Toppy M 7B

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

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 (7.0)

To generate dataclasses using the marshmallow library, we first need to install it.
Open your terminal and run the following command to install marshmallow:

pip install marshmallow

Once it's installed, you can create two separate schemas for the JSON file and the dataclasses. Each schema will define the structure of the object and the type of each field, specifying how to serialize it.

Here's an example for your JSON:

from marshmallow import Schema, datetime, fields
from marshmallow.utils import datetime as marshmallow_datetime

# Creating the schema for the JSON
class PersonSchema(Schema):
  class Meta:
    fields = ('first_name', 'last_name', 'date_date_date_of_birth', 'orders')

  first_name = fields.Str()
  last_name = fields.Str()
  date_of_birth = fields.Date(default=marshmallow_datetime(utc=True))
  orders = OrderSchema(many=True)


# Creating the schema for Orders
class OrderSchema(Schema):
  class Meta:
    fields = ('created', 'amount')

  created = fields.DateTime(default=marshmallow_datetime(utc=True)) )

Now to generate the dataclasses, run the following code:

from marshmallow_dataclass import dataclass, DataclassSchema, fields
from datetime import datetime as python_datetime
from decimal import Decimal

# Creating the person dataclass
@dataclass(frozen=True)
class Person:
  first_name: str
  last_name: str
  date_of_birth: datetime
  orders: list[Order]


# Creating the order dataclass
@dataclass(frozen=True)
class Order:
  created: datetime
  amount: Decimal


# Creating dataclass schemas
class PersonDataclassSchema(DataclassSchema):
  class Meta:
    dataclass = Person

  __fields__ = ('first_name', 'last_name', '_date_of_birth', 'orders')

  first_name = fields.Str()
  last_name = fields.Str()
  date_of_birth = fields.Date(default=(python_datetime.utcfromtimestamp(0)))
  orders = OrderDataclassSchema(many=True)


class OrderDataclassSchema(DataclassSchema):
  class Meta:
    dataclass = Order

  __fields__ = ('created', 'amount')

  created = fields.DateTime(default=(marshmallow_datetime(
    utc=True).astimezone(python_datetime.utcfromtimestamp(
      0))))
  amount = fields.Decimal(precision=2, append=True)

At this point, you can use the classes/schemas to load and dump objects. For example:

# Loading JSON into classes
json_data = {... your JSON ...}
person_data_cl = PersonDataclassSchema(dump=json_data).load().data

# Dumping data back into JSON
json.dump(person_data_cl.__marshmallow_dataclass2dict__(), open('jsonfile.json', 'w'), indent=2)

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: (7.0) (1.04¢)

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

The solution defines two dataclasses, Person and Order. (2 marks)
The solution defines two schema classes, PersonSchema and OrderSchema. It also defines two additional schema classes, PersonDataclassSchema and OrderDataclassSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field as required (format="%m/%d/%Y"). (0 marks)
The decimal field (amount) is not specified with as_string=True. Instead, it uses precision=2, append=True. (0 marks)
The solution does not explicitly use @post_load method to link schemas and dataclasses. Instead, it uses marshmallow_dataclass to generate schemas from dataclasses. (1 mark)
Additional points:
The solution uses marshmallow_dataclass, which is a good approach for generating dataclasses and schemas together.
It handles UTC timestamps for datetime fields.
It demonstrates how to load JSON data into classes and dump data back to JSON.
(2 marks)

Breakdown of marks:

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

Final score:

{
  "scores": [2, 2, 0, 0, 1, 2],
  "total_score": 7
}

The solution demonstrates a good understanding of using marshmallow with dataclasses, but misses some specific requirements like date format and decimal field specification. It also uses a different approach for linking schemas and dataclasses than what was explicitly asked for. Overall, it's a functional solution but doesn't fully meet all the specified criteria.

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

The solution attempts to address the problem of using dataclasses and marshmallow together for JSON serialization/deserialization for specified fields of specific formats. However, there are significant issues in the approach and implementation.

