Meta: Llama 3 8B Instruct

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.

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 (10.0)

You can achieve this using the marshmallow library. Here is an example:

import datetime
from marshmallow import Schema, fields, post_load

class OrderSchema(Schema):
    created = fields.DateTime(format='iso8601')
    amount = fields.Decimal(as_string=True)

@post_load
def transform_order(obj):
    return Order(**obj)

class Order:
    def __init__(self, created, amount):
        self.created = created
        self.amount = amount

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)

    @post_load
    def transform_person(obj):
        return Person(**obj)

class Person:
    def __init__(self, first_name, last_name, date_of_birth, orders):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = date_of_birth
        self.orders = orders

# Creating an instance of the schema
person_schema = PersonSchema()

# Deserializing the JSON string into a Python object
person = person_schema.load({
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
})

print(person)

In the example above, we define two schema classes, OrderSchema and PersonSchema, and two Python classes, Order and Person. The OrderSchema defines the structure of the Order class, and the PersonSchema defines the structure of the Person class. The post_load function is used to transform the deserialized object into an instance of the Python class.

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: (10.0) (0.90¢)

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

The solution defines two dataclasses, Person and Order. (2 marks)
The solution defines two schema classes, PersonSchema 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) is correctly specified with as_string=True. (1 mark)
The schema and dataclasses are linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution correctly uses fields.DateTime(format='iso8601') for the created field.
The solution demonstrates how to use the schema to deserialize JSON data.
(2 marks)

Breakdown of marks:

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

Score:

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

This solution correctly addresses all the requirements and demonstrates a good understanding of using marshmallow for data serialization and deserialization. It effectively creates dataclasses and schema classes, handles date and decimal fields appropriately, and uses the @post_load decorator to transform deserialized data into Python objects. The additional demonstration of how to use the schema for deserialization is a helpful touch.

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

The provided solution demonstrates the use of the Marshmallow library to deserialize JSON data into Python objects using schemas and data classes. Here’s the evaluation based on the marking scheme:

Defining Two Dataclasses (Person and Order): The solution has correctly defined two dataclasses, Person and order. These classes are used to encapsulate the data retrieved from JSON.
Score: 2/2
Defining Two Schema Classes (PersonSchema and OrderSchema): The solution includes both PersonSchema and OrderSchema, correctly aligning with the requirements of marshmallow for serialization and deserialization of the data.
Score: 2/2
Specifying Date Format for date_of_birth: The PersonSchema correctly defines the date_of_birth field with the specific date format ("%m/%d/%Y"). This format correctly interprets the string from the JSON into a Python date object.
Score: 1/1
Decimal Field with as_string=True: The solution properly sets as_string=True for the amount field in the OrderSchema, ensuring that the decimal values are correctly handled as strings during serialization to maintain precision.
Score: 1/1
Linking Schema and Dataclasses with @post_load Method: The solution does attempt to use @post_load decorator; however, it incorrectly places the decor

Response 2 (6.0)

Here is an example of how you can use the Marshmallow library to generate dataclasses from your JSON data:

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

pip install marshmallow

Then, here is the code:

from marshmallow import Schema, fields, DATE, DateTime, Decimal

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, default=[], missing=[])

person_schema = PersonSchema()
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(json.loads(data))

print(person)

In this code:

OrderSchema is a schema to validate and serialize/deserialize Order objects.
PersonSchema is a schema to validate and serialize/deserialize Person objects.
fields.DateTime is used to ensure that the created field is a datetime object and fields.Decimal is used to ensure that the amount field is a decimal number.
fields.Date is used to ensure that the date_of_birth field is a date object.
The fields.Nested field is used to create a nested schema for the orders field and set it as the default value to an empty list when it's missing from the input data.
The person_schema.load method is used to deserialize the JSON data into a Person object.

When you run this code, it will output:

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=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: (6.0) (0.98¢)

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

The solution doesn't explicitly define two dataclasses for the person and order. It only defines schemas. (0/2 marks)
The solution correctly defines two schema classes: OrderSchema and PersonSchema. (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 solution doesn't include @post_load methods to link the schemas and dataclasses. (0/2 marks)
Additional points:
The solution imports necessary modules from marshmallow.
It demonstrates how to use the schema to load and deserialize JSON data.
It shows the expected output format.
(2/2 marks)

Comments:
The solution provides a good implementation of marshmallow schemas for the given JSON structure. It correctly handles the date, datetime, and decimal fields as required. However, it doesn't create actual dataclasses, which was a key requirement. The solution also misses the @post_load decorator to create instances of dataclasses from the deserialized data. Despite these omissions, the solution demonstrates a good understanding of marshmallow's usage for data validation and deserialization.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 1,
  "Decimal field definition": 1,
  "Schema and dataclasses linking": 0,
  "Additional points": 2
}

Final score:

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

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

The provided solution partially handles the task of transforming JSON data into Python objects using the Marshmallow library, but there are notable deficiencies and inaccuracies in the approach:

Dataclasses Definition (0/2 marks).
The solution does not define dataclasses for either Person or Order, which are essential to fully encapsulate the data structure in a Pythonic way. Instead, it only defines schema classes.
Schema Classes Definition (2/2 marks).
It properly defines OrderSchema and PersonSchema to handle the deserialization of corresponding JSON structures into Python objects.
Date Formats Specification (1/1 marks).
The PersonSchema correctly specifies the date format for date_of_birth using fields.Date(format='%m/%d/%Y') which matches the required format.
Handling Decimal Types (1/1 marks).
The solution applies fields.Decimal(as_string=True) for the amount in OrderSchema, ensuring the decimal amount is treated as a string during serialization/deserialization.
Linking Schema to Dataclasses Via @post_load (0/2 marks).
The solution lacks the use of @post_load method (or similar functionality) to transform loaded data into instances of dataclasses. There is no logic provided to instantiate Python objects (from potentially validated data) corresponding to the specified schemas.
Additional Aspects (0/2 marks).
The explanation of the code's function within its context is somewhat helpful, yet key areas such as error handling, robust use of nested schemas, and the management of default values are omitted.
The special handling of missing fields (default=[], missing=[]) in PersonSchema is noted but not counted additionally because the primer solution structure is flawed without actual Python classes.

Overall, the solution demonstrates a basic use of the Marshmallow library for data validation and deserialization but fails in terms of advanced schema-to-object mapping, such as utilizing Marshmallow's support for dataclasses which would make it a more robust and Pythonic solution.

Final Score:

{
  "score": 4
}

Response 3 (3.0)

Here's how you can accomplish this:

import datetime
from marshmallow import fields, Schema

class OrderSchema(Schema):
    created = fields.DateTime(format='iso8601')
    amount = fields.Decimal(decimal_places=2)

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date('%d-%m-%Y')

person_schema = PersonSchema()
order_schema = OrderSchema()

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)

orders = [order_schema.load(order) for order in data.get('orders', [])]
print(orders)

In the above script, we define two schemata: OrderSchema and PersonSchema. We use Marhsmallow's built-in fields to define the fields in these schemata.

We then create instances of these schemata and use them to deserialize the JSON data.

The loads method is used to load JSON data into an instance of a schema.

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: (3.0) ()

Evaluation of the Solution:

Defining two dataclasses:
The solution does not define any Python dataclasses. A dataclass would typically use Python's built-in @dataclass decorator from the dataclasses module, and it is not seen here. Thus, no marks are awarded for dataclasses.
[0/2 points]
Defining two schema classes:
The solution correctly defines two schema classes: OrderSchema and PersonSchema, using the Marshmallow library.
[2/2 points]
Specification of the date format for date_of_birth:
The solution specifies a date format, but incorrectly uses %d-%m-%Y instead of the required %m/%d/%Y as per the originally provided date_of_birth. Therefore, this specification is incorrect.
[0/1 points]
Decimal field needs as_string=True:
The decimal field in the schema is declared but does not include the as_string=True parameter. This is necessary to ensure that serialized decimal values remain as strings, but it is missing in the provided schema.
[0/1 points]
Linking Schema and Dataclasses with @post_load method:
No @post_load method is implemented in the solution to instantiate dataclass instances upon schema loading. The use of @post_datetime decorator is crucial for correct operation of integrating schemas and dataclasses.
[0/2 points]
Additional considerations:
The use of loads method was mentioned but not applied; instead, load() is used, which is theoretically okay for loading Python dictionaries instead of JSON strings, but explaining this difference would be helpful.
Proper exception handling, validation errors handling or comments explaining each part could have been additional strengths.
[1/2 points]

Total Score

{
  "score": 3
}

The provided solution correctly defines Marshmallow schemas and manages basic serialization, but it fails to meet critical requirements including data validation, proper data format specification, incorporation of @post_load to utilize dataclasses effectively, and handling decimals as strings. This significantly limits the effectiveness of the solution in a real-world scenario.

anthropic/claude-3.5-sonnet says: (3.0) (0.85¢)

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

The solution does not define any dataclasses. It only defines schema classes. (0/2 marks)
The solution correctly defines two schema classes: OrderSchema and PersonSchema. (2/2 marks)
The date format for the date_of_birth field is incorrect. It uses '%d-%m-%Y' instead of the required '%m/%d/%Y'. (0/1 mark)
The decimal field (amount) is not specified with as_string=True. (0/1 mark)
The solution does not implement @post_load methods to link the schema and dataclasses. (0/2 marks)
Additional points:
The solution correctly uses fields.DateTime for the 'created' field.
It uses fields.Decimal for the 'amount' field, which is appropriate.
The solution demonstrates how to use the schemas to deserialize data.
(1/2 marks)

Overall, the solution partially addresses the requirements but misses several key points, particularly the use of dataclasses and the correct configuration of some fields.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field configuration": 0,
  "Schema and dataclasses linking": 0,
  "Additional points": 1
}

Final score:

{
  "scores": [0, 2, 0, 0, 0, 1],
  "total_score": 3
}

The solution scores 3 out of 10 points.

1.00	0.80	0.86
0.80	1.00	0.85
0.86	0.85	1.00

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