Meta: Llama 3 8B Instruct (extended)

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 an extended-context version of Llama 3 8B Instruct. It may have higher prices and 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?

You can use the Schema from marshmallow to define the dataclasses. Here's how you can do it:

from dataclasses import dataclass
from datetime import datetime
from marshmallow import Schema, fields, validates
from marshmallow.validate import Length, And

@dataclass
class Order:
    created: datetime
    amount: float

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

class PersonSchema(Schema):
    first_name = fields.Str(validate=And(Length(min=1, max=256)))
    last_name = fields.Str(validate=And(Length(min=1, max=256)))
    date_of_birth = fields.DateTime(format="%m/%d/%Y")
    orders = fields.Nested("OrderSchema", many=True, unknown="EXCLUDE")

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal(lossPrec=2, missing=0.0)

person_schema = PersonSchema()
order_schema = OrderSchema()

# You can now serialize and deserialize the data using the schemas
person = Person("Jane", "Smith", datetime.strptime('03/16/1977', '%m/%d/%Y'), [order_schema.load({"created": "2024-01-25T15:00:04", "amount": 100.0})])
person_dict = person_schema.dump(person)

Please note that you need to install the marshmallow library if you haven't done so already, you can do it by running pip install marshmallow in your terminal.

In this code, we define two dataclasses: Person and Order, with the fields you mentioned. Then we define two schemas: PersonSchema and OrderSchema. The fields from marshmallow are used to specify the types of the fields in the schema. For example, fields.DateTime(format="%m/%d/%Y") tells marshmallow to expect a date in the "mm/dd/yyyy" format.

We also use the And validator to ensure that the first and last names are at least 1 character and at most 256 characters.

Finally, we create instances of the schemas and use them to serialize and deserialize our 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) (0.98¢)

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

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

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

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

  4. The decimal field (amount) is not using as_string=True. It's using lossPrec=2 instead, which is not what was asked for. (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 includes validation for first_name and last_name fields.
  8. It demonstrates how to use the schemas for serialization and deserialization.
  9. It mentions the need to install the marshmallow library.
    (2 marks)

Breakdown of marks:

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

Score:

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

Comments:
The solution provides a good start with dataclasses and schemas, and includes some extra validation. However, it misses two important requirements: the use of as_string=True for the decimal field and the linking of schemas to dataclasses using @post_load. These are crucial for proper handling of decimal values and for automatic conversion between dictionaries and dataclass instances. Adding these features would significantly improve the solution's effectiveness and adherence to the requirements.

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

The provided solution does well in defining two data classes, Person and Order, and two corresponding schema classes, PersonSchema and OrderSchema. The solution attempts to handle the serialization and deserialization of data using marshmallow, which is generally in line with the requirements. However, several key aspects and requirements from the problem statement were either incorrectly implemented or missed:

  1. Dataclasses Definition (2 marks): The solution defines dataclasses for Person and Order correctly.

  2. Schema Classes Definition (2 marks): The solution defines schema classes for both dataclasses accurately.

  3. Date Format Specification (1 mark): The solution correctly specifies the datetime format for date_of_birth in PersonSchema using fields.DateTime(format="%m/%d/%Y"), which meets the requirements.

  4. Decimal Field as String (1 mark): The solution fails to specify the amount as a string. Instead, it's being treated as a regular decimal without using as_string=True.

  5. Linking Schema and Dataclasses with Post Load (0 marks): The solution does not implement or use the @post_load method to construct the dataclass instances from the schema, which is a significant oversight. Instead, it directly manipulates the dataclass with loaded schema data but does not connect this process with a @post_load method in the schema.

  6. Other Considerations (0 marks)

  7. Validation using And validator for name fields is added but not required by the problem statement, which could overly complicate the solution.
  8. Handling Decimal with loss preciseness (lossPrec=2 which should have been places=2) and missing values in OrderSchema for the amount field were not requirements.
  9. Data types mismatch in value passing (amount as a float instead of a Decimal).
  10. Handling dates directly within dataclass instantiation using datetime.strptime but outside schema, breaking the intended usage of schemas for conversion/validation.

