Meta: Llama 3 70B Instruct

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.

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?

You can use the marshmallow library to define dataclasses and deserialize the JSON data into them. Here's an example:

from dataclasses import dataclass
from marshmallow import Schema, fields
from marshmallow_enum import Enum
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
    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)

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

schema = CustomerSchema()
customer_data = schema.loads(customer_json)

print(customer_data)
# 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'))])

Here's what's happening:

  1. We define two dataclasses: Order and Customer.
  2. We define two schema classes: OrderSchema and CustomerSchema. These schemas define the structure of the data and the types of the fields.
  3. We use the fields module from marshmallow to specify the types of the fields. For example, fields.DateTime() specifies a field that should be deserialized as a datetime object.
  4. We use the Nested field to specify that the orders field in the Customer schema should be deserialized as a list of Order objects.
  5. We load the JSON data into the schema using the loads() method, which returns an instance of the Customer dataclass.

Note that we need to specify the many=True argument to the fields.Nested field to indicate that the orders field should be deserialized as a list of Order objects.

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) (1.08¢)

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

  1. The solution defines two dataclasses: Order and Customer. (2 marks)

  2. The solution defines two schema classes: OrderSchema and CustomerSchema. (2 marks)

  3. The solution doesn't specify the date format for the date_of_birth field. It should have used fields.Date(format="%m/%d/%Y"). (0 marks)

  4. The decimal field doesn't include as_string=True. It should have been fields.Decimal(as_string=True). (0 marks)

  5. The schema and dataclasses are not linked with a @post_load method on the schema classes. (0 marks)

  6. Additional points:

  7. The solution correctly uses fields.Nested for the orders field with many=True. (1 mark)
  8. The solution demonstrates how to use the schema to deserialize JSON data. (1 mark)

Breakdown of marks:

{
  "Dataclasses": 2,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal as string": 0,
  "Post load method": 0,
  "Additional points": 2
}

Score:

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

Comments:
The solution correctly defines the dataclasses and schema classes, which is a good start. However, it misses some important details:

  1. The date format for date_of_birth is not specified, which may lead to incorrect parsing of the date.
  2. The decimal field doesn't use as_string=True, which can cause issues with precision.
  3. There's no @post_load method to create instances of the dataclasses from the deserialized data.

Despite these omissions, the solution does provide a working example of how to use marshmallow to deserialize JSON data into Python objects, which is valuable. To improve, the solution should address the missing points mentioned above.

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

The given solution offers a structured approach to utilizing the marshmallow library for deserialization of JSON into Python dataclasses. Here's the evaluation based on the provided marking scheme:

  1. Dataclasses Definition: The solution correctly defines two dataclasses, Order and Customer. Each dataclass uses appropriate type hints for its fields. (2 marks)

  2. Schema Classes Definition: Two schema classes, OrderSchema and CustomerSchema, are defined. These classes map the relevant fields from the dataclasses to marshmallow fields, facilitating the serialization and deserialization processes. (2 marks)

  3. Date Format Specification: The solution uses fields.Date() for date_of_birth, but does not specify the format "%m/%d/%Y" as required. This could lead to potential issues if the input format varies or is unrecognized by the default parser settings in marshmallow. (0 marks)

  4. Decimal Field Handling: The solution utilizes fields.Decimal() for handling the amount field but fails to set as_string=True, which ensures that the field is serialized back to JSON as a string while maintaining the precision of Decimal. This omission may lead to precision issues when working with financial data. (0 marks)

  5. Linkage with @post_load method: The solution does not implement or mention the use of the @post_load decorator, which is vital for transforming the loaded data back into an instance of the corresponding dataclass (i.e., Customer from CustomerSchema). This means that although the types are correctly managed, the deserialization won't directly yield a Customer dataclass instance unless specifically handled. (0 marks)

  6. Additional Considerations:

  7. Correct use of fields.Nested() to handle nested structures (e.g., orders inside the customer), including correct parameter many=True to indicate a list of items.
  8. The example output seems accurate with proper type conversions, however, considering the @post_load and other required specific implementations aren't followed, the actual output handling might be prone to structural inaccuracies. (1 mark)

The solution has a well-intentioned use of basic Marshmallow functionalities but lacks completeness with the respect to the specifics required by the problem, particularly the precision aspects of date and decimal handling and object instantiation following deserialization.

