Mistral: Mistral 7B Instruct v0.2

Description

A high-performing, industry-standard 7.3B parameter model, with optimizations for speed and context length.

An improved version of Mistral 7B Instruct, with the following changes:

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?

To create dataclasses with Marshmallow for the given JSON structure, follow the steps below. first, you need to install the Marshmallow library if you haven't already. You can do that by running pip install Marshmallow.

Now, let's define the dataclasses:

from datetime import date
from decimal import Decimal
from pydantic import BaseModel
from marshmallow import fields, post_processor

class Person(BaseModel):
    first_name: str
    last_name: str
    date_of_birth: date

    class Config:
        allow_population_by_field_name = True

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

class OrderData(BaseModel):
    orders: List[Order]

class InputData(Person, OrderData):
    pass

class InputDataSchema(InputData):
    class Meta:
        unknown = EXclude

    date_of_birth = fields.Date('date_of_birth')
    created = fields.DateTime('created', format='%Y-%m-%d %H:%M:%S')
    amount = fields.Decimal('amount', precision=2)

    orders = fields.List(fields.Nested(Order))

    class Config:
        metadata = {'unknown': 'ignore'}

input_schema = InputDataSchema()

Let me explain this code snippet:

  1. We import the necessary modules, including Marshmallow's fields and post_processor, Pydantic's BaseModel and List, and Python's datetime and decimal modules.
  2. We define the Person dataclass with the first_name, last_name, and date_of_birth fields.
  3. We set the Config property of the Person class to allow the deserialization of fields by their names.
  4. We define the Order dataclass with the created and amount fields.
  5. We define the OrderData dataclass, which contains a list of Order objects.
  6. We define the InputData dataclass that inherits from both Person and OrderData.
  7. We define the InputDataSchema dataclass that inherits from InputData. Here, we set the Meta property to tell Marshmallow to ignore any unknown fields.
  8. We set the date_of_birth, created, and amount fields to use Marshmallow's Date, DateTime, and Decimal field types, respectively.
  9. We define the orders field as a list of nested Order objects using Marshmallow's List and Nested field types.

With this code, you should have dataclasses that Marshmallow can deserialize and serialize the provided JSON into and out of.

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) (1.01¢)

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

  1. The solution defines two dataclasses (Person and Order), but also includes additional classes. (2 marks)

  2. The solution doesn't explicitly define two schema classes as required. It defines one schema class (InputDataSchema) that combines both person and order information. (0 marks)

  3. The date format for the date_of_birth field is not specified correctly. It should be format="%m/%d/%Y", but this is missing. (0 marks)

  4. The decimal field (amount) is defined correctly, but as_string=True is missing. (0 marks)

  5. The solution doesn't include a @post_load method to link the schema and dataclasses. (0 marks)

  6. Additional points:

  7. The solution uses Pydantic's BaseModel instead of dataclasses, which is a different approach but can work for similar purposes. (1 mark)
  8. The solution attempts to handle nested structures with the OrderData class. (1 mark)

Comments:
The solution mixes concepts from Pydantic and Marshmallow, which leads to some confusion. It doesn't fully adhere to the requirements of using Marshmallow for creating dataclasses. The date format, decimal field configuration, and @post_load method are missing. The schema structure doesn't match the expected two separate schemas for Person and Order.

Here's the breakdown of marks:

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

Final score:

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

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

The provided solution seems to fundamentally misunderstand the task, particularly when integrating Python's dataclasses with the Marshmallow library for serialization and deserialization.

1. The solution states using Pydantic's BaseModel instead of Python's dataclasses. Since Pydantic is an entirely different framework from dataclasses and not required by the problem statement, it deviates from the expected structure. Pydantic's BaseModel is thereby irrelevant to a solution involving standard dataclasses and Marshmallow (2 marks lost).

2. There are no proper dataclass annotations for Person or Order. Both are defined as subclasses of BaseModel (from Pydantic), not dataclasses. Additionally, the use of dataclasses with specific types like date, datetime, and Decimal is missing (2 marks lost).

