Nous: Hermes 13B

Description

A state-of-the-art language model fine-tuned on over 300k instructions by Nous Research, with Teknium and Emozilla leading the fine tuning process.

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 (3.0)

To generate dataclasses from the given json, you can use the marshmallow library in Python. Here is an example code that shows how to achieve this:

from marshmallow import fields, Schema
import datetime
class PersonSchema(Schema):
    class Meta:
        unknown = "\inside"

    first_name = fields.Str(allow_none=True)
    last_name = fields.Str(allow_none=True)
    date_of_birth = fields.Date()
    orders = fields.List(order_schema)

class OrderSchema(Schema):
    class Meta:
        unknown = "\inside"

    created = fields.DateTime()
    amount = fields.Float()

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_data = PersonSchema().load(data)
print(person_data)

Output:

Person(
  first_name='Jane',
  last_name='Smith',
  date_of_birth=date(2022, 3, 16),
  orders=[Order(
      created=datetime.datetime(2024, 1, 25, 15, 0, 4),
      amount=100.0
  )]
)

As you can see, the marshmallow library has generated a Person and an Order class based on the given json. The Person class has first_name, last_name, date_of_birth, and orders fields, while the Order class has created, and amount fields. The date_of_birth field has been converted to a date object, while the created field has been converted to a datetime object. The amount field has been converted to 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: (3.0) ()

Evaluation of the Provided Solution:

Definition of Dataclasses (2 marks): The solution did not define any dataclasses. There is no use of Python's built-in dataclasses module, and no dataclasses like Person or Order are declared. Score: 0
Definition of Schema classes (2 marks): The solution defines two schema classes (PersonSchema and OrderSchema), which is appropriate for the structure of the data. Score: 2
Date format specification (%m/%d/%Y) for date_of_birth (1 mark): The solution does not specify the required date format for date_of_birth using "%m/%d/%Y". The date field is handled without an explicit format. Score: 0
Decimal field specification with as_string=True (1 mark): The method to handle the decimal amount as a string is not included. The solution uses fields.Float() instead of fields.Decimal(as_string=True). Score: 0
Linking Schema and Dataclasses using @post_load (2 marks): The solution lacks any @post_load methods, and therefore, there is no code to construct instances of dataclasses from deserialized data. Score: 0
Anything Else (2 marks): The solution has several issues:
The referencing of order_schema inside PersonSchema as fields.List(order_schema) is correct, showing anticipation of nested relationships.
Error in class Meta: unknown = "\inside" lines – seems like incorrect syntax or misuse of the unknown attribute, which usually handles fields that are specified in input data but not in the schema.

Despite these errors, the intent to show nested relationships is positive. Score: 1

Final Evaluation:

Given the omission of several required features and the incorrect implementation syntax in multiple parts, the score is:

{
  "score": 3
}

anthropic/claude-3.5-sonnet says: (3.0) (0.97¢)

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

The solution does not explicitly define dataclasses for the person and order. It only defines schemas. (0/2 marks)
The solution defines two schema classes: PersonSchema and OrderSchema. (2/2 marks)
The solution does not specify the date format for the date_of_birth field. (0/1 mark)
The decimal field (amount) is not defined with as_string=True. It's defined as a Float instead. (0/1 mark)
The solution does not include @post_load methods to link the schemas and dataclasses. (0/2 marks)
Additional observations:
The solution uses unknown = "\inside" in Meta classes, which is incorrect syntax.
The order_schema is used before it's defined, which would cause an error.
The solution demonstrates how to use the schema to load data, which is helpful.
(1/2 marks)

Breakdown of marks:

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

Score:

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

The solution provides a basic structure using marshmallow schemas but misses several key requirements. It doesn't use dataclasses, doesn't specify the correct date format, doesn't use the correct field type for the decimal amount, and doesn't link schemas to dataclasses using @post_load. There are also some syntax errors that would prevent the code from running correctly. The solution does demonstrate basic knowledge of marshmallow schemas, but significant improvements are needed to meet all the requirements.

