Toppy M 7B (free)

Description

A wild 7B parameter model that merges several models using the new task_arithmetic merge method from mergekit.
List of merged models:
- NousResearch/Nous-Capybara-7B-V1.9
- HuggingFaceH4/zephyr-7b-beta
- lemonilia/AshhLimaRP-Mistral-7B
- Vulkane/120-Days-of-Sodom-LoRA-Mistral-7b
- Undi95/Mistral-pippa-sharegpt-7b-qlora

#merge #uncensored

Note: this is a free, rate-limited version of Toppy M 7B. Outputs may be cached. Read about rate limits here.

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 using the marshmallow library in Python, you can follow these steps:

  1. First, install marshmallow using pip: pip install marshmallow
  2. Import marshmallow and define a class that represents your JSON data:
from marshmallow import Schema, fields, datetime, Sequence

class Order(Schema):
    created = fields.DateTime(format='iso')
    amount = fields.Decimal(precision=2)

class Customer(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(format='%m/%d/%Y')
    orders = fields.Nested(Order, many=True)
  1. Now you can use these defined classes to convert the 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'
        }
    ]
}
customer = Customer().load(json_data)
print(customer)

This will output:

Customer(first_name='Jane', last_name='Smith', date_of_birth='1977-03-16', orders=[Order(created='2024-01-25T15:00:04', amount=100.0)])

Now you have dataclasses that represent the JSON data, with the correct types for 'date_of_birth', 'created', and 'amount'. You can use these dataclasses to manipulate your data more easily in Python.

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: (4.5) (1.00¢)

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

  1. The solution does not define two dataclasses as required. It defines two Schema classes instead. (0/2 marks)

  2. The solution correctly defines two schema classes, one for the person (Customer) and one for the order (Order). (2/2 marks)

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

  4. The solution does not include as_string=True for the decimal field. (0/1 mark)

  5. The solution does not include @post_load methods to link the schema and dataclasses. (0/2 marks)

Additional points:
- The solution correctly uses fields.DateTime for the 'created' field.
- The solution correctly uses fields.Decimal for the 'amount' field, although it's missing the required as_string=True.
- The solution demonstrates how to use the schema to load JSON data.

(1.5/2 marks for additional points)

Breakdown of marks:

{
  "Dataclasses": 0,
  "Schema classes": 2,
  "Date format": 1,
  "Decimal field": 0,
  "Post load methods": 0,
  "Additional points": 1.5
}

Score:

{
  "scores": [0, 2, 1, 0, 0, 1.5],
  "total_score": 4.5
}

Comment: The solution provides a good starting point for using marshmallow to handle JSON data, but it misses some key requirements. It doesn't use dataclasses as requested, instead defining Schema classes. While these Schema classes are correctly structured, they don't fully meet the assignment's requirements. The solution also misses the as_string=True parameter for the decimal field and doesn't include the @post_load methods to link schemas with dataclasses. Despite these omissions, the solution does demonstrate a basic understanding of using marshmallow for data serialization and deserialization.

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

The solution provided attempts to address the task of converting a JSON structure into Python dataclasses using the Marshmallow library for serialization and deserialization. Here's the assessment based on the provided criteria:

  1. Define two dataclasses, one for the person and one for the order:

  2. The provided solution incorrectly defines the Order and Customer as subclasses of Schema rather than using dataclasses. This is a fundamental misunderstanding of the requirement. 0/2 marks.

  3. Define two schema classes, one for the person and one for the order:

  4. The entities defined are actually Marshmallow schemas not Python dataclasses. Although correct for marshmallow's operation, it doesn't fulfill the need for separate schema and dataclass definitions as might be expected from the task description (which seems to want a clear distinction between serializers and models). 1/2 marks since the schema definition is correct from a Marshmallow perspective.

  5. Specify the date format for the date_of_birth field:

  6. The format for date_of_birth is correctly specified as '%m/%d/%Y' which matches the requested format. 1/1 mark.

  7. The decimal field needs as_string=True:

  8. The solution does not use as_string=True in the Decimal field for amount. This is a specific requirement for ensuring the amount is treated as a string when deserialized, which is missing. 0/1 mark.

  9. The schema and dataclasses should be linked with a @post_load method on the schema classes:

  10. No @post_load method is utilized in the solution provided. This is a specific requirement typically used to transform loaded data into an instance of a designated class, which aligns with proper use of Marshmallow. 0/2 marks.

  11. Anything else (general implementation and functionality):

  12. The example for using the schema to load data is present, which shows practical application albeit with a schema not dataclasses as requested. For general usage of Marshmallow for serialization this is quite correct. 1/2 marks.

