Mistral: Mistral 7B Instruct v0.3

Description

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

An improved version of Mistral 7B Instruct v0.2, with the following changes:

Extended vocabulary to 32768
Supports v3 Tokenizer
Supports function calling

NOTE: Support for function calling depends on the provider.

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 the Marshmallow library in Python, you can define your data classes and their corresponding serialization schema classes. Here's how you can do it:

First, let's create the data classes:

from datetime import date
from decimal import Decimal
from dataclasses import dataclass

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date

@dataclass
class Order:
    created: datetime
    amount: Decimal

Next, we'll create the Marshmallow schema classes for our data classes:

from datetime import datetime
from dateutil.parser import isoparse
from marshmallow import Schema, fields, DateTime, Decimal

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

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

Now you can load the JSON data using the load method from the PersonSchema class, and it will automatically deserialize the JSON data to Person data classes.

import json

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

person = PersonSchema().load(data)

# Access the person's properties
print(person.first_name)  # Jane
print(person.last_name)   # Smith
print(person.date_of_birth)  # datetime.date(1977, 3, 16)
print(person.orders[0].created) # datetime.datetime(2024, 1, 25, 15, 0, 4, tzinfo=...)
print(person.orders[0].amount) # 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: (5.0) (0.93¢)

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 been fields.Date(format="%m/%d/%Y"). (0 marks)
The decimal field does not include as_string=True. It should have been Decimal(as_string=True). (0 marks)
The schema and dataclasses are not linked with a @post_load method on the schema classes. (0 marks)
Additional observations:
The solution imports necessary libraries and modules.
It demonstrates how to use the schema to load JSON data.
The Order dataclass uses datetime instead of date for the created field, which is correct.
(1 mark)

Here's the breakdown of the marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema-dataclass linking": 0,
  "Additional observations": 1
}

Final score:

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

The solution provides a good starting point but misses some important details required in the marking scheme. It correctly defines the dataclasses and schema classes but fails to implement the specific requirements for date format, decimal field, and linking schemas to dataclasses using @post_load. These improvements would make the solution more robust and aligned with the given requirements.

openai/gpt-4-0125-preview says: (3.5) ()

The provided solution offers a structured approach using the Marshmallow library to deserialize JSON data into Python data classes but falls short in several critical areas that were specified in the problem statement:

Definition of dataclasses (2 Marks)
The solution appropriately defines two dataclasses, Person and Order. This alignment perfectly satisfies the requirement.
Score: 2/2
Definition of schema classes (2 Marks)
Two schema classes, PersonSchema and OrderSchema, are defined. However, the Order data class incorrectly uses DateTime instead of fields.DateTime() and Decimal instead of fields.Decimal(as_string=True). Additionally, fields.DateTime should be fields.Date in PersonSchema to correctly parse dates.
Score: 1/2
Specification of date format for date_of_birth (1 Mark)
The solution missed specifying the proper date format (format="%m/%d/%Y") for the date_of_birth field. This is critical for proper parsing of dates in the specified format.
Score: 0/1
Decimal field with as_string=True (1 Mark)
The solution neglects the requirement to treat the decimal field by setting as_string=True.
Score: 0/1
Link between schema and dataclasses via @post_load method (2 Marks)
No @post_load method was implemented in the schema classes. This method is important to transform the loaded data into instances of the respective data classes.
Score: 0/2
Additional Criteria (2 Marks)
The incorrect assignment of field types in schema and missing proper error handling or any example regarding the sensitivity of the structure, especially around transformation specifics, leads to some issues. Though basic loading and printing were demonstrated, these aren't error-proof given the setup issues.
Score: 0.5/2

Overall Evaluation:
Some fundamental aspects of using Marshmallow are illustrated, such as loading JSON data and basic serialization. However, crucial details specific to this task, particularly around data formats and class instantiation (via @post_load), were omitted or incorrectly applied. Thus, while some progress is shown in the interpretation of JSON data, the implementation details pertinent to full functionality as per the specified needs are lacking.

