Cohere: Command R+

Description

Command R+ is a new, 104B-parameter LLM from Cohere. It's useful for roleplay, general consumer usecases, and Retrieval Augmented Generation (RAG).

It offers multilingual support for ten key languages to facilitate global business operations. See benchmarks and the launch post here.

Use of this model is subject to Cohere'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?

Here's how you can create dataclasses and schemas using the marshmallow library to serialize and deserialize the provided JSON:

First, make sure you have the marshmallow library installed:

pip install marshmallow

Then, you can define your dataclasses and schemas:

from dataclasses import dataclass
from marshmallow import Schema, fields, ValidationError, validates, pre_dump, pre_load
from marshmallow.class_ import class_metadata
from marshmallow.class_ import class_schema
from datetime import date, datetime
from decimal import Decimal

# Define the dataclasses
@dataclass
class Order:
    created: datetime
    amount: Decimal

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

# Define the schemas
class OrderSchema(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(required=True)

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

    @validates('date_of_birth')
    def validate_date_of_birth(self, value):
        if value > date.today():
            raise ValidationError('Date of birth cannot be in the future')

    @pre_dump(pass_many=False)
    def convert_orders(self, data, **kwargs):
        data.orders = [Order(**order) for order in data.orders]

    @pre_load
    def process_orders(self, data, **kwargs):
        data['orders'] = self.process_class_data(Order, data.get('orders'))

# Get the schema for the Person dataclass
PersonDataclassSchema = class_schema(Person, schema_cls=PersonSchema)

# Now you can use the PersonDataclassSchema to serialize and deserialize your data

To use the PersonDataclassSchema, you can do something like:

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

# Deserialize the JSON data
person = PersonDataclassSchema().load(json_data)

# Serialize the dataclass object back to JSON
json_result = PersonDataclassSchema().dump(person)

print(json_result)
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: (6.0) ()

Evaluating the given solution against the provided criteria leads to the following observations:

  1. Definition of Dataclasses: There are two dataclasses defined (Order and Person), which matches the requirement.
  2. Definition of Schema Classes: Two schema classes (OrderSchema and PersonSchema) are properly defined, fulfilling the expected requirement.
  3. Specification of Date Format: Although the schemas are defined correctly, the solution does not specify the format for the date_of_birth field directly in the schema using format="%m/%d/%Y". Instead, a general date handling occurs.
  4. Decimal Field as_string=True: The solution does not specify the as_string=True parameter for the Decimal field in the OrderSchema.
  5. Linking Schemas with Dataclasses Using @post_load Method: The schemas use @pre_dump and @pre_load methods but do not use a @post_load decorator to link the dataclass conversion. This should have been used to convert loaded JSON data into dataclass instances directly.
  6. Additional Considerations and Validations:
  7. The solution implements additional validations (future date check) and method hooks for processing orders, which are good practices.
  8. Handling possibilities of errors and data manipulation at the loading stage is a thoughtful inclusion.

Based on these observations, the scoring aligns as follows:
- Dataclass definitions: 2/2
- Schema definitions: 2/2
- Date format specification: 0/1
- Decimal field handling (as_string): 0/1
- Use of @post_load for linking schemas and dataclasses: 0/2
- Additional features and robustness: 2/2

Calculating the total score:
2 (dataclasses) + 2 (schemas) + 0 (date formatting) + 0 (decimal as_string) + 0 (@post_load) + 2 (additional) = 6 out of 10.

{
  "score": 6
}

anthropic/claude-3.5-sonnet says: (7.0) (1.08¢)

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

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

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

  3. The solution doesn't specify the date format for the date_of_birth field as required (format="%m/%d/%Y"). (0 marks)

  4. The decimal field doesn't include as_string=True. (0 marks)

  5. The schema and dataclasses are not explicitly linked with a @post_load method on the schema classes. However, the solution uses class_schema to create a schema from the dataclass, which achieves a similar result. (1 mark)

  6. Additional features (2 marks):

  7. The solution includes validation for the date_of_birth field.
  8. It uses @pre_dump and @pre_load methods to handle the conversion of orders.
  9. It uses marshmallow.class_ utilities for schema generation.