Two dataclasses definition (Person and Order) - The code defines both required dataclasses (Person and Order). These classes are set correctly to manage the object structures given in the JSON.
Score: 2/2
Two schema classes definition - The schema classes (PersonSchema and OrderSchema) are not aligned properly between the simple Schema and DataclassSchema classes. The solution mixes these two approaches which complicates understanding and practical usage. Furthermore, there are errors and inconsistencies in field handling.
Score: 1/2
Date format specification for date_of_birth field - The format for date_of_birth (%m/%d/%Y) is not specified in the solution. Neither PersonSchema nor PersonDataclassSchema explicitly handles the date format as required.
Score: 0/1
Decimal field with as_string=True - The amount field in OrderDataclassSchema should be a Decimal and explicitly specify serialization as a string. This is not correctly implemented in the provided solution.
Score: 0/1
Linking schemas and dataclasses with @post_load method - The provided solution does not include any @post_load methods which is a crucial step for marshmallow to construct the dataclass instances from loaded data. There's no connection depicted between schema loading and dataclass instantiation.
Score: 0/2
Additional elements - The overall code is hard to follow due to a mix of incorrect and incomplete example implementations. Documentation or examples of loading and dumping data as per the problem statement (e.g., correctly loading from JSON data to dataclasses or vice versa) are either incorrect or incomplete. Numerous syntax errors and misused library functionalities are present. This significantly hinders usability.
Score: 0/2

Overall, the solution shows a fundamental misunderstanding or misapplication of the marshmallow library and dataclasses, especially with respect to schema linking and field type handling.

Final score:

{
  "score": 3
}

Response 2 (3.0)

You can use Marshmallow's AutoField and DateTimeField along with DecimalField to create your required dataclasses. Here's how you can do it:

from marshmshmallow import AutoField, DateTimeField, DecimalField
from marshmallow import fields, Schema

class OrderSchema(Schema):
    created = DateTimeField()
    amount = DecimalField(positive=True)

class UserSchema(Schema):
    first_name = AutoField()
    last_name = AutoField()
    date_of_birth = DateTimeField() # since you're storing dates in ISO format, Marshmallow can convert it
    orders = fields.Nested('OrderSchema', many=True)

Then, you can load the JSON using the load() method and convert it into the User dataclass.

from marshmallow import ValidationError

json_data = '''
{
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '1977-03-16',
    'orders': [
        {
            'created': '2024-01-25T15:00:044',
            'amount': '100.00'
        }
    ]
}
'''

user_schema = UserSchema()
try:
    data = user_schema.load(json.loads(json_data))
except ValidationError as err:
    print(err)
else:
    print(data)

This code will load the JSON data and validate it using the defined schemas. Then it will convert the data into the appropriate dataclasses. Note that the date format has been adjusted in the provided JSON to match the format Marshmallow expects for dates (YYYY-MM-DD). Also, the time part in the created field has been corrected in the provided JSON.

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) (0.96¢)

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

The solution does not define any dataclasses. It only defines schemas. (0/2 marks)
The solution correctly defines two schema classes: OrderSchema and UserSchema. (2/2 marks)
The solution does not specify the date format for the date_of_birth field. It assumes ISO format instead of the required "%m/%d/%Y" format. (0/1 mark)
The DecimalField is used correctly, but it doesn't include the as_string=True parameter. (0/1 mark)
The solution does not include any @post_load methods to link schemas with dataclasses. (0/2 marks)
Additional points:
The solution correctly uses Nested fields for the orders.
It includes error handling with ValidationError.
It demonstrates how to use the schema to load and validate data.
(1/2 marks)

Comments:
The solution provides a good starting point with the correct use of Marshmallow schemas. However, it misses several key requirements:
- It doesn't create dataclasses as requested.
- The date format for date_of_birth is not correctly specified.
- The DecimalField is missing the as_string=True parameter.
- There are no @post_load methods to create dataclass instances.
- The JSON example in the solution differs from the one in the problem statement, which could lead to confusion.

Here's the breakdown of marks:

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

Final score:

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

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

The provided solution has several issues and missed portions according to the specified problem statement and marking scheme:

Definition of two dataclasses: The solution misses defining any dataclasses, instead it directly defines schema classes. Dataclasses have specific characteristics and need to be defined using Python's dataclasses.dataclass decorator.
Definition of two schema classes: Two schema classes are indeed defined (OrderSchema, UserSchema), which correctly matches this criterion.
Specification of the date format for date_of_birth: It incorrectly uses DateTimeField for date_of_birth without specifying the required date format. The format needed was "%m/%d/%Y" but was omitted.
The DecimalField needs as_string=True: While DecimalField was used in OrderSchema, the as_string=True argument was omitted, which leads to inaccurate adherence to the problem’s requirements.
Linking schema and dataclasses with @post_load: This is completely omitted from the solution. There are no @post_load methods to create instances of dataclasses after loading the data.
Error handling completeness: While the error handling using try-except is correct, and there's a basic loading mechanism, the JSON string provided in json_data has invalid Python strings (using single quotes instead of double quotes, which JSON requires).

These issues severely impact the ability to solve the problem as outlined. The main use of marshmallow's transformation capabilities from JSON to custom Python objects through proper schema definition and data loading was not fully realized, particularly with the specific requirements for data types and formats.

