Llama 3 Lumimaid 70B

Description

The NeverSleep team is back, with a Llama 3 70B finetune trained on their curated roleplay data. Striking a balance between eRP and RP, Lumimaid was designed to be serious, yet uncensored when necessary.

To enhance it's overall intelligence and chat capability, roughly 40% of the training data was not roleplay. This provides a breadth of knowledge to access, while still keeping roleplay as the primary strength.

Usage of this model is subject to Meta's Acceptable Use Policy.

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 (5.5)

Creating data classes using the Marshmallow library involves several steps:

First, create a Python data class.
Then, create a corresponding Marshmallow schema.
Use the schema to serialize and deserialize your JSON.

Please follow these steps to create your data classes based on the provided JSON:

First, create a Python data class:

from typing import List
from datetime import datetime
from decimal import Decimal

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

class Customer:
    def __init__(self, first_name: str, last_name: str, date_of_birth: datetime, orders: List[Order]):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = date_of_birth
        self.orders = orders

Create a corresponding Marshmallow schema:

from marshmallow import Schema, fields

class OrderSchema(Schema):
    created = fields.DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount = fields.Decimal(places=2)

class CustomerSchema(Schema):
    first_name = fields.String()
    last_name = fields.String()
    date_of_birth = fields.Date(format='%m/%d/%Y')
    orders = fields.Nested(OrderSchema, many=True)

order_schema = OrderSchema()
customer_schema = CustomerSchema()

Use the schema to serialize and deserialize your JSON:

from marshmallow import EXCLUDE

# Deserialize the JSON data into a Python object
customer = customer_schema.load({
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}, unknown=EXCLUDE)

print(customer)

This approach assumes that you have control over the input data and have the correct field types and structure. Otherwise, you might need additional validation and processing steps.<|eot_id|>

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: (5.5) (1.11¢)

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

Defining two dataclasses (2 marks):
The solution defines two classes: Order and Customer. However, these are not actual dataclasses as they don't use the @dataclass decorator. 1 mark.
Defining two schema classes (2 marks):
The solution correctly defines two schema classes: OrderSchema and CustomerSchema. 2 marks.
Specifying date format for date_of_birth (1 mark):
The solution correctly specifies the date format for date_of_birth as format='%m/%d/%Y'. 1 mark.
Decimal field with as_string=True (1 mark):
The solution doesn't include as_string=True for the decimal field. 0 marks.
Linking schema and dataclasses with @post_load (2 marks):
The solution doesn't use @post_load to link the schema and dataclasses. 0 marks.
Anything else (2 marks):
The solution includes a step to deserialize JSON data using the schema.
It uses fields.Nested for the orders field in CustomerSchema.
It imports necessary modules.
1.5 marks.

Breakdown of marks:

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

Score:

{
  "scores": [1, 2, 1, 0, 0, 1.5],
  "total_score": 5.5
}

Comments:
The solution provides a good starting point but misses some key requirements. It doesn't use actual dataclasses, which would simplify the class definitions. The decimal field is missing the as_string=True parameter. The solution also doesn't use @post_load to create instances of the dataclasses from the deserialized data. Despite these shortcomings, it does correctly define the schemas and handle the date format for date_of_birth.

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

The provided solution includes an effort to create dataclasses and corresponding Marshmallow schemas to serialize and deserialize JSON data based on a given specification. However, the solution has several shortcomings and omits essential parts of the problem requirements:

Defining two data classes (2 marks)
The solution correctly defines two Python classes (Order and Customer) which act as data classes. However, these classes are defined using traditional initializers rather than utilizing Python's dataclasses module, which would be more appropriate and efficient for this task. Based on the requirements specified, using Python's dataclasses.dataclass decorator would have been better to meet expectations.
Score: 1/2
Defining two schema classes (2 marks)
Two schema classes (OrderSchema and CustomerSchema) have been defined appropriately. The solution uses Marshmallow’s Schema and field declarations well to correspond to the data classes.
Score: 2/2
Specifying date format for date_of_birth (1 mark)
The format for the date_of_birth is explicitly set to '%m/%d/%Y' in the CustomerSchema, which correctly meets the requirement.
Score: 1/1
Decimal field as_string=True (1 mark)
The solution fails to set as_string=True for the amount field in the OrderSchema. This is critical for ensuring that decimal fields are serialized as strings.
Score: 0/1
Linking schema and dataclasses with @post_load method (2 marks)
The solution does not implement the @post_load method in the schema classes to return instances of the data classes after loading the data. This step is essential for integrating Marshmallow schemas with data classes.
Score: 0/2
Additional considerations (2 marks)
The lack of using the dataclasses module and failure to implement @post_on_load are significant omissions. Although the basic structure for serializing and deserializing is outlined, missing these crucial integration pieces significantly reduces the effectiveness and conciseness of the code.
Nonetheless, setting unknown=EXCLUDE during loading to handle unexpected fields shows some good practice in dealing with real-world data.
Score: 1/2

Overall, while the solution sets a basis for using Marshmallow schemas with custom classes, it falls short in fully leveraging Python features (like dataclasses) and Marshmallow functionalities (like @post_load) that are essential for the task.

