Qwen 1.5 7B Chat

Description

Qwen1.5 7B 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 (4.0)

To create dataclasses from a JSON-like structure using the Marshmallow library in Python, you first need to install Marshmallow by running pip install marshmallow, and then you'll follow these steps. Marshmallow has a Schema class for defining the data structure, and a marshmallow.validate for specific data types.

Step 1: Define a Schema
A Schema will map your JSON structure to a Python dictionary:

from marshmallow import Schema, fields
from datetime import datetime

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

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()

class JsonToDataclasses:
    def __init__(self, json_data):
        self.data = json_data

    def convert(self):
        orders_schema = OrderSchema()
        person_schema = PersonSchema()

        # Parse the JSON using the Schema
        orders = orders_schema.load(json_data['orders'])
        person_data = person_schema.load(json_data)

        person_data['date_of_birth'] = datetime.strptime(person_data['date_of_birth'], '%m/%d/%Y')  # Convert date to datetime

        return person_data, orders

Step 2: Use the JsonToDataclasses class
Now, you can create an instance of JsonToDataclasses and call the convert method to convert your JSON data to dataclasses:

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

json_to_dataclasses = JsonToDataclasses(json_data)
person, orders = json_to_dataclasses.convert()

print(person)
# Output: {'first_name': 'Jane', 'last_name': 'Smith', 'date_of_birth': datetime.date(1977, 3, 16), 'orders': [{'created': datetime.datetime(2024, 1, 25, 15, 0, 4), 'amount': Decimal('100.00')}]}

print(orders)
# Output: [Order(created=datetime.datetime(2024, 1, 25, 15, 0, 4), amount='100.00')]

In this example, Person and Order dataclasses are created with the expected schema, and the date is converted from a string to a datetime.date object using the strptime method.

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) (1.06¢)

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

The solution doesn't explicitly define two dataclasses for person and order. It only defines schemas. (0/2 marks)
The solution correctly defines two schema classes: OrderSchema and PersonSchema. (2/2 marks)
The solution doesn't specify the date format for the date_of_birth field in the schema. It's handled manually in the convert method. (0/1 mark)
The decimal field is not specified with as_string=True. (0/1 mark)
The solution doesn't use @post_load method to link schemas and dataclasses. (0/2 marks)
Additional points:
The solution creates a custom JsonToDataclasses class to handle the conversion.
It uses datetime.strptime to convert the date string to a datetime object.
It provides an example of how to use the conversion class.
(2/2 marks)

Breakdown of marks:

{
  "Dataclass definition": 0,
  "Schema definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema-dataclass linking": 0,
  "Additional features": 2
}

Score:

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

Comments:
The solution provides a basic structure for converting JSON to Python objects using Marshmallow schemas. However, it falls short in several areas according to the marking scheme. It doesn't create actual dataclasses, doesn't use some of the required Marshmallow features like specifying date formats in the schema or using as_string=True for decimals, and doesn't use the @post_load decorator to link schemas with dataclasses. The solution does provide a functional approach to converting the JSON data, but it doesn't fully align with the specific requirements outlined in the marking scheme.

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

The provided solution has attempted to use the Marshmallow library for data serialization, but there are significant issues and missing points based on the requirements provided in the problem statement:

Dataclasses Definition (0/2):
The solution does not define any Python dataclasses (Person and Order). It rather handles the serialization purely within the schema without creating respective classes.
Schema Classes (2/2):
Proper schema classes are defined (OrderSchema, PersonSchema) that accurately reflect the JSON structure.
Date Format Specification (0/1):
The PersonSchema does not specify the format of the date_of_birth field (format="%m/%d/%Y" is missing). The format is incorrectly being handled in the convert method rather than in the schema.
Decimal Field Handling (0/1):
as_string=True for the amount field in OrderSchema is not mentioned. This would be necessary to ensure that the decimal values are handled as strings which can be important for JSON serialization purposes or preserving exact numeric data without floating point errors.
Linking Schema and Dataclasses with @post_load (0/2):
No @post_load method is employed to convert loaded data into instances of any dataclasses (mostly because no dataclasses were defined). The transformation and instantiation should be managed using @post_load to ensure encapsulation and proper data handling.
Additional Marks (0/2):
No use of additional useful features such as error handling, or complete and correct use of schema-level validation or additional properties (like setting up nested schemas correctly).

