NousResearch: Hermes 2 Pro

Description

Hermes 2 Pro is an upgraded, retrained version of Nous Hermes 2, consisting of an updated and cleaned version of the OpenHermes 2.5 Dataset, as well as a newly introduced Function Calling and JSON Mode dataset developed in-house.

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 (5.0)

You can use the Marshmallow library in Python to create data classes and deserialize the JSON. Here's an example:

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

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal(2, validate=validate.Decimal('0.00'))

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

# In this case, the JSON string should be
json_data = """
{
  "first_name": "Jane",
  "last_name": "Smith",
  "date_of_birth": "03/16/1977",
  "orders": [
    {
      "created": "2024-01-25T15:00:04",
      "amount": "100.00"
    }
  ]
}
"""

 schemas = UserSchema()
 user = schemas.load(json_data)
 print(user)

First, we define two separate schemas - OrderSchema and UserSchema. In the OrderSchema, we specify fields for 'created' and 'amount', and in the UserSchema, we define fields for 'first_name', 'last_name', 'date_of_birth', and 'orders'.

The OrderSchema uses fields.DateTime() for the 'created' field, which will convert the string into a datetime object, and fields.Decimal() for 'amount' with precision 2 and validation to ensure the input is a valid decimal with two decimal places.

The UserSchema uses fields.Nested(OrderSchema(), many=True) for the 'orders' field, which means it will contain multiple instances of the OrderSchema.

In the code, we create an instance of UserSchema and use the load() method to parse the JSON string into the specified data classes. Finally, we print the deserialized data.

This way, you can transform the JSON data into Python objects (data classes) with the specified fields and formats using the Marshmallow library.

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) (0.95¢)

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

The solution does not define dataclasses. It only defines schemas. (0/2 marks)
The solution correctly defines two schema classes: OrderSchema and UserSchema. (2/2 marks)
The solution correctly specifies the date format for the date_of_birth field using format='%m/%d/%Y'. (1/1 mark)
The solution does not include as_string=True for the decimal field. (0/1 mark)
The solution does not include @post_load methods to link schemas and dataclasses. (0/2 marks)
Additional points:
The solution correctly uses nested fields for the orders.
It includes validation for the decimal field.
It provides a sample JSON and demonstrates how to use the schema.
(2/2 marks)

Breakdown of marks:

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

Score:

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

Comments:
The solution provides a good start with the Marshmallow schemas, but it misses some key requirements. It doesn't define dataclasses, which was a major part of the task. The decimal field is missing the as_string=True parameter. The solution also lacks the @post_load methods to create instances of dataclasses from the deserialized data. While the schema definitions are correct and include some nice additional features like validation, the solution doesn't fully meet the requirements of the task.

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

The presented solution has the following points to evaluate according to the marking scheme:

Dataclasses Definition: The solution lacks the definitions of data classes for both the person and the order. It only uses schema classes provided by the Marshmallow library. Therefore, no points can be awarded here. (0/2)
Schema Classes Definition: The solution correctly defines two schema classes (OrderSchema and UserSchema) using the Marshmallow library. (2/2)
Date Format Specification: While the date_of_birth in the UserSchema intended to be a fields.DateTime(format='%m/%d/%Y'), it is incorrectly typed as DateTime instead of Date. This is problematic for correct serialization because DateTime expects time information as well. (0/1)
Decimal Field Property: In the OrderSchema, the fields.Decimal was used but without specifying as_string=True. Hence, the serialized output is not specified if it should be handled and converted as a string. (0/1)
@post_load Usage: The solution does not include any @post_load methods in the schemas to link back to data classes (which were never defined). No points are awarded here. (0/2)
Additional Aspects:
Uses Marshmallow field definitions accurately for nested handling.
Provides a basic example of loading JSON into defined schemas, meant to illustrate deserialization, though it does have errors in expected output types, particularly in date handling and decimal conversion.
Does not effectively manage to print the resulting parsed data since loading needs proper use of actual dictionaries and error handling which was not demonstrated or discussed. (1/2)

