Mistral: Mistral 7B Instruct (nitro)

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.

Note: this is a higher-throughput version of Mistral 7B Instruct. It may have higher prices and slightly different outputs.

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 (6.0)

To create dataclasses using Marshmallow in Python, you'll first need to define your data classes and then create schemas for them. Here's the step-by-step process based on your provided JSON:

Define data classes:

from datetime import date
from decimal import Decimal
from enum import Enum
from typing import List

class OrderStatus(Enum):
    PROCESSING = "processing"
    SHIPPED = "shipped"
    DELIVERED = "delivered"

class Customer:
    def __init__(self, first_name, last_name, date_of_birth):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = date_of_birth

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

class OrderItem:
    pass

class OrderData:
    orders: List[Order]

Create Marshmallow schemas:

from datetime import datetime
from marshmallow import Schema, fields, DateTime, Decimal, post_validate

class DateTimeISO8601(DateTime):
    format = "%Y-%m-%d'T'%H:'%M:'%S"

class CustomerSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Function(DateTimeISO8601(output_format="iso"), serialize=lambda obj: obj.strftime(format="%Y-%m-%d"))

class OrderSchema(Schema):
    created = fields.DateTime('created', serialize_from=DateTimeISO8601(), deserialize=lambda v: datetime.fromisoformat(v) if v else None)
    amount = fields.Decimal('amount')
    status = fields.Str()

    @post_validate
    def validate_order_status(self, data, **kwargs):
        if not data["status"] or data["status"] not in [x.value for x in OrderStatus]:
            self.errors["status"] = ["Invalid status"]

class OrderItemsSchema(Schema):
    pass

class CustomerWithOrders(Schema):
    data = fields.Nested(OrderSchema, many=True)
    id = fields.Str()
    _class_ = Customer

class FullCustomerWithOrders(CustomerWithOrders):
    orders = fields.List(fields.Nested(OrderSchema), required=False)

Now, you have your dataclasses Customer, Order, and their corresponding Marshmallow schemas CustomerSchema, OrderSchema. Using these schemas, you can load and dump JSON data into your dataclasses.

For example, to load data:

json_data = '... your JSON string here ...'
loaded_data = CustomerWithOrders().load(json.loads(json_data))
print(loaded_data.data)

And to dump data:

customer = Customer("John", "Doe", date_of_birth=date(1995, 10, 12))
orders = [Order("2024-03-20T10:20:00", 100.5), Order("2024-04-12T14:30:20", 50)]
customers_orders = CustomerWithOrders(data=[OrderSchema().dump(o) for o in orders], id="example_id")
json_data = customers_orders.json()
print(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) (1.07¢)

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

The solution defines two dataclasses, Customer and Order. (2 marks)
The solution defines two schema classes, CustomerSchema and OrderSchema. (2 marks)
The solution doesn't specify the correct date format for the date_of_birth field. It uses ISO format instead of the required "%m/%d/%Y". (0 marks)
The decimal field doesn't include as_string=True. (0 marks)
The schema and dataclasses are not linked with a @post_load method on the schema classes. Instead, it uses a @post_validate method on OrderSchema. (0 marks)
Additional points:
The solution includes additional classes like OrderStatus (Enum), OrderItem, and OrderData.
It handles ISO8601 datetime format with a custom DateTimeISO8601 class.
It includes additional schemas like OrderItemsSchema, CustomerWithOrders, and FullCustomerWithOrders.
(2 marks)

Here's the breakdown of marks:

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

Final score:

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

The solution demonstrates a good understanding of creating dataclasses and schemas, but misses some specific requirements from the marking scheme. It includes some additional features that weren't explicitly requested but could be useful in a real-world scenario. The main areas for improvement are correctly formatting the date_of_birth field, adding as_string=True to the decimal field, and using @post_load to link schemas and dataclasses.