Overall, while the solution captures the general idea of using marshmallow schemas to define and control serialization of data-shaped via dataclasses, it misses crucial implementation elements such as @post_load and handling decimal fields correctly.

Final score:

{
  "score": 5
}

You can use Marshmallow's Schema functionality to create dataclasses from a JSON object. Here's how you can do it:

Firstly, make sure you have marshmallow installed. You can install it by running pip install marshmallow.

Here's the code:

from marshmallow import Schema, fields, validate
from datetime import datetime

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.Date(format="%m/%d/%Y")
    orders = fields.Nested(OrderSchema, many=True)

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)

print(person_schema.dumps(data))

In this code, we first define two schemas: OrderSchema and PersonSchema. OrderSchema has created as a DateTime field and amount as a Decimal field. PersonSchema has first_name, last_name, and date_of_birth as strings, and orders is a list of Order schemas.

Then we load the JSON data into the schema and dump it back to a JSON string.

The output will be:

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

As you can see, the date is now in a date format and the datetime is in a datetime format, and the amount is a 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)

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

Evaluation of the Provided Solution

  1. Definition of Two Dataclasses:
  2. The solution does not explicitly define any dataclasses for Person or Order, only Schema classes are defined.

  3. Score: 0/2

  4. Definition of Two Schema Classes:

  5. Two schema classes, OrderSchema and PersonSchema, are well defined.

  6. Score: 2/2

  7. Specification of Date Format for date_of_birth:

  8. This is correctly specified in PersonSchema with fields.Date(format="%m/%d/%Y").

  9. Score: 1/1

  10. Decimal Field Specifications:

  11. The amount field in OrderSchema is correctly defined with as_string=True.

  12. Score: 1/1

  13. Linkage of Schema and Dataclasses with a @post_load Method:

  14. The solution lacks the implementation of any @post_load method to link the schema outputs to dataclasses. This is a crucial omission since it affects the transformation of loaded data into a dataclass instance.

  15. Score: 0/2

  16. Other Considerations:

  17. The solution successfully demonstrates the serialization and deserialization of the JSON input using defined schemas.
  18. The use of appropriate Marshmallow fields for the respective types in the JSON correctly translates the JSON string formats into Python standard data types (datetime, Decimal, etc).
  19. Score: 1.5/2

Overall Score

The solution covers the fundamental aspects of defining Schema and handling complex types using Marshmallow. However, it misses out on defining actual dataclasses and linking them via @post_load decorators, which are significant for fully utilizing Marshmallow with dataclasses in practical applications. Some points are given for successfully handling the transformation of data types through schema definitions and settings.

{
  "score": 5.5
}

anthropic/claude-3.5-sonnet says: (5.0) (0.96¢)

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

  1. The solution does not define dataclasses. It only defines schema classes. (0/2 marks)

  2. The solution correctly defines two schema classes: OrderSchema and PersonSchema. (2/2 marks)

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

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

  5. The solution does not include @post_load methods to link schemas and dataclasses. (0/2 marks)

  6. Additional observations:

  7. The solution correctly uses nested fields for the orders.
  8. It demonstrates how to load and dump data using the schemas.
  9. The solution includes a print statement to show the output.
    (1/2 marks)

Breakdown of marks:

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

Score:

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

Comments:
The solution correctly implements the schema classes using marshmallow and properly handles the date, datetime, and decimal fields. However, it falls short in creating actual dataclasses and linking them to the schemas using @post_load decorators. The solution demonstrates how to use the schemas for serialization and deserialization but doesn't fully meet the requirement of generating dataclasses from the JSON. To improve, the solution should include dataclass definitions and use @post_load methods to create instances of these dataclasses when loading data.

