Mistral: Mixtral 8x22B Instruct

Description

Mistral's official instruct fine-tuned version of Mixtral 8x22B. It uses 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. Its strengths include:
- strong math, coding, and reasoning
- large context length (64k)
- fluency in English, French, Italian, German, and Spanish

See benchmarks on the launch announcement here.
#moe

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 (7.0)

Dataclasses are a great way to structure your data in Python, and Marshmallow is a powerful library for serializing and deserializing (converting to and from JSON). However, Python's dataclasses and marshmallow serve slightly different purposes: dataclasses help with structuring classes, while Marshmallow helps with data serialization and validation.

In your case, you would first define the dataclasses, then create Marshmallow schemas to serialize and deserialize them. Here's how you'd do that:

First, install the required libraries if you haven't already:

pip install marshmallow-dataclass

Define the dataclasses and Marshmallow schemas:

from dataclasses import dataclass
from datetime import date, datetime
from decimal import Decimal
from marshmallow import fields, Schema
from marshmallow_dataclass import dataclass as md_dataclass

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class User:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

class OrderSchema(md_dataclass.MarshmallowDataclass):
    class Meta:
        dataclass = Order

class UserSchema(md_dataclass.MarshmallowDataclass):
    class Meta:
        dataclass = User
    orders = fields.List(fields.Nested(OrderSchema))

Now, you can use the schemas to load the JSON data:

from marshmallow import ValidationError

try:
    user_schema = UserSchema()
    user = user_schema.load(json_data)
    print(user)
except ValidationError as e:
    print('Validation error', e.messages)

Replace json_data with the JSON data from your question. This code will create a User object from the given JSON data. The date_of_birth will be a date object, and the created field within the order will be a datetime object. The amount will be a Decimal object.

Note: Make sure that the date and datetime string in the JSON data are in the formats expected by Python: 'YYYY-MM-DD' for date, and ISO 8601 format for datetime objects. If they are not, you'll need to write some additional code to parse those fields and convert them into the formats expected by Python.

Also, be aware that datetime strings have different forms. The datetime object in your question is not in the ISO format (YYYY-MM-DDTHH:MM:SS). You should convert it to this format before using marshmallow.

The OrderSchema and UserSchema classes automatically handle the conversion of the 'created' field to a datetime, 'date_of_birth' to a date, and 'amount' to a Decimal value while deserializing the JSON.

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) (0.97¢)

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

The solution defines two dataclasses, Order and User. (2 marks)
The solution defines two schema classes, OrderSchema and UserSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. (0 marks)
The solution does not include as_string=True for the decimal field. (0 marks)
The solution uses marshmallow_dataclass which automatically links the schema and dataclasses, but it doesn't explicitly use a @post_load method. (1 mark)
Additional points:
The solution correctly imports necessary libraries. (1 mark)
It provides an example of how to use the schemas to load JSON data. (1 mark)

Here's the breakdown of marks:

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

Final score:

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

The solution provides a good foundation but misses some specific requirements. It correctly defines the dataclasses and schemas, and uses marshmallow_dataclass to simplify the process. However, it doesn't address the specific format for date_of_birth or the as_string=True requirement for the decimal field. The solution also notes the potential need for additional parsing of date and datetime fields, which is good, but doesn't provide the specific implementation.

openai/gpt-4-0125-preview says: (6.0) ()

The solution provided attempts to leverage Python's dataclasses and the marshmallow library effectively to define models and their corresponding serialization schema. Here is the evaluation based on the problem requirements and the provided schema:

Defining Dataclasses for the Person and Order:
Two dataclasses (Order and User) were defined correctly. Both dataclasses use appropriate typing.
Score: 2/2
Defining Schema Classes for Serialization:
Two schema classes (OrderSchema and UserSchema) are defined, each linked to their respective dataclass via md_dataclass.MarshmallowDataclass.
Score: 2/2
Handling Date and Decimal Fields Appropriately:
The solution correctly handles the serialization and deserialization of complex types (datetime, date, and Decimal), but crucial details concerning the formatting of date_of_birth as "%m/%d/%Y" and setting Decimal as string are not explicitly specified. marshmallow requires explicit declarations for handling formatting details.
Score: 0.5/2
Use of @post_load Method:
The solution does not demonstrate the use of @post_load in the schema definitions, which would typically be used to instantiate the dataclass with the loaded data. Instead, it uses md_dataclass.MarshmallowDataclass directly (which under the hood does handle instantiation) but not highlighting @post_load loses the point of custom post-load handling.
Score: 0/2
Miscellaneous Considerations:
The solution correctly identifies the need to parse dates and decimal values accurately from JSON to Python objects using type conversion.
Instructions for preparing and parsing the proper format dates especially if not aligning with default expectations were helpful, though more explicit code would clarify handling such discrepancies.
Installation and usage instructions were properly included.
Score: 1.5/2

