Mistral Tiny

Description

This model is currently powered by Mistral-7B-v0.2, and incorporates a "better" fine-tuning than Mistral 7B, inspired by community work. It's best used for large batch processing tasks where cost is a significant factor but reasoning capabilities are not crucial.

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)

To achieve this using Marshmallow library in Python, you'll first need to define your data classes and their corresponding schema classes. Here's how you can do it:

Define the data classes:

from datetime import date
from decimal import Decimal

class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

class Order:
    created: datetime
    amount: Decimal

Define the schema classes for serialization and deserialization:

from marshmallow import fields, Schema, DateTime, DateTimeStr, Decimal

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(format='%m/%d/%Y')
    orders = fields.List(fields.Nested(OrderSchema))

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal(precision=2)

Use the defined schema classes to load and dump 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"
    }
  ]
}
'''

# Load JSON data into a Person instance
person_data = PersonSchema().load(json.loads(json_data))

# Dump the Person instance into JSON format
json_person_data = PersonSchema().dump(person_data).data

print(json_person_data)

This way, you can use Marshmallow library to define your data classes and their corresponding JSON schema classes, making it easier to serialize and deserialize data while maintaining strong type hints.

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: (5.0) ()

Evaluation of the provided solution:

Data classes defined: The solution defines two data classes, Person and Order, as required. Each class has appropriate fields matching the problem description. (+2 marks)
Schema classes defined: The solution includes PersonSchema and OrderSchema classes using the Marshmallow library. (+2 marks)
Date format specification: The date_of_birth field in the PersonSchema class uses the fields.Date with the specified format '%m/%d/%Y' compliant with the requirements. (+1 mark)
Decimal as string: The requirement is to specify as_string=True for the decimal field, but the solution uses fields.Decimal(precision=2) without this property. The amount field requires the as_string=True flag to ensure it is serialized as a string. (-1 mark)
Linking schema with dataclasses via @post_load method: The solution provided does not contain the application of the @post_load methods in the schema classes. This method is necessary to ensure that the loaded JSON data is converted into instances of the data classes (Person and Order). (-2 marks)
Additional aspects (miscellaneous):
The code tries to handle serialization and deserialization but misses critical points like error handling and type validation in the runtime.
Good practices, such as using dataclass decorator for data classes and proper use of type hints, could enhance the solution. (-2 marks)

Total Score:

{
  "score": 5
}

This solution introduces the basics of using Marshmallow for handling JSON data according to a schema but needs improvements in handling decimals as strings and converting deserialized data back into data class instances properly. Moreover, integrating error handling and enhancing code robustness could further improve its effectiveness and reliability.

anthropic/claude-3.5-sonnet says: (5.0) (1.06¢)

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

Defining two dataclasses (2 marks):
The solution defines two classes: Person and Order. However, they are not defined using Python's @dataclass decorator. Half marks awarded.
Defining two schema classes (2 marks):
The solution correctly defines two schema classes: PersonSchema and OrderSchema. Full marks awarded.
Date format for date_of_birth (1 mark):
The solution correctly specifies the date format for date_of_birth as format='%m/%d/%Y'. Full mark awarded.
Decimal field with as_string=True (1 mark):
The solution uses fields.Decimal(precision=2) but doesn't include as_string=True. No marks awarded.
Linking schema and dataclasses with @post_load (2 marks):
The solution doesn't implement @post_load methods to link the schema classes with the dataclasses. No marks awarded.
Anything else (2 marks):
The solution imports necessary modules and demonstrates how to use the schema classes for loading and dumping JSON data.
It uses type hints in the class definitions.
However, it doesn't use the dataclasses module as requested.
1 mark awarded.

Breakdown of marks:

{
  "Dataclasses": 1,
  "Schema classes": 2,
  "Date format": 1,
  "Decimal field": 0,
  "Post load": 0,
  "Anything else": 1
}

Final score:

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

The solution demonstrates a good understanding of using Marshmallow for JSON serialization and deserialization. However, it misses some key requirements, such as using the dataclasses module, implementing @post_load methods, and setting as_string=True for the decimal field. The total score is 5 out of 10, indicating that there's room for improvement to fully meet the specified requirements.