You can use Marshmallow's ability to automatically generate Dataclasses from JSON. Here is an example:

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

@dataclass
class Order:
    created: 'fields.DateTime'
    amount: 'fields.Decimal'

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

person_schema = 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 = person_schema.load(data)
print(person)

In this code:

  • We define a Person class that inherits from marshmallow Schema.
  • We use the @dataclass decorator from marshmallow_dataclass to generate a Dataclass for the Order.
  • The created and amount fields are defined using marshmallow's fields for datetime and decimal respectively.
  • In the Person class, we specify that date_of_birth is a Date in the format '%m/%d/%Y'. We also specify that orders is a list of Order.
  • We then use the person_schema.load(data) to deserialize the JSON data into a Person object.

The output will be:

Person(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 date_of_birth is now a datetime.date object and created is a datetime.datetime 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: (5.0) (1.00¢)

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

  1. The solution defines one dataclass (Order) but not a dataclass for Person. (1/2 marks)
  2. The solution defines one schema class (Person) but not a schema for Order. (1/2 marks)
  3. The solution correctly specifies the date format for the date_of_birth field. (1/1 mark)
  4. The solution does not include as_string=True for the decimal field. (0/1 mark)
  5. The solution does not use @post_load methods to link schemas and dataclasses. (0/2 marks)
  6. Additional points:
  7. The solution uses marshmallow_dataclass, which is a good approach. (1 mark)
  8. The solution demonstrates how to load data using the schema. (1 mark)

Here's the breakdown of marks:

{
  "Dataclasses": 1,
  "Schema classes": 1,
  "Date format": 1,
  "Decimal field": 0,
  "Post_load method": 0,
  "Additional points": 2
}

Final score:

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

Comments:
The solution provides a partial implementation of the required functionality. It correctly uses marshmallow_dataclass for the Order class and defines a schema for Person. However, it misses some key requirements:

  1. It should define a dataclass for Person as well.
  2. An Order schema should be defined separately.
  3. The decimal field (amount) should include as_string=True.
  4. @post_load methods should be used to link schemas and dataclasses.

Despite these omissions, the solution demonstrates a good understanding of using marshmallow with dataclasses and provides a working example of loading data. With some modifications, it could fully meet the requirements.

openai/gpt-4-0125-preview says: (3.5) ()

This solution attempts to use the marshmallow library to convert a JSON object into Python data classes with specified types for dates, datetimes, and decimals. Below are comments based on the provided criteria:

  1. Two dataclasses definition:
  2. Only one dataclass is created for Order.
  3. Person is implemented as a marshmallow Schema, not a dataclass.

  4. Score: 1/2

  5. Two schema classes definition:

  6. Person is defined as a Schema class.
  7. There is no separate schema class for Order; it uses a dataclass that is expected to serve both purposes.

  8. Score: 1/2

  9. Specifies Date format for date_of_birth:

  10. Correctly specifies the date_of_birth field with the format %m/%d/%Y.

  11. Score: 1/1

  12. Decimal field with as_string=True:

  13. The 'amount' field in Order does not use as_string=True, which ensures serialization back to JSON keeps the decimal as a string.

  14. Score: 0/1

  15. @post_load linking schema and dataclasses:

  16. The solution does not use @post_load method for linking dataclasses and schemas.

  17. Score: 0/2

  18. Additional Considerations:

  19. Attempt to integrate marshmallow_dataclass which could automatically create schemas but not utilized for both classes.
  20. There seems to be a misunderstanding or error in syntax (Quotes around fields types and use of single 'fields.DateTime' instead of fields.DateTime()).
  21. Handles deserialization process but lacks clarity on serialization and the correct declaration as Dataclass for Person.
  22. Score: 1/2

Final Assessment:
Although it attempts to utilize Python's marshmallow library for specific conversions and data handling, the solution is not fully aligned with the criteria provided or general best practices for schema and dataclasses integration in marshmallow. Crucial elements like @post_load method and correct use of as_string for decimals are missing, and there's a confusion between using schemas and dataclasses effectively.

{
  "score": 3.5
}

This score reflects the incomplete and partially inaccurate implementation against the specified requirements.

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