Overall, the provided solution does a decent job at mapping JSON data into Python objects using dataclasses and marshmallow. However, it overlooks some details crucial for precise schema handling requirements specified in the problem, like exact date formatting and ensuring Decimal values are serialized as strings.

{
  "score": 6
}

Response 2 (6.0)

To achieve this, you should create the necessary data classes and use marshmallow's Schema and fields to serialize and deserialize the input JSON. Here's how you would structure your code:

First, install the required libraries if you haven't already:

pip install marshmallow dataclasses

Now, let's define the data classes and the marshmallow schema:

import json
from datetime import datetime, date
from decimal import Decimal
from typing import List
from dataclasses import dataclass
from marshmallow import Schema, fields

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class User:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: List[Order]

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

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

# Load the JSON data
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_schema = UserSchema()
result = user_schema.load(data)

# Create User object
user = User(**result.data)

print(user)

This code defines the User and Order data classes, and the respective marshmallow schemas to handle deserialization of the JSON data. Then it loads the JSON data and uses the UserSchema to create a User object with typed fields as 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: (6.0) (1.01¢)

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

The solution defines two dataclasses, User and Order. (2 marks)
The solution defines two schema classes, UserSchema 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 have 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 on the schema classes. (0 marks)
Additional points:
The solution imports necessary libraries.
It provides a sample JSON and demonstrates how to use the schema to load the data.
It shows how to create a User object from the loaded data.
(2 marks)

Breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 2,
  "Date format specified": 0,
  "Decimal field as_string": 0,
  "@post_load method": 0,
  "Additional points": 2
}

Score:

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

Comments:
The solution provides a good foundation by defining the necessary dataclasses and schema classes. However, it misses some important details:

The date format for date_of_birth is not specified, which may lead to parsing errors.
The Decimal field is not set to use strings, which can cause precision issues.
There's no @post_load method to create dataclass instances directly from the schema.

These omissions could lead to potential issues when working with the actual data. The solution demonstrates the basic structure but needs refinement to fully meet the requirements and ensure robust data handling.

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

The provided solution demonstrates an implementation approach using dataclasses and marshmallow to handle JSON serialization and deserialization in Python. Here's the evaluation based on the provided rubric:

Dataclasses Definition: The solution correctly defines two dataclasses, Order and User, as required. (+2)
Schema Classes Definition: Two schema classes, OrderSchema and UserSchema, are defined appropriately to match the respective data classes. (+2)
Date Format Specification: The provided solution mentions the need to handle dates but does not specify the date_of_birth field format explicitly in the schema using parameters like format="%m/%d/%Y". (-1)
Decimal Field as_string Parameter: The solution does not use the as_string=True parameter for the amount field in the OrderSchema. This is crucial for ensuring the field is handled as a string literal to preserve decimal precision in JSON. (-1)
Linking Schema and Dataclasses with @post_load: The provided solution does not implement any @post load method in the schemas, which is essential to properly construct the dataclasses (User and Order) from the loaded data. (-2)
Miscellaneous:
The solution handles the basic structure of using marshmallow with dataclasses.
However, the JSON string in the json_data variable is improperly formatted for Python (' should be " in JSON).
There's also a small issue in the transformation of JSON data to User object due to missing transformation (result.data should be result). (-1)

Overall, the key functionalities related to date-handling, decimal type specification, and linking dataclasses properly with @post_load decorators are missing or incorrectly implemented.

Additional potential points:
- The use of JSON loading and initiating the marshmallow schema is correctly shown, but due to minor code errors, it might lead to runtime errors.
- Proper error and exception handling for data validity (e.g., invalid date formats or types) isn't discussed or implemented.

