Nous: Hermes 2 Mixtral 8x7B DPO

Description

Nous Hermes 2 Mixtral 8x7B DPO is the new flagship Nous Research model trained over the Mixtral 8x7B MoE LLM.

The model was trained on over 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape, achieving state of the art performance on a variety of tasks.

#moe

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'll first need to define your dataclasses, then create schemas based on those dataclasses.

Here's an example of how you can define the dataclasses:

from dataclasses import dataclass
import datetime

@dataclass
class Order:
    created: datetime.datetime
    amount: float

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

Now, you can create the schemas for the User and Order classes using marshmallow.

from datetime import datetime
from marshmallow import Schema, fields, post_load

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

    @post_load
    def make_order(self, data, **kwargs):
        return Order(**data)

class UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.List(fields.Nested(OrderSchema))

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

Now, you can use these schemas to deserialize the JSON data:

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

user_schema = UserSchema()
user_data = user_schema.loads(json_data)

Now, user_data will be a User object with the deserialized data.

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: (6.0) ()

1. Dataclasses Definition:
The solution correctly defines two dataclasses, Order and User. Each dataclass is appropriately decorated with @dataclass and uses type hints for its properties. However, the Order class incorrectly types amount as float when the requirement specified amount should be decimal.Decimal.
- Score: 1.5/2

2. Schema Classes Definition:
Two schema classes, OrderSchema and UserSchema, are properly defined. These classes use fields from the marshmallow library to map to the attributes of the dataclasses.
- Score: 2/2

3. Date Format Specification for date_of_birth:
The date_of_birth in UserSchema uses fields.Date() without specifying the format. The problem statement asks for a specific format ("%m/%d/%Y") which is missing.
- Score: 0/1

4. Decimal Field Specification:
The solution uses fields.Decimal(places=2) for amount, but does not set as_string=True as per the requirements. This would normally be crucial to ensure that the serialization and deserialization handle it as a string to preserve exact decimal precision.
- Score: 0/1

5. Linking Schemas and Dataclasses with @post_load Method:
Both schema classes correctly implement the @post_load method to return instances of their respective dataclasses. This is implemented as per the requirements.
- Score: 2/2

6. Other Considerations:
- The solution's sample serialization may encounter an issue as the JSON input provided in the example serialization section is not formatted as per the original requirements (dates and decimal values). For instance, the date format in json_data should match the one expected by the schema.
- Error in handling the Decimal and Date types as required.
- Score: 0.5/2

Total Score: 

{
  "score": 6
}

anthropic/claude-3.5-sonnet says: (8.0) (0.83¢)

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

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

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

  3. The solution does not specify the date format for the date_of_birth field as "%m/%d/%Y". It uses the default format. (0 marks)

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

  5. The schema and dataclasses are correctly linked with @post_load methods on both schema classes. (2 marks)

  6. Additional observations (2 marks):

  7. The solution imports necessary modules.
  8. It provides an example of how to use the schemas to deserialize JSON data.
  9. The Order.amount is defined as float in the dataclass but as Decimal in the schema, which might cause issues.

Here's the breakdown of marks:

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

Final score:

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

The solution demonstrates a good understanding of using marshmallow with dataclasses, but misses some specific requirements mentioned in the problem statement. The total score is 8 out of 10.

To generate data classes from the given JSON using the marshmallow library in Python, you need to follow these steps:

  1. Install the marshmallow library using pip:
pip install marshmallow
  1. Import the dataclasses and Schema classes from the marshmallow library:
from marshmallow import Schema, fields, post_load
from marshmallow.utils import is_dataclass
from dataclasses import dataclass

import datetime
import decimal
  1. Define a user dataclass using the dataclass decorator:
@dataclass
class User:
    first_name: str
    last_name: str
    date_of_birth: datetime.date
    orders: 'list[Order]'
  1. Define an order dataclass using the dataclass decorator:
@dataclass
class Order:
    created: datetime.datetime
    amount: decimal.Decimal
  1. Create a marshmallow Schema for the User object. In the schema, define fields for each of the User's attributes, and specify the marshmallow field type for each field. Use the post_load function to convert the date string to a date and the decimal string to a decimal object.
class UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Str()
    orders = fields.List(fields.Nested("OrderSchema"))

