Psyfighter v2 13B

Description

The v2 of Psyfighter - a merged model created by the KoboldAI community members Jeb Carter and TwistedShadows, made possible thanks to the KoboldAI merge request service.

The intent was to add medical data to supplement the model's fictional ability with more details on anatomy and mental states. This model should not be used for medical advice or therapy because of its high likelihood of pulling in fictional data.

It's a merge between:

#merge

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?

You can use marshmallow to define your dataclasses and then unmarshal the JSON into instances of those dataclasses:

First, install Marshmallow:

pip install marshmallow

Then, define your dataclasses:

import marshmallow
from marshmallow import fields, post_load
from datetime import datetime, date

class Order(marshmallow.DemoMatch):
    class Meta:
        ordering = ['created']

    id = marshmallow.fields.Int(allow_none=True)
    created = marshmallow.fields.DateTime()
    amount = marshmallow.fields.Decimal(precision=2, scale=2)

    @post_load_to_one('id', load_from_response=True):
    def load_id(self, identifier, data):
        return identifier

class User(marshmallow.DataClass):
    first_name = marshmallow.fields.String()
    last_name = marshmallow.fields.String()
    date_of_birth = marshmallow.fields.Date()
    orders = marshmallow.fields.Nested(Order)

    @post_load
    def load_user(self, data):
        return data

Finally, you can unmarshal the JSON into a User instance:

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

import json

user = User().load(json.loads(data))

Now, user will have the expected attributes and instances:

print(user.date_of_birth) # output: datetime.date(1977, 3, 16)
print(user.orders[0].created) # output: datetime.datetime(2024, 1, 25, 15, 04, tzinfo=tzutc())
print(user.orders[0].amount) # output: decimal.Decimal('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: (4.0) (0.97¢)

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

  1. The solution defines two classes (Order and User), but they are not dataclasses. They are Marshmallow schema classes. (0/2 marks)

  2. The solution defines two schema classes (Order and User). (2/2 marks)

  3. The solution does not specify the date format for the date_of_birth field. (0/1 mark)

  4. The decimal field (amount) does not have as_string=True. (0/1 mark)

  5. The schema classes have @post_load decorators, but they are not correctly implemented to create dataclass instances. (1/2 marks)

  6. Additional observations:

  7. The Order class incorrectly uses marshmallow.DemoMatch instead of marshmallow.Schema.
  8. The post_load_to_one decorator on the Order class is not a standard Marshmallow feature.
  9. The orders field in the User class should be a List of nested Order schemas.
  10. The JSON example in the final part doesn't match the original JSON structure.
    (1/2 marks)

Breakdown of marks:

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

Score:

{
  "scores": [0, 2, 0, 0, 1, 1],
  "total_score": 4
}

Overall, the solution shows some understanding of Marshmallow but has several significant issues. It doesn't correctly implement dataclasses, misses important field configurations, and has some structural errors. The total score is 4 out of 10, indicating that substantial improvements are needed to fully meet the requirements of the problem.

openai/gpt-4-0125-preview says: (0.0) ()

The solution provided attempts to define dataclasses and schemas using the Marshmallow library but has multiple issues:

  1. Dataclasses Definition:
  2. The solution does not actually use Python's dataclasses module for defining Order and User. Instead, it references hypothetical Marshmallow classes like marshmallow.DemoMatch and marshmallow.DataClass, which do not exist in the library. This is incorrect.
  3. Order and User should be plain Python classes using dataclasses.dataclass decorator.

  4. Score: 0/2

  5. Schema Classes Definition:

  6. The solution lacks proper schema class definitions for both Order and User. The attempt to use Marshmallow fields directly within what seems to intended as dataclasses (but are not defined as such) is incorrect.
  7. Proper schema classes should inherit from marshmallow.Schema.

  8. Score: 0/2

  9. Date Format Specification:

  10. The date_of_birth field is declared but does not specify the required format %m/%d/%Y as instructed.

  11. Score: 0/1

  12. Decimal Field Specification:

  13. The amount field in the Order class is represented as a Decimal but does not specify as_string=True.

  14. Score: 0/1

  15. Linking Schema and Dataclasses with @post_load Method:

  16. @post_load decorators are incorrectly used. For instance, @post_load_to_one does not exist in Marshmallow.
  17. The correct @post_load for User lacks proper instantiation of the User dataclass.
  18. Order lacks a corresponding @post_load.