Given these evaluations, the overall score would be a sum of the points awarded:

{
  "score": 3
}

Response 2 (9.0)

You can achieve this by defining separate classes for the base information and orders respectively and then serializing them using the marshmallow library. Here's a step-by-step guide:

Step 1: Install the marshmallow library.

pip install marshmallow

Step 2: Import the necessary modules from the marshmallow library.

from marshmallow import Schema, fields, post_load

Step 3: Define the classes for the base information, orders, and a schema for serialization.

class BaseInfoSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date("%m/%d/%Y")

    @post_load
    def make_base_info(self, data):
        return BaseInfo(**data)

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

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

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

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

In the above code, we define BaseInfoSchema and OrderSchema classes which define the fields for serialization. The @post_load decorator is used to convert the serialized data into instance of BaseInfo and Order classes.

Step 4: Serialize your json data using the defined schemas.

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

base_info_schema = BaseInfoSchema()
base_info = base_info_schema.load(
    {k: v for k, v in json_data.items() if k != 'orders'})

order_schema = OrderSchema()
orders_data = json_data.get('orders')
orders = [order_schema.load(order) for order in orders_data]

# Now `base_info`, `orders` are instances of `BaseInfo` and `Order` defined by you.

In the above code, we are loading the base information and orders separately and into different instances of the defined BaseInfo and Order classes.

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: (8.0) ()

Evaluation:

Defining Data Classes: The solution does define two dataclasses (BaseInfo and Order), although they are implemented via manual class definition with __init__ methods rather than using the Python dataclasses module. Additionally, the Order class lacks fields like date_of_birth, critical for inputs like this in the description. Nonetheless, it matches the basic requirement to a degree.
Score: 1/2
Defining Schema Classes: The solution defines two schema classes (BaseInfoSchema and OrderSchema) corresponding to the BaseInfo and Order classes which is as required.
Score: 2/2
Specifying Date Format: The date_of_birth field correctly uses the Marshmallow fields with the fields.Date setup specifying %m/%d/%Y format. This is done correctly as per the requirement.
Score: 1/1
Decimal Field: The solution correctly sets the Decimal field in OrderSchema with as_string=True, ensuring that the amount field will be correctly formatted as a string which matched the requirement.
Score: 1/1
Post Load Method Linking: Both schema classes implement the @post_load method that should correctly create instances of BaseInfo and Order. This matches the solution requirement.
Score: 2/2
Additional Considerations: While the solution handles serialization of the fields, it omits any error handling or validation beyond the built-in capabilities of Marshmallow. There's lack of nesting between the defined schemas to handle orders as a nested structure under a person's basic information which was implied in the problem statement. Therefore, an additional consideration like nested handling of 'orders' isn't fully considered as the explanation within the solution mixes plain dictionaries with object mappings.
Score: 1/2

Total Score:

{
  "score": 8
}

anthropic/claude-3.5-sonnet says: (9.0) (0.97¢)

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

The solution defines two classes: BaseInfo and Order. These aren't dataclasses, but they serve a similar purpose. (1 mark out of 2)
The solution defines two schema classes: BaseInfoSchema and OrderSchema. (2 marks)
The solution correctly specifies the date format for the date_of_birth field using fields.Date("%m/%d/%Y"). (1 mark)
The decimal field correctly uses as_string=True in the OrderSchema. (1 mark)
The schema and classes are linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution imports necessary modules from marshmallow.
It provides a step-by-step guide, including installation instructions.
It demonstrates how to use the schemas to deserialize JSON data.
(2 marks)

Breakdown of marks:

{
  "dataclasses": 1,
  "schema classes": 2,
  "date format": 1,
  "decimal field": 1,
  "post_load methods": 2,
  "additional points": 2
}

Score:

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

Comments:
The solution is generally well-structured and addresses most of the requirements. It uses regular classes instead of dataclasses, which is the main point of improvement. However, the implementation is functional and demonstrates a good understanding of marshmallow's usage for JSON deserialization with custom data types. The additional explanations and the demonstration of how to use the schemas are particularly helpful.

