Qwen 1.5 14B Chat

Description

Qwen1.5 14B 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 use the Marshmallow library to create dataclasses from the given JSON, you'll need to follow these steps:

Install the necessary libraries (if not already): You'll need the marshmallow and marshmallow_dataclass libraries for this task. You can install them using pip:

pip install marshmallow marshmallow_dataclass

Import the required modules:

from typing import List
import datetime
from decimal import Decimal
from marshmallow import Schema, fields, ValidationError
from marshmallow_dataclass import class_schema

Define the dataclass and the associated Marshmallow schema:

from dataclasses import dataclass
from typing import Optional

@dataclass
class Order:
    created: datetime.datetime
    amount: Decimal

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: datetime.date
    orders: List[Order]

OrderSchema = class_schema(Order)
PersonSchema = class_schema(Person)

Create a function to deserialize the JSON and convert it to a dataclass:

def json_to_dataclass(json_data: dict) -> Person:
    try:
        parsed_data = PersonSchema.load(json_data)
        parsed_data.date_of_birth = datetime.datetime.strptime(json_data['date_of_birth'], '%m/%d/%Y').date()
        return parsed_data
    except ValidationError as e:
        print(f"Invalid JSON structure: {e}")
        return None

Example usage:

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

json_data = json.loads(json_string)
converted_person = json_to_dataclass(json_data)

if converted_person:
    print(converted_person)

This code will parse the JSON, convert the date_of_birth to a datetime.date, and create a Person dataclass using the PersonSchema. If the JSON structure is valid, it will return the Person object; otherwise, it will print an error message.

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) (1.00¢)

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

The solution defines two dataclasses, Order and Person. (2 marks)
The solution defines two schema classes using class_schema: OrderSchema and PersonSchema. (2 marks)
The solution specifies the date format for the date_of_birth field, but not in the schema. It's handled in the json_to_dataclass function. (0 marks)
The decimal field is not specified with as_string=True. (0 marks)
The schema and dataclasses are not linked with a @post_load method. Instead, a custom json_to_dataclass function is used. (0 marks)
Additional points:
The solution imports necessary modules. (1 mark)
It provides a custom function to handle deserialization and date conversion. (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": 0,
  "Additional points": 2
}

The solution partially meets the requirements but misses some key aspects of using Marshmallow effectively. It doesn't use the schema to handle field conversions and validations as expected. The date and decimal conversions are not handled in the schema, which is a missed opportunity to leverage Marshmallow's features.

Here's the final score:

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

The solution scores 6 out of 10. While it does create dataclasses and schemas, it doesn't fully utilize Marshmallow's capabilities for field conversion and validation, which were key requirements in the marking scheme.

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

The provided solution has several key elements for converting JSON to dataclasses using Marshmallow, but there are some misses in the exact requirements and best practices. Let's break down the evaluation based on the provided criteria:

Defining Two Dataclasses (2 marks):
The solution correctly defines two dataclasses, Order and Person. Each class is appropriately decorated with @dataclass and includes the specified fields: created and amount for Order, and first_name, last_name, date_of_birth, and orders for Person.
Score: 2/2
Defining Two Schema Classes (2 marks):
The solution partially addresses this by auto-generating schema classes using class_schema from marshmallow_dataclass for both Order and Person. However, it does not manually define schema classes where specific formatting (e.g., for dates and decimals) and custom logic (e.g., @post_load method) could be implemented.
Score: 1/2
Specifying Date Format for date_of_birth Field (1 mark):
The proposed solution does not explicitly define the format for the date_of_birth field in a schema definition. Instead, date conversion is handled separately in the deserialization function. This approach misses the requirement to specify the date format in the schema.
Score: 0/1
Decimal Field as_string=True (1 mark):
The instructions for ensuring the amount is treated as a decimal with as_string=True in a schema definition are not followed or mentioned. The solution does not demonstrate how to achieve this requirement within the Marshmallow schema.
Score: 0/1
Linking the Schema and Dataclasses with @post_load (2 marks):
The provided solution does not utilize the @post_load method to link the schema and dataclasses. Instead, it opts for direct manipulation of the loaded data in the deserialization function, which misses the essence of using @post_load for object creation.
Score: 0/2
Anything Else (2 marks):
The use of class_schema to automatically generate schemas is clever and shows an understanding of the marshmallow_dataclass utility, which simplifies schema creation from dataclasses.
Correct handling of JSON deserialization and initiation of a manual conversion for the date_of_birth field demonstrates an ability to use Marshmallow and understand Python's datetime module.
The solution does lack the explicit control over field formatting and validation that manual schema definitions provide.
Score: 1/2

Overall, while the solution touches on critical components necessary for using Marshmallow to convert JSON to dataclasses, it falls short in the detailed requirements of the task, specifically around schema customization, field formatting, and the structured use of @post_load.

