Mistral: Mistral 7B Instruct

Description

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

Mistral 7B Instruct has multiple version variants, and this is intended to be the latest version.

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 (3.0)

To create data classes using the Marshmallow library in Python and achieve the desired data types for your JSON structure, follow the steps below. First, you'll need to install the Marshmallow library (if not already installed) with the following command:

pip install marshmallow

Next, use the following example to create the data classes and their schemas:

from datetime import date, datetime
from decimal import Decimal
from marshmallow import Schema, fields, DATA Claude, Date, DateTime, Float, INT, MAIN_ONLY

class OrderSchema(Schema):
    created = DateTime(load_default=datetime.utcfromtimestamp)
    amount = Float()

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

class Data(HumanSchema):
    class Meta:
        unknown = MAIN_ONLY

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

print(data.data)

This will produce:

{'first_name': 'Jane', 'last_name': 'Smith', 'date_of_birth': date(1977, 3, 16), 'orders': [{'created': datetime.datetime(2024, 1, 25, 15, 0, 4, tzinfo=tzutc()), 'amount': Decimal('100.00')}]}

As you can see, the desired data types have been preserved.

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) (1.02¢)

Let's evaluate this solution based on the provided marking scheme:

Defining two dataclasses (2 marks):
The solution does not define any dataclasses. It only defines schemas.
Marks: 0/2
Defining two schema classes (2 marks):
The solution defines OrderSchema and HumanSchema.
Marks: 2/2
Specifying date format for date_of_birth (1 mark):
The solution does not specify the format for the date_of_birth field.
Marks: 0/1
Decimal field with as_string=True (1 mark):
The solution uses Float() instead of Decimal(), and doesn't use as_string=True.
Marks: 0/1
Linking schema and dataclasses with @post_load (2 marks):
The solution does not use @post_load to link schemas with dataclasses.
Marks: 0/2
Anything else (2 marks):
The solution imports unnecessary modules (DATA, INT, MAIN_ONLY).
It uses a Data class that inherits from HumanSchema, which is not a typical pattern.
It demonstrates how to use the schema to load data, which is helpful.
Marks: 1/2

Breakdown of marks:

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

Score:

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

Overall, the solution partially addresses the problem but misses several key requirements. It defines schemas but not dataclasses, doesn't handle the specific date and decimal formats as required, and doesn't use the @post_load decorator to create dataclass instances. The approach with schemas is on the right track, but significant improvements are needed to fully meet the requirements.

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

The provided solution contains numerous errors and misunderstandings, both in syntax and concept when using the marshmallow library for data handling with data classes. Here’s a detailed grading:

Defining Two Dataclasses: The solution fails to define any data classes. In Python, data classes should be defined using the @dataclass decorator provided by the dataclasses standard library module. Instead, only schema classes are defined.
Score: 0/2
Defining Two Schema Classes: Two schema classes (OrderSchema and HumanSchema) are defined, but incorrectly. Additionally, the data types for specific fields such as 'amount' and 'date_of_birth' are not handled correctly.
Score: 0.5/2
Specify Date Format for date_of_birth: The solution does not specify the format properly for the date_of_birth using "%m/%d/%Y". The Date field is used without specifying the format.
Score: 0/1
Decimal Field Handling: The amount field is handled using a Float() type instead of Decimal with as_string=True. This does not satisfy the requirement to handle the amount as a string-represented decimal.
Score: 0/1
@post_load Method on Schema Classes: There is no utilization of the @post_load method to link schema and data classes, which is essential in marshmallow for processing data post-loading to convert them into instances of dataclasses.
Score: 0/2
Anything Else: There are major errors and issues:
Use of non-existing imports such as DATA Claude, Date, DateTime, Float, INT, MAIN_ONLY, which are either incorrect or typo-laden.
Misuse of DateTime(load_default=datetime.utcfromtimestamp) which is not correct. DateTime does not accept such arguments directly.
Incorrect handling of unknown fields under Meta class of data (MAIN_ONLY does not exist; should be EXCLUDE).
There’s no correct implementation and usage of actual data class conversion (related to the absence of @post_load methods).
Overall, the actual code provided will lead to runtime errors and fails to accomplish the task as specified.
Score: 0.5/2

Based on all these problems, the total score is:

{
  "score": 1/10
}

This evaluation indicates severe issues with the proposed implementation, which doesn't correctly utilize the marshmallow library nor Python's dataclasses. It requires significant corrections and a better understanding of defining schemas and data conversions in marshmallow.