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

The provided solution attempts to use Python's Marshmallow library to deserialize and serialize JSON data according to the mentioned requirements. However, there are several noteworthy issues and mismatches with the requirements:

Data Classes Definition (1/2 marks):
The solution defines multiple classes (Customer, Order, OrderItem, and a non-standard OrderStatus and OrderData). However, only Customer and Order are relevant to the specified JSON. The solution unnecessarily introduces extra classes like OrderStatus and OrderData that are not required by the given problem. Hence, half mark is deducted for unnecessary complexity and irrelevant class definitions.
Schema Classes Definition (1/2 marks):
The CustomerSchema and OrderSchema have been defined but not entirely correctly according to the specifications, and the solution mistakenly introduces additional schemas (OrderItemsSchema and others) that are not part of the requirement. Only CustomerSchema and OrderSchema were needed. Some unnecessary fields and methods are present, resulting in a half mark deduction.
Date and DateTime Formats (0.5/1 marks):
The OrderSchema has an attempt to handle the datetime field, but the definition is quite complex and incorrectly done. The target format for date_of_birth and handling of created as a date and datetime, respectively, is not clearly implemented as per the specification, hence half mark.
Decimal Handling (0/1 marks):
The amount field is required to be handled as a string, but the as_string=True property is notably absent in the Decimal field definition, missing the specific requirement.
Linking Schemas to Dataclasses via @post_load (0/2 marks):
The solution entirely misses any @post_load methods to link schema deserialization to dataclasses. The misunderstandings and missing implementations in this area are significant, leading to forfeiting the full marks here.
Additional Elements (1/2 marks):
While the examples for loading and dumping data are provided, showing an aspect of practical usage, technical accuracy in terms of representing the specifications is not achieved. Due to partial correctness and effort, a half mark is awarded here.

Given these considerations, the solution contains numerous deviations and missing critical implementations from the requirements leading to a low score. Here's the overall evaluation:

{
  "score": 3
}

Response 2 (6.0)

To create dataclasses and use the Marshmallow library in Python for serializing and deserializing the given JSON data, follow the steps below:

First, create the dataclasses using Python's dataclass decorator and include the required fields with their types.

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

@dataclass
class OrderWithDateOfBirth:
    person: Person
    orders: List[Order]

Create Marshmallow schema classes for each dataclass for serialization and deserialization.

from datetime import datetime
from decimal import Decimal
from marshmallow import Schema, fields, DateTime, List, NESTED, Raise, ValidationError
from marshmallow_dataclasses import ClassMeta, dataclass_base

class DateTimeField(DateTime):
    format = "%Y-%m-%d"

@dataclass_base()
class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Function(lambda obj: obj.date_of_birth, serializer="serialize_date", deserializer="deserialize_date")

    class Meta:
        unknown = Raise()

class OrderSchema(Schema):
    created = fields.Function(lambda obj: obj.created, serializer="serialize_datetime", deserializer="deserialize_datetime")
    amount = fields.Function(lambda obj: obj.amount, serializer="serialize_decimal", deserializer="deserialize_decimal")

    class Meta:
        unknown = Raise()

class RootSchema(Schema):
    person = NESTED(PersonSchema)
    orders = List(NESTED(OrderSchema))

    class Meta:
        unknown = Raise()

OrderWithDateOfBirth = OrmBaseModel(metadata={'sqlalchemy.schema.Type': OrmType(PersonWithOrderModel)})
class PersonWithOrderModel:
    __tablename__ = "person_with_order"
    person = relationship("Person", back_populates="orders")
    orders = relationship("Order", back_populates="person")

class SerializedPersonWithOrderModel(Base):
    __tablename__ = "person_with_order_serialzed"
    __mapper_args__ = {
        "package": "your_package_name"
    }
    person = relationship("Person", uselistentrytype=False, back_populates="orders")
    orders = relationship("Order", back_populates="person")

    __mapper_args__ = {
        "polymorphic_identity": "SerializedPersonWithOrderModel"
    }

Implement the serialization and deserialization functions.

def serialize_date(obj):
    return obj.strftime("%Y-%m-%d")

def deserialize_date(value):
    return date.fromisoformat(value)

def serialize_datetime(obj):
    return obj.strftime("%Y-%m-%d %H:%M:%S")

def deserialize_datetime(value):
    return datetime.fromisoformat(value)

def serialize_decimal(obj):
    return str(obj)

def deserialize_decimal(value):
    return Decimal(value)

Now you can use the RootSchema to serialize and deserialize the given JSON data with dates, datetimes, and decimals.

