Mistral OpenOrca 7B

Description

A fine-tune of Mistral using the OpenOrca dataset. First 7B model to beat all other models <30B.

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 create dataclasses by defining the API endpoint schema. The date_of_birth field should be converted to a datetime before being passed to the dataclass wrapper. The created field can be mapped to a datetime type automatically. The amount field should be specified with a Decimal type.

Here is an example schema to create dataclasses for the given JSON:

from typing import List
from datetime import datetime
from marshmallow importfields, validate
from marshmallow_dataclass import dataclass

class Order(fields.Object):
    created: fields.DateTime(required=True)
    amount: fields.Decimal(required=True, precision=2, max_precision=3)

class OrderSchema(Order):
    # Add any additional fields or validations here
    class Meta:
        model = Order

class PersonSchema(OrderSchema):
    first_name: fields.Str(required=True)
    last_name: fields.Str(required=True)
    date_of_birth: fields.DateTime(required=True)
    orders: OrderSchema(many=True)

person_schema = PersonSchema(allow_instance=True)
person_data = person_schema.load(json_data)
print(person_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: (4.0) (0.89¢)

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

  1. The solution defines two classes: Order and PersonSchema. However, these are not proper dataclasses. (0/2 marks)

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

  3. The solution does not specify the correct date format for the date_of_birth field. It uses fields.DateTime instead of fields.Date with the required format. (0/1 mark)

  4. The decimal field (amount) is not specified with as_string=True. (0/1 mark)

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

  6. Additional points:

  7. The solution uses marshmallow_dataclass, which is a good approach for creating dataclasses from Marshmallow schemas. (1 mark)
  8. The solution includes precision settings for the decimal field. (1 mark)
    (2/2 marks)

Comments:
The solution has a good start but misses several key requirements. It doesn't properly define dataclasses, doesn't use the correct date format for date_of_birth, and doesn't include @post_load methods. The use of marshmallow_dataclass is a good idea, but it's not fully implemented correctly in this solution.

Breakdown of marks:

{
  "Dataclass definition": 0,
  "Schema definition": 2,
  "Date format": 0,
  "Decimal field": 0,
  "Post_load methods": 0,
  "Additional points": 2
}

Final score:

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

openai/gpt-4-0125-preview says: (1.5) ()

The solution provided uses Marshmallow but contains mistakes and oversights in relation to the requirements given by the problem statement.

  1. Defining dataclasses for person and order (2 marks):

    • The solution directly uses fields to define content in what appears to be a schema definition, but does not actually define dataclasses using the Marshmallow @dataclass or any other mechanism. The supposed classes Order and PersonSchema are incorrectly outlined as schema definitions and not as plain dataclasses. Therefore, 0 marks.

  2. Defining schema classes for person and order (2 marks):

    • Although the intent is to define schema classes, the classes are incorrectly structured, conflating dataclass and schema features. Moreover, they do not extend Marshmallow's Schema class or correctly use the marshmallow-dataclass library. Thus, this answer partially meets the task without fully correct technical implementation, 0.5 marks.

  3. Specifying the date format for date_of_birth (1 mark):

    • No date format has been defined in the provided solution. The field is treated as fields.DateTime without a format such as '%m/%d/%Y', which the problem explicitly requires. Therefore, 0 marks.

  4. Using as_string=True for amount field (1 mark):

    • The solution does not use as_string=True on the Decimal field. This is needed as per the problem statement to treat decimal fields correctly as strings. Therefore, 0 marks.

  5. Linking schema and dataclasses with @post_load method (2 marks):

    • The solution lacks the use of the @post_load decorator to connect the marshmallow schema to constructing a dataclass instance. This is crucial for effectively converting loaded data into a dataclass object. Accordingly, 0 marks.

  6. Anything else (2 marks):

    • Good attempt to create custom marshmallow schema. Potentially valid use of new fields.Object (though incorrect in context), and reasonable attempt to initialize and load data using person schema. Some intent to integrate various components of Marshmallow, but issues in understanding and application reflect a lack of complete coherence. Thus, 1 mark.

Overall, the provided solution misunderstands or incorrectly implements key aspects necessary to satisfy the problem's requirements using Marshmallow and dataclasses.

Final score:

{
  "score": 1.5
}

To generate dataclasses from a JSON schema using Marshmallow in Python, you can follow these steps:

  1. Install the marshmallow library using pip: pip install marshmallow
  2. Define a dataclass for each of the fields in the JSON schema. For example:
from datetime import datetime
from decimal import Decimal
from pydantic import BaseModel

class FirstName(BaseModel):
    first_name: str

class LastName(BaseModel):
    last_name: str

class DateOfBirth(BaseModel):
    date_of_birth: datetime

class Order(BaseModel):
    created: datetime
    amount: Decimal

class User(BaseModel):
    first_name: FirstName
    last_name: LastName
    date_of_birth: DateOfBirth
    orders: List[Order]
  1. Create a Marshmallow schema for the JSON schema by mapping the dataclasses to the keys in the JSON schema. For example:
from marshmallow import Schema, fields

class UserSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.DateTime(default_factory=datetime.utcnow.isoformat)
    orders = fields.List(OrderSchema())

The default_factory argument sets the default value for the date_of_birth field to the current ISO-formatted datetime.

