Qwen 1.5 110B Chat

Description

Qwen1.5 110B 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:

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?

To generate dataclasses with specific types using the Marshmallow library, you first define a Schema for your data, specifying the field types, and then create dataclasses based on those schemas. The marshmallow_dataclass package can help you create the dataclasses directly from the Schema. If you don't have it installed, install it with pip install marshmallow marshmallow-dataclass.

Here's how you can do it:

  1. Install marshmallow and marshmallow-dataclass:
pip install marshmallow marshmallow-dataclass
  1. Define your Schema:
from dataclasses import dataclass
from datetime import date, datetime
from decimal import Decimal
from marshmallow import Schema, fields
from marshmallow_dataclass import class_schema

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Customer:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

CustomerSchema = class_schema(Customer)  # This generates a Schema from the Customer dataclass
  1. Use the Schema to load your JSON data:
import json

# Your JSON data as a 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 JSON data
data_parsed = json.loads(json_data)

# Load data into dataclass instances using the schema
customer = CustomerSchema().load(data_parsed)

print(customer)

This code defines two dataclasses, Order and Customer, and a corresponding CustomerSchema. The class_schema function generates a Schema from the Customer dataclass automatically, which knows how to serialize and deserialize dates and decimals according to the specified types. The JSON data is then loaded into an instance of the Customer dataclass using this schema.

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: (7.0) ()

Evaluation of the Provided Solution:

  1. Dataclasses Definition:
  2. The solution correctly defines two dataclasses: Order and Customer.
  3. Order dataclass fields very well capture the structuring of individual orders which are within the given JSON data.
  4. Customer dataclass encompasses the overall JSON structure and inlines the Order via a list composition, thereby perfectly accommodating nested relationships.

  5. Score: 2/2

  6. Schema Classes Definition:

  7. The solution employs class_schema(Customer) to create a schema directly from the Customer dataclass, which implicitly incorporates both Customer and Order through its fields.
  8. Although it does not explicitly create an OrderSchema, it is implicitly handled through the Customer dataclass.

  9. Score: 1.5/2 because while effective and concise, the explicit creation of both schemas can add clarity.

  10. Date Format Specification:

  11. The solution lacks explicit specification of the date_of_campaign format using %m/%d/%Y. The date deserialization happens through the date type hint; however, exact formatting requirements are not clearly stated.

  12. Score: 0/1

  13. Decimal Field Specification:

  14. The solution does not specify the amount field as a string using as_string=True.
  15. This misses ensuring that the decimal value retains necessary format during JSON parsing which is critical for financial precise calculations.

  16. Score: 0/1

  17. Linking Schema and Dataclasses:

  18. The use of class_schema to generate the schema from the dataclass allows for automatic linking between the schema and the dataclasses, handling the deserialization as per the type hinting and the structure given.
  19. Given the automatic functionality of marshmallow_dataclass, it takes care of the @post_load logic implicitly.

  20. Score: 1.5/2 due to not explicitly showing or mentioning how @post_load is being used or implied.

  21. Additional Observations:

  22. Installation and utilization of marshmallow and marshmallow-dataclass libraries are appropriately described.
  23. Example code showing how to load JSON into the Python application using Marshmallow is effectively demonstrated.
  24. Handling and parsing the JSON data into actual dataclass instances using the schema is showcased well.
  25. Score: 2/2

Overall, while the solution adeptly demonstrates the integration of marshmallow with dataclasses for JSON parsing and object mapping, it lacks specific detail in fields configuration for date formats and decimal handling which are crucial per the requirements stated.

{
  "score": 7
}

anthropic/claude-3.5-sonnet says: (5.0) (1.00¢)

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

  1. The solution defines two dataclasses, Order and Customer. (2 marks)

  2. The solution defines one schema class, CustomerSchema, which is automatically generated from the Customer dataclass using class_schema. However, it doesn't explicitly define separate schema classes for both person and order. (1 mark)

  3. The solution doesn't specify the date format for the date_of_birth field. (0 marks)

  4. The solution doesn't specify as_string=True for the decimal field. (0 marks)

  5. The solution doesn't use a @post_load method to link the schema and dataclasses. Instead, it uses marshmallow_dataclass to automatically generate the schema from the dataclass. While this achieves a similar result, it doesn't match the specific requirement. (0 marks)

  6. Additional points:

  7. The solution uses marshmallow_dataclass, which is a good approach for simplifying the process of creating schemas from dataclasses. (1 mark)
  8. The solution includes code to parse and load the JSON data into the dataclass instances. (1 mark)

Here's the breakdown of the marks:

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

The total score:

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

The solution demonstrates a good understanding of using dataclasses with Marshmallow, but it doesn't fully meet all the specific requirements outlined in the marking scheme. It takes a different approach by using marshmallow_dataclass, which simplifies the process but doesn't allow for the detailed customization requested in the marking scheme.