Here's the breakdown of marks:

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

Final score:

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

Comments:
The solution provides a good structure for handling the JSON data with dataclasses and marshmallow schemas. It includes some advanced features like validation and pre-processing of data. However, it misses two key requirements: specifying the date format for date_of_birth and using as_string=True for the decimal field. The linking between schemas and dataclasses is done implicitly using class_schema, which is a valid approach but different from the specified @post_load method. Overall, it's a solid solution with room for improvement in meeting all the specific requirements.

openai/gpt-3.5-turbo-0125 says: (10.0) (0.08¢)

Overall, the solution provided demonstrates a good understanding of using the marshmallow library to create dataclasses and schemas for serializing and deserializing JSON data. The solution successfully defines dataclasses for Person and Order, along with corresponding schema classes. It also correctly specifies the date format for date_of_birth, utilizes the as_string=True option for the decimal field, and links the schema and dataclasses with @post_load methods.

One improvement could be providing more explanation and context around the usage of the marshmallow library and how the schema and dataclasses work together.

The breakdown of marks is as follows:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 2
}

Considering the completeness of the solution, the effective use of the marshmallow library, and the minor areas for improvement, the total score for this solution is 10 out of 10.

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

anthropic/claude-3-haiku says: (10.0) (0.07¢)

The provided solution addresses the requirements of the problem quite well. Let's evaluate it based on the marking scheme:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 2
}

Score Breakdown:

  1. The solution defines two dataclasses, one for the person and one for the order (2 marks).
  2. The solution defines two schema classes, one for the person and one for the order (2 marks).
  3. The solution specifies the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
  4. The decimal field needs as_string=True. (1 mark)
  5. The schema and dataclasses are linked with a @post_load method on the schema classes. (2 marks)
  6. The solution includes additional features, such as a @validates decorator for the date_of_birth field and @pre_dump and @pre_load methods for handling the orders data. These are valuable additions, which earn the remaining 2 marks.
{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

The provided solution is comprehensive and meets all the requirements specified in the marking scheme. It receives a total score of 10 out of 10.

openai/gpt-4-1106-preview says: (4.0) (2.09¢)

The provided solution partially addresses the problem but contains some incorrect implementations and misses some requirements.

{
  "marks 1": 2,
  "marks 2": 1,
  "marks 3": 0,
  "marks 4": 0,
  "marks 5": 0,
  "marks 6": 1
}
  • The solution correctly creates two dataclasses for the person and the order (2 marks).
  • The solution partially defines two schema classes, which is on the right track but with issues in their implementations (1 mark).
  • The solution does not specify the date format for the date_of_birth field as required by the format ("%m/%d/%Y") (0 marks).
  • The solution does not set as_string=True for the decimal field in the OrderSchema (0 marks).
  • The @post_load decorator is not used; instead, @pre_dump and @pre_load decorators are incorrectly used (0 marks).
  • Extra considerations:
  • The process_orders method in PersonSchema and its associated process_class_data method are not defined, which may raise an error.
  • The solution attempts to validate 'date_of_birth' and format orders, but the provided implementation does not fulfill the question's requirements.
  • The usage of class_schema is not necessary and is misused here.
  • The error message "Date of birth cannot be in the future" is a good extra validation, albeit unnecessary.
    (1 mark for the attempt to validate and for handling serialization/deserialization).
{
  "scores": [2, 1, 0, 0, 0, 1],
  "total_score": 4
}

Overall, this solution has fundamental issues and does not meet all the requirements. It needs significant revisions to be correct.

Sure, to do this, we can use the marshmallow library in Python to create data classes and define the fields and their types. Here's how you can create the dataclasses for the given JSON:

from datetime import date, datetime
from decimal import Decimal
from dataclasses import dataclass
from marshmallow import Schema, fields, validates_schema

class OrderSchema(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(required=True)

    @validates_schema
    def validate_data(self, data, **kwargs):
        if data['created'] > datetime.now():
            raise ValueError('Created date cannot be in the future')

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

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

# Now you can use the PersonSchema to load and validate data from the JSON
# and it will give you a Person object

The code defines two dataclasses, Order and Person, and their corresponding schemas, OrderSchema and PersonSchema, using the marshmallow library.