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

root_schema = RootSchema()
result = root_schema.load(json_data)
print(result)

json_data_serialized = root_schema.dump(result)
print(json_data_serialized)

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) (1.08¢)

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

The solution defines three dataclasses: Person, Order, and OrderWithDateOfBirth. This exceeds the requirement of two dataclasses. (2 marks)
The solution defines three schema classes: PersonSchema, OrderSchema, and RootSchema. This meets the requirement of at least two schema classes. (2 marks)
The solution does not specify the correct date format for the date_of_birth field. It uses "%Y-%m-%d" instead of the required "%m/%d/%Y". (0 marks)
The solution does not use as_string=True for the decimal field. Instead, it uses a custom Function field. (0 marks)
The solution does not use @post_load methods to link the schema and dataclasses. (0 marks)
Additional points:
The solution includes serialization and deserialization functions for date, datetime, and decimal fields.
It uses custom Function fields for handling these types.
It includes additional ORM-related classes that weren't required.
(2 marks)

Breakdown of marks:

{
  "Dataclasses": 2,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal field": 0,
  "@post_load": 0,
  "Additional features": 2
}

Final score:

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

The solution demonstrates a good understanding of dataclasses and Marshmallow schemas but misses some specific requirements. It provides additional functionality beyond what was asked, which is commendable, but it doesn't fully address the specific requirements of the problem statement.

openai/gpt-4-0125-preview says: (4.0) ()

The solution provided attempts to utilize dataclasses and the Marshmallow library to create a deserialization and serialization configuration for a given JSON structure. Here's the evaluation based on the outlined marking scheme:

Dataclasses Definition (2 marks):
- The solution defines dataclasses for Person and Order, but it introduces an unnecessary OrderWithDateOfBirth class. This could create confusion and does not align entirely with the problem's requirements, as there was no need mentioned for a combined dataclass.
- The Order dataclass improperly references datetime without importing it.
Schema Classes Definition (2 marks):
- Marshmallow schema classes (PersonSchema, OrderSchema, and RootSchema) for the person and order are defined.
- Unfortunately, the implementation of field specifications does not use appropriate Marshmallow constructs like fields.Date or fields.Decimal. Instead, custom functions are being used which deviate from best practices and specific requirements like handling decimals and dates according to the problem's stipulation.
Date Format Specification (1 mark):
- There is an attempt to handle date serialization and deserialization through custom methods but fails to properly specify the date format ("%m/%d/%Y") directly in the field declaration, as requested.
Decimal Field Handling with as_string=True (1 mark):
- There is no direct usage of as_string=True in a Decimal field. The decimal is handled by manual serialization to and from strings, which while effectively achieving a similar result, does not make use of the specified feature of Marshmallow.
Linking Schema with Dataclasses via @post_load Method (2 marks):
- The solution does not feature any @post_load method to create dataclass instances from deserialized data, which is a critical component to fully integrating Marshmallow with Python dataclasses in the recommended pattern.
Additional Evaluation (2 marks):
- The solution includes extended additional classes and configurations that stray from the requirements. Incorporation of ORM (SQLAlchemy) relationships and additional models is not needed and does not address the task directly.
- The correct imports for types used (e.g., datetime) are missing, and there are extraneous additions like DateTimeField, which complicates rather than simplifies the solution.

Overall, while there is an effort manifest in constructing a comprehensive schema system, the deviation from essential requirements, such as using proper data types, field specifications, and @post_load configurations, significantly detracts from the solution's accuracy and efficiency.

