Qwen 1.5 32B Chat

Description

Qwen1.5 32B is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:

Significant performance improvement in human preference for chat models
Multilingual support of both base and chat models
Stable support of 32K context length for models of all sizes

For more details, see this blog post and GitHub repo.

Usage of this model is subject to Tongyi Qianwen LICENSE AGREEMENT.

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 generate dataclasses using the Marshmallow library in Python, you can follow these steps:

Install the Marshmallow library (if not already installed):

pip install marshmallow marshmallow-dataclass

Import the necessary modules and create your JSON dictionary:

from datetime import datetime
from marshmallow import Schema, fields
from marshmallow_dataclass import dataclass_schema

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

Define the dataclasses for the nested Order and the main object (Person):

from dataclasses import dataclass

@dataclass
class Order:
    created: datetime
    amount: float

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: datetime
    orders: list[Order]

Generate the Marshmallow Schema for each dataclass:

PersonSchema = dataclass_schema(Person)
OrderSchema = dataclass_schema(Order)

Parse the JSON data into your dataclasses using the Marshmallow Schema:

person_data = json_data.copy()
orders_data = person_data.pop('orders')

person = PersonSchema().load(person_data)
orders = [OrderSchema().load(order) for order in orders_data]

person.orders = orders

Now person is a Person dataclass instance with the parsed data, and date_of_birth and created fields are of the correct types:

print(person.date_of_birth)
print(person.orders[0].created)
print(person.orders[0].amount)

Keep in mind that Marshmallow expects dates to be in a standardized format (like YYYY-MM-DD) or ISO format (like YYYY-MM-DDTHH:MM:SS) for parsing. In the provided JSON, date_of_birth is in a non-standard format. You can create a custom Marshmallow field to handle the conversion, but for simplicity, in this example, I've assumed date_of_birth will be in a standardized format. If not, you'll need to preprocess it or create a custom field for conversion.

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) (0.96¢)

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

Defining two dataclasses (Person and Order):
The solution correctly defines two dataclasses, one for Person and one for Order.
Defining two schema classes:
The solution uses dataclass_schema to generate schema classes for both Person and Order.
Specifying date format for date_of_birth:
The solution doesn't specify the date format for the date_of_birth field.
Decimal field with as_string=True:
The solution doesn't use a decimal field with as_string=True for the amount field.
Linking schema and dataclasses with @post_load:
The solution doesn't use @post_load to link the schema and dataclasses.
Additional points:
The solution correctly imports necessary modules.
It provides a method to parse the JSON data into dataclasses.
The solution mentions the potential issue with non-standard date format and suggests creating a custom field for conversion.

Here's the breakdown of marks:

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

Final score:

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

The solution provides a good starting point but misses some important details required in the marking scheme. It correctly defines dataclasses and generates schema classes, but doesn't address the specific requirements for date format, decimal field configuration, and linking schemas with dataclasses using @post_load. The additional explanations and suggestions are helpful, earning it some extra points.

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

The provided solution offers some correct structures but fails to address specific issues related to the correct implementation of data conversion using the marshmallow library for Python, as per the requirements provided in the problem description. Let's break down the solution based on the marking scheme:

Dataclasses Definition: The solution defines two dataclasses, Order and Person, as required. These classes represent the respective JSON structures appropriately.
Score: 2/2
Schema Classes Definition: The use of dataclass_schema from marshmallow_dataclass to automatically generate schema classes (PersonSchema and OrderSchema) is correct but lacks customization for the specific types (date, datetime, and decimal) as required. The transformation isn't explicitly outlined.
Score: 1/2
Date Format Specification: The solution did not modify the default settings or field attributes for the date_of_birth to be correctly interpreted in the specified format ("%m/%d/%Y").
Score: 0/1
Decimal Field Handling: The solution does not set amount in the Order schema to be handled as a decimal as_string=True. It leaves amount as a float, which does not meet the requirements.
Score: 0/1
Linking Schema with @post_load Method: The solution does not include a @post_load method to ensure that the objects loaded from the JSON can be properly instantiated into their respective dataclasses. The creation of instances is a manual process which would not be necessary with a proper @post_load implementation.
Score: 0/2
Additional Considerations: The solution skips handling non-standard date formats by relying on potentially pre-processed or standardized data. This indicates a mismatch with real-world scenarios where data may not come preformatted. Furthermore, there's no exception handling or validation presented.
Score: 0/2

Based on the marking scheme, the solution significantly falls short in terms of handling and transforming the specialized data types through the marshmallow library, crucial for meeting the specific serialization and deserialization requirements.