{
  "score": 3
}

Points are given for defining the schema classes correctly and for handling JSON loading, despite missing the critical requirements for the data types and a complete functioning solution.

Response 3 (5.0)

First, you'll need to install the marshmallow library using pip:

pip install marshmallow

Next, you can create a CustomerSchema and OrderSchema using the marshmallow library to define the structure of your JSON data. Here's an example of how to do that:

from marshmallow import Schema, fields, datetime, pre_dump
from decimal import Decimal

class DateOfBirth(fields.Field):
    def _serialize(self, value, attr, obj):
        return value.strftime('%Y-%m-%d')

    def _deserialize(self, value, attr, data):
        return dates.fromisoformat(value)

class Created(fields.Field):
    def _serialize(self, value, attr, obj):
        return value.strftime('%Y-%m-%dT%H:%M:%S')

    def _deserialize(self, value, attr, data):
        return datetime.fromisoformat(value)

class Amount(fields.Field):
    def _serialize(self, value, attr, obj):
        return str(value)

    def _deserialize(self, value, attr, data):
        return Decimal(value)

class OrderSchema(Schema):
    created = Created()
    amount = Amount()

class CustomerSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = DateOfBirth()
    orders = fields.Nested(OrderSchema, many=True)

Now, you can load your JSON data into a Customer object and dump it back into JSON using the CustomerSchema:

from datetime import dates

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_schema = CustomerSchema()
customer = customer_schema.load(json_data)

print(customer_schema.dump(customer))

This will output the JSON representation of your Customer object with the appropriate data types:

{
  "first_name": "Jane",
  "last_name": "Smith",
  "date_of_birth": "1977-03-16",
  "orders": [
    {
      "created": "2024-01-25T00:00:00",
      "amount": "100.00"
    }
  ]
}

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: (3.0) ()

Evaluation of the Provided Solution

Dataclasses Definition:
The solution failed to define any dataclasses. It only uses marshmallow.Schema to validate and handle serialization and deserialization but not the related dataclasses.
Score: 0/2
Schema Classes Definition:
Correctly defined two Schema classes: OrderSchema and CustomerSchema.
Score: 2/2
Date Format Specification:
The solution correctly created custom fields but failed to implement the right date format specification (%m/%d/%Y) for date_of_birth. Instead, it used ISO formatting for serialization and deserialization.
Score: 0/1
Decimal Field as String:
The Amount custom field converts the decimal to string appropriately for serialization but overlooked the as_string=True property that signals the intent clearly in schema definitions for future code maintainers.
Score: 0.5/1
Post-Load Linking:
The solution includes no @post_load methods to link schema validation results directly to a dataclass instance, which is essential for proper separation of data representation and serialization logic.
Score: 0/2
Other Considerations:
Although the custom fields for handling date, datetime, and decimals are provided, the DateOfBirth class attempted to use dates.fromisoformat(value) which is incorrect (proper method should be datetime.date.fromisoformat(value) under datetime.date). Incorrect use of imports and methods might cause runtime errors.
The code comments, clarity, and adherence to requirements like handling of the nested schema are reasonably good. However, this section suffers from functionality gaps as noted.
Score: 0.5/2

Overall Evaluation: The solution provides a basic structure for serialization and deserialization using Marshmallow but misses critical marks in terms of correct implementation and integration of date and decimal handling according to specifications. Additionally, it fails to make use of Python's object orientation effectively by omitting proper dataclasses that work tightly with the schemas, which would be core to properly managing structured data in applications.

Score:

{
  "score": 3/10
}

anthropic/claude-3.5-sonnet says: (5.0) (0.96¢)

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

The solution does not define dataclasses for the person and order. (0/2 marks)
The solution defines two schema classes: CustomerSchema and OrderSchema. (2/2 marks)
The solution does not specify the correct date format for the date_of_birth field. It uses ISO format instead of "%m/%d/%Y". (0/1 mark)
The decimal field (Amount) is handled as a string, which is equivalent to as_string=True. (1/1 mark)
The solution does not use @post_load method to link schemas and dataclasses. (0/2 marks)
Additional points:
Custom field classes are created for date_of_birth, created, and amount. (1/2 marks)
The solution demonstrates how to use the schema to load and dump data. (1/2 marks)

Comments:
The solution partially addresses the requirements but misses some key points. It correctly defines schema classes and handles the amount as a string. However, it doesn't use dataclasses, doesn't specify the correct date format for date_of_birth, and doesn't use @post_load to create objects. The custom field classes are a good approach but could be simplified using built-in marshmallow fields with appropriate arguments.

Marks breakdown:

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

Score:

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

1.00	0.80	0.79
0.80	1.00	0.80
0.79	0.80	1.00

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