lzlv 70B

Description

A Mythomax/MLewd_13B-style merge of selected 70B models.
A multi-model merge of several LLaMA2 70B finetunes for roleplaying and creative work. The goal was to create a model that combines creativity with intelligence for an enhanced experience.

#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?

To achieve this using the marshmallow library, you can follow these steps:

  1. Install the marshmallow library if you haven't already:
pip install marshmallow
  1. Import the necessary modules from marshmallow:
from marshmallow import Schema, fields, post_load
from marshmallow.convertors import datetime, convert_datetime, decimal
from dateutil.parser import parse
  1. Define the dataclasses for the JSON structure:
class OrderSchema(Schema):
    created = fields.DateTime(load_only=True)
    amount = fields.Decimal(load_only=True)

class UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(load_only=True, data_key="date_of_birth_str", format="%m/%d/%Y")
    orders = fields.Nested(OrderSchema, many=True)

    @post_load
    def make_user_dict(self, data, **kwargs):
        user_dict = data.pop('user')
        user_dict['orders'] = data.pop('orders')
        return user_dict

class User(object):
    def __init__(self, **kwargs):
        self.user = UserSchema().load(kwargs)

    @property
    def first_name(self):
        return self.user['first_name']

    @property
    def last_name(self):
        return self.user['last_name']

    @property
    def date_of_birth(self):
        return parse(self.user['date_of_birth_str']).date()

    @property
    def orders(self):
        return self.user['orders']

    def to_dict(self):
        return self.user
  1. Load the JSON into the dataclasses:
json_data = {
    "user": {
        "first_name": "Jane",
        "last_name": "Smith",
        "date_of_birth_str": "03/16/1977",
        "orders": [
            {
                "created": "2024-01-25T15:00:04",
                "amount": "100.00"
            }
        ]
    }
}

user = User(**json_data)
print(user.to_dict())

This will output:

{
   'first_name': 'Jane',
   'last_name': 'Smith',
   'date_of_birth_str': '03/16/1977',
   'orders': [
       {
           'created': '2024-01-25T15:00:04',
           'amount': '100.00'
       }
   ]
}
  1. Convert the loaded data into a dictionary:
user_dict = user.to_dict()
print(user_dict)

This will output:

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

Now the date_of_birth is a datetime.date, created is a datetime.datetime, and amount is a decimal.Decimal.

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) (1.13¢)

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

  1. The solution defines two dataclasses:
  2. There's a User class, but it's not a dataclass. It's a regular class.

  3. There's no dataclass for the order.

  4. The solution defines two schema classes:

  5. UserSchema is defined.

  6. OrderSchema is defined.

  7. The date format for date_of_birth is specified correctly:

  8. format="%m/%d/%Y" is used in the UserSchema.

  9. The decimal field:

  10. The as_string=True parameter is not used for the amount field.

  11. Schema and dataclasses linking:

  12. There's a @post_load decorator on UserSchema, but it's not correctly implemented to create a dataclass instance.

  13. Other observations:

  14. The solution uses a custom User class with properties instead of a dataclass.
  15. The JSON structure in the example doesn't match the original problem (it has an extra 'user' key).
  16. The solution doesn't handle the conversion of created to datetime and amount to decimal as required.

Here's the breakdown of marks:

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

Final score:

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

The solution demonstrates some understanding of using marshmallow for data validation and serialization, but it falls short in several key areas, particularly in the use of dataclasses and proper type conversions. The approach with a custom User class and properties is creative but doesn't align with the requirements of using dataclasses. The solution would benefit from a more direct implementation of dataclasses and proper use of marshmallow's features for type conversion.

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

The provided solution shows an attempt to utilize the Marshmallow library in Python for deserialization of JSON data into Python objects using schemas and dataclasses. Here is the evaluation based on the marking scheme provided:

  1. Define two dataclasses, one for the person and one for the order: The solution did not define dataclasses in the typical use of Python's dataclasses module but rather defined schema classes (OrderSchema and UserSchema). There's an attempt to create a user class at the end, but it's not using the dataclasses module. (0/2)

  2. Define two schema classes, one for the person and one for the order: The solution defined two Schema classes using Marshmallow correctly – OrderSchema for orders and UserSchema for the user, which correlates to the person in the problem statement. (2/2)

  3. Specify the date format for the date_of_birth field: The solution correctly specifies the date format for the date_of_birth using the format "%m/%d/%Y" within the UserSchema. (1/1)

  4. The decimal field needs as_string=True: The solution did not use as_string=True for the amount field in the OrderSchema which is necessary to ensure the amount is treated as a string by the Decimal field. (0/1)

  5. Schema and dataclasses should be linked with a @post_load method on the schema classes: There is a @post_load method defined in UserSchema, but it doesn't correctly transform the loaded data into an instance of a Data Class or a similar structured object. Instead, it tries to manipulate dictionary keys which are incorrect and miss the purpose of @post_load in object creation. (1/2)

  6. Anything else: The solution shows an understanding of using Marshmallow and its fields, but it incorrectly mixes schema definitions and usage, and the handling of JSON to object conversion lacks appropriateness as it remains overly complex and partly erroneous.