{
  "score": 5
}

Response 2 (8.0)

You can define a dataclass using the marshmallow library to serialize and deserialize your JSON data. Below is an example code:

from dataclasses import dataclass
from marshmallow import Schema, post_load
from marshmallow.fields import Str, List, Nested, Decimal
from marshmallow.validate import OneOf, Range
from datetime import datetime

@dataclass
class Order:
    created: datetime
    amount: float

@dataclass
class Customer:
    first_name: str
    last_name: str
    date_of_birth: datetime
    orders: List[Order]

class OrderSchema(Schema):
    created = fields.DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount = fields.Decimal(as_string=True)

class CustomerSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(format='%m/%d/%Y')
    orders = fields.Nested(OrderSchema, many=True, data_key='orders')
    LOAD_INSTANCE = True

    @post_load
    def make_customer(self, data, **kwargs):
        return Customer(**data)

customer_schema = CustomerSchema()

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

result, _ = customer_schema.load(data)
print(result)

In the above code, we define two dataclasses: Order and Customer. Then we define the corresponding Marshmallow schema for each class. In the OrderSchema, created field is defined as DateTime and amount is a Decimal.

In the CustomerSchema, we define it with first_name, last_name, date_of_birth, and orders fields. The date_of_birth field is defined as a Date and orders is a nested field that loads a list of Order.

@post_load is used to make an instance of Customer from the deserialized data.

Finally, we can use the customer_schema.load method to transform the JSON data into an instance of Customer.<|eot_id|>

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) (0.91¢)

Thank you for providing the solution. I'll evaluate it based on the given marking scheme:

The solution defines two dataclasses, Customer and Order. (2 marks)
The solution defines two schema classes, CustomerSchema and OrderSchema. (2 marks)
The date format for the date_of_birth field is correctly specified as "%m/%d/%Y". (1 mark)
The decimal field (amount) is correctly defined with as_string=True. (1 mark)
The schema and dataclasses are linked with a @post_load method on the CustomerSchema class. However, it's missing for the OrderSchema. (1 mark)
Additional observations:
The solution imports necessary modules.
It demonstrates how to use the schema to load data.
The Order dataclass uses float for amount instead of Decimal. (1 mark)

Comments:
The solution is generally well-structured and addresses most of the requirements. However, it could be improved by adding a @post_load method to the OrderSchema and using Decimal instead of float in the Order dataclass. The use of LOAD_INSTANCE = True in CustomerSchema is unnecessary when using @post_load.

Here's the breakdown of marks:

{
  "Dataclasses": 2,
  "Schema classes": 2,
  "Date format": 1,
  "Decimal field": 1,
  "@post_load": 1,
  "Additional observations": 1
}

Final score:

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

openai/gpt-4-0125-preview says: (None) ()

The provided solution demonstrates the use of dataclasses and the marshmallow library for handling JSON serialization and deserialization effectively. Let's evaluate it according to the criteria provided:

Dataclasses Defined:
The solution defines two dataclasses, Order and Customer, as required by the problem statement.
Points: 2/2
Schema Classes Defined:
Corresponding schema classes OrderSchema and CustomerSchema are properly defined.
Points: 2/2
Date Format Specification:
The date_of_identity field in the CustomerSchema correctly uses the specified date format (%m/%d/%Y).
Points: 1/1
Decimal Field as_string=True:
The amount field is handled using Decimal with the as_string=True parameter correctly to keep the decimal precision.
Points: 1/1
Linking Schema with Dataclasses via @post_load:
The @post_load decorator is correctly utilized in CustomerSchema to create an instance of Customer after loading data.
There is a slight error in the code regarding the LOAD_INSTANCE attribute which is outdated syntax or syntax from another library/version. In recent versions of marshmallow, it's not necessary or used. However, this error doesn't affect the core functionality being discussed.
Points: 1.5/2
Additional Considerations:
Import corrections are needed, specifically, fields is referenced but not imported, instead, it should be all under from marshmallow.fields import ....
The Order dataclass uses float for amount instead of decimal.Decimal, which doesn't match the requirement of using decimal, hence could lead to precision errors in a real-world scenario.
Exception handling or showing how errors are handled was not covered.
Also, there's no print function to show any possible validation or loading errors (_, errors).
Points: 1/2

In summary, while the solution structures the dataclasses and schema largely as required, it misses some key points such as correct imports and decimal precision in the dataclass, and it does not address error outputs, which is crucial for real-world applications.