Final Score:

{
  "score": 3.5
}

Response 2 (6.0)

To achieve this using the Marshmallow library in Python, you'll first need to install it if you haven't already. You can do so using pip:

pip install marshmallow

Next, create a person.py file and define your dataclasses and their corresponding Marshmallow schema:

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

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

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

class Person:
    def __init__(self, first_name, last_name, date_of_birth, orders=None):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = date_of_birth
        self.orders = orders or []

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

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

Now, you can use the schemas 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'
    }
  ]
}
'''

person_data = json.loads(json_data)
person_schema = PersonSchema()
person = person_schema.load(person_data)

# Access the person object
print(person.first_name)  # Jane
print(person.date_of_birth)  # 1977-03-16
print(person.orders[0].created)  # 2024-01-25 15:00:04
print(person.orders[0].amount)  # 100.0

This example demonstrates how to create dataclasses with Marshmallow and handle different data types like date, datetime, and decimal. The post_load method is used to create instances of the Person and Order classes when loading the JSON 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: (6.0) (1.02¢)

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 does not include as_string=True. It should have been fields.Decimal(as_string=True). (0 marks)
The solution includes a @post_load method for the Person class, but it's incorrectly placed inside the Person class instead of the PersonSchema class. The Order class is missing a @post_load method. (1 mark)
Additional observations:
The solution imports necessary modules.
It provides an example of how to use the schemas to load JSON data.
The Order class correctly uses Decimal(amount) in its constructor.
(1 mark)

Here's the breakdown of marks:

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

Final score:

{
  "scores": [2, 2, 0, 0, 1, 1],
  "total_score": 6
}

The solution demonstrates a good understanding of using Marshmallow with dataclasses, but it misses some important details like specifying the date format and using as_string=True for the decimal field. The @post_load method implementation is partially correct but misplaced. Overall, the solution provides a functional base but requires some improvements to fully meet the requirements.

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

The provided solution does a fairly good job, but there are specific shortcomings with respect to the points outlined in the marking scheme:

Definition of two dataclasses, one for the person and one for the order: This has been done correctly with the Person and Order classes defined accurately. (2 marks)
Definition of two schema classes, one for the person and one for the order: The PersonSchema and OrderSchema classes have been defined. (2 marks)
Specification of the date format for the date_of_birth field: The solution fails to specify the date format within the PersonSchema. The field date_of_is of_birth in PersonSchema should have used fields.Date(format='%m/%d/%Y') but this was omitted. (0 marks)
Decimal field with as_string=True: The solution neglects to add as_string=True to the amount field in the OrderSchema, which is necessary for ensuring that the field is handled as a string representing a decimal. (0 marks)
Link schema and dataclasses using @post_load: The post_load method is used correctly to link the Person dataclass with PersonSchema, though the usage inside OrderSchema is missing to correspondingly instantiate Order objects. The use of post_load in PersonSchema correctly injects instances but the method isn't present in OrderSchema. Thus, only partial credit can be given. (1 mark)
Anything else: The code uses conventional imports and structuring effectively, the integration of the Marshmallow library seems fluent, and JSON data loading and usage were handled appropriately. Example usage in script format is helpful for understanding. (2 marks)

The overall score for this solution, based on the marking scheme provided, would be:

{
  "score": 7
}

These points reflect fairly well-structured code but miss important details crucial for correct data handling as per the requirements (like date formatting and precise Decimal representation).

Response 3 (4.0)

To achieve this using the Marshmallow library in Python, you first need to install it if you haven't already. You can do this by running:

pip install marshmallow

Next, create a new Python file and define the data classes for each part of your JSON. Here's the code for the data classes:

from datetime import date, datetime
from decimal import Decimal
from marshmallow import Schema, fields, DateTime, Date, Float

class BirthDate(fields.Field):
    def _serialize(self, value, attr, obj):
        return value.strftime('%Y-%m-%d')

    def _deserialize(self, value, attr, data):
        return date.fromisoformat(value)

class OrderCreated(fields.Field):
    def _serialize(self, value, attr, obj):
        return value.strftime('%Y-%m-%dT%H:%M:%S')

    def _deserialize(self, value, attr, data):
        return datetime.fromisoformat(value.replace('T', ' '))

class OrderAmount(fields.Float):
    pass

class User(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = BirthDate()

class Order(Schema):
    created = OrderCreated()
    amount = OrderAmount()

class RootSchema(Schema):
    user = User()
    orders = fields.List(Order())