{
  "score": 4/10
}

Response 2 (4.0)

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

Install Marshmallow and the necessary dependencies:

pip install marshmallow marshmallow-dataclass

Create a Python module (e.g., data_classes.py) and define the dataclasses:

from typing import List, Union
from datetime import datetime
from decimal import Decimal
from marshmallow_dataclass import dataclass

# Custom fields
from marshmallow import fields

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

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

    # Optional: if you want to use fields.Nested for the list of orders
    # orders: List[OrderData] = fields.List(fields.Nested(OrderData))

@dataclass
class Order:
    created: datetime = fields.DateTime()
    amount: Decimal = fields.Decimal()

# Assuming you have a JSON string
json_data = """
{
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  'orders': [
    {
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
    }
  ]
}
"""

# Parse the JSON string into a Python object
data = Person.from_dict(json.loads(json_data))

# You can now access the dataclasses easily
first_name = data.first_name
last_name = data.last_name
date_of_birth = data.date_of_birth
orders = data.orders  # If you used fields.Nested, orders will be a list of OrderData objects

# If you want to validate the data, you can use Marshmallow's Schema
from marshmallow import Schema

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.DateTime()
    orders = fields.Nested(OrderData, many=True)

schema = PersonSchema()
parsed_data, errors = schema.load(json.loads(json_data))

if errors:
    print("Validation errors:", errors)
else:
    print("Parsed data:", parsed_data)

In this example, I created two dataclasses, Person and Order. The OrderData class is a helper class that contains common fields for the 'orders' list items. The Person class uses dataclass from marshmallow_dataclass to define the required fields.

You can use from_dict to create a Person object from the JSON string. If you want to validate the data, you can create a PersonSchema using marshmallow.Schema and load the JSON data. If there are any validation errors, they will be logged. If the data is valid, you can access the parsed data as shown.

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) (1.01¢)

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

Defining two dataclasses (Person and Order): 2 marks
The solution defines both Person and Order as dataclasses.
Defining two schema classes: 1 mark
The solution only defines one schema class (PersonSchema). It's missing a separate schema for Order.
Specifying date format for date_of_birth: 0 marks
The solution doesn't specify the format for the date_of_birth field.
Decimal field with as_string=True: 0 marks
The solution doesn't include as_string=True for the Decimal field.
Linking schema and dataclasses with @post_load: 0 marks
The solution doesn't use @post_load to link the schema and dataclasses.
Additional points:
The solution imports necessary libraries: 0.5 marks
It provides a sample JSON and demonstrates how to use the classes: 0.5 marks

Here's the breakdown of marks:

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

The solution addresses some of the requirements but misses several key points. It defines the dataclasses correctly but falls short on schema implementation, date format specification, and proper decimal field configuration. The absence of @post_load method is also a significant omission.

Here's the final score:

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

The solution scores 4 out of 10 based on the marking scheme provided.

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

{
  "score": 6
}

Evaluation:

Dataclasses for Person and Order: The solution provides a method to create a Person dataclass but mixes up in defining the Order dataclass appropriately with marshmallow_dataclass syntax, rather defining a simple Python class. However, it still produces a structure that could be recognized as an attempt to define data classes (1/2 mark).
Schema Classes Definition: The solution does an adequate job of starting to define a PersonSchema to validate and load the JSON data. However, it lacks a corresponding schema class for the order specifically and does not properly utilize marshmallow_dataclass for schema generation automatically from dataclasses (1/2 mark).
Date Format Specification: The solution fails to specify the date format for the date_of_birth field using the format parameter ("%m/%d/%Y" is not specified anywhere in the solution). (0/1 mark)
Decimal Field Specification: The solution does not include as_string=True for the decimal field, which is important to ensure that decimal fields are handled correctly according to the requirements. (0/1 mark)
@post_load Method Linkage: The solution does not make use of the @post_load method to link the schema and data classes correctly. This method is crucial for transforming the loaded data into an instance of the defined dataclass. (0/2 marks)
Additional Considerations:
- Installation instructions and importing necessary libraries are provided, which is helpful for setting up the environment.
- The example attempts to parse JSON and interact with the data, showing practical use cases.
- Misunderstands the direct use of marshmallow_dataclass in transforming JSON directly into dataclass instances; it doesn't leverage marshmallow's full capability in deserialization directly into dataclasses, especially missing the points for the specific handling of date, datetime, and Decimal types as given in the problem statement.
For these reasons, an additional (2/2 marks) for effort and partial implementation.