  1. Use the load method of the schema to validate and deserialize the JSON data into a dictionary:
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)

user_data = UserSchema().load(data)

print(user_data)

This will output the deserialized user_data with the validated fields and their corresponding values.

Note: This method requires the JSON data to have the same keys and nullable fields as the defined dataclasses. If not, the schema complaint error will raise.

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: (1.5) ()

Evaluation of the Provided Solution:

  1. Definition of Dataclasses:
  2. The solution uses Pydantic's BaseModel instead of Python's built-in dataclasses.
  3. There is an incorrect mix of frameworks; the task asked explicitly to use the marshmallow library together with Python dataclasses.

  4. Score: 0/2

  5. Definition of Schema Classes:

  6. The solution incorrectly utilizes Marshmallow for schema definition but fails to define an OrderSchema.
  7. The attributes defined in UserSchema are incomplete and do not match the structured data types (e.g., DateTime is used instead of handling specific formatting and conversion for the date_of_birth).

  8. Score: 0.5/2

  9. Date Format Specification:

  10. The solution does not correctly specify the date format for date_of_birth as %m/%d/%Y.
  11. Instead, it completely mishandles the field by setting a default current datetime value.

  12. Score: 0/1

  13. Decimal Field Handling:

  14. The solution does not address the requirement of representing the amount as a decimal with as_string=True.

  15. Score: 0/1

  16. Linkage of Schema and Dataclasses with @post_load:

  17. The solution does not use or mention the use of a @post_load method to link the parsing operation to the dataclasses.
  18. This is a crucial misstep in the context of marshmallow's intended usage pattern.

  19. Score: 0/2

  20. Additional Elements & Best Practices:

  21. The solution does attempt to integrate JSON loading and parsing, which shows some understanding of the end goal.
  22. Inadequate attention to exception handling or validation error capture, which would be essential in practical use.
  23. Score: 1/2

Overall, the provided solution suffers significantly from a misunderstanding or misapplication of the technologies specified in the task. It largely fails to meet the requirements outlined and demonstrates confusion between Pydantic's and marshmallow's differing roles in data validation and serialization.

{
  "score": 1.5
}

anthropic/claude-3.5-sonnet says: (3.0) (0.96¢)

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

  1. The solution defines dataclasses, but uses pydantic's BaseModel instead of Python's dataclass. This is not exactly what was asked for. (1/2 marks)

  2. The solution defines only one schema class (UserSchema), missing the OrderSchema. (1/2 marks)

  3. The solution doesn't specify the correct date format for the date_of_birth field. It uses DateTime instead of Date and doesn't include the format. (0/1 mark)

  4. The solution doesn't include as_string=True for the decimal field. (0/1 mark)

  5. The solution doesn't use @post_load to link the schema and dataclasses. (0/2 marks)

  6. Additional points:

  7. The solution imports and uses pydantic, which wasn't mentioned in the problem.
  8. It correctly uses marshmallow's Schema and fields.
  9. It provides a method to load and validate the JSON data.
    (1/2 marks)

Here's the breakdown of marks:

{
  "dataclasses": 1,
  "schema classes": 1,
  "date format": 0,
  "decimal field": 0,
  "post_load": 0,
  "additional": 1
}

Final score:

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

The solution demonstrates some understanding of the problem but misses several key requirements. It uses pydantic instead of pure dataclasses, doesn't properly handle the date and decimal fields, and doesn't use the @post_load decorator to link schemas and dataclasses. The solution would benefit from closer adherence to the problem specifications and better use of marshmallow's features.