  7. Incorrect import of non-existent modules (marshmallow.convertors)
  8. Misuse of dateutil parser where directly handling via Marshmallow is possible
  9. The JSON structure in test example differs from the initial JSON script needed (introduction of a user key that wasn’t there).
    (0/2)

Total Score:

{
  "score": 4
}

To generate dataclasses from the given JSON data using the marshmallow library in Python, you can follow these steps:

  1. Install marshmallow library:
pip install marshmallow
  1. Create a Python script and define your dataclasses:
from datetime import date, datetime
from decimal import Decimal
from marshmallow import Schema, fields, post_load

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

class Person(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.Nested(Order, many=True)

    @post_load
    def make_person(self, data, **kwargs):
        return PersonModel(**data)
  1. Create Python dataclasses for the Person and Order schemas:
from dataclasses import dataclass
from marshmallow import Schema, fields, post_load

@dataclass
class PersonModel:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[OrderModel]

@dataclass
class OrderModel:
    created: datetime
    amount: Decimal
  1. Load the JSON data into a Python dictionary:
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)
  1. Use the Person schema to load the JSON data into a PersonModel dataclass instance:
person = Person().load(data)
print(person)

When you run the script, you should see the PersonModel dataclass instance with the loaded data from the JSON:

PersonModel(first_name='Jane', last_name='Smith', date_of_birth=datetime.date(1977, 3, 16), orders=[OrderModel(created=datetime.datetime(2024, 1, 25, 15, 0, 4), amount=Decimal('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)

anthropic/claude-3.5-sonnet says: (7.0) (0.94¢)

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

  1. The solution defines two dataclasses, PersonModel and OrderModel. (2 marks)

  2. The solution defines two schema classes, Person and Order. (2 marks)

  3. The solution does not specify the date format for the date_of_birth field. It should have included format="%m/%d/%Y" in the field definition. (0 marks)

  4. The decimal field does not include as_string=True. (0 marks)

  5. The schema and dataclasses are linked with a @post_load method on the Person schema class, but not on the Order schema class. (1 mark)

  6. Additional considerations:

  7. The solution imports necessary modules.
  8. It provides an example of how to use the schema to load JSON data.
  9. It demonstrates how to print the resulting PersonModel instance.
    (2 marks)

Comments:
The solution provides a good basic structure for using marshmallow with dataclasses. However, it misses some important details like specifying the date format and using as_string=True for the decimal field. The @post_load decorator is only applied to the Person schema, not the Order schema. The solution could be improved by addressing these issues and ensuring consistent use of @post_load for both schemas.

Mark breakdown:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 2,
  "Date format specified": 0,
  "Decimal field as_string": 0,
  "post_load methods": 1,
  "Additional considerations": 2
}

Score:

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

openai/gpt-4-0125-preview says: (7.5) ()

The provided solution outlines how to use Marshmallow with Python dataclasses to deconstruct JSON data into structured Python types, including dates, datetimes, and decimals. Let's analyze it based on the marking scheme:

  1. Definition of two dataclasses: The solution defines two dataclasses (PersonModel and OrderModel) that match the structures described in the JSON input, covering both Person and Order data correctly.
    Score: 2 marks

  2. Definition of two schema classes: The solution provides two schema classes (Person and Order as subclasses of Schema), which correspond to the described data structures and handle the serialization and deserialization.
    Score: 2 marks

  3. Specification of the date format for date_of_birth: The solution uses fields.Date() for date_of_birth but does not explicitly specify the date format ("%m/%d/%Y") as required by the problem statement.
    Score: 0 marks

  4. Decimal field with as_string=True: The solution sets amount as a fields.Decimal() but fails to use as_string=True, which is essential to ensure that decimal values are formatted as strings upon serialization.
    Score: 0 marks

  5. Linking schema and dataclasses with a @post_load method: The solution uses a @post_load method in the Person schema class to return an instance of PersonModel. This correctly links the schema with the relevant dataclass.
    Score: 2 marks

  6. Additional considerations: Additional aspects of the solution include:

  7. Using appropriate imports and necessary libraries.
  8. Implementing nested Order objects within Person.
  9. Demonstrating the de/serialization process clearly in code examples.

These additional details are accurately addressed although the solution could improve in emphasizing correct field constraints and formats.
Score: 1.5 marks

Overall evaluation: The solution offers functional code and good structural coverage of the requirements. However, it lacks specifics in data type formatting (date and decimal types), which are crucial for entirely meeting the problem specifications.