Now, you can use the RootSchema to load and dump data into the JSON format:

import json

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

loaded_data = RootSchema().load(data)

# To save the data back to JSON
json_data = RootSchema().dump(loaded_data)
print(json.dumps(json_data, indent=2))

This code uses the custom BirthDate, OrderCreated, and OrderAmount fields to handle the date, datetime, and decimal types in your JSON data, respectively. The RootSchema takes care of loading and dumping the overall JSON structure.

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) (1.05¢)

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

The solution does not define dataclasses for the person and order. Instead, it defines Schema classes. (0/2 marks)
The solution defines three schema classes: User, Order, and RootSchema. (2/2 marks)
The solution does not specify the correct date format for the date_of_birth field. It uses ISO format instead of "%m/%d/%Y". (0/1 mark)
The solution does not use as_string=True for the decimal field. It uses a custom OrderAmount class that inherits from fields.Float. (0/1 mark)
The solution does not use @post_load methods to link schemas and dataclasses. (0/2 marks)
Additional points:
The solution provides custom field classes for handling date and datetime serialization and deserialization.
It includes a sample usage of the schema for loading and dumping data.
(2/2 marks)

Breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field configuration": 0,
  "@post_load method usage": 0,
  "Additional features": 2
}

Score:

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

Comments:
The solution provides a working implementation using Marshmallow schemas, but it doesn't fully meet the requirements specified in the marking scheme. It lacks the use of dataclasses, doesn't use the correct date format for date_of_birth, doesn't configure the decimal field as required, and doesn't use @post_load methods. However, it does provide custom field classes for date and datetime handling, which is a good approach. To improve, the solution should incorporate dataclasses, use the correct date format, properly configure the decimal field, and use @post_load methods to link schemas and dataclasses.

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

The provided solution incorporates the use of the Marshmallow library to serialize and deserialize data but fails to comply with the original problem's requirements in several key areas. Here’s a detailed analysis:

Dataclasses Definition (0/2 marks):
The solution specified uses Marshmallow's Schema classes but does not leverage actual Python dataclasses. This misses ensuring that Python's native dataclass is used for defining data models for User and Order which is a requirement.
Schema Classes Definition (2/2 marks):
The schemas for User and Order are correctly structured using Marshmallow Schema. The RootSchema setup also aligns with best practices for handling nested data structures.
Specifying Date Format (0/1 mark):
The solution uses custom fields for date and datetime but fails to explicitly set the date_of_birth date format to "%m/%d/%Y" as required. It handles ISO formats instead.
Decimal Field as String (0/1 mark):
The OrderAmount uses a floating point field without setting it to serialize the decimal amount as a string using as_string=True. This is a specific requirement given in the problem statement to ensure precision.
Linking Schema with Dataclasses (@post_load) (0/2 marks):
The solution does not use any @post_load method to link the Marshmallow schema with any dataclass, as there is no dataclass defined. This function is crucial for transforming the loaded data into an instance of a defined dataclass.
Additional Considerations (1/2 marks):
While the intentions behind custom fields (BirthDate, OrderCreated, OrderAmount) are in the correct direction to handle serialization and deserialization of specific formats, the execution diverts from the exact requirements of handling formats as stated, especially for dates. The overall structure and understanding of handling nested JSON is good.

Conclusion: The solution shows a misunderstanding or overlook of specific Python features like dataclasses and the exact details in the problem specifications regarding the handling and formatting of different data types.

Final Score:

{
  "score": 3
}

1.00	0.93	0.83
0.93	1.00	0.85
0.83	0.85	1.00

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