Qwen 1.5 72B Chat

Description

Qwen1.5 72B 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)

First, you need to install the marshmallow library and its dependencies for handling datetimes and decimals. You can install them using pip:

pip install marshmallow marshmallow-date marshmallow-decodable decimal

Now, you can define the dataclasses and the corresponding marshmallow schemas for serializing and deserializing the JSON data:

from decimal import Decimal
from datetime import datetime
from marshmallow import Schema, fields, post_load
from marshmallow_date import DateField
from marshmallow_decodable import Decodable

class Order:
    def __init__(self, created, amount):
        self.created = created
        self.amount = amount

class OrderSchema(Schema, Decodable):
    created = fields.DateTime()
    amount = fields.DecimalField(decimal_places=2)

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

class UserSchema(Schema, Decodable):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = DateField()
    orders = fields.List(fields.Nested(OrderSchema))

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

# Deserialize JSON to Python objects
user_schema = UserSchema()
user_dict = user_schema.loads(json_data)
user = user_schema.objectify(user_dict)

# Print the deserialized user object
print(user)

# Serialize Python object back to JSON
serialized_data = user_schema.dumps(user)
print(serialized_data)

This code defines the necessary dataclasses and marshmallow schemas. The post_load decorator is not needed here because we're not modifying the data during deserialization. The Decodable mixin is used to decode the JSON data into a dictionary, which is then used to create the 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: (6.0) (1.01¢)

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

The solution defines two dataclasses, Order and User. (2 marks)
The solution defines two schema classes, OrderSchema and UserSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. It should have been DateField(format="%m/%d/%Y"). (0 marks)
The decimal field (amount) is not specified with as_string=True. It should have been fields.DecimalField(as_string=True, decimal_places=2). (0 marks)
The schema and dataclasses are not linked with a @post_load method. Instead, the solution uses the Decodable mixin, which is an alternative approach but not the one specified in the marking scheme. (0 marks)
Additional points:
The solution imports necessary libraries and includes a complete working example. (1 mark)
The solution demonstrates how to serialize and deserialize the data. (1 mark)

Here's the breakdown of the marks:

{
  "dataclasses": 2,
  "schema classes": 2,
  "date format": 0,
  "decimal field": 0,
  "post_load method": 0,
  "additional points": 2
}

Final score:

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

The solution provides a working implementation but misses some specific requirements from the marking scheme. It uses alternative approaches for some aspects, which, while functional, don't align with the specific requirements. The main areas for improvement would be specifying the date format, adding as_string=True to the decimal field, and using @post_load decorators instead of the Decodable mixin.

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

The provided solution meets some of the objectives but fails in several critical areas according to the problem and marking scheme provided. Here's the breakdown:

Dataclass Definition:
The solution provided correctly defines two classes, Order and User, intending to mirror the structure of the JSON data.
However, these are not defined as dataclasses (using the @dataclass decorator from the dataclasses module). They are regular classes.
Score for this section: 0.5/2
Schema Definition:
Two schema classes, OrderSchema and User', are defined using marshmallow.Schema.
This aligns with the requirements.
Score for this section: 2/2
Date Formatting:
The sample solution uses marshmallow_date.DateField() for the date_of_birth without specifying the required format (%m/%d/%Y).
This does not fulfill the requirement of setting the date format specifically.
Score for this section: 0/1
Decimal Field Handling:
The schema for the amount in OrderSchema uses fields.DecimalField but does not specify as_string=True. This is mandatory to meet the provided requirements.
Score for this section: 0/1
@post_load Use:
The solution lacks the use of the @post_load method to link schema and dataclass instantiations, opting instead for another approach using a Decodable mixin.
This approach diverges from the requirements of utilizing the @post_load decorator.
Score for this section: 0/2
Additional Aspects:
The code imports and uses certain libraries (marshmallow_date, marshmallow_decodable) that do not exist in the standard Python package index and are not part of the marshmallow library's ecosystem. This can lead to confusion and misrepresentation of how Marshmallow is typically used.
Also, it could potentially introduce an unnecessary complexity or errors in the code due to non-existent dependencies if someone attempts to run this code in a standard setup.
Score for this section: 0.5/2

Given all these points, the final score for this solution in the review is:

{
  "score": 3/10
}

This solution, while structured in terms of correct schema definition, inadequately meets the specific requirements, especially concerning date and decimal handling, and misuse of the @post_load decorator for linking dataclasses and schemas.

