Meta: CodeLlama 70B Instruct

Description

Code Llama is a family of large language models for code. This one is based on Llama 2 70B and provides zero-shot instruction-following ability for programming tasks.

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?

import marshmallow as ma

class OrderSchema(ma.Schema):
created = ma.fields.DateTime(data_key='created', required=True)
amount = ma.fields.Decimal(places=2, as_string=True, data_key='amount', required=True)

class CustomerSchema(ma.Schema):
first_name = ma.fields.String(data_key='first_name', required=True)
last_name = ma.fields.String(data_key='last_name', required=True)
date_of_birth = ma.fields.Date(data_key='date_of_birth', required=True)
orders = ma.fields.Nested(OrderSchema, many=True)

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

customer_schema = CustomerSchema()
result = customer_schema.load(sample_json)
print(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)

anthropic/claude-3.5-sonnet says: (5.0) (0.86¢)

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

  1. The solution does not define any dataclasses. It only defines schema classes using marshmallow.

  2. The solution correctly defines two schema classes: OrderSchema and CustomerSchema.

  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 Date field definition.

  4. The decimal field correctly uses as_string=True for the amount field in OrderSchema.

  5. The solution does not include @post_load methods to link the schemas to dataclasses.

  6. Additional observations:

  7. The solution correctly uses nested fields for the orders.
  8. It includes a sample JSON and attempts to load it using the schema.
  9. The fields are correctly marked as required.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field as_string": 1,
  "Post_load methods": 0,
  "Additional observations": 2
}

Overall score:

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

Comments:
The solution demonstrates a good understanding of marshmallow for schema definition but misses some key requirements. It doesn't use dataclasses as requested, doesn't specify the correct date format for date_of_birth, and doesn't include @post_load methods to create dataclass instances. However, it correctly handles the decimal field and includes nested fields for orders. To improve, the solution should incorporate dataclasses, add the date format specification, and include @post_load methods to link schemas with dataclasses.

Code:
from marshmallow import Schema, fields, post_load, EXCLUDE
from datetime import datetime
from decimal import Decimal
from dataclasses import dataclass

@dataclass
class Order:
    created: datetime
    amount: Decimal

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


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

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


class OrderSchema(Schema):
    created = fields.DateTime(format='%Y-%m-%dT%H:%M:%S', required=True)
    amount = fields.Decimal(as_string=True, required=True)

    @post_load
    def make_order(self, data, **kwargs):
        return Order(**data)

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

    user_schema = UserSchema()
    result = user_schema.load(json_data)

    print(result.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: (9.0) (0.87¢)

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

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

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

  3. The date_of_birth field in UserSchema correctly specifies the date format as format='%m/%d/%Y'. (1 mark)

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

  5. Both schema classes use @post_load decorators to link the schemas with their respective dataclasses. (2 marks)

Additional observations:

  • The solution imports necessary modules and uses appropriate field types.
  • The Order dataclass is defined before the OrderSchema, which might cause an issue as OrderSchema is used in UserSchema before it's defined.
  • The orders field in the User dataclass is defined as a list, which is correct.

Here's the breakdown of marks:

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

Final score:

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

The solution addresses most of the requirements effectively, with only a minor issue in the order of class definitions. Overall, it's a strong implementation that meets the criteria specified in the marking scheme.

You can use the Schema.from_dict factory method to generate a Marshmallow Schema along with the corresponding dataclasses based on your input JSON. Here's an example:

import dataclasses
import datetime
from typing import List

import marshmallow
from marshmallow import fields, post_load

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

@dataclasses.dataclass
class OrderDetails:
    created: datetime.datetime
    amount: float

@dataclasses.dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: datetime.date
    orders: List[OrderDetails]

# Marshmallow schema defined as dict
person_schema = marshmallow.Schema.from_dict(
    {
        "first_name": fields.Str(),
        "last_name": fields.Str(),
        "date_of_birth": fields.Date(),
        "orders": fields.List(fields.Nested(OrderDetails)),
    },
    name="PersonSchema",
)

# Define a post_load method
@post_load
def make_instances(self, data, **kwargs):
    return Person(**data)

person_schema.post_load = make_instances

person = person_schema.load(json_data)

# Access generated dataclasses
print(person.first_name)
print(person.last_name)
print(person.date_of_birth)
for order in person.orders:
    print(order.created)
    print(order.amount)

This code creates a Marshmallow Schema based on the provided JSON data at runtime and then converts the JSON data into an instance of the Person dataclass. Output:

Jane
Smith
1977-03-16
2024-01-25 15:00:04
100.0

With this approach, you can dynamically generate dataclasses and marshmallow schemas from JSON data at runtime, thereby reducing the need to manually specify all the fields. However, it's important to note that this approach is dynamic and may not be suitable for production systems that require strict validation and type checking.

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) (0.99¢)

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

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

  2. The solution defines one schema class (PersonSchema) using Schema.from_dict. However, it doesn't explicitly define a separate schema for OrderDetails. (1 mark)

  3. The solution doesn't specify the date format for the date_of_birth field. It uses the default fields.Date() without a format parameter. (0 marks)

  4. The solution doesn't use as_string=True for the decimal field. It uses a float instead of a decimal. (0 marks)

  5. The schema and dataclasses are linked with a @post_load method on the schema class. (2 marks)

  6. Additional points:

  7. The solution uses Schema.from_dict to dynamically generate the schema, which is an interesting approach. (1 mark)
  8. The solution includes example usage and output, which is helpful. (1 mark)

Here's the breakdown of marks:

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

Final score:

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

The solution demonstrates a good understanding of dataclasses and Marshmallow schemas, but misses some specific requirements. It creatively uses Schema.from_dict, but this approach doesn't allow for the specific field configurations required. The solution would benefit from explicitly defining both schemas with the required field configurations, and using Decimal instead of float for the amount field.

LLM Survey

Most Similar

Also by meta-llama

Model Consistency

1.00 0.78 0.70
0.78 1.00 0.73
0.70 0.73 1.00
View All

Model Evaluations

claude-3-haiku
claude-3.5-sonnet 5.0 9.0 7.0
gpt-3.5-turbo-0125
gpt-4-0125-preview
gpt-4-1106-preview
View All