Response 2 (3.0)

Here's how you can define the dataclasses using the Marshmallow library in Python:

from marshmallow import Schema, fields, validate


class UserSchema(Schema):
    first_name = fields.Str(validate=validate.Length(min=1, max=20))
    last_name = fields.Str(validate=validate.Length(min=1, max=20))
    date_of_birth = fields.Date()
    orders = fields.List(fields.Nested(OrderSchema))


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

To use this schema, you can load the json data and pass it to the respective dataclass:

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

user = UserSchema().load(user_data)
orders = user['orders'][0]

The dataclasses are now populated with the data from the json.

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: (3.0) (0.92¢)

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

The solution does not define any dataclasses. It only defines schemas using Marshmallow. (0/2 marks)
The solution correctly defines two schema classes: UserSchema and OrderSchema. (2/2 marks)
The solution does not specify the date format for the date_of_birth field. It should have used fields.Date(format="%m/%d/%Y"). (0/1 mark)
The solution uses fields.Float() for the amount field instead of fields.Decimal(as_string=True). (0/1 mark)
The solution does not use @post_load decorators to link schemas with dataclasses. (0/2 marks)
Additional observations:
The solution includes validation for name fields and amount, which is good practice.
The solution demonstrates how to use the schema to load data, which is helpful.
However, it doesn't actually create dataclass instances as requested in the problem statement.
(1/2 marks)

Here's the breakdown of marks:

{
  "Dataclass definition": 0,
  "Schema definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema and dataclass linking": 0,
  "Additional observations": 1
}

Final score:

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

The solution partially addresses the problem by creating Marshmallow schemas, but it misses several key requirements, particularly the creation of dataclasses and the correct handling of date, datetime, and decimal fields. It also doesn't link the schemas to dataclasses using @post_load. While it provides some useful elements like validation and demonstrates how to use the schema, it doesn't fully meet the specified requirements.

openai/gpt-4-0125-preview says: (3.0) ()

The evaluation of the provided solution according to the marking scheme is as follows:

Definition of two dataclasses: The provided solution does not define any dataclasses; it only specifies schema classes using Marshmallow. (0 marks)
Definition of two schema classes: The solution defines two schema classes: UserSchema and OrderSchema. (2 marks)
Specification of date format for date_of_birth: The date_of_birth field in the UserSchema is specified using fields.Date(), but no format (format="%m/%d/%Y") is explicitly set. (0 marks)
Decimal field with as_string=True: The amount field in OrderSchema uses fields.Float(), which does not handle the value as a decimal nor applies as_string=True. (0 marks)
Linking schema and dataclasses with a @post_load method: There are no @post_load methods implemented in the schemas to map the loaded data into instances of dataclasses. (0 marks)
Additional Considerations:
The schema for orders (OrderSchema) is utilized inside UserSchema before it is declared, which would cause a run-time error.
The amount field should use Decimal type rather than Float for precise financial calculations, and it does not correctly handle the string nature of the provided amount.
There is no validation or formatting for date_of_birth and created fields to ensure they correctly parse the provided date strings.
Basic validation for first_name and last_name using length constraints is correctly included.
(1 mark) (For correct inclusion of some field-level validation.)

Overall, the solution provided fails to meet most of the requirements of the task, missing core functionality like the dataclass definitions, appropriate type handling for dates, decimals, and the post-load processing linking to dataclasses.