The final score, therefore, is:

{
  "score": 3
}

Response 2 (5.0)

You can use the Marshmallow library to generate dataclasses for the JSON you provided. First, you need to install Marshmallow if you haven't already:

pip install marshmallow marshmallow-dataclass

Now, you can create dataclasses using Marshmallow's dataclass_schema method. Here's how you can do it for your JSON:

from datetime import datetime
from decimal import Decimal
from marshmallow import fields
from marshmallow_dataclass import dataclass_schema

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

UserSchema = dataclass_schema(User)
OrderSchema = dataclass_schema(Order)

This code defines two dataclasses, User and Order, along with their respective Marshmallow schemas, UserSchema and OrderSchema. The dataclass_schema method generates the necessary Marshmallow fields based on the dataclass fields.

To deserialize the JSON into a User object and serialize it back, you can use the load and dump methods of the schema:

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"
    }
  ]
}
'''

# Deserializing JSON to a User object
user_data = json.loads(json_data)
user_instance = UserSchema().load(user_data)

# Serializing the User object back to JSON
serialized_user = UserSchema().dump(user_instance)
print(serialized_user)

This code will parse the JSON data, create a User object, and then serialize the object back to JSON. Note that for the date and time conversion to work, you may need to provide a custom date format. You can do this when loading the JSON using a format argument in the load method:

user_instance = UserSchema().load(user_data, formattz="ddd MMM DD YYYY HH:mm:ss")

Please replace the format string "ddd MMM DD YYYY HH:mm:ss" with the appropriate format that matches your date and time strings.

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) (1.00¢)

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 using dataclass_schema(User) and dataclass_schema(Order). (2 marks)
The solution does not specify the date format for the date_of_birth field. It mentions using a format argument in the load method, but this is not the correct approach. (0 marks)
The solution does not include as_string=True for the decimal field. (0 marks)
The solution does not use @post_load method to link schema and dataclasses. The dataclass_schema method is used instead, which doesn't require @post_load. (0 marks)
Additional points:
The solution correctly imports necessary modules.
It provides an example of how to deserialize and serialize data.
It mentions the need for custom date format, although the implementation is incorrect.
(1 mark)

Here's the breakdown of marks:

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

Final score:

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

The solution demonstrates a basic understanding of using Marshmallow with dataclasses, but it misses several key requirements specified in the problem statement. It doesn't properly handle the date and decimal fields, and it doesn't use the @post_load decorator as requested. The approach of using dataclass_schema is valid, but it doesn't align with the specific requirements of the question.

openai/gpt-4-0125-preview says: (None) ()

The provided solution has attempted to use the Marshmallow library along with marshmallow-dataclass for converting JSON data into Python objects using dataclasses. Here's the evaluation based on the given criteria:

Definition of Two Dataclasses: The solution correctly defines two Python dataclasses, Order and User, using the @dataclass decorator from the Marshmallow-dataclass library. (+2 marks)
Definition of Two Schema Classes: The solution uses dataclass_schema to automatically create schema classes for the User and Order dataclasses. While the use of dataclass_schema is valid, it is misattributed in the explanation as manual definition of schema classes. However, technically, the task is achieved. (+1 mark)
Date Format Specification for date_of_birth: Although dataclasses are correctly defined, the solution does not specify the date format ("%m/%d/%Y") for the date_of_date field as required, potentially leading to incorrect parsing of date strings. (+0 marks)
Decimal Field Representation as String: The solution also fails to specify the field configuration for marshalling the amount as a decimal string representation, which is critical given JSON's handling of numbers. (+0 marks)
Link Between Schemas and Dataclasses Using @post_load: The solution does not implement or explain the use of any @post_load methods to customize the creation of dataclass instances after loading data. This means that any specialized behavior after loading data, such as additional validations or transformations, won't be handled. (+0 marks)
Additional Aspects (Error Handling, Custom Formats, etc.): The final portion of code attempts to handle date and time parsing with a custom format string, but the format string offered ("ddd MMM DD YYYY HH:mm:ss") does not match any standard datetime format and is starkly different from the needed format. This shows a misunderstanding or oversight regarding datetime formats. Additionally, it implies using a formattz parameter in the load method which doesn't exist in the Marshmallow library. (+0 marks)

The provided solution did manage to use the Marshmallow library and automated the creation of schema classes but lacked critical customizations and accurate implementations for the specific needs outlined in the problem statement. The misunderstanding about customization options and incorrect details also weaken the solution.