Response 2 (7.5)

To create dataclasses with Marshmallow that meet your requirements, follow these steps:

First, install Marshmallow using pip:

pip install marshmallow marshmallow-enum marshmallow-datetimes

Next, create your dataclasses and Marshmallow schema:

from dataclasses import dataclass
from marshmallow import fields, DateTime, Decimal, Schema, post_load

@dataclass
class Order:
    created: DateTime
    amount: Decimal

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: DateTime
    orders: List[Order] = field(default_factory=list)

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

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.DateTime()
    orders = fields.List(fields.Nested(OrderSchema))

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

Now you should have your Person and Order dataclasses, as well as their corresponding Marshmallow schemas, which meet your requirements for date formats and decimal handling.

Using the schema, you can load and serialize data as needed:

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

person_schema = PersonSchema()
loaded_person = person_schema.load(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: (7.5) (0.96¢)

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's missing the format="%m/%d/%Y" parameter. (0 marks)
The decimal field uses quantity_as_string=True instead of as_string=True. While this is not exactly correct, it shows an understanding of the need for string representation. (0.5 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 load data.
The solution uses type hints in the dataclasses.
(2 marks)

Comments:
The solution is generally good, but it misses a few key details. The date format for date_of_birth is not correctly specified, which could lead to parsing errors. The decimal field uses a slightly incorrect parameter name. The @post_load decorator is missing for the OrderSchema, which means Order objects won't be automatically created when deserializing data. Despite these issues, the overall structure and approach are correct.

Here's the breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 2,
  "Date format specification": 0,
  "Decimal field specification": 0.5,
  "post_load methods": 1,
  "Additional points": 2
}

Final score:

{
  "scores": [2, 2, 0, 0.5, 1, 2],
  "total_score": 7.5
}

openai/gpt-4-0125-preview says: (5.0) ()

The solution provided attempts to fulfill the problem requirements by using the marshmallow library to create dataclasses and schemas for serializing and deserializing JSON data. Let's evaluate it based on the provided rubric:

Two dataclasses definition:
The solution defines two dataclasses Order and Person. (2 marks)
Two schema classes definition:
The solution correctly defines two schema classes, OrderSchema and PersonSchema. (2 marks)
Specify date format for the date_of_birth field:
The solution fails to specify the correct date format for date_of_birth. It's mentioned in the problem that the format should be %m/%d/%Y, but this has not been implemented in the PersonSchema definition. (0 marks)
Decimal field 'as_string' attribute:
The attribute quantity_as_string=True is incorrectly noted—it should be as_string=True. Moreover, the fields.Decimal should include as_string=True to meet the requirement. This is incorrectly or inadequately addressed. (0 marks)
Link schema and dataclasses with a @post_load method:
The @post_load method is correctly implemented in the PersonSchema to create a Person instance from the loaded data. (2 marks)
Additional aspects (imports and overall integration):
Most necessary imports seem correctly stated except List and DateTime from dataclasses are correctly imported, but DateTime isn't a valid dataclass field type, and Decimal should be from decimal, not directly as a dataclass type. Required fields from fields are used correctly, but there's an incorrect attribute quantity_as_string=True instead of as_string=True. The data format for date_of_birth in PersonSchema is not set properly according to the problem’s requirements. (-1 mark for the incorrect types and missing imports related to dataclass fields, and for insufficient attribute application)

Total Score: 5 out of 10.