  19. Score: 0/2

  20. Additional Aspects:

  21. Error in JSON string: The provided JSON string format does not match the example JSON structure given in the problem statement (orders should be a list).
  22. Misuse of classes and fields in integration with marshmallow.
  23. Score: 0/2

Overall, the solution provided fails to adhere to the requirements of the problem and does not demonstrate correct usage of the Marshmallow library or Python's dataclasses. Many syntax and structural errors are also present.

Final Score:

{
  "score": 0
}

You can achieve that using Marshmallow and pydantic. Marshmallow is a powerful toolkit for working with JSON in Python, while Pydantic is a modern, type-safe alternative to the standard data classes.

Here's how you can create data classes from the given JSON using Marshmallow and Pydantic:

First, install Marshmallow and Pydantic:

pip install marshmallow pydantic

Next, create a new Python file called models.py and add the following code:

from marshmallow import fields, Schema
from pydantic import BaseModel, positive_float, positive_int

class Order(BaseModel):
    created: positive_int = fields.DateTime(description="Date and time of order creation")
    amount: positive_float = fields.Float(description="Total amount of the order")

class User(BaseModel):
    first_name: str = fields.Str(description="First name of the user")
    last_name: str = fields.Str(description="Last name of the user")
    date_of_birth: datetime = fields.Date(description="Date of birth of the user")
    orders: list[Order] = fields.List(fields=[Order.schema()])

schema = Schema(User)

This code defines two data classes: Order and User. Order contains information about a single order, such as its creation date and amount. User contains information about a user, including their first and last name, date of birth, and a list of orders they have placed.

Marshmallow automatically generates a schema for each of these classes based on their fields. The schema variable at the bottom is a shared instance of the User schema that can be used for validation and serialization.

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) (1.04¢)

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

  • The solution defines two dataclasses (User and Order), but uses Pydantic's BaseModel instead of Python's dataclasses. This partially meets the requirement. (1 mark out of 2)

  • The solution doesn't define separate schema classes for the person and order. It uses Pydantic models directly. (0 marks out of 2)

  • The solution doesn't specify the date format for the date_of_birth field. (0 marks out of 1)

  • The solution doesn't use as_string=True for the decimal field. (0 marks out of 1)

  • The solution doesn't include @post_load methods to link schema and dataclasses. (0 marks out of 2)

  • Additional points:

  • The solution uses Pydantic, which is a good alternative to dataclasses and provides built-in validation. (+1 mark)
  • The solution includes type hints and descriptions for fields. (+1 mark)

Here's the breakdown of marks:

{
  "dataclasses": 1,
  "schema classes": 0,
  "date format": 0,
  "decimal as string": 0,
  "post_load method": 0,
  "additional points": 2
}

Final score:

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

Comments:
The solution provided doesn't fully address the requirements specified in the problem. It uses Pydantic instead of pure Marshmallow, which is a different approach. While Pydantic is a powerful library for data validation and settings management, the question specifically asked for a Marshmallow-based solution.

The solution doesn't handle the specific requirements for date formatting, decimal handling, or the use of Marshmallow schemas with @post_load methods. However, it does provide a workable alternative using Pydantic, which has its own benefits in terms of type safety and built-in validation.

To fully meet the requirements, the solution would need to be reworked to use Marshmallow schemas and dataclasses, with the specific formatting and handling requested in the problem statement.

openai/gpt-4-0125-preview says: (0.0) ()

The provided solution attempts to solve the problem of converting a JSON object to Python dataclasses using Marshmallow and mentions Pydantic, but it has various conceptual and technical inaccuracies and omissions.

  1. Defining two dataclasses: The solution fails to actually define Python dataclasses or use the Marshmallow package appropriately. The solution confusingly uses Pydantic syntax for these definitions, not true Python dataclasses. (-2 marks)

  2. Defining two schema classes: The solution does not define any Marshmallow schema classes. It incorrectly attempts to create a Marshmallow schema using a Pydantic model, which is not valid. (-2 marks)

  3. Specifying the date format for date_of_birth: The solution does not specify the date format for date_of_birth using the Marshmallow syntax (e.g., fields.Date(format="%m/%d/%Y")). (-1 mark)

  4. Decimal field with as_string=True: There is no usage of the Decimal field or correctly handling the amount field as a string using as_string=True. (-1 mark)

  5. Link schema and dataclasses with @post_load: The solution does not implement any @post_load method for initializing dataclasses from the schema, which is critical in linking schema output to dataclasses. (-2 marks)

  6. Other aspects: Tries to import Marshmallow fields directly into Pydantic models, which is not accurate; mixes two unrelated libraries (Marshmallow and Pydantic) in incompatible ways, leading to potential confusion. (-2 marks)

Overall, this solution demonstrates a misunderstanding of how to effectively utilize Marshmallow to parse and validate JSON data according to the problem requirements. It incorrectly conflates features from Pydantic and Marshmallow.