To automatically generate dataclasses from your provided JSON data using Marshmallow library in Python, you need to perform these steps:

  1. Install the required libraries:
    pip install marshmallow dataclasses datetime-iso-extensions underscore-to-camelcase-snake

  2. Import the necessary modules:
    python from datetime import datetime from dataclasses import dataclass, field from marshmallow import fields, Schema

  3. Define the date datatype using the date_iso_extensions library:
    ```python
    from datetime_iso_extensions import FixedDateTimeZone

class MyDate(FixedDateTimeZone):
def init(self, date_time, timezone=datetime.timezone.utc):
super().init(date_time=date_time, timezone=timezone)
```

  1. Define the order datatype with required fields like date_of_birth, created, and amount with their respective data types:
    ```python
    @dataclass
    class Order:
    created: datetime = field(metadata={'type': 'iso_datetime_local'})
    amount: float = field(metadata={'type': 'decimal'})
    date_of_birth: MyDate = field(metadata={'type': 'date'})

    def init(self, kwargs):
    super().init(    kwargs)
    ```

  2. Define the order schema with required fields and their respective data types:
    ```python
    class OrderSchema(Schema):
    created: fields.DateTime(format='iso_datetime_local')
    amount: fields.Decimal(places=2, max_digits=10)
    date_of_birth: fields.Str(format='%Y-%m-%d')

    class Meta:
    model = Order
    load_instance = True
    ```

  3. Create an instance of the order:
    python # Assuming the provided JSON data is stored in json_data variable order = Order[json_data]

  4. Finally, you can use the order schema to serialize the order object back to JSON data:
    python order_json = order_schema.jsonify(order)

Please note that in the above example, amount field is defined as a float, but it's represented as string in the output. If you need to keep it as a float, you should use the following method:

```python
@dataclass
class Order:
created: datetime
amount: float
date_of_birth: MyDate

   def __init__(self, **kwargs):
       super().__init__(**kwargs)

# Define a separate decorator to handle decimal digits with leading zeros if required
@classmethod
def with_decimal_padding(cls):
def metaclass(cls, name, bases, attrs):
attr = attrs[name]
if attr.is_required and attr.is_float:
attr_parameters.default = str(attr_parameters.default).zfill(2)
return attr

   return metaclass(cls, name, bases, attrs)

   # Define a parameter to control the number of decimal digits that need to be padded
   attr**_parameters = namedtuples('DecimalDigitParameters', 'digits')

Order = cls.with_decimal_padding()
```

With this change, the amount field will have two decimal digits with leading zeros if required.

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) (0.96¢)

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

  1. Defining two dataclasses (Person and Order):
    The solution only defines one dataclass (Order). It's missing the Person dataclass.

  2. Defining two schema classes:
    The solution only defines one schema class (OrderSchema). It's missing the PersonSchema.

  3. Specifying the date format for date_of_birth:
    The solution uses '%Y-%m-%d' format instead of the required '%m/%d/%Y'.

  4. Decimal field with as_string=True:
    The solution doesn't use as_string=True for the amount field.

  5. Linking schema and dataclasses with @post_load:
    The solution doesn't use @post_load to link the schema and dataclasses.

  6. Additional points:

  7. The solution imports some unnecessary libraries.
  8. It defines a custom MyDate class, which wasn't required.
  9. The solution attempts to handle decimal padding, which wasn't part of the requirements.

Here's the breakdown of marks:

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

Final score:

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

The solution partially addresses the problem but misses several key requirements and includes unnecessary complexity. It would benefit from a more focused approach that directly addresses the specified requirements.

openai/gpt-4-0125-preview says: (1.5) ()

The provided solution has some noticeable issues and gaps concerning the requirements:

  1. Dataclasses Definition: The solution defines one dataclass (Order), but not the second dataclass for Person. There should have been a separate Person dataclass, including fields like first_name and last_name. Furthermore, the Order dataclass incorrectly includes date_of_birth which should belong to Person.

  2. Score: 0.5/2

  3. Schema Classes Definition: A single schema class OrderSchema is defined, but no schema for the Person is present. Additionally, the schema defined doesn't set up conversions correctly (e.g., using fields.Str for a date field which should be fields.Date).

  4. Score: 0.5/2

  5. Date of Birth Format Specification: There is no correct set up in the OrderSchema for the date format of date_of_birth, especially since %Y-%m-%d is noted, but the original requirement specified a different format ("%m/%d/%Y").

  6. Score: 0/1

  7. Decimal Field Representation: Although the decimal type is mentioned, the solution incorrectly sets the amount type to float instead of Decimal. Additionally, as_string=True is completely missing which is required to ensure the amount remains a string representation of a decimal.

  8. Score: 0/1

  9. @post_load Method Usage: There is no usage of the @post_load method for converting loaded data into instances of dataclasses.

  10. Score: 0/2

  11. Additional Points:

  12. Importing unnecessary libraries and incorrect usage of datatypes.
  13. Misuse of classes and methods (e.g., using Python techniques that are not standard such as cls.with_decimal_padding() which is pseudocode and not applicable to Python).
  14. There lacks proper integration and testing snippets that show how to load JSON data using schemas.
  15. Score: 0.5/2

Considering these points, the solution deviates significantly from best practices and requirements, and it exhibits a lack of fundamental understanding of the Marshmallow library for serialization and using dataclasses in Python.

Final score:

{
  "score": 1.5
}
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