3. While the solution specifies a date format for date_of_birth correctly, it mixes the format notation and does not employ Marshmallow's schema formatting correctly. Also, the code snippet erroneously shows a 'date_of_birth' being set outside the fields setup for Meta class (1 mark lost).

4. There is no usage of as_string=True directive for the amount (Decimal type) field, which is key for ensuring the marshaled data retains its precision and type-specific nature intact (1 mark lost).

5. No proper use of Marshmallow’s @post_load decorator is present to link data deserialized by the schema back to corresponding dataclasses, culminating in the absence of deserialized objects being turned back into respective dataclass instances (2 marks lost).

6. Several other errors and limitations include:
- Misuse of EXclude in unknown = EXclude, which is neither a valid keyword nor correct syntax (note the lack of Python identifier rules compliance and spelling). If aiming to exclude unknown fields, the correct syntax for the marshmallow should be EXCLUDE (case-sensitive).
- No demonstration of tying input JSON through schema for serialization/deserialization.
- Various syntax and logical structure errors in the code presented as a so-called "solution" (2 marks lost).

Summary:
- Incorrect use of Pydantic instead of dataclasses.
- Misconfiguration and syntax errors in handling Marshmallow schema.

Based on the evaluation factors, this solution fails to properly demonstrate essential elements of the task and introduces incorrect libraries and methods. Hence, the following score feels appropriate:

{
  "score": 0
}

To define dataclasses with Marshmallow that meet your requirements, you'll first need to install the Marshmallow library if you haven't already:

pip install marshmallow marshmallow-datetimes marshmallow-sqlalchemy

Now, create three separate files for the following components: a schema, a model, and a helper for converting to Marshmallow's native data types:

  1. _ conversion helper: "marshmallow_converters.py"
from marshmallow import fields
from marshmallow.utils import formalize_datetime, iso8601

class DateTimeConverter(fields.Converter):
    def convert(self, value, **kwargs):
        if value is not None:
            return iso8601.parse(value)

class DateConverter(fields.Converter):
    def convert(self, value, **kwargs):
        if value is not None:
            return value.replace(year=1, month=1, day=1).replace(hour=0, minute=0, second=0, microsecond=0)

class DecimalConverter(fields.Converter):
    def convert(self, value, **kwargs):
        if value is not None:
            return decimal.Decimal(value)
  1. Model: "model.py"
from datetime import date
from marshmallow import fields, Schema, BaseSchema, class_maybe
import decimal
from marshmallow_datetimes import DateTime, Date
from ._converters import DateTimeConverter, DateConverter, DecimalConverter

@class_maybe
class OrderSchema(BaseSchema):
    created = fields.Field(DateTime(), sort=True)
    amount = fields.Field(Decimal(), required=True)

class CustomerSchema(BaseSchema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Field(Date())
    orders = fields.Nested(OrderSchema, many=True)

class Customer(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Field(DateConverter())
    orders = fields.List(fields.Nested(OrderSchema), required=True)
  1. Main: "main.py"
from marshmallow import validate
from ._converters import DateTimeConverter
from .model import CustomerSchema, Customer

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 = CustomerSchema().load(json_data, unknown=EXinclude)
print(data)

The "CustomerSchema" and "Customer" classes in your "model.py" file define your dataclasses using Marshmallow Schema classes. The "_converters.py" file defines custom converters for the native Marshmallow data types.

Make sure that "marshmallow-sqlalchemy" is imported in your main file ("main.py") if it's necessary for your use case.

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) (1.15¢)

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

  1. Defining two dataclasses:
    The solution doesn't explicitly define dataclasses. It defines schema classes using Marshmallow, but not Python dataclasses.

