Qwen 1.5 4B Chat

Description

Qwen1.5 4B 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 (3.0)

You can use the marshmallow transforms library to convert the json data into date, datetime and decimal dataclasses. Here is an example:

from marshmallow import fields
from marshmallow.transforms import _Dtype


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

    class Meta:
        fields = ('created', 'amount')


class CustomerSchema(ma.Schema):
    first_name = fields.String()
    last_name = fields.String()
    date_of_birth = fields.Date()
    orders = fields.Nested(OrderSchema, many=True)

    class Meta:
        fields = ('first_name', 'last_name', 'date_of_birth', 'orders')

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

OrderSchema().load(data)
# Output: OrderSchema

In above example, we defined OrderSchema which has three fields created, amount and orders which is a nested schema with many field. Then we created CustomerSchema which has two classes OrderSchema and UserSchema to convert the json data into date, datetime and decimal dataclasses.

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) (0.88¢)

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

The solution does not define any dataclasses. It only defines schema classes. (0/2 marks)
The solution correctly defines two schema classes: OrderSchema and CustomerSchema. (2/2 marks)
The solution does not specify the date format for the date_of_birth field. (0/1 mark)
The decimal field does not include as_string=True. (0/1 mark)
The solution does not include @post_load methods to link schema and dataclasses. (0/2 marks)
Additional observations:
The solution imports marshmallow.transforms, which is not a standard part of marshmallow.
The _Dtype import is unused.
The solution uses ma.Schema, but ma is not defined.
The example usage at the end is incomplete and incorrect.
(1/2 marks for attempting to show usage, despite errors)

Comments:
The solution provides a basic structure for using marshmallow to define schemas, but it falls short in several areas. It doesn't create dataclasses, misses important field configurations, and lacks the connection between schemas and dataclasses. The example usage is incomplete and contains errors. The solution needs significant improvements to meet the requirements fully.

Marks breakdown:

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

Score:

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

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

The provided solution has several imperatives to achieve the intended task of parsing JSON into dataclasses using marshmallow. However, the solution suffers from multiple discrepancies and omissions as per the specifics of the task and the requirements mentioned in the problem:

Definition of Data Classes:
The solution does not define any dataclasses for either Order or Customer. Data classes in Python are typically defined using the @dataclass decorator from the dataclasses module, which is not utilized in the given solution.
Definition of Schema Classes:
Two schema classes, OrderSchema and CustomerSchema, are defined using marshmallow, which aligns with the task requirements. Correct usage of marshmallow.Schema is shown, and it's proper according to the basic schema structure in marshmallow.
Date Formatting Specification:
The solution does not specify the date format for the date_of_birth field. It uses the default settings of fields.Date(), which may not correctly parse the date 03/16/1977 unless the format is explicitly stated with format="%m/%d/%Y".
Decimal Field Specification with as_string=True:
The amount field under the OrderSchema is assigned as a fields.Decimal() but does not use as_string=True, thereby not ensuring amount is treated as a string for formatted output, which is a specific requirement.
Linkage of Schema and Data Classes via @post_load:
The solution omits the @post_load decorator and does not include any methods that would convert loaded data into instances of dataclasses. This method is crucial for integrating marshmallow schemas with Python dataclasses.
Additional Review Points:
The solution inappropriately attempts to load Order data using only OrderSchema directly on a dictionary which includes both customer and order data, which is incorrect and would lead to a parsing error. Furthermore, the example usage should employ CustomerSchema for the given dictionary (data) structure.

Overall, while the solution partially sets up marshmallow schemas, it fails to follow precise specifications like date and decimal formatting, utilizing dataclasses, and correctly demonstrating practical usage of schema for nested structures. Therefore, the following score is appropriate:

{
  "score": 3
}

This score reflects the partial creation of schema classes but misses many critical aspects of the complete solution as per the specific task's requirements.