To generate dataclasses using the Marshmallow library in Python, you'll need to first install Marshmallow and its dataclass extension. You can install them using pip:

pip install marshmallow marshmallow-dataclass

Then, you can define your dataclasses and schema as follows:

from dataclasses import dataclass
from datetime import date, datetime
from decimal import Decimal
from marshmallow import Schema, fields
from marshmallow_dataclass import class_schema

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

# Create a schema for the dataclass
PersonSchema = class_schema(Person)

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

# Deserialize (load data from JSON to dataclass)
schema = PersonSchema()
deserialized_person = schema.load(person_data)

# Serialize (dump dataclass to JSON)
serialized_person = schema.dump(deserialized_person)

In this code:

  1. We define Order and Person as dataclasses.
  2. We use @dataclass decorator to automatically generate special methods like __init__, __repr__, etc.
  3. We define the types for each field according to your requirements, such as date for date_of_birth, datetime for created, and Decimal for amount.
  4. We use class_schema from marshmallow_dataclass to create a Schema for each dataclass.
  5. We demonstrate how to load (deserialize) JSON data into a Person dataclass instance and then how to dump (serialize) it back to JSON.

Remember to handle potential exceptions when parsing with schema.load, as invalid input can raise marshmallow.exceptions.ValidationError.

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) (1.01¢)

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

  1. The solution defines two dataclasses, Order and Person. (2 marks)

  2. The solution uses class_schema(Person) to create a schema for the Person dataclass. However, it doesn't explicitly define separate schema classes for Person and Order. (1 mark)

  3. The solution doesn't specify the date format for the date_of_birth field. (0 marks)

  4. The solution doesn't include as_string=True for the decimal field. (0 marks)

  5. The solution doesn't use @post_load method to link schema and dataclasses. Instead, it uses marshmallow_dataclass.class_schema, which automatically generates the schema from the dataclass. While this is a valid approach, it doesn't match the specific requirement in the marking scheme. (0 marks)

  6. Additional points:

  7. The solution correctly imports necessary modules.
  8. It demonstrates how to serialize and deserialize data using the schema.
  9. It mentions handling potential exceptions.
    (2 marks)

Here's the breakdown of marks:

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

Final score:

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

The solution provides a working approach using marshmallow_dataclass, which simplifies the process of creating schemas from dataclasses. However, it doesn't meet some specific requirements outlined in the marking scheme, such as explicitly defining schema classes, specifying date formats, and using @post_load decorators. Despite these omissions, the solution demonstrates a good understanding of using dataclasses with Marshmallow for JSON serialization and deserialization.

openai/gpt-4-0125-preview says: (5.5) ()

The provided solution does cover significant aspects of marshmallow and the use of marshmallow-dataclass to handle serialization and deserialization of JSON data into and from dataclasses, but there are notable issues and missing elements based on the specified marking scheme:

  • Defining dataclasses: The solution correctly defines the Person and Order dataclasses. (+2 marks)

  • Defining schema classes: It correctly uses class_schema from marshmallow_dataclass to automatically generate the schema class for the Person dataclass. This technically fulfills the requirement, although generating the schema for Order would have been explicitly shown. (+1.5 marks)

  • Date format specification: The solution does not specify the date format for the date_of_birth field using fields.Date with format="%m/%d/%Y". This could lead to incorrect date deserialization. (+0 marks)

  • Decimal field representation as string: The Decimal field is not specified to be serialized as a string (as_string=True in fields.Decimal). This may not directly affect internal handling but does impact JSON output. (+0 marks)

  • @post_load method for loading: The solution does not define any post_load method in the schemas to parse and create a dataclass instance from the loaded data. The schema is being generated using class_schema, which handles dataclass creation, but nothing is manual about custom parsing. (+0 marks)

  • Other aspects:

  • The usage of marshmallow-dataclass is appropriate and demonstrates serialization and deserialization effectively. (+1 mark)
  • It correctly demonstrates the construction and use of a schema object for the Person dataclass. (+1 mark)

Considering the shortcomings, particularly in the handling of date and decimal fields according to specifics given, plus the lack of manual @post_load customization, this solution would score lower than expected.