  2. Defining two schema classes:
    The solution defines two schema classes: OrderSchema and CustomerSchema. (2 marks)

  3. Specifying date format for date_of_birth:
    The solution doesn't specify the date format as required (format="%m/%d/%Y"). (0 marks)

  4. Decimal field with as_string=True:
    The solution doesn't use as_string=True for the decimal field. (0 marks)

  5. Linking schema and dataclasses with @post_load:
    The solution doesn't use @post_load to link schema and dataclasses. (0 marks)

  6. Anything else:

  7. The solution provides custom converters for DateTime, Date, and Decimal fields.
  8. It uses Marshmallow's fields and Schema classes.
  9. It includes a main script to load and print the data.
    (1 mark)

Breakdown of marks:

{
  "Defining dataclasses": 0,
  "Defining schema classes": 2,
  "Date format specification": 0,
  "Decimal field as_string": 0,
  "post_load usage": 0,
  "Additional features": 1
}

Score:

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

Comments:
The solution uses Marshmallow to define schemas, which is a step in the right direction. However, it misses several key requirements:
1. It doesn't use Python dataclasses.
2. It doesn't specify the correct date format for date_of_birth.
3. It doesn't use as_string=True for the decimal field.
4. It doesn't use @post_load to link schemas and dataclasses.

The custom converters are a good addition, but they don't fully meet the requirements of the task. The solution would need significant modifications to fully address the problem as specified.

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

The provided solution outlines an attempt to create dataclasses and schemas using the Marshmallow library in Python to deserialize JSON data to objects. However, there are several fundamental issues and errors that undermine the proper application and functionality of Marshmallow for the stated requirements.

Breakdown of the Submitted Solution

  • Two Dataclasses and Schemas: The solution does not define Python dataclasses; instead, it attempts to use Marshmallow's Schema class to represent both Orders and Customers, misinterpreting the use of actual dataclasses (1/2 mark). Definitions for schemas are attempted but are incorrectly structured and contain errors (1/2 mark).

  • Date and Datetime Formatting: There is no specific configuration for date_of_birth to parse the date with format %m/%d/%Y, and created to parse datetime. Also, manual converters like DateTimeConverter and DateConverter are wrongly used and implemented (0 marks).

  • Decimal Field Handling: The solution fails to specify as_string=True for handling decimal fields, which is necessary for ensuring amount is treated as a string representation of Decimal (0 marks).

  • @post_load Method: There is no use of the @expecteded, the Marshmallow post_load decorator that is crucial for linking loaded data back to a corresponding dataclass or plain object. This is essential for converting loaded data to model instances (0 marks).

  • Other Considerations & Errors:

  • Erroneous and misunderstood use of class_maybe and misconceptions regarding schema bindings (0/2 marks).
  • Incorrect attempt to load JSON string directly without parsing it into a Python dictionary first.
  • Inclusion of redundant and incorrect imports and methods.
  • Misuse and misunderstanding of Marshmallow concepts and how they relate to Python dataclasses.

Conclusion

The solution provided misinterpretes the use of Marshmallow with respect to Python dataclasses and fails to meet several of the criteria established by the task. Significant misunderstanding of schema definition, data conversion, and the proper utilization of Marshmallow features such as date conversion, data loading, and serialization options heavily impairs the functionality of the proposed implementation.

{
  "score": 1.5
}

This score reflects fundamental misunderstandings and missing elements required for a functional and correct Marshmallow implementation based on the requirements.

To achieve this using the Marshmallow library in Python, you first need to define your data classes and their corresponding schema classes. Here's an example:

from datetime import date, datetime
from decimal import Decimal
from pydantic import BaseModel
from marshmallow import fields, Schema, post_load

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

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

class Person(BaseModel):
    first_name: str
    last_name: str
    date_of_birth: date
    orders: List[Order] = fields.List(OrderSchema(many=True))

class PersonSchema(Schema):
    first_name = fields.String('first_name')
    last_name = fields.String('last_name')
    date_of_birth = fields.Date('date_of_birth')
    orders = fields.List(fields.Nested(OrderSchema))

    @post_load
    def make_date(self, data, **kwargs):
        data['date_of_birth'] = date.fromisoformat(data['date_of_birth'].isoformat().split('T')[0])
        return data