Final score:

{
  "service": 8
}

Response 3 (7.0)

To generate dataclasses for the given JSON data using the marshmallow library in Python, you'll first need to install the marshmallow and marshmallow-dataclass libraries, if not already installed:

pip install marshmallow marshmallow-dataclass

Now, you can use the following script to define the dataclasses:

from typing import List

from marshmallow import Schema, post_load
from marshmallow_dataclass import dataclass
from marshmallow.fields import Date, DateTime, Decimal
from datetime import datetime

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

class UserSchema(Schema):
    class Meta:
        unknown = 'include'

    @post_load
    def make_user(self, data, **kwargs):
        return User(**data)

class OrderSchema(Schema):
    class Meta:
        unknown = 'include'

    created = DateTime()
    amount = Decimal(as_string=True)

user_schema = UserSchema()
order_schema = OrderSchema()

In this script, two dataclasses Order and User are defined with their corresponding fields. For date_of_birth, created and amount, datetime, DateTime and Decimal types are used, respectively.

Note that Decimal(as_string=True) is used in OrderSchema to ensure that the amount field is always converted as a string when deserializing. This provides a workaround for potential rounding errors that could happen when working with decimals.

The post_load decorator in UserSchema allows marshmallow to create an instance of the User dataclass once the data is deserialized.

Now you can use the UserSchema and OrderSchema instances to deserialize your JSON data:

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

Load user_data:
user_data = user_schema.load(json_data)

print(user_data)
```<|eot_id|>

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) (0.97¢)

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, UserSchema and OrderSchema. (2 marks)
The solution doesn't specify the date format for the date_of_birth field. It should have used format="%m/%d/%Y". (0 marks)
The decimal field correctly uses as_string=True. (1 mark)
The schema and dataclasses are linked with a @post_load method on the UserSchema class. However, it's missing for the OrderSchema. (1 mark)
Additional observations:
The solution uses marshmallow-dataclass, which simplifies the creation of dataclasses.
It handles unknown fields with unknown = 'include' in both schemas.
The User dataclass uses datetime for date_of_birth instead of date.
(1 mark)

Here's the breakdown of marks:

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

Final score:

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

The solution demonstrates a good understanding of using marshmallow with dataclasses, but missed some specific requirements like the date format for date_of_birth and didn't fully implement the @post_load method for both schemas. Overall, it's a solid attempt that could be improved with minor adjustments.

openai/gpt-4-0125-preview says: (8.0) ()

The provided solution successfully defines Python dataclasses and uses the marshmallow library to handle JSON serialization and deserialization, but there are some issues and missed requirements:

Dataclasses Defined (2 marks): The solution defines two dataclasses Order and User, successfully meeting the specification's requirements.
Schema Classes Defined (2 marks): Appropriate schema classes UserSchema and OrderSchema are declared.
Date Format Specification (0 marks): The solution fails to specify the dateFormat for date_of_birth using marshmallow. The date_of_birth should be parsed using a specific format (%m/%d/%Y), which is not mentioned or implemented in the UserSchema.
Decimal Field Specification (1 mark): This requirement is met, as the solution specifies Decimal(as_string=True) in OrderSchema.
Linkage of Schema and Dataclasses with @post_load (1.5 marks): The solution attempts to link dataclasses with schemas using @post_attachments, but it doesn't accurately map all fields, particularly it doesn't convert date_of_birth to datetime.date nor handle list of orders properly in UserSchema. The linkage in OrderSchema is missing the post-load conversion to Order dataclass.
Additional Considerations (1.5 marks):
The script correctly uses post_load to create an instance of data from schema. Incorrect date format in JSON instantiation in test code and wrong type (should be date instead of datetime) for date_of_body deducts some marks.
Error handling, custom validations, or other advanced marshmallow features are not discussed or implemented, which limits the functionality in real-world scenarios.

Based on these considerations, the script has some limitations and misses a few key requirements.

Final score:

{
  "score": 8
}

1.00	0.87	0.87
0.87	1.00	0.86
0.87	0.86	1.00

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