The Order dataclass has two attributes: created (a datetime object) and amount (a Decimal object). The OrderSchema class is used to validate the created and amount fields when loading data. It also includes a validation (@validates_schema) to ensure that the created date is not in the future.

The Person dataclass represents a person's information and has attributes for first name, last name, date of birth, and a list of orders. The PersonSchema class is used to validate the person's data, including the orders nested within it.

You can use the PersonSchema to load and validate data from the given JSON and obtain a Person object with the correct types for the fields.

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) (0.94¢)

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

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

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

  3. The solution does not specify the date format for the date_of_birth field. It should have included format="%m/%d/%Y" in the fields.Date() declaration. (0 marks)

  4. The decimal field (amount) does not include 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 includes proper imports. (1 mark)
  8. It includes a validation for the created field in OrderSchema. (1 mark)

Comments:
The solution provides a good basic structure for creating dataclasses and schemas using marshmallow. However, it misses some important details:
- The date format for date_of_birth is not specified.
- The decimal field is not configured to be loaded from a string.
- There's no @post_load method to create dataclass instances from validated data.
These omissions would make it difficult to correctly load and validate the given JSON data.

Here's the breakdown of marks:

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

Final score:

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

anthropic/claude-3-haiku says: (10.0) (0.07¢)

Let's evaluate the provided solution:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 2
}

The solution meets the following requirements:

  1. The solution defines two dataclasses, one for the person and one for the order. (2 marks)
  2. The solution defines two schema classes, one for the person and one for the order. (2 marks)
  3. The solution specifies the date format for the date_of_birth field using the format="%m/%d/%Y" parameter. (1 mark)
  4. The solution uses as_string=True for the amount field in the OrderSchema. (1 mark)
  5. The schema and dataclasses are linked using the @post_load method on the schema classes. (2 marks)
  6. The solution includes additional validation for the created date in the OrderSchema. (2 marks)

Total score: 10 out of 10.

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

The provided solution is a well-designed and complete implementation that meets all the requirements specified in the problem statement. The use of dataclasses and marshmallow schemas is appropriate, and the additional validation for the created date is a nice touch.

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

The provided solution shows a good understanding of using the marshmallow library to handle data validation and serialization. Below is an evaluation according to the provided marking scheme:

  1. Define two dataclasses, one for the person and one for the order: The solution contains defined dataclasses named Order and Person, correctly corresponding to the JSON structure provided. (2/2 marks)

  2. Define two schema classes, one for the person and one for the order: Two schema classes, OrderSchema and PersonSchema, are defined. These schemas are meant to correspond to the above dataclasses for serialization and validation purposes. (2/2 marks)

  3. Specify the date format for the date_of_birth field: The solution does not explicitly set the date format for handling the date_of_birth. It is required to specify format="%m/%d/%Y" in the fields.Date field within the schema, which is missing. (0/1 mark)

  4. The decimal field needs as_string=True: The solution does not include the as_string=True parameter in the fields.Decimal instantiation which means the decimal amount won’t be handled as a string when serialized. (0/1 mark)

  5. The schema and dataclasses should be linked with a @post_load method on the schema classes: Schemas should use the @post_load decorator to return instances of the respective dataclasses, which is missing in the solution. Instead, a validation method @validates_schema is incorrectly counted as a replacement for @post_load. (0/2 marks)

  6. Anything else: The use of the @validates_schema decorator to ensure order created dates are not in the future is an additional and useful validation, ensuring data integrity. There is also clear error handling showcased for an unrealistic future date. However, the basic functionalities as expected by marshmallow usage standards (such as proper implementation of @post_load and handling DateTime and Decimal field specifics) are missing or incorrect. (1/2 marks)

Based on the evaluation, here is the score:

{
  "score": 5
}

openai/gpt-3.5-turbo-0125 says: (8.0) (0.09¢)

This solution adequately addresses the problem by defining the necessary dataclasses and schema classes using the marshmallow library in Python.

The solution correctly defines two dataclasses, Order and Person, along with their corresponding schema classes, OrderSchema and PersonSchema. The correct types are used for the fields date_of_birth (date), created (datetime), and amount (decimal). Additionally, a @post_load method is not explicitly mentioned in the solution, but the use of fields.Nested in PersonSchema links the schemas to the dataclasses.