Response 2 (3.0)

Here is an example code snippet that demonstrates how to use the marshmallow library to generate dataclasses from a JSON object:

from marshmallow import fields, Embedded, Schema

class AddressSchema(Schema):
    street = fields.String()
    city = fields.String()
    state = fields.String()
    zip_code = fields.String()

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

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

order_schema = OrderSchema()
order_data = order_schema.load(json_data)

class Order:
    def __init__(self, order_data):
        self.created = order_data['created']
        self.amount = order_data['amount']
        self.address = order_data['address']

# Usage
order = Order(order_data)
print(order.created)  # 2024-01-25T15:00:04
print(order.amount)  # 100.00
print(order.address.street)  # Jane Street
print(order.address.city)  # Smithville
print(order.address.state)  # Indiana
print(order.address.zip_code)  # 47551

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) (0.85¢)

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

The solution defines only one dataclass (Order) instead of two. It's missing a dataclass for the person. (1/2 marks)
The solution defines two schema classes (AddressSchema and OrderSchema), but it's missing a schema for the person. (1/2 marks)
The solution does not specify the date format for the date_of_birth field. (0/1 mark)
The decimal field is not specified with as_string=True. (0/1 mark)
The solution does not use @post_load method to link schema and dataclasses. (0/2 marks)
Additional observations (1/2 marks):
The solution includes an AddressSchema, which is not part of the original JSON structure.
The Order class is manually created instead of using dataclasses.
The solution doesn't handle the 'date_of_birth' field at all.
The example usage at the end doesn't match the actual JSON structure provided.

Here's the breakdown of marks:

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

Final score:

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

The solution misses several key requirements and contains elements not relevant to the given JSON structure. It needs significant improvements to meet the specified requirements.

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

The provided solution attempts to use the marshmallow library to deserialize JSON data into Python objects. However, there are significant issues and errors in how it addresses the problem requirements. Here is a detailed evaluation against the marking scheme:

Definition of two dataclasses: The solution does not define any dataclasses using Python's built-in dataclasses module for Person and Order. Instead, a single class Order is defined typically, which neither uses the dataclasses module nor matches the requirement structure (0/2 marks).
Definition of two schema classes: The solution defines OrderSchema and mistakenly includes an AddressSchema, which is irrelevant to the provided JSON structure and requirements. Additionally, there is no schema for the Person entity (0.5/2 marks as it only partially implemented one correct schema).

3, Specifying the date format for date_of_birth: The JSON structure mentions a date_of_birth field, but the provided solution does not handle this field at all, neither in the schema definition nor in the class. The date format customization required by the task is entirely absent (0/1 marks).

Decimal handling with as_string=True: Although the solution defines a decimal field, it does not specify as_string=True. This parameter is important for ensuring that the decimal data is handled as a string when loading or dumping to preserve precision and format in text (0/1 marks).
Linking schema and dataclasses via @post_load: The provided solution does not use marshmallow’s @post_load decorator which is crucial for integrating data loaded by marshmallow schemas directly into an instance of a dataclass or another structured object. The solution thus lacks one of the suggested proper methods for constructing objects from schemas (0/2 marks).
Any other aspects: The solution provided additional AddressSchema classes and handling, which is entirely unrelated to the problem statement. Although including extra structure could be insightful for other contexts, in this specific task, it introduces confusion and distracts from the actual requirements (0.5/2 marks).

Given these considerations, the provided solution does not adequately meet the criteria outlined in the problem, falsely incorporates unrelated elements, and skips essential implementation specifics for a correct marshmallow-based serialization/deserialization approach as required by the task.