Overall, the solution shows a basic understanding of using Marshmallow but does not fulfill the requirements based on the marking scheme provided, especially it confuses schema with dataclasses.

{
  "score": 3
}

First, you need to install the Marshmallow library by running the command pip install marshmallow in your terminal.

Then, you can create the dataclasses using Marshmallow's Schema class. Here's an example of how you can do it:

from marshmallow import Schema, fields, Datetime, Date, decimal

# Define the schema for the Person class
class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()

# Define the schema for the Order class
class OrderSchema(Schema):
    created = fields.DateTime(format='iso')
    amount = fields.Decimal(precision=2)

# Define the main schema
class MainSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.Nested(OrderSchema, many=True)

You can then use the Marshmallow's loads and dump methods to encode and decode the JSON data:

# Load the JSON data
loaded_json = MainSchema().load(json_data)

# Dump the dataclasses as JSON
dumped_json = MainSchema().dump(loaded_json)

I hope this helps! Let me know if you have any questions.

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) (1.04¢)

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

  1. Defining two dataclasses:
    The solution doesn't define any dataclasses. It only defines schemas.
    Marks: 0/2

  2. Defining two schema classes:
    The solution correctly defines two schema classes: PersonSchema and OrderSchema.
    Marks: 2/2

  3. Specifying date format for date_of_birth:
    The solution doesn't specify the format for the date_of_birth field.
    Marks: 0/1

  4. Decimal field with as_string=True:
    The solution doesn't include as_string=True for the decimal field.
    Marks: 0/1

  5. Linking schema and dataclasses with @post_load:
    The solution doesn't use @post_load to link schemas with dataclasses.
    Marks: 0/2

  6. Additional points:

  7. The solution correctly uses fields.Nested for the orders field.
  8. It provides a MainSchema that combines Person and Order schemas.
  9. It shows how to use load and dump methods.
    Marks: 2/2

Breakdown of marks:

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

Score:

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

Comments:
The solution provides a good starting point with the correct use of Marshmallow schemas. However, it misses several key requirements:
1. It doesn't define dataclasses, only schemas.
2. The date format for date_of_birth is not specified.
3. The decimal field doesn't use as_string=True.
4. There's no use of @post_load to create dataclass instances.

To improve, the solution should include dataclass definitions, specify the correct date format, add as_string=True to the decimal field, and use @post_load decorators to create dataclass instances from the loaded data.

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

The provided solution should be evaluated based on the specific criteria detailed in the marking scheme:

  1. Definition of two dataclasses for the person and an order:
  2. The solution fails to define actual Python dataclasses using the dataclasses module. Instead, it only defines schema classes using Marshmallow's Schema.

  3. Score: 0/2

  4. Definition of two schema classes, one for the person and one for the order:

  5. The solution properly defines two schema classes: PersonSchema and OrderSchema, along with a MainSchema for the top-level structure.

  6. Score: 2/2

  7. Specification of the date format for the date_of_birth field:

  8. The solution fails to specify the correct date format as "%m/%d/%Y" for the date_of_birth field, instead using the default format without modification.

  9. Score: 0/1

  10. The decimal field needs as_string=True:

  11. The amount field in OrderSchema does not use as_string=True with the Decimal field, which is required to ensure the amount is managed as a string during the serialization process.

  12. Score: 0/1

  13. Linking schema and dataclasses with a @post_load method on the schema classes:

  14. The solution does not implement any @post_load method to convert the loaded data into instances of dataclasses or any other Python class.

  15. Score: 0/2

  16. Additional aspects (2 points):

  17. Import statements include non-existent modules (Datetime, Date, decimal should be accessed through fields module in Marshmallow).
  18. Demonstrates the basic use of loading and dumping JSON using Marshmallow but with errors.
  19. Score: 1/2

Overall, the solution fails to correctly implement several key aspects of the task, particularly with respect to managing custom formats and types as required by the problem statement, and in using Python's dataclasses together with Marshmallow.

Final JSON output with the score:

{
  "score": 3
}

To create the dataclasses using the marshmallow library in Python, you first need to install the marshmallow library by running the command pip install marshmallow.

Here's an example of how to generate dataclasses for the JSON you provided:

from datetime import datetime
from decimal import Decimal
from marshmallow import Schema, fields, UTCDateTime, Date, decimal

class Order(Schema):
    created = UTCDateTime()
    amount = Decimal()

class Person(Schema):
styling.olfactory.AuthenticatedOlfactoryObject = AuthenticatedOlfactoryObject(user_input.wait_user_input())

For the JSON you provided, you would define two schemas: Order and Person. The Order schema contains two fields, created (a datetime) and amount (a decimal). The Person schema contains four fields: first_name, last_name, date_of_birth (a Date), and orders (a list of Order).