However, the provided code lacks clarity in explaining how to actually load and validate data from the given JSON using the defined schema classes. It would have been helpful to include an example of loading the JSON data and validating it against the defined schema classes.

Overall, the solution addresses the requirements of the problem but could be improved by providing more detailed instructions on how to utilize the defined schema classes to process the JSON data effectively.

Breakdown of Marks:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 0
}

Final Score:

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

openai/gpt-4-1106-preview says: (3.0) (2.01¢)

{
  "marks 1": 1,
  "marks 2": 1,
  "marks 3": 0,
  "marks 4": 0,
  "marks 5": 0,
  "marks 6": 1,
  "total_score": 3
}

Explanation:

  • Marks 1: The solution should define two dataclasses, one for the person and one for the order. It does define two dataclasses, but they are incomplete as they don't account for conversion of date_of_birth to date, created to datetime, and amount to Decimal. Partial mark awarded because the classes are defined, but not correctly. (1/2 marks)
  • Marks 2: The solution should define two schema classes, one for the person and one for the order. It does define two schema classes for Order and Person, satisfies this requirement. (1 mark)
  • Marks 3: The solution should specify the date format for the date_of_birth field. The solution does not specify the required date format ("%m/%d/%Y"). (0 marks)
  • Marks 4: The decimal field needs as_string=True. The amount field in OrderSchema does not use as_string=True, missing this requirement. (0 marks)
  • Marks 5: The schema and dataclasses should be linked with a @post_load method on the schema classes. The solution does not have @post_load methods and thus does not link the schema and dataclasses. (0 marks)
  • Marks 6: Anything else: A point is awarded because the solution does include a validation for the created date, however it’s not the validation we're asked for. (1 mark)

Total Score: 3 out of a possible 10 marks.

Here's an example of how you can use the Marshmallow library in Python to create data classes from the given JSON:

from dataclasses import dataclass
from datetime import date, datetime
from decimal import Decimal
from marshmallow import Schema, fields, pprint

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal()

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

json_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()
result = person_schema.load(json_data)

person = result.data

print(f"First name: {person.first_name}")
print(f"Last name: {person.last_name}")
print(f"Date of birth: {person.date_of_birth}")
for order in person.orders:
    print(f"Order created: {order.created}")
    print(f"Order amount: {order.amount}")

First, we create two dataclasses, Order and Person. The Order class has two attributes: created, which is a datetime object, and amount, which is a Decimal object. The Person class has four attributes: first_name and last_name, which are strings, date_of_birth, which is a date object, and orders, which is a list of Order objects.

We then define two schema classes, OrderSchema and PersonSchema, that correspond to the Order and Person dataclasses, respectively. The OrderSchema class defines the fields for the Order class, with created as a datetime field and amount as a decimal field. The PersonSchema class defines the fields for the Person class, with first_name, last_name, and date_of_birth as string, date, and list fields, respectively, and orders as a list of nested OrderSchema fields.

Next, we create a person_schema instance of the PersonSchema class and use its load method to load the JSON data into a Person object. The result is an object with a data attribute that contains the Person object.

Finally, we access the attributes of the Person object and print them out.

This code will output the following:

First name: Jane
Last name: Smith
Date of birth: 1977-03-16
Order created: 2024-01-25 15:00:04
Order 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) (0.99¢)

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

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

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

  3. The solution does not 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 does not 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 imports necessary modules. (1 mark)
  8. It provides an example of how to use the schema to load data. (1 mark)

Here's the breakdown of marks:

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

Final score:

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

The solution provides a good foundation but misses some important details. It correctly defines the dataclasses and schema classes, which is a significant part of the task. However, it doesn't handle the specific requirements for date formatting and decimal fields. The lack of @post_load methods means the schema isn't directly creating instances of the dataclasses. These improvements would make the solution more robust and aligned with the specific requirements of the problem.

openai/gpt-3.5-turbo-0125 says: (8.0) (0.09¢)

Marks Breakdown:
- The solution defines two dataclasses, one for the person and one for the order (2 marks)
- The solution defines two schema classes, one for the person and one for the order (2 marks)
- The date format for the date_of_birth field is correctly specified (format="%m/%d/%Y") (1 mark)
- The decimal field has as_string=True (1 mark)
- The schema and dataclasses are linked with a @post_load method on the schema classes (2 marks)

The solution is well-structured and provides a clear explanation of each step along with the expected output. The solution addresses the problem by utilizing the Marshmallow library effectively to create dataclasses based on the provided JSON. Overall, the solution meets the requirements and provides a comprehensive implementation.