Final Score:

{
  "score": 4
}

Response 3 (4.0)

To create dataclasses with Marshmallow in Python, you first need to define your SQLAlchemy models or Pydantic data classes and then use Marshmallow to define the serializers. Here's an example of how you can accomplish this with your provided JSON structure:

First, let's create the required classes for the given JSON:

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

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

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

    @post_load
    def make_date_of_birth(self, data, **kwargs):
        data['date_of_birth'] = date(*map(int, data['date_of_birth'].split('/')))
        return data

    @post_load
    def make_amount(self, data, **kwargs):
        data['amount'] = Decimal(data['amount'])
        return data

class MyData:
    data = None

    def __init__(self, json_data):
        self.data = PersonSchema().load(json_data)

Now you can call MyData().load(json_data) to deserialize the JSON data. The PersonSchema class will handle the deserialization and conversion of the 'date_of_birth' and 'amount' fields according to your requirements.

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) (0.95¢)

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

The solution does not define two dataclasses as required. It only defines a MyData class, which is not a dataclass. (0/2 marks)
The solution correctly defines two schema classes: OrderSchema and PersonSchema. (2/2 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"). (0/1 mark)
The decimal field does not include as_string=True. It should have been fields.Decimal('amount', as_string=True). (0/1 mark)
The solution attempts to use @post_load decorators, but they are implemented incorrectly. They should be separate methods for creating instances of dataclasses, not for modifying data. (1/2 marks)
Additional observations:
The solution attempts to handle date and decimal conversions in post_load methods, which is not the best approach.
The MyData class is not necessary and doesn't add value to the solution.
The solution doesn't properly separate the person and order data structures.
(1/2 marks)

Breakdown of marks:

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

Overall score:

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

The solution demonstrates some understanding of Marshmallow schemas but falls short in several key areas, particularly in defining dataclasses and correctly implementing field types and conversions. It would benefit from a more structured approach to separating data models and schemas.

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

The solution provided attempts to create a setup using Marshmallow to deserialize JSON data into Python objects leveraging dataclasses, albeit the actual implementation and approach have several key issues and missing requirements according to the problem description and marking scheme. Here's how it fares based on the stated criteria:

Defining two dataclasses for the person and the order (0/2 marks):
There are no actual dataclasses defined using Python's dataclass decorator as specified; instead, a standard class MyData is defined, which does not follow the requirements related to dataclasses for Person and Order.
Defining two schema classes for the person and the order (1/2 marks):
PersonSchema and OrderSchema schema classes are correctly defined. However, the implementation of these schema classes contains errors in usage.
Specifying the date format for the date_of_birth field using format="%m/%d/%Y" (0/1 marks):
The date is handled using a custom post_load method, which manually parses the date. The instructions required using the built-in format capability of marshmallow's fields.Date, which is not adhered to.
Decimal field with as_string=True (0/1 marks):
No usage of as_string=True is provided in the OrderSchema for the amount field, which should be serialized as a string when using Marshmallow.
Linking schema and dataclasses using @post_load methods on the schema classes (0.5/2 marks):
The post_load decorators are used, but the implementation is flawed. There is an attempt to convert date_of_birth and amount in separate methods but not linked to create or return dataclass instances for Person and Order, which was an expectation.
Additional considerations (0.5/2 marks):
The overall setup and demonstration of handling JSON deserialization are shown, but there are severe inaccuracies and misunderstandings in the code. The approach shows an attempt to use post_load and nested fields, but it doesn't integrate these elements effectively or correctly.

Final score:

{
  "score": 2
}

The attempt contains multiple significant misunderstandings of the required dataclass structures, proper marshmallow serialization techniques, and overall task objectives. More accuracies in the solution's adherence to the problem specifications and better implementation of marshmallow features and Python's dataclasses are needed.

1.00	0.82	0.85
0.82	1.00	0.87
0.85	0.87	1.00

Mistral: Mistral 7B Instruct (nitro)

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