Response 3 (7.0)

To create dataclasses from the JSON using the marshmallow library in Python, follow these steps:

Step 1: Install the marshmallow library using pip:

pip install marshmallow

Step 2: Import the necessary modules and define the dataclasses using marshmallow.

Here's an example for your JSON data:

from datetime import datetime
from decimal import Decimal
from marshmallow import Schema, fields, post_load

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

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

    @post_load
    def make_person(self, data):
        return Person(**data)

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

# OR

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

The above code defines the dataclasses using marshmallow's Schema. The OrderSchema and PersonSchema define the structure of the order and person objects, respectively. The @post_load decorator is used to convert the deserialized data into instances of the Person and Order classes.

Step 3: Deserialize the JSON string using the Schema:

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()
data = person_schema.load(json.loads(json_data))

This will load the JSON data into the person_schema and deserialize the data into instances of the defined dataclasses.

Finally, you can access the deserialized data:

print(data.first_name)       # Output: Jane
print(data.last_name)       # Output: Smith
print(data.date_of_birth)    # Output: 1977-03-16
print(data.orders[0].created)  # Output: 2024-01-25T15:00:04
print(data.orders[0].amount)  # Output: 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)

openai/gpt-4-0125-preview says: (7.0) ()

Here's an evaluation of the provided solution based on the marking scheme:

Definition of two dataclasses, one for the person and one for the order (+1 mark only): The person and order classes are structured simply with __init__ constructors as regular Python classes, not using dataclasses, thereby missing the attribute of using Python's dataclass feature as required.
Definition of two schema classes, one for the person and one for the order (+2 marks): The solution correctly defines OrderSchema and PersonSchema using marshmallow, which matches the requirement to deal with JSON structure and types.
Specification of date format for the 'date_of_hire' field (+0 marks): The date format for date_of_birth should be specified as "%m/%d/%Y" in the fields.Date() call, but this format is missing in the solution.
The decimal field as string setting (+0 marks): The solution incorrectly used as_string=False; however, the requirement was as_string=True to ensure decimals are accurately serialized.
Linking schema and dataclasses through @post_load method (+1 mark): The @post_last method make_person does help convert deserialized data into an instance of the Person class, but its effectivity is part of a broader correct implementation, which missed the proper use of dataclasses and other settings.
Additional evaluation points:
Correct implementation of nested fields and serialization handling (+1 mark).
Use of many=True for handling multiple orders in the JSON is done correctly (+1 mark).
Import of required modules and correct usage of marshmallow’s fields in schemas (+1 mark).

Total Score Calculation:
- Two dataclasses: +1
- Two schema classes: +2
- Correct date format: +0
- Correct decimal handling: +0
- Correct use of @post_load: +1
- Additional considerations: +3

Total: 7 / 10

{
  "score": 7
}

anthropic/claude-3.5-sonnet says: (7.0) (1.04¢)

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

The solution defines two dataclasses, Person and Order. (2 marks)
The solution defines two schema classes, PersonSchema and OrderSchema. (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 marks)
The decimal field is defined with as_string=False, which is incorrect. It should be as_string=True. (0 marks)
The schema and dataclasses are linked with a @post_load method on the PersonSchema class. However, it's missing for the OrderSchema. (1 mark)
Additional points:
The solution imports necessary modules.
It provides an example of how to use the schema to deserialize JSON data.
It shows how to access the deserialized data.
(2 marks)

Breakdown of marks:

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

Score:

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

Comments:
The solution provides a good starting point but misses some important details. It correctly defines the dataclasses and schema classes, and demonstrates how to use them. However, it fails to specify the correct date format for the date_of_birth field and incorrectly sets as_string=False for the decimal field. The @post_load method is implemented for the PersonSchema but missing for the OrderSchema. Despite these issues, the solution shows a good understanding of using marshmallow for JSON deserialization and dataclass creation.

1.00	0.74	0.87
0.74	1.00	0.74
0.87	0.74	1.00

NousResearch: Hermes 2 Pro - Llama-3 8B

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Evaluation:

Total Score:

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