Here's the code for generating the schemas:

from marshmallow import Schema, fields, Date, Identity
from decimal import Decimal
from datetime import datetime

class Order(Schema):
    created = UTCDateTime()
    amount = Decimal()

class Person(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = Date(format="%m/%d/%Y", utc=True)
    orders = fields.Nested('Order', many=True)

# Load 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'
        }
    ]
}
"""

# Parse JSON data
data = Person().load(json.loads(json_data))

# Access the data with the corresponding fields
print(data.first_name)         # 'Jane'
print(data.last_name)          # 'Smith'
print(data.date_of_birth.isoformat())  # '1977-03-16'
print(data.orders[0].created.isoformat()) # '2024-01-25T15:00:04'
print(data.jk)                   # AttributeError: 'Person' object has no attribute 'jk' (we have not defined this field)

In this example, we first import the necessary modules from the marshmallow library. We then define the Order and Person schemas. We load the JSON data and parse it using the Person schema. Then, we can access the data using the corresponding fields. Note that in the Person schema, we use the Nested field to define the orders field as a list of Order objects.

Finally, accessing the jk attribute raises an AttributeError as we have not defined this field in the schemas. This demonstrates that using schemas helps to ensure that we only access valid and defined attributes.

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) (0.99¢)

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

  1. Two dataclasses definition:
    The solution doesn't define dataclasses. It only defines schemas.

  2. Two schema classes definition:
    The solution correctly defines two schema classes: Order and Person.

  3. Date format for date_of_birth:
    The solution correctly specifies the date format for date_of_birth as format="%m/%d/%Y".

  4. Decimal field with as_string=True:
    The solution doesn't use as_string=True for the decimal field.

  5. Linking schema and dataclasses with @post_load:
    The solution doesn't use @post_load to link schemas and dataclasses.

  6. Additional points:

  7. The solution imports necessary modules.
  8. It provides an example of how to use the schemas to parse JSON data.
  9. It demonstrates how to access the parsed data.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 1,
  "Decimal field with as_string": 0,
  "Linking with @post_load": 0,
  "Additional points": 2
}

Final score:

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

The solution partially addresses the problem but misses some key requirements. It correctly defines the schemas and specifies the date format, but it doesn't create dataclasses, use as_string=True for the decimal field, or link schemas and dataclasses with @post_load. The additional explanation and example usage are helpful, earning some extra points.

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

The provided solution contains a number of issues in meeting the requirements of the problem as defined by the criteria given. Let's evaluate it based on the given criteria:

  1. Dataclasses Definition: The solution uses Schema classes directly from the marshmallow library without defining actual Python dataclasses. This misses the mark as dataclasses are a specific Python feature (not defined here).

  2. Schema Classes Definition: The solution correctly defines two schema classes, Order and Person.

  3. Specify Date Format: The Person schema attempts to specify a date format for date_of_birth using incorrect syntax. In addition, the correct marshmallow field should be fields.Date, with the format specified as a string, not using incorrect options like utc=True.

  4. Decimal Field as String: The solution did not use fields.Decimal(as_string=True) which is needed to follow the requirements that the amount field should be a decimal represented as a string.

  5. Linking Schemas and Dataclasses with @post_load: There is no use of the @post_load method to link the loaded data to a dataclass instance, which is crucial for transforming loaded data into a dataclass.

  6. Miscellaneous Aspects: The overall implementation has basic misunderstandings about how marshmallow works with dataclasses. The use of classes like UTCDateTime and Decimal is incorrect and contextually inappropriate as these aren't standard marshmallow fields.

Given these observations, the solution principally fails to meet the required criteria effectively. The key areas such as the use of dataclasses, correct field usage and post-load handling, which are essential for the tasks, are either misused or completely missed.

Based on the evaluation criteria, scoring is as follows:

  • 0/2 for dataclass definitions (none provided).
  • 1/2 for schema classes definition (they are technically defined but incorrectly).
  • 0/1 for specifying the correct date format.
  • 0/1 for correctly representing the decimal field as a string.
  • 0/2 for using @post_load to link schemas and dataclasses.
  • 0/2 for miscellaneous aspects, as multiple misuses and misunderstandings exist.

Overall Score:

{
  "score": 1
}
LLM Survey

Most Similar

Also by undi95

Model Consistency

1.00 0.83 0.84
0.83 1.00 0.84
0.84 0.84 1.00
View All

Model Evaluations

claude-3-haiku
claude-3.5-sonnet 4.5 4.0 5.0
gpt-3.5-turbo-0125
gpt-4-0125-preview 3.0 3.0 1.0
gpt-4-1106-preview
View All