{
  "score": 5
}

Here's an example of how you can define dataclasses and use the Marshmallow library to deserialize the JSON data:

from datetime import date, datetime
from decimal import Decimal
from dataclasses import dataclass
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(as_string=True)

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

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

# Deserialize JSON data
schema = CustomerSchema()
customer_data = schema.loads(json_data)

# Create dataclass instances
customer = Customer(**customer_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. We also define two schemas: OrderSchema and CustomerSchema. The schemas define the fields and their corresponding types.

In the CustomerSchema, we use the fields.Nested field to specify that the orders field is a list of Order objects, and we use the many=True parameter to indicate that it's a list.

Finally, we deserialize the JSON data using the CustomerSchema instance, and create a Customer dataclass instance by passing the deserialized data to the Customer constructor.

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) (0.98¢)

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

  1. The solution defines two dataclasses, Order and Customer. (2 marks)

  2. The solution defines two schema classes, OrderSchema and CustomerSchema. (2 marks)

  3. 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)

  4. The decimal field correctly uses as_string=True. (1 mark)

  5. The solution does not implement @post_load methods to link the schemas and dataclasses. (0 marks)

  6. Additional points:

  7. The solution correctly uses fields.Nested for the orders field in CustomerSchema.
  8. It provides an example of how to use the schema to deserialize JSON data.
  9. It demonstrates how to create a dataclass instance from the deserialized data.
    (2 marks)

Comments:
The solution provides a good basic structure for using Marshmallow with dataclasses. However, it misses a few important details:
- The date format for date_of_birth is not specified, which may lead to parsing errors.
- The @post_load decorators are not implemented, which means the schema doesn't automatically create dataclass instances.
- The solution could be improved by adding error handling and validation.

Here's the breakdown of marks:

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

Final score:

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

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

The provided solution does a good job in demonstrating how to use dataclasses and the Marshmallow library to deserialize JSON data:

  1. Dataclass definition:
  2. The solution correctly defines two dataclasses, Order and Customer, adhering to the requirements of encapsulating order details and customer details respectively. Each dataclass is equipped with the appropriate fields: Order with created and amount, and Customer with first_name, last_name, date_of_birth, and orders.

  3. Score for this section: 2/2

  4. Schema class definition:

  5. Two corresponding schema classes, OrderSchema and CustomerSchema, are also well defined. These schema classes correctly map to their respective dataclasses using Marshmallow’s fields.

  6. Score for this section: 2/2

  7. Date and DateTime Formats:

  8. The OrderSchema correctly initializes the created field using fields.DateTime(), which is suitable for handling ISO formatted datetime strings.
  9. The CustomerSchema uses fields.Date() for the date_of_birth field without explicitly specifying the format as %m/%d/%Y.

  10. Score for this section: 0.5/1

  11. Decimal Field Representation:

  12. The amount field in OrderSchema uses fields.Decimal(as_string=True), which adheres to the requirement that the amount should be handled as a string to maintain precision.

  13. Score for this section: 1/1

  14. Linking Schema and Dataclasses with @post_load:

  15. The solution is missing the @post_load decorator that is necessary to bind schema deserialization directly to dataclass instantiation. This linkage is significant for effectively mapping the loaded JSON data into the dataclass instances.

  16. Score for this section: 0/2

  17. Additional Aspects (Error Handling, Commenting, Execution flow):

  18. The solution effectively showcases the deserialization of JSON and the instantiation of dataclasses, but lacks any form of error handling or validations that might be necessary in a real-world scenario.
  19. There’s no commentary explaining the code, which might be helpful for clarity, especially for those new to using Marshmallow or dataclasses.
  20. Score for this section: 1.5/2

Total score:

{
  "score": 7
}

This score reflects the solution's success in addressing several key points while illustrating areas for enhancement, particularly in format-specific serialization details and linking mechanisms between schema and dataclasses.