{
  "score": 5
}

Response 3 (4.0)

To create dataclasses using Marshmallow in Python with the specified data types for date_of_birth, created, and amount, follow these steps:

Install the required libraries: You need Marshmallow, Marshmallow-dataclasses, and Decimal (python-decimal) packages. You can install them using pip:

pip install marshmallow marshmallow-dataclasses python-decimal

Below is an example of how you can create dataclasses with Marshmallow using your JSON data as a reference:

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

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

class Person(BaseModel):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.List(field_handler=OrderSchema())

    @post_load
    def make_person(self, data, **kwargs):
        return self.model(**{k: Decimal(v) if k in ('amount',) else v for k, v in data.items()})

class PersonSchema(Schema):
    model = Person

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

result = PersonSchema().load(json_data)
person = result.data

print(person.first_name)    # 'Jane'
print(person.last_name)     # 'Smith'
print(person.date_of_birth) # datetime.date(1977, 3, 16)
print(person.orders)        # [Order(created=datetime.datetime(2024, 1, 25, 15, 0, tzinfo=<DstTzInfo 'EST' EST-5:00>, amount=Decimal('100.00')))]

In this example:
- We created two classes OrderSchema and Person using dataclasses mixin (BaseModel from pydantic).
- OrderSchema handles the lists of orders as lists of OrderSchema instances.
- PersonSchema is used for loading JSON data into the Python dataclasses using Marshmallow.
- Additionally, the make_person method in the Person class converts amount to a Decimal object after loading the 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) (1.04¢)

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

The solution defines two classes, Person and OrderSchema, but they are not proper dataclasses. Person is a Pydantic model, and OrderSchema is a Marshmallow Schema. (0/2 marks)
The solution defines two schema classes, OrderSchema and PersonSchema. (2/2 marks)
The solution does not specify the date format for the date_of_birth field. (0/1 mark)
The solution does not use as_string=True for the decimal field. (0/1 mark)
The solution includes a @post_load decorator on the Person class, but it's not correctly implemented as it should be on the schema class. (1/2 marks)
Additional observations:
The solution mixes Pydantic and Marshmallow, which is not ideal.
The make_person method attempts to convert values to Decimal, but it's not correctly implemented.
The solution doesn't properly handle the nested structure of orders.
(1/2 marks)

Here's the breakdown of marks:

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

Final score:

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

The solution demonstrates some understanding of Marshmallow and attempts to solve the problem, but it has several issues and doesn't fully meet the requirements. It mixes different libraries (Pydantic and Marshmallow) which wasn't asked for, and doesn't correctly implement the dataclasses or handle the specific field requirements. The total score is 4 out of 10.

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

The provided solution uses a combination of Python libraries including Marshmallow, but also pydantic, which is not part of the original problem's requirement. Here's a detailed evaluation based on the criteria:

Dataclasses Definition (2 marks):
- The solution does not properly use Python's dataclasses module. It instead uses the BaseModel from pydantic, which is conceptually similar but not the same as dataclasses.
- Score given: 0/2
Schema Classes Definition (2 marks):
- The solution defines OrderSchema and PersonSchema using the Marshmallow library as required.
- Score given: 2/2
Date Format Specification for date_of_birth (1 mark):
- The date format for date_of_birth should have been specified using format="%m/%d/%Y", but this was not explicitly done in the solution.
- Score given: 0/1
Decimal Field as_string=True (1 mark):
- The solution does not set the amount field with as_string=True in the fields.Decimal() constructor.
- Score given: 0/1
Linking Schema and Dataclasses with @post_load Method (2 marks):
- Although a @post_load method is mentioned (make_person), its implementation and usage is incorrect and conceptually flawed due to the misuse of pydantic's BaseModel in place of a dataclass. Moreover, the conversion logic for the decimal within make_person is inappropriate for this setup, as it should be an innate part of marshmallow's schema handling.
- Score given: 0/2
Other Considerations (2 marks):
- The implementation suggests a mix-up between the purposes and functionalities of pydantic and marshmallow, which indicates a fundamental misunderstanding of the problem requirements.
- Explicit datatypes for date_of_birth and created are not handled correctly.
- Score given: 0/2

Final Score:

{
  "score": 2
}

This assessment is based on the effectiveness and accuracy in meeting the given requirements, and the correctness of the solution provided. Consequently, the score reflects significant misunderstandings and misapplications in the given solution.

1.00	0.87	0.86
0.87	1.00	0.89
0.86	0.89	1.00

Mistral Tiny

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