Overall, the core functionality needed to meet the requirements outlined in the marking scheme is mostly missing. Specifically, the lack of dataclasses and their integration with Marshmallow schemas via the @post_load decorator is a significant shortfall. Also, proper handling of field-specific serialization details such as date formats and decimal presentation was not adequately addressed.

{
  "score": 2
}

Response 2 (4.0)

You can use the marshmallow library in Python to define and Unmarshal your dataclasses from the given JSON with the provided types. First, install marshmallow if you haven't already, then follow these steps:

Install marshmallow (if not already):

pip install marshmallow

Import the necessary modules:

from datetime import datetime
from marshmallow import Schema, fields, validates, ValidationError

Define the dataclasses:

from dataclasses import dataclass

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

@dataclass
class Order:
    created: datetime
    amount: decimal

Create a Marshmallow Schema for the dataclass:

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.DateTime(format="%m/%d/%Y", validates=validates.date)
    orders = fields.List(fields.Nested(OrderSchema))

    @validates('date_of_birth')
    def validate_date(self, value):
        try:
            datetime.strptime(value, '%m/%d/%Y')
        except ValueError:
            raise ValidationError("Invalid date format. Use 'MM/DD/YYYY'.")

Unmarshal the JSON to a Person object:

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

# Load the JSON数据 into a Python dictionary
data = json.loads(json_data)

# Create a marshmallow `PersonSchema` instance
person_schema = PersonSchema()
person = person_schema.load(data)

print(person)

With the provided schema, the JSON will be successfully unmarshalled into the Person and Order dataclasses. If any validation fails, a ValidationError will be raised.

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:

Defining two dataclasses (Person and Order):
The solution defines the Person dataclass but doesn't properly define the Order dataclass. It's mentioned in the Person class, but not fully implemented.
Defining two schema classes:
The solution defines the PersonSchema, but the OrderSchema is not defined.
Specifying the date format for the date_of_birth field:
The solution correctly specifies the date format as "%m/%d/%Y".
Decimal field with as_string=True:
The solution doesn't implement this requirement for the amount field.
Linking schema and dataclasses with @post_load:
The solution doesn't implement the @post_load method to link schemas and dataclasses.
Additional points:
The solution includes a custom validator for the date_of_birth field.
It provides a basic example of how to use the schema to load data.

Here's the breakdown of marks:

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

Final score:

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

The solution provides a good start but misses several key requirements. It correctly defines the Person dataclass and PersonSchema, and handles the date_of_birth format correctly. However, it lacks the Order dataclass and schema, doesn't implement the decimal field correctly, and doesn't use the @post_load method to link schemas and dataclasses. With some improvements, this solution could meet all the requirements.

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

The solution provided attempts to use the marshmallow library to deserialize JSON data into Python dataclasses, which represent a Person and their associated Orders. Here is the evaluation against the provided criteria:

Definition of two data classes (Person and Order):
The solution defines both Person and Order dataclasses correctly.
Score: 2/2
Definition of two schema classes (PersonSchema and OrderSchema):
The solution only partially implements the PersonSchema but does not define or show the OrderSchema. Absence of OrderSchema results in incomplete functionality.
Score: 1/2
Specification of the date format for the date_of_birth:
The field date_of_birth uses the fields.DateTime with format="%m/%d/%Y", which matches the requirement perfectly.
Score: 1/1
Decimal field with as_string=True:
This requirement is not implemented in the provided solution. There is no use of as_string=True for the decimal field.
Score: 0/1
Linking the schema and dataclasses with @post_load method:
The solution does not use the @postload method to link the schema classes back to the dataclasses. Instead, it employs direct conversion with potential validation, which is not the same as proper linkage with @post_load.
Score: 0/2
Additional Considerations:
Import errors and incorrect usage: The solution misses the import for decimal and incorrectly references it as a type hint (decimal instead of Decimal).
Error in fields.DateTime, which does not have a validates argument and the inappropriate use of validates decorator for a method expecting a different format than it operates under.
Missing import correction and handling for json.loads.
Score: 0/2

Overall, while the solution has a good base structure and attempts most of the requirements, it contains several fundamental flaws in execution, missing schemas, import errors, incorrect use of marshmallow decorators, and lacks crucial configurations.