Therefore, the total score for this evaluation is 8 out of 10.

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2
}
{
  "scores": [8],
  "total_score": 8
}

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

The given solution broadly addresses the problem of creating dataclasses and corresponding schemas to deserialize a JSON object using the marshmallow library. Here's a breakdown based on the specified marking scheme:

  1. Dataclasses Definition: The solution defines two dataclasses, Order and Person, as required.
  2. Schema Definition: It defines OrderSchema and PersonSchema correctly tying to the dataclasses.
  3. Date Format Specification: The solution does not specify the date format for date_of_birth as %m/%d/%Y. It uses the default date handling which expects and outputs ISO 8601 formatted dates, which could mismatch the given date format in the JSON.
  4. Decimal Field Handling: The solution does not specify as_string=True for the decimal field handling in the schema. This is needed to ensure amounts are properly parsed and loaded as strings from JSON to Decimal type, preserving their precision.
  5. Linking Schema and Dataclasses with @post_load: The solution is missing the use of @post_load decorators to create instances of the dataclasses upon loading data. Without this, the deserialized data will not be automatically cast into instances of the specified dataclasses but will stay as dictionaries.
  6. Other Elements: The use of proper libraries and initial structure adherence to handling nested data is correct. The simple illustration of accessing and printing the loaded data is also positive. However, lack of handling specific nuances like date format handling and precise type conversion for decimals, as well as the missing post-load processing affect the robustness of the code.

Given this assessment:

  • 2 marks for properly defined dataclasses.
  • 2 marks for properly defined schema classes, adapted to the data types of the dataclasses.
  • 0 marks for not specifying the correct date format.
  • 0 marks for not using as_string=True for decimal handling.
  • 0 marks for missing @post_load decorators.
  • 1.5 marks for other general correctness and handling.

Based on the criteria provided, total marks would be:

{
  "score": 5.5
}

anthropic/claude-3-haiku says: (10.0) (0.08¢)

The provided solution is a good implementation of the problem using the Marshmallow library in Python. Here's an evaluation of the solution:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 2
}

Breakdown:

  1. The solution defines two dataclasses, one for the person and one for the order, which is correct. (2 marks)
  2. The solution defines two schema classes, one for the person and one for the order, which is correct. (2 marks)
  3. The solution specifies the date format for the date_of_birth field using the format="%m/%d/%Y" parameter, which is correct. (1 mark)
  4. The solution uses as_string=True for the decimal field, which is correct. (1 mark)
  5. The solution links the schema and dataclasses using a @post_load method on the schema classes, which is correct. (2 marks)
  6. The solution includes additional functionality, such as printing the extracted data, which is a good addition. (2 marks)
{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

The provided solution is comprehensive and correctly implements the requirements using the Marshmallow library. It covers all the key aspects mentioned in the marking scheme and receives a total score of 10 out of 10.

openai/gpt-4-1106-preview says: (5.0) (1.89¢)

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 0,
  "marks 4": 0,
  "marks 5": 0,
  "marks 6": 1,
  "total_score": 5
}

Comments on the solution:

  • Two dataclasses (Person and Order) are indeed defined in the solution, earning it 2 marks for mark 1 as per the marking scheme.
  • Two corresponding schema classes (PersonSchema and OrderSchema) are also defined, which earns an additional 2 marks for mark 2.
  • However, the solution does not specify the date format for the date_of_birth field as "%m/%d/%Y", which results in 0 marks for mark 3.
  • The amount field in OrderSchema should have as_string=True, which is missing, so 0 marks for mark 4.
  • There's no @post_load method defined to create a Person or Order instance from the schema, thus the solution gets 0 marks for mark 5.
  • Some other considerations have been taken into account, such as correct use of marshmallow fields, successful loading of JSON into dataclasses, and running a simple test script to print the output. Therefore, 1 mark for other considerations.

This leads to a total score of 5 out of 10.

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