Here's an example of how you can use the Marshmallow library to generate dataclasses from the provided JSON:

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

@dataclass
class Order:
    created: datetime = fields.DateTime(format='iso')
    amount: Decimal = fields.Decimal(as_string=True)

@dataclass
class Customer:
    first_name: str = fields.Str()
    last_name: str = fields.Str()
    date_of_birth: datetime = fields.Date(format='%m/%d/%Y')
    orders: list[Order] = fields.Nested('Order', many=True)

customer_schema = Customer.Schema()

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.load(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'))])

Here's a brief explanation of the code:

  1. We define two dataclasses: Order and Customer. The Order dataclass has two fields: created with a datetime type and amount with a Decimal type. The Customer dataclass has four fields: first_name, last_name, date_of_birth with a datetime type, and orders with a list of Order objects.
  2. We use the fields module from Marshmallow to specify the types and formats for each field. For example, created is a DateTime field with an ISO format, and amount is a Decimal field represented as a string.
  3. We create a schema for the Customer dataclass using the Customer.Schema() method.
  4. We load the JSON data into the schema using the load() method, which returns an instance of the Customer dataclass.

Note that you'll need to install the marshmallow and marshmallow-dataclass libraries to run this code. You can do this using pip: pip install marshmallow marshmallow-dataclass.

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: (6.5) ()

Here's the evaluation based on the provided marking scheme:

  1. Dataclasses Defined:
  2. Two dataclasses (Order and Customer) are defined using @dataclass from marshmallow_dataclass. This fulfills the requirement.

  3. Score: 2/2

  4. Schema Classes Defined:

  5. While there isn't a separate explicit schema class defined for each dataclass (Order and Customer), the solution does use Customer.Schema() to interact with the Customer dataclass in a manner consistent with marshmallow's features. According to the typical marshmallow_dataclass usage pattern, this is appropriate.

  6. Score: 1.5/2

  7. Date Format Specification for date_of_birth:

  8. The date_of_birth field in the Customer dataclass correctly uses the %m/%d/%Y format as specified in the problem statement.

  9. Score: 1/1

  10. Decimal Field Handling:

  11. The amount field in the Order dataclass is defined correctly with as_string=True to ensure that the decimal value is treated as a string.

  12. Score: 1/1

  13. Linking Schema and Dataclasses with @post_load:

  14. The solution does not include a @post_load method in the schema class to create instances of the dataclass. Instead, the automatic integration provided by marshmallow_dataclass is used.

  15. Score: 0/2

  16. Additional Considerations:

  17. The effective use of marshmallow_dataclass for seamless integration and transformation between JSON data and Python objects.
  18. Decent usage of fields.DateTime and fields.Decimal with correct formatting and properties.
  19. The solution lacks detailed error handling or customization that might be necessary for more complex scenarios.
  20. It misses including imports or mention of necessary fields for using list typing directly which may cause errors in certain environments or IDE setups.
  21. Score: 1/2

Overall, while the solution effectively uses marshmallow and marshmallow_dataclass to accomplish the task of serializing and deserializing JSON to dataclass instances with requisite formatting, it misses out on explicitly defined schema classes for each dataclass and the @post_load linkage. Also, certain improvements and details in explanation or error handling could enhance the quality of the implementation.

{
  "score": 6.5
}

anthropic/claude-3.5-sonnet says: (9.0) (1.04¢)

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

  1. The solution defines two dataclasses, Order and Customer. (2 marks)

  2. The solution implicitly defines two schema classes through the use of marshmallow_dataclass. While not explicitly defined, they are created when using Customer.Schema(). (2 marks)

  3. The solution correctly specifies the date format for the date_of_birth field using format='%m/%d/%Y'. (1 mark)

  4. The decimal field (amount) correctly uses as_string=True. (1 mark)

  5. The solution doesn't explicitly use a @post_load method to link the schema and dataclasses. However, marshmallow_dataclass handles this automatically. (1 mark)

  6. Additional points:

  7. The solution uses marshmallow_dataclass, which simplifies the process.
  8. It provides a complete working example with sample data and output.
  9. It includes installation instructions for required libraries.
    (2 marks)

Breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 2,
  "Date format specified": 1,
  "Decimal field as string": 1,
  "Schema and dataclasses linked": 1,
  "Additional points": 2
}

Total score:

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

The solution effectively addresses the problem, using marshmallow_dataclass to simplify the process of creating dataclasses with Marshmallow schemas. It correctly handles the date, datetime, and decimal fields as requested. The only minor point it misses is explicitly using @post_load, but this is handled automatically by marshmallow_dataclass. Overall, it's a very good solution that demonstrates a solid understanding of using Marshmallow with dataclasses.

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