Response 2 (7.5)

In order to use the marshmallow library to define dataclasses based on the provided JSON, you'll first need to install marshmallow and its extensions for handling datetimes and decimals:

pip install marshmallow marshmallow-dataclass marshmallow-enum

Then, you can create the necessary dataclasses and a schema to deserialize the JSON. Here's an example:

from decimal import Decimal
from datetime import datetime
from typing import List
from marshmallow import fields, Schema, validate, pre_load
from marshmallow_dataclass import class_schema, dataclass

# Create custom fields for marshmallow
class DateTimeField(fields.DateTime):
    format = '%Y-%m-%dT%H:%M:%S'

class DecimalField(fieldsetSocketAddressSphere):
    decimal_places = 2

@dataclass
class Order:
    created: datetime = fields.DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount: Decimal = DecimalField()

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date = fields.DateField(input_formats=['%m/%d/%Y'])
    orders: List[Order] = fields.List(fields.Nested(OrderSchema))

# Create a schema for the Order dataclass
OrderSchema = class_schema(Order)(exclude=['__class__'])

# Create a schema for the Person dataclass
PersonSchema = class_schema(Person)(exclude=['__class__'])


def parse_json(json_data):
    # Deserializing the JSON data
    person_data = PersonSchema().load(json_data)
    return person_data

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

parsed_data = parse_json(json_input)
print(parsed_data)

This code defines two dataclasses, Order and Person, with the correct types for date_of_birth, created, and amount. The PersonSchema and OrderSchema are created based on these dataclasses, and the parse_json function is used to deserialize the provided JSON input.

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)

openai/gpt-4-0125-preview says: (5.0) ()

Dataclasses Definition: The solution defines two dataclasses, Order and Person. Each dataclass uses type annotations for its fields, indicating the types of the data expected, which is a correct way for defining dataclasses. (+2 marks)
Schema Classes Definition: The solution defines schema classes using the class_schema function from marshmallow_dataclass, which is a correct way to generate schemas for dataclasses in marshmallow. (+2 marks)
Date Format Specification for date_of_birth: The date format for the date_of_birth in Person dataclass is correctly specified using fields.DateField(input_formats=['%m/%d/%Y']). (+1 mark)
Decimal Field as String: The solution introduces DecimalField for handling decimal values, but it missed using as_string=True setting for converting amount to and from JSON string. Instead, it has implemented an incorrect and undefined attribute decimal_places = 2, which is not a recognized or correct implementation in marshmallow for representing decimal as string. (-1 mark)
Linking Schema and Dataclasses with @post_load: The solution does not include any @post_load methods for linking the deserialized data back to the respective dataclasses. Instead, it uses class_schema for automatic schema generation without manually reconnecting data to the original dataclass structures after loading. (-2 marks)
Additional Aspects:
The custom DateTimeField and incorrect DecimalField creation do not enhance the needed logic for decimal handling as a string.
The JSON parsing and printing of the resolved data are done well.
Use of parameter exclude=['__class__'] in class_schema is unnecessary and not required in given context. (+1 mark due to some positives like JSON parsing but also recognising some misinformation or unnecessary inclusions).

The solution overall corrects in schema generation and defining dataclasses but lacks in crucial marshmallow specific implementations and attributes correctness.

{
  "score": 5
}

anthropic/claude-3.5-sonnet says: (7.5) (1.06¢)

Let's evaluate this solution based on the provided marking scheme:

The solution defines two dataclasses, Order and Person. (2 marks)
The solution defines two schema classes, OrderSchema and PersonSchema, using class_schema. (2 marks)
The date format for the date_of_birth field is specified, but not correctly. It uses input_formats=['%m/%d/%Y'] instead of format="%m/%d/%Y". (0.5 marks)
The decimal field (DecimalField) is defined, but it doesn't use as_string=True. (0 marks)
The schema and dataclasses are not explicitly linked with a @post_load method. The class_schema function is used instead, which does create a schema from the dataclass, but it's not the exact method specified in the marking scheme. (1 mark)

Additional points:
- The solution imports necessary libraries and defines custom fields for DateTime and Decimal. (1 mark)
- The solution includes a parse_json function to demonstrate how to use the schema. (1 mark)

Comments:
The solution is generally on the right track, but it misses some specific requirements. The date format for date_of_birth is close but not exact, and the decimal field lacks the required as_string=True parameter. The use of class_schema is a valid approach, but it doesn't explicitly use the @post_load decorator as requested. The solution also includes some extra elements not specifically asked for, which shows good initiative.

