areas. Here is the detailed evaluation based on the given marking scheme:

Defining two dataclasses for person and order: The solution correctly defines two dataclasses, Customer and Order, which match the requirements of the problem statement.
Defining two schema classes, one for the person (Customer) and one for the order (Order): The solution properly defines two schema classes, CustomerSchema and OrderSchema, that reflect the structure needed for marshmallow to serialize and deserialize data.
Date format specification for the date_of_birth field: The solution correctly uses ma.fields.DateTime(format='%m/%d/%Y') for the date_of_birth in the CustomerSchema, effectively meeting the requirements for date formatting.
Handling Decimal type with as_string=True: The code correctly defines the amount field in the OrderSchema with ma.fields.Decimal(as_string=True), ensuring that the decimal amounts are handled as strings during serialization to preserve decimal precision.
Linkage of schema and dataclasses using @post_load: The solution failed to implement a @post_party method in the CustomerSchema and OrderSchema. This method is essential for ensuring that instances of dataclasses (Customer and Order) are created when the schema's load method is called. The absence of this linkage means the deserialization may not handle nested structs as expected, particularly missing the conversion directly into the Customer and Order classes.
Additional elements (installation note and usage example): The solution adds a helpful note about installing the marshmallow-dataclass library, which is an important requirement and a good practice to mention. There is also an example of parsing JSON and outputting results that correctly demonstrates the library usage, increasing the utility of the example for practical implementations.

Summarily, the solution does an excellent job in the definition of classes and schemas and accurately setting field behaviors. However, its failure to demonstrate usage of the @post_load method significantly affects the functionality for creating the dataclass instances. This omission makes the solution less effective in situations where correct and seamless deserialization from JSON into dataclass instances is required.

Final Score

{
  "score": 8
}

Response 2 (6.0)

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

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

@dataclass
class Order:
    created: datetime = fields.DateTime(format="%Y-%m-%dT%H:%M:%S")
    amount: Decimal = fields.Decimal(as_string=True)

@dataclass
class Customer:
    first_name: str = fields.Str()
    last_name: str = fields.Str()
    date_of_birth: date = fields.Date(format="%m/%d/%Y")
    orders: list[Order] = fields.List(fields.Nested(Order))

customer_schema =Customer.Schema()

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

customer = customer_schema.loads(json_data)
print(customer)

This will output:

Customer(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'))])

Note that we define two dataclasses: Order and Customer. The Order dataclass has two fields: created which is a datetime field with a specific format, and amount which is a Decimal field represented as a string.

The Customer dataclass has four fields: first_name, last_name, date_of_birth which is a date field with a specific format, and orders which is a list of Order objects.

We then create a Schema instance for the Customer dataclass, and use it to load the JSON data into a Customer object.

Make sure to install the marshmallow and marshmallow-dataclass libraries using pip install marshmallow marshmallow-dataclass before running this code.

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) (1.05¢)

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

The solution defines two dataclasses, Order and Customer. (2 marks)
The solution does not explicitly define separate schema classes. Instead, it uses marshmallow_dataclass to automatically generate schemas from the dataclasses. While this is a valid approach, it doesn't strictly meet the requirement of defining two schema classes. (0 marks)
The solution correctly specifies the date format for the date_of_birth field as format="%m/%d/%Y". (1 mark)
The decimal field (amount) correctly uses as_string=True. (1 mark)
The solution does not implement @post_load methods to link the schemas and dataclasses. The marshmallow_dataclass approach handles this automatically, but it doesn't meet the specific requirement. (0 marks)
Additional points:
The solution uses marshmallow_dataclass, which is an efficient way to create both dataclasses and schemas.
It provides an example of how to use the schema to load JSON data.
The solution handles the datetime format for the created field correctly.
(2 marks)

Breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 0,
  "Date format specified": 1,
  "Decimal field configuration": 1,
  "@post_load method": 0,
  "Additional points": 2
}

Score:

{
  "scores": [2, 0, 1, 1, 0, 2],
  "total_score": 6
}

The solution demonstrates a good understanding of using marshmallow with dataclasses, but it doesn't strictly adhere to all the requirements in the marking scheme. It takes a different approach by using marshmallow_dataclass, which simplifies the process but doesn't meet the specific requirement of defining separate schema classes and using @post_load methods. However, it correctly handles the data types and formats as required.

openai/gpt-4-0125-preview says: (6.5) ()

The provided solution demonstrates the use of the marshmallow library together with marshmallow_dataclass to deserialize a JSON structure into Python dataclasses. Among the key elements to evaluate based on the marking scheme:

Dataclasses Definition:
The solution defines two data classes: Order and Customer. This aligns with the first requirement of the marking scheme.
Score: 2/2
Schema Classes Definition:
The schema classes for Order and Customer are implicitly created using marshmallow_dataclass. However, the explicit creation or customization of schema classes in the conventional sense (by subclassing Schema) is not shown, which might confuse the purpose of separate schema definition.
Score: 1/2
Specification of the Date Format:
The date_of_birth field is correctly specified with the format "%m/%d/%Y".
Score: 1/1
Decimal Field Representation:
The amount field in the Order class is correctly set with as_string=True, ensuring its representation as a string.
Score: 1/1
Linking of Schema and Dataclasses with @post_load:
The solution does not demonstrate the use of the @post_load method to manually handle the loading process, which may typically be used to explicitly create an object from the deserialized data. Instead, it relies on the automatic behavior facilitated by marshmallow_dataclass.
Score: 0/2
Other Considerations:
Usage and explanation are well noted, advising on library installation and providing an example usage which demonstrates the practical application of the defined classes and schema.
Serialization and deserialization processes are implicitly managed well by the marshmallow_dataclass, which is a strength in simplicity but might be a downside for misunderstanding direct interaction with marshmallow.
Adequate handling of imports and utilizing Python standard libraries for types like Decimal and datetime.
Score: 1.5/2

Conclusion:
The solution overall accommodates most requirements properly, but it slightly misses out on delivering an explicit demonstration of marshmallow schemas (especially on the @postload functionality) which is crucial for deeper customizations in more complex scenarios. As a result, the score reflects this observation.

{
  "score": 6.5
}

Response 3 (9.0)

You can use Marshmallow's fields module to define custom fields for your dataclasses. Here's an example of how you can achieve this:

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

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

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

class UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(format='%m/%d/%Y')
    orders = fields.Nested(OrderSchema, many=True)

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

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

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

print(user)
# Output:
# User(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'))])

In this example, we define two dataclasses: Order and User. We then define two schemas: OrderSchema and UserSchema.

In OrderSchema, we define created as a DateTime field with the iso format, and amount as a Decimal field with as_string=True to parse the amount as a string.

In UserSchema, we define first_name and last_name as Str fields, date_of_birth as a Date field with the format parameter set to '%m/%d/%Y', and orders as a nested OrderSchema field with many=True to indicate that it's a list of orders.

The @post_load decorator is used to define a method that will be called after the data has been deserialized. In this case, we use it to create an instance of the User dataclass with the deserialized data.

Finally, we load the JSON data using the UserSchema instance and print the resulting User 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: (9.0) (1.01¢)

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

The solution defines two dataclasses, Order and User. (2 marks)
The solution defines two schema classes, OrderSchema and UserSchema. (2 marks)
The solution specifies the date format for the date_of_birth field as format='%m/%d/%Y'. (1 mark)
The decimal field (amount) is defined with as_string=True. (1 mark)
The schema and dataclasses are linked with a @post_load method on the UserSchema class. However, it's missing for the OrderSchema. (1 mark)
Additional points:
The solution includes example usage and demonstrates how to use the schema to deserialize JSON data.
It correctly imports necessary modules and uses appropriate field types (e.g., fields.DateTime, fields.Decimal).
(2 marks)

Breakdown of marks:

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

Score:

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

Comments:
The solution is well-structured and covers most of the requirements effectively. It correctly defines dataclasses and schema classes, uses appropriate field types, and demonstrates how to use the schema for deserialization. The only minor omission is the lack of a @post_load method for the OrderSchema, which would have made the solution perfect. Overall, it's a very good implementation that meets the majority of the specified criteria.

openai/gpt-4-0125-preview says: (None) ()

The provided solution effectively demonstrates how to use the Marshmallow library to deserialize JSON into dataclasses using custom field types and formats. Here's a breakdown of the evaluation based on the provided marking scheme:

Defining dataclasses for the person and the order: The solution defines an Order and a User dataclass correctly, representing an order and a user respectively. (+2 marks)
Defining schema classes for the person and the order: The OrderSchema and UserGirlSchema are both defined correctly corresponding to the dataclasses. (+2 marks)
Specifying the date format for the date_of_graph field: The date_of_birth field in the UserSchema is correctly set to utilize the format '%m/%d/%Y' for date parsing. (+1 mark)
Specifying as_string=True for the decimal field: The amount field in OrderSchema correctly uses as_string=True for the Decimal type, ensuring that amounts are handled as strings, not floats, during serialization. (+1 mark)
Linking schema and dataclasses with a @post_load method on the schema classes: The UserSchema uses the @post_load decorator effectively to instantiate the User dataclass with deserialized data. (+2 marks)
Anything else: The example includes a usage snippet showing how to load a JSON string into the data structures, which demonstrates practical implementation. The dataclasses correctly accommodate lists of orders within a user and handle advanced serialization rules, such as formatting dates and handling decimals. Additionally, the decision to handle created as an ISO formatted datetime is a suitable choice, ensuring precise handling of datetime serialization. (+2 marks)

Despite overlooking the slight issue that the date field within the dataclass was typed as datetime instead of date, everything else in terms of clarity, definition, and explanation is straightforward and follows best practices.

The overall evaluation yields:

{
  "mono_score": 10
}

1.00	0.93	0.81
0.93	1.00	0.82
0.81	0.82	1.00

Meta: Llama 3 70B Instruct (nitro)

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Final Score

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