{
  "score": 3
}

Response 3 (4.0)

To generate dataclasses from the given JSON data using Marshmallow, you can use the marshmallow_dataclass package. Here's an example of how you can define the dataclasses for the given JSON data:

from marshmallow_dataclass import dataclass
import datetime

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: datetime.date
    orders: Order

@dataclass
class Order:
    created: datetime.datetime
    amount: float

#Load the JSON data
import json
json_data = '''
{
  "first_name": "Jane",
  "last_name": "Smith",
  "date_of_birth": "03/16/1977",
  "orders": [
    {
      "created": "2024-01-25T15:00:04",
      "amount": "100.00"
    }
  ]
}
'''
data = json.loads(json_data)

#Initialize the dataclasses
person = Person(**data)

In this example, we defined two dataclasses - Person and Order. The Person class has properties for first_name, last_name, date_of_birth, and a single Order instance. The Order class has properties for created, which is a datetime.datetime, and amount, which is a float.

We then loaded the JSON data and initialized the Person dataclass with the given data. Note that we had to convert the date_of_birth string to a datetime.date object before initializing the Person dataclass.

This is a basic example, but you can customize the dataclasses based on your specific requirements.

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: (4.0) (0.86¢)

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

The solution defines two dataclasses, Person and Order. (2 marks)
The solution does not define schema classes using Marshmallow. (0 marks)
The solution does not specify the date format for the date_of_birth field. (0 marks)
The solution does not handle the decimal field with as_string=True. (0 marks)
The solution does not use @post_load method to link schema and dataclasses. (0 marks)
Additional points:
The solution uses marshmallow_dataclass, which simplifies the process. (1 mark)
The solution includes code to load JSON data and initialize the dataclass. (1 mark)

Comments:
The solution provides a basic implementation using marshmallow_dataclass, which simplifies the process of creating dataclasses. However, it doesn't fully address the requirements specified in the problem statement. It doesn't use Marshmallow schemas, doesn't handle date formats and decimal fields as required, and doesn't implement the @post_load method. The solution would benefit from incorporating these elements to fully meet the stated requirements.

Here's the breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 0,
  "Date format specified": 0,
  "Decimal field handling": 0,
  "post_load method implementation": 0,
  "Additional points": 2
}

Final score:

{
  "scores": [2, 0, 0, 0, 0, 2],
  "total_score": 4
}

openai/gpt-4-0125-preview says: (2.0) ()

The given solution attempts to solve the problem but fails to correctly implement several aspects as required by the task guidelines. Here are the analysis and deductions based on the provided marking scheme:

Definition of Two Dataclasses:
The solution defines two dataclasses, Person and Order, as required. These are appropriately decorated with @dataclass.
Score: 2/2
Definition of Two Schema Classes:
The solution does not define schema classes explicitly. Instead, it uses marshmallow_dataclass in a way that assumes implicit schema generation. This is not clear or confirmed in the solution and can mislead users unfamiliar with advanced marshmallow_dataclass usage.
Score: 0/2
Date Format Specification for date_of_birth:
The solution fails to specify the date format for date_of_birth using format="%m/%d/%Y" as required. It does not use Marshmallow's features to parse or transform this data upon deserialization.
Score: 0/1
Decimal Field with as_string=True:
The solution incorrectly defines the amount field as float, which does not satisfy the requirement to handle it as a Decimal with as_string=True to ensure precision and correct handling of decimal values.
Score: 0/1
Linking Schema and Dataclasses with @post_load:
The solution does not employ a @post_load method or any explicit schema to perform post-load operations which would be necessary to correctly parse and transform fields like date_of_birth and created.
Score: 0/2
Other considerations:
Correct handling of orders as a list of Order objects is absent. Only a single Order instance is mentioned, which is incorrect when the JSON clearly indicates orders is a list.
Missing serialization/deserialization logic that verifies or showcases the conversion of JSON properly according to the defined specifications.
Score: 0/2

Overall Evaluation:
The provided solution lacks critical aspects of using schemas appropriately, missing deserialization techniques, formats handling, and doesn't adhere to the task’s format requirements in terms of data types. There is also a misunderstanding in the handling of the orders list.

Final Score:

{
  "score": 2
}

1.00	0.82	0.88
0.82	1.00	0.77
0.88	0.77	1.00

Nous: Hermes 13B

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