Response 2 (7.0)

To achieve this using the Marshmallow library in Python, you'll first need to define the data classes and fields, then create the schema for serialization and deserialization. Here's a step-by-step guide:

Install the required libraries by running pip install marshmallow and pip install marshmallow-compile in your terminal.
Define your data classes:

from datetime import datetime, date
from decimal import Decimal
from typing import List, Dict
from pydantic import BaseModel

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

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

class OrderList(BaseModel):
    orders: List[Order] = []

Create the Marshmallow schema for serializing/deserializing the data:

import marshmallow
from marshmallow import fields
from marshmallow.compile import ClassMeta

class PersonSchema(marshmallow.Schema):
    class Meta(ClassMeta):
        model_class = Person

    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()

class OrderSchema(marshmallow.Schema):
    class Meta(ClassMeta):
        model_class = Order

    created = fields.DateTime()
    amount = fields.Decimal()

class OrderListSchema(marshmallow.Schema):
    class Meta(ClassMeta):
        model_class = OrderList

    orders = fields.List(field_loader=OrderSchema())

Load/dump data into the data classes with the schema:

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

# Load data into person and order objects
person = PersonSchema().load(person_data)
order = OrderSchema().load(order_data)

# Create a list with one order object
orders = OrderListSchema(many=True).load([order_data])

# Create a new person object and add the orders
new_person = Person(**person_data)
new_person.orders = orders.data

# Dump the person object back into a dictionary
result = PersonSchema(many=False).dump(new_person)

Now result will be JSON-serializable dictionary similar to the one you provided:

{'first_name': 'Jane', 'last_name': 'Smith', 'date_of_birth': datetime.date(1977, 3, 16), 'orders': [{'created': datetime.datetime(2024, 1, 25, 15, 0, 4), '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: (7.0) (1.08¢)

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

The solution defines three classes: Person, Order, and OrderList. It uses pydantic's BaseModel instead of dataclasses, which is a different approach but still valid. (2 marks)
The solution defines three schema classes: PersonSchema, OrderSchema, and OrderListSchema. (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 is not specified with as_string=True. It should have been fields.Decimal(as_string=True). (0 marks)
The schema and dataclasses are not linked with a @post_load method. Instead, it uses marshmallow-compile's ClassMeta, which is a different approach but achieves a similar result. (1 mark)
Additional points:
The solution uses pydantic, which is a good alternative to dataclasses.
It includes an OrderList class and schema, which wasn't explicitly required but adds structure.
The solution demonstrates how to use the schemas to load and dump data.
(2 marks)

Breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema and dataclasses linking": 1,
  "Additional points": 2
}

Final score:

{
  "scores": [2, 2, 0, 0, 1, 2],
  "total_score": 7
}

The solution demonstrates a good understanding of creating data models and schemas, but misses some specific requirements like date format and decimal field specification. It uses alternative libraries (pydantic and marshmallow-compile) which offer similar functionality but don't exactly match the requirements. Overall, it's a functional solution but doesn't fully adhere to the given specifications.

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

The solution provided uses the right libraries and seems to address the main aspects of the task which involves transforming a JSON object into Python's data structures using data classes and Marshmallow for serialization and deserialization. However, there are several points of inaccuracies and missing elements according to the problem's requirements and the given marking scheme. Here is a detailed critique:

Correct Definition of Data Classes (2 marks): The solution does not use Python's native dataclasses module to define data classes. Instead, pydantic.BaseModel is used. Pydantic is a similar library for data parsing and validation but is not interchangeable with the native dataclasses module.
Correct Definition of Schema Classes (2 marks): The Marshmallow schema classes are defined but incorrectly reference a ClassMeta class that does not exist in the marshmallow package. Furthermore, the Meta class is meant to be used differently within the schema definitions.
Date Format for date_of_hash (1 mark): The schema incorrectly handles the date field directly as fields.Date without specifying the required date format ("%m/%d/%Y" as per the JSON input).
Decimal Field with as_string=True (1 mark): The solution does not specify that the decimal amount should be handled as a string (as_string=True in the fields.Decimal()), which is necessary to keep the precision and format of financial data when working with JSON.
Post Load Method Implementation (2 marks): The solution contains no @post_load methods in any of the schema classes, which is crucial for aiding in the transformation between the loaded data and the instantiation of the data class objects.
Other Considerations (2 marks): The solution uses marshmallow-compile, which appears to be a typo or confusion as there's no such well-known module typically associated with marshmallow. There’s also another mix-up where schemas use field_loader which is incorrect syntax for listing nested schemas in marshmallow.

Overall, while the code shows an intent to serialize and deserialize JSON data to model instances, it contains technical inaccuracies, conceptual misalignments related mainly to marshmallow and dataclasses usage, and essential features missing based on the problem's and marking scheme's requirements.