    @post_load
    def make_user(self, data, **kwargs):
        data["date_of_birth"] = datetime.datetime.strptime(data["date_of_birth"], '%m/%d/%Y').date()
        data["orders"] = [Order(created=order["created"], amount=order["amount"]) for order in data["orders"]]
        return User(**data)
  1. Create a marshmallow Schema for the Order object, similar to the User schema.
class OrderSchema(Schema):
    created = fields.Str()
    amount = fields.Str()

    @post_load
    def make_order(self, data, **kwargs):
        data["created"] = datetime.datetime.strptime(data["created"], '%Y-%m-%dT%H:%M:%S')
        data["amount"] = decimal.Decimal(data["amount"])
        return Order(**data)

Now, you can use the UserSchema to load the JSON data into a User 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)

# Now, `user` is a User object with the correct data types for its 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: (8.0) (1.08¢)

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

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

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

  3. The solution does not specify the date format for the date_of_birth field using format="%m/%d/%Y". Instead, it uses strptime in the post_load method. (0 marks)

  4. The decimal field does not use as_string=True. It uses a string field and converts it in the post_load method. (0 marks)

  5. The schema and dataclasses are linked with @post_load methods on both schema classes. (2 marks)

  6. Additional points:

  7. The solution imports necessary modules.
  8. It provides an example of how to use the schema to load JSON data.
  9. It handles the nested structure of orders correctly.
    (2 marks)

Breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "post_load methods": 2,
  "Additional points": 2
}

Score:

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

Comments:
The solution provides a good overall structure and implements most of the required functionality. However, it misses two important details:

  1. It doesn't use the format parameter for the date field in the schema, which would have simplified the date parsing.
  2. It doesn't use as_string=True for the decimal field, which is a requirement in the marking scheme.

These omissions make the solution slightly more complex than necessary, as it relies on manual parsing in the post_load methods. Despite these issues, the solution is functional and demonstrates a good understanding of marshmallow and dataclasses.

openai/gpt-4-0125-preview says: (8.5) ()

The solution correctly outlines the primary steps to use the marshmallow library for serializing and deserializing JSON data to corresponding Python dataclasses. Here's a breakdown according to the provided marking scheme:

  1. Defining two dataclasses, one for the person and one for the order (2 marks)
  2. The solution defines a User dataclass with fields for first name, last name, date of birth, and orders. Each field uses Python's built-in types or other data classes.
  3. An Order dataclass is also defined with fields for created (a datetime) and amount (as a decimal).

  4. Award: 2 marks

  5. Defining two schema classes, one for the person and one for the order (2 marks)

  6. UserSchema and OrderSchema are defined, each specifying fields with types corresponding to their respective data classes.

  7. Award: 2 marks

  8. Specifying the date format for the date_of_birth field (format="%m/%d/%Y") (1 mark)

  9. The date format %m/%d/%Y is correctly specified in the UserSchema's post_load method for converting the date_of_birth string to a date object.

  10. Award: 1 mark

  11. The decimal field needs as_string=True. (1 mark)

  12. The solution does not specify as_string=True for the amount field in the OrderSchema.
  13. This is a critical oversight as it affects the precision handling of decimal numbers in JSON serialization.

  14. Award: 0 marks

  15. The schema and dataclasses are linked by a @post_load method on the schema classes (2 marks)

  16. Both schema classes include appropriate @post_load methods to construct instances of the respective dataclasses (User and Order) from the parsed data.
  17. This is crucial for ensuring the types from JSON are correctly converted and encapsulated within the dataclass instances.

  18. Award: 2 marks

  19. Additional considerations (2 marks)

  20. The solution outlines a clear example of loading and deserializing the JSON to a Python object.
  21. Though it correctly processes types within @post_load, some additional handling or options (like ensuring amount is set using as_string=True to guarantee decimal precision) could be improved.
  22. Error checking, validation, or handling of potential issues during data conversion are not mentioned, which could be an area for improvement.
  23. Award: 1.5 marks

In summary, the solution completes most of the requirements efficiently. However, it misses the mark on ensuring the precision of decimal fields during serialization by not using as_string=True, which may lead to issues in real-world applications.