{
  "approval": 3
}

Response 3 (10.0)

You can use the Marshmallow library in Python to define data structures and serialize/deserialize JSON. First, you'll need to install Marshmallow and its typings package if you haven't already:

pip install marshmallow marshmallow-coreschema

Now, let's define the dataclasses and marshmallow schemas:

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

@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(as_string=True)

    @post_load
    def make_order(self, data):
        return Order(**data)

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

    @post_load
    def make_user(self, data):
        return User(**data)

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

loaded_user_data = UserSchema().load(json.loads(json_data))
user = loaded_user_data.make_user()
print(user)

This code defines two dataclasses, Order and User, along with corresponding Marshmallow schemas, OrderSchema and UserSchema. The OrderSchema has a @post_load decorator that creates an Order instance from the deserialized JSON data. Similarly, the UserSchema creates a User instance in its @post_load decorator.

In the example usage, we deserialize the JSON data into a User object, and then print the resulting User instance.

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: (10.0) (0.90¢)

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

The solution correctly defines two dataclasses, Order and User. (2 marks)
The solution correctly defines two schema classes, OrderSchema and UserSchema. (2 marks)
The solution specifies the correct date format for the date_of_birth field using format="%m/%d/%Y". (1 mark)
The decimal field (amount) is correctly defined with as_string=True. (1 mark)
The schema and dataclasses are linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution imports necessary modules and types.
It includes an example of how to use the schemas to deserialize JSON data.
(2 marks)

Breakdown of marks:

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

Score:

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

The solution addresses all the requirements specified in the marking scheme and provides a complete, working example of how to use Marshmallow to create dataclasses from JSON data. It correctly handles the date, datetime, and decimal fields as required. The solution also demonstrates good practices by including type hints and using the @dataclass decorator. Overall, it's an excellent implementation that meets all the criteria.

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

The solution has addressed most of the required components specified in the problem statement regarding the use of the Marshmallow library in conjunction with Python dataclasses. Let's evaluate it according to the given marking scheme:

Defining two dataclasses, one for the person and one for the order: The solution correctly defines a User dataclass for the person and an Order dataclass for the order. Each class correctly uses types that match the problem requirements. (+2 marks)
Defining two schema classes, one for the person and one for the order: The solution provides UserSchema for the person and OrderSchema for the order. Each schema class maps to its respective dataclass. (+2 marks)
Specifying date format for the date_of_birth: The UserSchema uses the fields.Date() with the parameter format="%m/%d/%Y", correctly formatting the date_ofard_birth field according to the problem's requirements. (+1 mark)
Decimal field with as_string=True: In the OrderSchema, the amount field is properly declared as fields.Decimal(as_string=True), ensuring that the serialization keeps the decimal precision as a string. (+1 mark)
Linking schema and dataclasses with @post_load method: Both the OrderSchema and the UsercerSchema contain @post_load methods (make_order and make_user respectively) that instantiate and return their related dataclass objects (Order and User). The example usage, however, incorrectly attempts to call make_user() method on loaded_user_data, which is not required since load already performs this action through the @post_load decorator. This is incorrect usage but does not affect the marking on this part. (+2 marks)
Anything else (additional considerations): The imports and setup are correctly handled, and necessary Python standard library and third-party packages (Marshmallow) are imported. The use of json.loads implies an import of the json module, which should have been explicitly mentioned but is likely overlooked. The use of List from the typing module correctly corresponds to JSON's lists portrayal. (-1 mark for the example usage error and missing explicit json import).

Final assessment based on the outlined rubric:

{
  "score": 7
}

1.00	0.86	0.83
0.86	1.00	0.86
0.83	0.86	1.00

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