{
  "score": 1
}

Response 3 (3.0)

You can use the built-in datetime and decimal classes to create the necessary dataclasses. Here's an example of how you can do this using the marshmallow library in Python:

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

class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

    @classmethod
    def from_Root(cls, data: dict):
        return cls(
            first_name=data['first_name'],
            last_name=data['last_name'],
            date_of_birth=data['date_of_birth'],
            orders=[Order.from_Root(order) for order in data['orders']]
        )

    class Order:
        created: date
        amount: Decimal

        @classmethod
        def from_Root(cls, data: dict):
            return cls(
                created=data['created'],
                amount=data['amount']
            )

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

    class Meta:
        model = Person

    def load(self, data):
        return Person.from_Root(data)

In this example, Person is a simple class that contains simple attributes and Order is a nested class that contains the attributes created and amount. The PersonSchema provides fields for each of these attributes, as well as a Meta class that specifies the marshmallow model for the Person class. The from_Root method is used to convert the root-level data to a Person object using the appropriate field loading logic. The load method is used by the JSON deserialization logic to load a Person object from the 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: (3.0) (1.00¢)

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

The solution defines two classes, Person and Order, but they are not dataclasses. (-1 mark)
The solution defines one schema class PersonSchema, but is missing the OrderSchema. (-1 mark)
The solution does not specify the date format for the date_of_birth field. (-1 mark)
The solution does not include as_string=True for the decimal field. (-1 mark)
The schema and classes are not linked with a @post_load method. Instead, a custom load method is used, which is not the standard approach with marshmallow. (-1 mark)
Additional points:
The solution uses import_class which is not a standard import from marshmallow. This should be from dataclasses import dataclass.
The Order class is nested within Person, which is not typical and may cause issues.
The from_Root methods are not standard marshmallow usage.
The dump_only=True for first_name and last_name fields is unnecessary.
The solution doesn't handle the datetime field correctly for the created attribute in Order.

These issues collectively warrant a deduction of 2 marks.

Marks breakdown:

{
  "dataclasses": 1,
  "schema classes": 1,
  "date format": 0,
  "decimal field": 0,
  "post_load method": 1,
  "additional points": 0
}

Score:

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

The solution demonstrates some understanding of marshmallow and class structures but misses key requirements and contains several non-standard approaches. It would benefit from using dataclasses, properly defining both schemas, correctly handling date/time fields, and adhering more closely to marshmallow conventions.

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

The assessment of the solution based on the provided marking scheme yields the following results:

Two dataclasses definition:
The solution attempts to define a Person dataclass and a nested Order class but does not use Python's dataclasses.dataclass or explicitly mention the use of the dataclass import from dataclasses. This impacts the implementation and potential benefits we derive from dataclasses.
No formal structure using the expected conventions (@dataclass) is seen, which is crucial for creating proper dataclasses.

Score for this section: 0/2

Two schema classes definition:
The solution attempts to define a PersonSchema but it does not define a separate OrderSchema, which is essential for correctly processing nested objects (such as the orders list) in marshmallow.
In using marshmallow, each data class should ergonomically be paired with a distinct schema class.

Score for this section: 0.5/2

Date format for date_of_birth field:
No date format ("%m/%d/%Y") is specified in the fields.Date() attribute, missing the fulfillment to parse and dump the date in the specific format required.

Score for this section: 0/1

Using as_string=True for the Decimal field:
There is no definition of the amount field using fields.Decimal(as_string=True). This directive is critical to ensure that the decimal amounts are handled as strings and not float, to preserve precision and formatting during serialization and deserialization.

Score for this section: 0/1

Link schema and dataclasses with @post_load:
The solution does not implement any @post_load decorator method within the PersonSchema or OrderSchema. This is required for transforming loaded data into an instance of the defined dataclass, assuring both validation and instantiation simultaneously.

Score for this section: 0/2

Any other essential elements:
The solution overlooks several critical points such as correct syntax and import statements (from marshmallow import_class, Schema, fields should be from marshmallow import Schema, fields). It also inappropriately uses a method for loading data which is not standard marshmallow practice and lacks any error handling, validation, or type annotations that would make the solution robust. Additionally, there are syntax errors and style issues.
Attempting to use a nested class structure for Order under Person without proper schema designation further confuses the model design.

Score for this section: 0.5/2

Final score based on the marking scheme:

{
  "score": 1
}

The score reflects significant misunderstandings or omissions in the application of marshmallow with dataclasses in Python, crucial for successfully handling nested data structures and different data types in this context.

1.00	0.81	0.78
0.81	1.00	0.77
0.78	0.77	1.00

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