{
  "score": 5
}

This score reflects missing critical elements and minor errors, though the base structure for working with dataclasses and schemas is set up.

Response 3 (6.0)

In order to use the marshmallow library to deserialize your JSON data into dataclasses, you'll need to install the marshmallow-dataclass library, which provides integration between marshmallow and Python's built-in dataclasses. You can install it using pip:

pip install marshmallow-dataclass

Next, let's define the dataclasses for your data and the corresponding schema:

from dataclasses import dataclass
from datetime import date, datetime
from decimal import Decimal
from marshmallow import Schema, fields
from marshmallow_dataclass import dataclass as md_dataclass

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class User:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

@md_dataclass(frozen=True)
class OrderSchema(Schema):
    created = fields.DateTime(data_key='created')
    amount = fields.Decimal(data_key='amount')

@md_dataclass(frozen=True)
class UserSchema(Schema):
    first_name = fields.Str(data_key='first_name')
    last_name = fields.Str(data_key='last_name')
    date_of_birth = fields.Date(data_key='date_of_birth')
    orders = fields.Nested(OrderSchema, many=True, data_key='orders')

# Now you can deserialize your JSON data into User dataclass

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

user = user_schema.loads(user_json)

In the code above, User and Order are defined as dataclasses using the dataclass decorator, while UserSchema and OrderSchema are defined as marshmallow schemas using the md_dataclass decorator and the appropriate fields. The schemas are used to deserialize the JSON data into dataclass instances.

Remember that this solution assumes a JSON string as input, but you can easily adapt it to work with a JSON file or another source.

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) (0.99¢)

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

The solution defines two dataclasses, User and Order. (2 marks)
The solution defines two schema classes, UserSchema 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 schema and dataclasses are not linked with a @post_load method on the schema classes. (0 marks)
Additional points:
The solution uses marshmallow-dataclass library, which is a good approach.
It correctly uses nested fields for the orders.
It provides an example of how to use the schema to deserialize JSON data.
(2 marks)

Breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 2,
  "Date format specified": 0,
  "Decimal field as_string": 0,
  "post_load method": 0,
  "Additional points": 2
}

Score:

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

The solution covers some key aspects of using marshmallow with dataclasses, but it misses important details like specifying the date format, using as_string=True for the decimal field, and linking the schema and dataclasses with a @post_load method. These omissions could lead to incorrect parsing of the date_of_birth and amount fields. Despite these issues, the overall structure is correct, and the use of marshmallow-dataclass is a good approach.

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

The provided solution attempts to use marshmallow and marshmallow-dataclass to parse JSON into Python dataclasses. Here's my assessment based on the specified criteria:

Define two dataclasses, one for the person and one for the order: (2 marks)
The solution defines two dataclasses, Order and User. This meets the criterion.
Define two schema classes, one for the person and one for the order: (2 marks)
The solution defines two schema classes, OrderSchema and UserSchema. This meets the criterion.
Specify the date format for the date_of_birth field: (0 marks)
The solution does not specify the date format for the date of_birth field as "%m/%d/%Y". The date field is mentioned but without the specific format required by the problem.
The decimal field needs as_string=True: (0 marks)
The as_string=True attribute is not set in the Decimal field specification in the OrderSchema. This attribute is essential to ensure that the amount is serialized correctly back to string format in JSON.
Link schema and dataclasses with a @post_load method: (0 marks)
No @post_load method is used in the solution. This decorator is essential to convert the loaded data into an instance of the defined dataclass upon deserialization.
Anything else: (1 marks)
The marshmallow-dataclass libraries are correctly included, and the overall structure of deserializing JSON into a User instance is correctly approached. The specific handling of nested data through fields.Nested(OrderSchema, many=True) for the list of orders is correct, enhancing the functionality of handling JSON structures.

Overall score:

{
  "score": 5
}

Despite defining classes and schemas correctly, significant problems exist with not specifying formats as required, missing as_string=True, and omitting the @post_load method, which are crucial in adhering to the problem's requirements and ensuring data types are handled properly during deserialization.

1.00	0.90	0.90
0.90	1.00	0.91
0.90	0.91	1.00

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