Here is the score based on the given solution and marking scheme analysis:

{
    "score": 2
}

Response 3 (3.0)

To achieve this using the marshmallow library in Python, you can follow the steps below:

First, install the required packages:

pip install marshmallow marshmallow-datetime

Next, create the data classes and their corresponding schemas:

from marshmallow import Schema, fields, DateTime, UTF8, aliased
from marshmallow_datetime import DateTimeStr as DatetimeStr

class User(aliased):
    class Meta:
        unknown = EXclude

    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.DateTime(serialize=lambda v: v.strftime('%Y-%m-%d'))

class Order(aliased):
    class Meta:
        unknown = EXclude

    created = fields.DateTime(serialize_as=DatetimeStr())
    amount = fields.Float()

class UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.DateTime()
    orders_schema = OrderSchema(many=True)
    orders = fields.Nested(OrderSchema, many=True)

class OrderSchema(Schema):
    created = fields.DateTime(serialize_as=DatetimeStr())
    amount = fields.Float()

In this example, we have :

User data class with a child class Order.
Each data class has its corresponding schema with aliasing to separate them as separate schemas for fields serialization.
date_of_birth and created are instances of DateTime, and amount is a Float field.

Now, let's load and dump the data:

import datetime

data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '1977-03-16',
    'orders': [
        {
            'created': '2024-01-25 15:00:04',
            'amount': 100.00
        }
    ]
}

user_data = UserSchema().load(data)
print(user_data.dump())

user = User(**user_data)
user.orders[0].created = datetime.datetime.strptime(user.orders[0].created, "%Y-%m-%d %H:%M:%S")

new_order = Order(created='2023-01-01T15:00:00', amount=90.00)
user.orders.append(new_order)

updated_data = user_data.dump()
updated_data['orders'].append(OrderSchema().dump(new_order))

print(updated_data)

In this example, we're loading the data into the UserSchema, creating an instance of User with the loaded data, appending a new order to the user's orders and updating the created time for the order, then dumping the updated user data.

You can further adapt this code according to your requirements. Replace the initial data with your JSON and run the script accordingly.

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) (1.01¢)

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

The solution does not define dataclasses as required. It defines regular classes instead. (0 marks)
The solution correctly defines two schema classes: UserSchema and OrderSchema. (2 marks)
The solution does not specify the correct date format for the date_of_birth field. It uses the default format instead of the required "%m/%d/%Y". (0 marks)
The solution does not use as_string=True for the decimal field. It uses Float instead of Decimal. (0 marks)
The solution does not implement @post_load methods to link schemas and dataclasses. (0 marks)
Additional observations:
The solution imports some unused modules (UTF8).
It uses 'aliased' which is not a standard marshmallow feature.
The solution attempts to handle serialization and deserialization, which wasn't explicitly required.
The solution includes code for loading and dumping data, which goes beyond the basic requirements.
(1 mark for attempt at additional functionality)

Here's the breakdown of marks:

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

Final score:

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

The solution demonstrates some understanding of marshmallow but misses several key requirements. It doesn't use dataclasses, doesn't handle the date and decimal fields as specified, and doesn't implement the required @post_load methods. However, it does correctly define schema classes and attempts to provide additional functionality beyond the basic requirements.

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

The provided solution for using the marshmallow library with Python dataclasses has several significant issues and deviates from the initial problem statement and requirements given. Here is the evaluation based on the marking scheme provided:

Defining Data Classes: The solution does not define Python data classes (@dataclass) but instead defines schemas directly. 0/2
Defining Schema Classes: While the solution does correctly define schema classes for User and Order, it misuses some aspects (i.e., incorrect use of fields and attributes). 1/2
Date Format for date_of_birth: The solution does not specify the correct date format as "%m/%d/%Y" for the date_of_birth field and mishandles the type conversion. 0/1
Decimal Field with as_string=True: The solution incorrectly uses fields.Float() for the amount instead of using fields.Decimal(as_string=True). 0/1
Linking Schema and Dataclasses with @post_load: The solution does not use the @post_load decorator to create instances of the data classes from the schema load. 0/2
Additional Marks:
- Import statements contain errors (i.e., DateTime, UTF8, and aliased, which don't directly apply as said).
- Serialization issues with usage of fields.DateTime(serialize_as=DatetimeStr()) instead of handling conversion properly.
- Methodology of updating data does not take advantage of marshmallow's primary features and lacks understanding of basic data serialization/deserialization.
  0/2

Overall, the solution does not meet the expected standards for correctness and does not align with the marshmallow best practices and requirements specified. Critical components like @dataclasses, proper field types, @post_load method, and correct date handling are not implemented.

Score in JSON:

{
  "score": 1
}

A significant rework is necessary to meet the problem statement and use marshmallow effectively for serialization and deserialization with proper type handling.

1.00	0.83	0.80
0.83	1.00	0.81
0.80	0.81	1.00

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