Final score is calculated as follows:

{
  "score": 4
}

Response 3 (4.0)

To create data classes in Python using the Marshmallow library, you need to first install it if you haven't already (pip install marshmallow), and then follow these steps. Marshmallow provides a simple way to define data schemas, which will map JSON objects to Python classes. Here's how you can achieve your goal:

Import the required modules:

from datetime import datetime
from marshmallow import Schema, fields

Define the UserSchema for your user data:

class UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.List(fields.Nested(order_schema), load_only=True)

# Define the order schema (order_schema.py, if separate file)
class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal()

In your main file, load the JSON string and create a user instance using the UserSchema:

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 order_schema
from order_schema import OrderSchema as Order

# Load JSON data
data = json.loads(json_data)

# Create a UserSchema instance
user_schema = UserSchema()
user = user_schema.load(data)

# If you need to save the user to a database, you can use the dumped method
dumped_user = user_schema_dumped.dump(user)

The user_schema_load will parse the JSON and create a user object with the correct data types. For the order data, we load only the nested orders list and use fields.List(fields.Nested(order_schema)) to represent it in the schema.

If you want to save the user back to the JSON format, you can use user_schema_dumped.dump(user) to convert the object back to a serialized JSON string.

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) (0.92¢)

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

Defining two dataclasses:
The solution doesn't define any dataclasses. It only defines schemas.
Defining two schema classes:
The solution correctly defines two schema classes: UserSchema and OrderSchema.
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 include as_string=True for the decimal field.
Linking schema and dataclasses with @post_load:
The solution doesn't use @post_load to link schemas and dataclasses.
Other considerations:
The solution imports necessary modules.
It provides a sample of how to use the schema to load JSON data.
It mentions dumping the data back to JSON format.

Here's the breakdown of marks:

{
  "Dataclasses": 0,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal field": 0,
  "Post_load method": 0,
  "Other considerations": 2
}

Final score:

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

Comments:
The solution partially addresses the problem by creating schema classes using Marshmallow, but it misses several key requirements. It doesn't create dataclasses, doesn't specify the required date format, doesn't set as_string=True for the decimal field, and doesn't use @post_load to link schemas and dataclasses. While it provides a basic structure for using Marshmallow, it doesn't fully meet the specified requirements.

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

The provided solution utilizes Marshmallow, a popular Python library for handling serialization and deserialization of objects. Let's evaluate how it addresses the problem requirements:

Define two dataclasses, one for the person and one for the order (2 marks):
The solution does not define any dataclasses. It instead directly uses Schema classes without corresponding data classes.
Define two schema classes, one for the person and one for the order (2 marks):
The schema classes UserSchema and OrderSchema are defined, fulfilling this requirement.
Specify the date format for the date_of_birth field (format="%m/%d/%Y") (1 mark):
The solution does not specify the formatting for the date_of_birth field explicitly. It should have used something like fields.Date(format="%m/%d/%Y").
The decimal field needs as_string=True (1 mark):
The amount field is defined using fields.Decimal() but lacks the as_string=True parameter, which is essential for ensuring that the decimal field is serialized as a string.
The schema and dataclasses should be linked with a @post_load method on the schema classes (2 marks):
The solution does not define any @post_load method to link the loaded data to a data class instance, which means the JSON data is not converted to a specific class instance.
Anything else (2 marks):
General organization and presentation of the code are on point.
The provided solution mentions how to handle nested fields with fields.List(fields.Nested(order_schema)), which is correctly implemented for nested structures.
It also shows basic serialization using dump and deserialization using load.
However, there are issues with the use of the order_schema variable and import before defining or providing it correctly, which can confuse the reader and cause errors in actual implementation.

Final thoughts:
- The candidate seems to know some aspects of Marshmallow but struggles with fully applying best practices for defining proper serialization and deserialization methods that align with pythonic data handling using data classes.
- Key aspects like data typing via dataclasses, decimal representation, and date formatting are not optimally implemented.

Here's the total score:

{
  "score": 4
}

1.00	0.89	0.84
0.89	1.00	0.83
0.84	0.83	1.00

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