    @post_load
    def make_datetime(self, data, **kwargs):
        data['orders'] = [o.update({'created': datetime.fromisoformat(o['created'].isoformat().split('T')[0])}) for o in data['orders']]
        return data

json_data = '''
{
  "first_name": "Jane",
  "last_name": "Smith",
  "date_of_birth": "2001-03-16",
  "orders": [
    {
      "created": "2024-01-25T15:00:04Z",
      "amount": "100.00"
    }
  ]
}
'''

person_data = PersonSchema().load(json.loads(json_data))
print(person_data)

Here's an explanation of the code:

  1. We define Order and OrderSchema classes for the 'orders' item in the JSON.
  2. We define Person and PersonSchema classes for the main JSON object.
  3. We use the post_load decorator to convert the strings representing dates into date objects and strings representing datetimes into datetime objects.
  4. We parse and load the JSON data into a Person object using the PersonSchema.

Note that you'll need to install the required libraries by running pip install marshmallow pydantic.

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) (1.09¢)

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

  1. The solution defines two dataclasses, Order and Person. However, these are Pydantic models, not dataclasses. (1/2 marks)

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

  3. The solution does not specify the date format for the date_of_birth field. It should have used format="%m/%d/%Y". (0/1 mark)

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

  5. The schema and dataclasses are linked with @post_load methods on the PersonSchema class. However, the implementation is not correct and doesn't properly create the dataclass instances. (1/2 marks)

  6. Additional observations:

  7. The solution uses Pydantic models instead of dataclasses, which wasn't asked for.
  8. The date parsing in the @post_load methods is unnecessarily complex and doesn't match the required format.
  9. The solution doesn't handle the specific date format given in the problem (03/16/1977).
  10. The make_datetime method incorrectly tries to update a dictionary instead of creating an Order instance.
    (1/2 marks)

Breakdown of marks:

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

Score:

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

The solution attempts to solve the problem but has several issues and doesn't fully meet the requirements. It uses Pydantic models instead of dataclasses, doesn't handle the specified date formats correctly, and has some implementation errors in the @post_load methods. The total score is 5 out of 10, indicating that there's significant room for improvement to fully meet the problem's requirements.

openai/gpt-4-0125-preview says: (1.0) ()

The provided solution contains several aspects to evaluate based on your criteria:

  1. Dataclass Definition: The solution inaccurately defines the Order and Person classes using BaseModel from Pydantic, not dataclass from the standard library, which was the expected in the task. (-2 points)

  2. Schema Definition: They used OrderSchema and PersonSchema based on Marshmallow's Schema, which fits the provided marking scheme. However, due to errors in data types and method implementations, it partially meets the criteria. (-1 point)

  3. Date Format Specification: The format for the date_of_birth is not specified explicitly using format="%m/%d/%Y". Instead, they make an approximation via a post-load function, yet this does not handle the original format shown in the provided JSON. (-1 point)

  4. Decimal Field Configuration: The solution doesn't include as_string=True in the Decimal field specification for the amount. This was specifically requested in the marking scheme. (-1 point)

  5. Post Load Method: Two @post_load methods are defined within PersonSchema. However, these methods are largely ineffective and incorrect in their implementation. The methods attempt to handle date transformations via incorrect operations that will not run successfully. The explanation provided is inadequate, leading to confusion and misinterpretation. (-2 points)

  6. Additional Requirements: The solution introduces additional errors and confusions:

  7. Use of BaseModel (Pydantic) instead of a traditional dataclass which is not compatible with marshmallow in typical usage.
  8. Misuses JSON key access with date formats that do not match those in the provided JSON.
  9. Implementation confusion between Pydantic and Marshmallow methods and decorators.
  10. Error in JSON handling at the end script likely leading to import errors and incorrect data parsing (-2 points).

Overall, significant issues and discrepancies from the requested task and intended use of libraries have led to the following score:

{
  "score": 1
}

The output simply does not meet the criteria efficiently and is laden with critical mistakes, misunderstandings, and incorrect library usage, thus achieving the lowest mark with 1 out of 10.

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