{
  "score": 5.5
}

To generate dataclasses with the specified types using the marshmallow library in Python, first, you'll need to install marshmallow and marshmallow-dataclass. If you haven't installed them already, you can do so with the following command:

pip install marshmallow marshmallow-dataclass

Next, you can define your dataclasses and a schema that specifies the types and format for deserialization and serialization. Here's how you'd define the dataclasses and the schema for your JSON structure:

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

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Customer:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

class CustomerSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(format='%m/%d/%Y')
    orders = fields.List(fields.Nested(Order.Schema()))

# 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"
    }
  ]
}
'''

# Deserializing
customer_schema = CustomerSchema()
customer = customer_schema.loads(json_data)
print(customer)

# Serializing back to JSON
 serialized_data = customer_schema.dumps(customer)
print(serialized_data)

In the code snippet above, dataclass annotations define the structure and types of your data. The CustomerSchema class defines how marshmallow should serialize and deserialize the data. For date_of_birth, we specify the format to match the incoming JSON string. The Order dataclass represents the nested orders data, and it is nested within the Customer dataclass.

Remember to adjust the format string in fields.Date(format='%m/%d/%Y') if the date format in your input data differs. The amount field automatically works with Decimal as specified without requiring additional configuration since marshmallow can deserialize strings into Decimal by default.

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) (0.95¢)

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

  1. The solution defines two dataclasses, Customer and Order. (2 marks)

  2. The solution defines one schema class, CustomerSchema. It's missing a separate schema for Order. (1 mark)

  3. The solution correctly specifies the date format for the date_of_birth field with format='%m/%d/%Y'. (1 mark)

  4. The solution doesn't include as_string=True for the decimal field. (0 marks)

  5. The solution doesn't include a @post_load method to link the schema and dataclasses. (0 marks)

  6. Additional points:

  7. The solution imports necessary modules.
  8. It provides example usage for deserialization and serialization.
  9. It uses nested fields for the orders list.
    (2 marks)

Breakdown of marks:

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

Score:

{
  "scores": [2, 1, 1, 0, 0, 2],
  "total_score": 6
}

Comments:
The solution provides a good starting point but misses some key requirements. It correctly defines the dataclasses and partially implements the schema. However, it lacks a separate schema for the Order class, doesn't include the required as_string=True for the decimal field, and misses the @post_load method to link schemas and dataclasses. The example usage and additional imports are helpful additions. To improve, the solution should address these missing elements and separate the Order schema from the Customer schema.

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

The provided solution attempts to implement two dataclasses (Order and Customer) and corresponding schema classes (CustomerSchema) using the marshmallow library. Here's the evaluation based on the marking scheme provided:

  1. Dataclasses for person and order: The solution defines an Order dataclass with datetime and Decimal fields, and a Customer dataclass with str, date, and a list of Order dataclasses. This meets the requirement.

    • Score: 2 marks

  2. Schema classes for person and order: The CustomerSchema schema class is defined. However, an OrderSchema within a CustomerSchema is referred to, but not separately defined, which makes it somewhat unclear whether the scheme for Order is fully defined. Nonetheless, the attempt is there.

    • Known Issue: Order schema isn't explicitly defined but nested as Order.Schema() which is incorrect.
    • Score: 1 mark

  3. Date format for date_of_birth: The format for the date_of_birth field is correctly defined as %m/%d/%Y in the CustomerSchema.

    • Score: 1 mark

  4. Decimal field with as_string=True: There is no use of as_string=True in the solution for the Decimal field. Marshmallow needs explicit instruction to serialize Decimal back to string, otherwise, it will serialize it to a float or decimal depending on the configuration.

    • Score: 0 marks

  5. Linking schema and dataclasses with @post_load: The solution lacks any implementation of the @post_load method, which is necessary to ensure that the loaded JSON data is indeed converted into respective dataclass instances.

    • Score: 0 marks

  6. Additional elements: Correct imports and basic handling using marshmallow are well demonstrated. The example of deserializing and serializing the JSON is good for understanding.

    • Score: 1 mark

Final score calculation:

{
  "score": 5
}
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