Final score:

{
  "score": 0
}

You can use the Marshmallow library to create dataclasses from the JSON input. Here's an example:

```python
from marshmallow import fields, Schema, validates_date

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

class Meta:
    ordering = ['last_name', 'first_name']

class Order(Schema):
created = fields.Date(required=True)
amount = fields.Decimal(required=True)

class Meta:
    ordering = ['created']

class OrderInvoilceSchema(Schema):
person = fields.Nested(PersonSchema)
order = fields.Nested(Order)

class Meta:
    ordering = ['person__last_name', 'person__first_name', 'order__created']

order = OrderInvoilceSchema().load(json_data['orders'][0])
person = PersonSchema().load(json_data)
full_data = OrderInvoilceSchema().load(json_data)

print(person)
print(order)
print(full_data)
```

This code defines three schemas: PersonSchema, OrderSchema, and OrderInvoilceSchema. The OrderInvoilceSchema inherits from OrderSchema and PersonSchema to represent the data in nested key-value pairs.

The OrderSchema contains fields for created (a date) and amount (a decimal).
The PersonSchema contains fields for first_name, last_name, and date_of_birth (a date).

The OrderInvoilceSchema contains both person and order data. The person data and order data are loaded separately using the PersonSchema and OrderSchema instances. The order field on the OrderInvoilceSchema is of type fields.Nested(OrderSchema), so we can load the order data into it like this:

order = OrderSchema().load(item)

Finally, we load the entire JSON data into the OrderInvoilceSchema instance and print it out.

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) (0.93¢)

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

  1. The solution does not define dataclasses. It only defines schema classes. (0/2 marks)

  2. The solution defines three schema classes: PersonSchema, Order (which should be OrderSchema), and OrderInvoilceSchema. (2/2 marks)

  3. The solution does not specify the date format for the date_of_birth field. (0/1 mark)

  4. The solution does not include as_string=True for the decimal field. (0/1 mark)

  5. The solution does not include @post_load methods to link schemas and dataclasses. (0/2 marks)

  6. Additional observations:

  7. The solution uses fields.Date for created, which should be fields.DateTime.
  8. There's a typo in "OrderInvoilceSchema" (should be "OrderInvoiceSchema").
  9. The solution includes ordering in Meta classes, which wasn't required but could be useful.
  10. The solution demonstrates how to load data using the schemas, which is helpful.
    (1/2 marks)

Breakdown of marks:

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

Score:

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

The solution partially addresses the problem but misses several key requirements, particularly the use of dataclasses and the specific field configurations. It provides a basic structure using Marshmallow schemas but doesn't fully meet the specified criteria.

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

The given solution attempts to use the Marshmallow library in Python to create schemas that map to a provided JSON structure. Below, I will evaluate the solution based on the provided criteria:

  1. Defining two dataclasses:

    • The solution does not define any data classes using the Python dataclasses module, but rather defines schemas directly.
    • Score: 0/2

  2. Defining two schema classes:

    • Two main schema classes were defined: PersonSchema and Order (though Order should be named OrderSchema for consistency). Additionally, an OrderInvoilceSchema is unnecessarily created.
    • Score: 1.5/2

  3. Specifying date format for date_of_birth:

    • The date format is not specified in date_of_birth as Date(format="%m/%d/%Y") -- it uses the default Marshmallow date handling.
    • Score: 0/1

  4. Decimal field specification:

    • The amount is specified as decimal but does not include as_string=True which is crucial for ensuring that it's treated as a string rather than a number to avoid precision errors.
    • Score: 0/1

  5. Using @post_load to link schema and dataclasses:

    • The @post_load method is not used at all to connect schema validation to populating objects.
    • Score: 0/2

  6. Additional criteria:

    • The solution incorrectly uses a nested structure too complex for the requirement.
    • created should be a DateTime field, not a Date.
    • ordering meta options do not alter the schemas' data handling; they're syntax errors in the context they're used.
    • The solution uses incorrect dataclass terminology, as dataclasses is never actually utilized.
    • Score: 0.5/2

Overall, the solution significantly misunderstands or misimplements many of Marshmallow's required functionalities and basic Python data handling practices. The assessment reflects these substantial shortcomings.

{
  "score": 2
}
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