Here's the breakdown of marks:

{
  "Dataclasses": 2,
  "Schema classes": 2,
  "Date format": 0.5,
  "Decimal field": 0,
  "Schema-dataclass linking": 1,
  "Additional elements": 2
}

Final score:

{
  "scores": [2, 2, 0.5, 0, 1, 2],
  "total_score": 7.5
}

Response 3 (8.0)

To use the marshmallow library in Python to define dataclasses for the given JSON, you need to install marshmallow and its extensions marshmallow-dataclass and marshmallow-precise-date first. You can install them using pip:

pip install marshmallow marshmallow-dataclass marshmallow-precise-date

After that, you can define the necessary dataclasses and schema as follows:

from dataclasses import dataclass
from datetime import datetime
from decimal import Decimal
from marshmallow import Schema, fields, post_load
from marshmallow_dataclass import class_schema
from marshmallow_precise_date import fields as precise_date_fields

@dataclass
class Order:
    created: datetime = fields.DateTime(data_key='created')
    amount: Decimal = fields.Decimal(as_string=True)

@dataclass
class Customer:
    first_name: str = fields.Str(data_key='first_name')
    last_name: str = fields.Str(data_key='last_name')
    date_of_birth: date = precise_date_fields.Date(data_key='date_of_birth')
    orders: list[Order] = fields.Nested(lambda: OrderSchema(), many=True)

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

CustomerSchema = class_schema(Customer)

Here, we've defined two dataclasses Order and Customer, along with a nested OrderSchema for deserializing the orders field. The @post_load decorator on the OrderSchema is used to create an Order instance from the deserialized data.

Now, you can use CustomerSchema to deserialize the JSON:

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"
    }
  ]
}'''

data = json.loads(json_data)
customer = CustomerSchema().load(data)

This will create a Customer object with the appropriate types for date_of_birth, created, and amount.

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) (0.96¢)

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

The solution defines two dataclasses, Order and Customer. (2 marks)
The solution defines two schema classes, OrderSchema and CustomerSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. It uses precise_date_fields.Date without specifying the format. (0 marks)
The decimal field (amount) correctly uses as_string=True. (1 mark)
The schema and dataclasses are linked with a @post_load method on the OrderSchema class. However, this is not done for the CustomerSchema. (1 mark)
Additional observations:
The solution imports necessary libraries.
It uses marshmallow-dataclass and marshmallow-precise-date for better handling of dates and dataclasses.
It provides an example of how to use the schema to deserialize JSON data.
(2 marks)

Breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field configuration": 1,
  "Schema and dataclass linking": 1,
  "Additional features": 2
}

Score:

{
  "scores": [2, 2, 0, 1, 1, 2],
  "total_score": 8
}

The solution provides a good implementation overall, but it misses a few key points from the marking scheme. The main areas for improvement are specifying the date format for date_of_birth and ensuring that both schema classes use the @post_load decorator to link with their respective dataclasses.

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

The solution's overall structure and intent are mostly correct, aiming to use the marshmallow library with dataclasses for JSON data transformation. The approach to implementing the requirements is generally in line with the problem's demands. Let's evaluate based on the mentioned criteria:

Dataclasses for Person and Order: The solution defines two dataclasses, Order and Customer. However, the use of marshmallow fields directly in the dataclass definition is incorrect. Marshmallow fields should be specified in schema classes, not in the dataclass.
Score for this section: 1/2
Schema classes: The solution includes an OrderSchema for the order, but incorrectly attempts to generate CustomerSchema using class_schema from marshmallow-dataclass without correctly defining Customer as a data field based schema. Instead, it mixes dataclass and schema definitions.
Score for this section: 1/2
Date format specification: There is no explicit specification of the date format in the date_of_birth field. While the solution uses precise_date_fields.Date, it does not specify the format and relies on implicit handling, which does not strictly follow the instruction to use a specific date format ("%m/%d/%Y").
Score for this section: 0/1
Decimal field with as_string=True: This is correctly implemented in the Order dataclass for the amount field.
Score for this section: 1/1
Link schema and dataclasses with a @post_load method: There is a @post_load method in the OrderSchema, which correctly constructs an Order instance from the deserialized data. However, the absence of a proper CustomerSchema class means that the linkage between Customer dataclass and schema is not implemented.
Score for this section: 1/2
Additional observations:
The mix of fields in the dataclass definitions (which should instead be plain Python types) is a major error.
No explicit handling or instruction on handling errors or ensuring correct data types beyond what marshmallow handles inherently.
The solution does not ensure proper custom handling where needed, for example, overriding default format settings for dates.
There would be runtime errors if attempting to execute the script as-is. Specifically, Python would throw errors regarding date and other imports and usages that are incorrect.
Score for this, including clarity, correctness, and execution potential: 0/2

Total score calculation:

{
  "score": 4/10
}

1.00	0.86	0.88
0.86	1.00	0.91
0.88	0.91	1.00

Qwen 1.5 72B Chat

Description

Most Similar

Also by qwen

Model Consistency

Model Evaluations

claude-3-haiku
claude-3.5-sonnet	6.0	7.5	8.0
gpt-3.5-turbo-0125
gpt-4-0125-preview	3.0	5.0	4.0
gpt-4-1106-preview