Overall, the solution demonstrates an initial attempt at using marshmallow and marshmallow-dataclass to work with JSON data and Python dataclasses but lacks full compliance with specific requirements of the task and correct use of marshmallow's features for data conversion and validation.

Response 3 (8.0)

To create dataclasses and use the marshmallow library in Python to define and validate the input JSON, you'll first need to install marshmallow if you haven't already. You can do this using pip:

pip install marshmallow

Next, you'll create a Python class for the person's data and another for the order data. Then, you'll use marshmallow to define the schema. Here's how you can do it:

Define the Python classes:

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


@dataclass
class Order:
    created: datetime
    amount: Decimal


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

Create the schema using marshmallow:

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

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

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

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

Now, you can use the schema to load the JSON and validate it:

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

# Parse the JSON using marshmallow
parsed_data = PersonSchema().load(input_json)

# The parsed_data variable will now contain an instance of the Person class
person = parsed_data

This code will create a Person object with the input JSON data, where date_of_birth is a datetime object, created is also a datetime object, and amount is a Decimal object. The Order and Person classes and their corresponding schemas handle the conversion and validation of 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: (8.0) (1.00¢)

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 have as_string=True. It should have been fields.Decimal(as_string=True). (0 marks)
The schema and dataclasses are linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution imports necessary modules.
It provides an example of how to use the schema to load and validate JSON data.
The solution uses type hints in the dataclasses.
(2 marks)

Breakdown of marks:

{
  "Dataclasses": 2,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal as_string": 0,
  "Post_load methods": 2,
  "Additional points": 2
}

Score:

{
  "scores": [2, 2, 0, 0, 2, 2],
  "total_score": 8
}

The solution provides a good foundation for using marshmallow with dataclasses to handle JSON data. However, it misses two important details: specifying the correct date format for the date_of_birth field and setting as_string=True for the decimal field. These omissions could lead to parsing errors or unexpected behavior when handling the JSON data. Despite these issues, the overall structure and implementation of the solution are correct and demonstrate a good understanding of marshmallow and dataclasses in Python.

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

{
  "score": 7
}

Evaluation:

Define two dataclasses (Person and Order): The provided solution correctly defines two dataclasses, Person and Order, fulfilling this requirement. (+2 marks)
Define two schema classes (PersonSchema and OrderSchema): The solution includes a PersonSchema and OrderSchema that are well-defined and correspond to the data classes. (+2 marks)
Specify the date format for date_of_birth: While fields.Date() is correctly used for date_of_birth, the solution does not explicitly set the date format to "%m/%d/%Y" as required by the problem statement. (0 marks)
Decimal field with as_string=True: The solution fails to specify as_string=True for the Decimal field. This is necessary to ensure that the decimal amount is correctly handled as a string when loading from JSON. (0 marks)
Link schema and dataclasses with a @post_load method: The solution correctly uses the @post_load method in both schema classes to create instances of the corresponding dataclasses upon loading the JSON. (+2 marks)
Anything else (Completeness, Correct JSON parsing, etc.): The code successfully demonstrates how to parse JSON into defined Python classes using Marshmallow, providing a complete workflow from JSON input to dataclass instances. However, it would not work as intended due to the JSON string needing to be deserialized into a Python dictionary before loading with Marshmallow, which is a significant oversight. Additionally, there's a notable missing instruction to use make_order=self.make_order and make_person=self.make_person within the post_load decorators to correctly address the scope. Given these considerations, a partial score is assigned. (+1 mark)

The provided solution demonstrates a good understanding of the Marshmallow library and its integration with Python dataclasses for JSON serialization/deserialization. However, critical mistakes such as the omission of the as_string=True for decimals and not specifying the date format string reduce its completeness and correctness.

1.00	0.89	0.90
0.89	1.00	0.90
0.90	0.90	1.00

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