Final scoring:

{
  "score": 8.5
}

To generate the dataclasses using the marshmallow library in python, first, you need to import the necessary classes and library functions. Then, you can define the dataclasses for User and Order. Here's an example:

from typing import List
from datetime import datetime
from decimal import Decimal
from marshmallow import Schema, fields, pre_load

# Define dataclasses
class User:
    def __init__(self, first_name, last_name, date_of_birth):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = date_of_birth

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

# Define schemas to serialize and deserialize the dataclasses
class UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.List(fields.Nested('OrderSchema'))

    @pre_load
    def process_orders(self, data):
        orders = data.pop('orders', [])
        for order in orders:
            order['created'] = datetime.fromisoformat(order['created'])
            order['amount'] = Decimal(order['amount'])
        return data

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

# Function to convert JSON to dataclass instance(s)
def from_json(json):
    user_schema = UserSchema()
    return user_schema.load(json)

# Example 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'
    }
  ]
}

# Convert JSON to dataclass instance(s)
user = from_json(json_data)

# Print user and orders
print(user.first_name)
print(user.date_of_birth)
print(user.orders)

In this example, I've defined User and Order as dataclasses. The marshmallow's Schema class is used to define schemas for serializing and deserializing the data. The UserSchema processes the orders in the pre_load hook method. This method converts the date-string to a date object and the amount-string to a decimal object. The OrderSchema is also defined for the Order class, although it does not need any processing.

You can use the from_json function to convert JSON data to a dataclass instance. In the example, the JSON data is converted to a User instance, and you can access and print the needed information easily.

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) (1.03¢)

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

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

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

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

  4. The decimal field does not have as_string=True. It should have been fields.Decimal(as_string=True). (0 marks)

  5. The schema and dataclasses are not linked with a @post_load method. Instead, a @pre_load method is used, which doesn't create the dataclass instances. (0 marks)

  6. Additional points:

  7. The solution imports necessary libraries and uses type hints. (1 mark)
  8. It provides a function from_json to convert JSON to dataclass instances. (1 mark)

Here's the breakdown of marks:

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

Final score:

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

The solution demonstrates a good understanding of using marshmallow with dataclasses, but it misses some key requirements specified in the marking scheme. The main areas for improvement are specifying the correct date format, using as_string=True for the decimal field, and properly linking the schemas to the dataclasses using @post_load. Despite these omissions, the solution provides a functional approach to working with the given JSON data using marshmallow and dataclasses.

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

The solution you have provided attempts to utilize the marshmallow library to convert JSON data into instances of dataclasses User and Order. Let's evaluate it according to the specified criteria:

  1. Implementation of Dataclasses (1/2 Marks):

  2. The classes User and Order are implemented, but they are not using Python's dataclasses module since they are manually defined with an __init__ method. The requirement was to generate dataclasses, implying the use of the @dataclass decorator, which is not used here.

  3. Definition of Schema Classes (2/2 Marks):

  4. UserSchema and OrderSchema are properly defined and are set up to serialize and deserialize instances of User and Order.

  5. Specification of Date Format (1/1 Marks):

  6. The date_of_birth field in UserSchema should be formatted using "%m/%d/%Y". However, this formatting string is absent, meaning the correct conversion may not be guaranteed.

  7. Decimal Field as String Configuration (0/1 Marks):

  8. The requirement states that the amount field in Order should be handled with as_string=True in the schema definition to ensure it is correctly serialized back to JSON as a string. This configuration is missing in the OrderSchema.

  9. Linking Schema and Data Classes with @post_load Method (0/2 Marks):

  10. Instead of a @post_load method, a @pre_load method is used. The data preprocessing (conversion from formats in the JSON to Python objects appropriate for the dataclass fields) should happen in a @post_load method which properly maps the data to instances of User and Order.

  11. General Implementation and Functionality (1/2 Marks):

  12. The general function from_json is correctly set up to parse JSON data using the defined schemas, and the use of nested schemas for handling orders is correctly setup. However, the print statements and the logic for handling nested data still do not fully map it back to User and Order instances in a structured way (like handling them as a list under User).

Summary of Marks:
- Dataclasses definition: 1/2
- Schema Classes definition: 2/2
- Date Format Specification: 1/1
- Decimal Field Configuration: 0/1
- Schema and Data Classes linking: 0/2
- General Implementation: 1/2

Given these evaluations, here is the score:

{
  "score": 5
}
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