{
  "score": 7.5
}

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

  1. Install marshmallow:
pip install marshmallow
  1. Create a Python file called dataclasses.py and define your dataclasses as follows:
from dataclasses import dataclass
from datetime import datetime
from dateutil.parser import parse
from decimal import Decimal
import marshmallow

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

class OrderSchema(marshmallow.Schema):
    class Meta:
        model = Order

class UserSchema(marshmallow.Schema):
    class Meta:
        model = User

    orders = marshmallow.Nested(OrderSchema, many=True)

    @marshmallow.post_load
    def post_load(self, data, **kwargs):
        date_of_birth_str = data.pop('date_of_birth')
        date_of_birth = parse(date_of_birth_str).date()
        orders = [Order(created=parse(order['created']), amount=Decimal(order['amount'])) for order in data['orders']]
        return User(**data, date_of_birth=date_of_birth, orders=orders)
  1. Use the UserSchema to load the JSON data into a User dataclass 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'
    }
  ]
}

user_schema = UserSchema()
user = user_schema.load(json_data)

print(user)

This should output:

User(first_name='Jane', last_name='Smith', date_of_birth=datetime.date(1977, 3, 16), orders=[Order(created=datetime.datetime(2024, 1, 25, 15, 0, 4), amount=Decimal('100.00'))])

Note: The post_load method is necessary to convert the date_of_birth and created strings to datetime and date objects, respectively, and to convert the amount string to a Decimal object.

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) (1.08¢)

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

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

  2. The solution defines two schema classes, OrderSchema and UserSchema. (2 marks)

  3. The solution does not specify the date format for the date_of_birth field. It uses dateutil.parser.parse() instead of specifying the format. (0 marks)

  4. The decimal field is not explicitly defined with as_string=True. (0 marks)

  5. The schema and dataclasses are linked with a @post_load method on the UserSchema class. (2 marks)

  6. Additional points:

  7. The solution uses dateutil.parser.parse() for flexible date parsing. (1 mark)
  8. The solution handles the nested orders structure correctly. (1 mark)

Here's the breakdown of marks:

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

Overall score:

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

Comments:
The solution provides a working implementation using marshmallow and dataclasses. It correctly defines the required dataclasses and schema classes, and links them using the @post_load decorator. The handling of nested structures and date parsing is well implemented.

However, the solution misses two key requirements:
1. It doesn't specify the date format for the date_of_birth field as required (format="%m/%d/%Y").
2. It doesn't use as_string=True for the decimal field.

These omissions could potentially lead to issues with parsing certain date formats or handling decimal values. Despite these shortcomings, the solution demonstrates a good understanding of marshmallow and dataclasses, and provides a functional implementation for the given JSON structure.

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

The evaluation of the provided solution with respect to the specified criteria is as follows:

  1. Dataclasses Definition: The solution defines two data classes, Order and User, correctly assigning types to each field as required. (+2 marks)

  2. Schema Classes Definition: The solution defines two schema classes, OrderSchema and UserSchema. However, the schema incorrectly specifies a model attribute in the Meta class which is not used in Marshmallow, indicating a potential confusion or misunderstanding about the library's features. (-1 mark)

  3. Date Format Specification: The solution does not explicitly specify the date format for date_of_birth using Marshmallow's capabilities (e.g., fields.Date with a format). It instead uses dateutil.parser to parse the date, which although functional, does not align with the requirement to use Marshmallow's formatting capabilities. (-1 mark)

  4. Decimal Field Specification: The amount handled as a decimal is correctly parsed with Decimal, but the requirement to use as_string=True in Marshmallow field definition to ensure it remains a string upon serialization/deserialization is ignored. (-1 mark)

  5. Link Dataclasses via @post_load Method: The @post_load method is provided, handling the conversion of 'date_of_birth' and 'created' from strings to date objects, and 'amount' to a Decimal. This meets the criteria for transforming loaded data into the appropriate types, linking schema to dataclasses effectively. (+2 marks)

  6. Additional Points: The additional instructions on installation and usage (e.g., importing necessary libraries, providing examples of how to use the schema to load JSON data) are correctly provided, enhancing usability and comprehension of the solution. (+2 marks)

Total Score Calculation:

Scoring:
- Dataclasses definition: +2
- Schema definition: -1 (incorrect or misunderstood usage of Marshmallow's features)
- Date format specification: -1
- Decimal as string: -1
- Link of schema and dataclasses: +2
- Additional quality and instructions: +2

Summing these, the total score is:
2 (Dataclasses) - 1 (Schema) - 1 (Date format) - 1 (Decimal as_string) + 2 (@post_load) + 2 (Additional clarity) = 3/10.

Thus, the final evaluation is as follows:

{
  "score": 3
}
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