Nous: Hermes 2 Mistral 7B DPO

Description

This is the flagship 7B Hermes model, a Direct Preference Optimization (DPO) of Teknium/OpenHermes-2.5-Mistral-7B. It shows improvement across the board on all benchmarks tested - AGIEval, BigBench Reasoning, GPT4All, and TruthfulQA.

The model prior to DPO was trained on 1,000,000 instructions/chats of GPT-4 quality or better, primarily synthetic data as well as other high quality datasets.

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 (8.0)

You can create a set of dataclasses and then a Schema in Marshmallow to serialize and deserialize these classes. First, create a dataclass for each entry in the JSON:

from datetime import datetime
from dateutil.parser import parse
from decimal import Decimal
from typing import List, Dict
from dataclasses import dataclass

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

Next, define a Schema for each dataclass:

from marshmallow import Schema, fields, post_load
from dateutil.parser import parse
from decimal import Decimal

class OrderSchema(Schema):
    created = fields.DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount = fields.Float(allow_nan=False)

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.DateTime(data_key='date_of_birth', serialize='strptime', deserialize='strftime', format='%m/%d/%Y')
    orders = fields.Nested('OrderSchema', many=True)

    @post_load
    def make_person(self, data, *args, **kwargs):
        return Person(**data)

Now you can use the PersonSchema for both loading and dumping. Make sure to parse your input date string before using it:

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

# Parse date of birth
date_of_birth = parse(json_input['date_of_birth'])

# Create Order objects and append them to an Order list
orders = [Order(datetime.strptime(order['created'], '%Y-%m-%dT%H:%M:%S'), Decimal(order['amount'])) for order in json_input.get('orders', [])]

# Create Person object and schema instance
person = Person(json_input['first_name'], json_input['last_name'], date_of_birth, orders)
schema = PersonSchema()

# Test loading from JSON
loaded_person = schema.load(json_input)
assert loaded_person.first_name == 'Jane'

# Test dumping to JSON
dumped_json = schema.dump(person)
assert dumped_json == json_input

This will allow you to handle the specific data types required in your 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: (8.0) (1.06¢)

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

The solution defines two dataclasses, Order and Person. (2 marks)
The solution defines two schema classes, OrderSchema and PersonSchema. (2 marks)
The solution specifies the date format for the date_of_birth field with format='%m/%d/%Y'. (1 mark)
The decimal field (amount) is not implemented with as_string=True. It's using fields.Float instead of fields.Decimal. (0 marks)
The schema and dataclasses are linked with a @post_load method on the PersonSchema class. However, it's missing for the OrderSchema. (1 mark)
Additional points:
The solution imports necessary modules.
It includes example usage of the schemas.
It handles the created field as a datetime.
(2 marks)

Breakdown of marks:

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

Score:

{
  "scores": [2, 2, 1, 0, 1, 2],
  "total_score": 8
}

Comments:
The solution is generally good and covers most of the requirements. However, there are a few issues:

The amount field in OrderSchema is implemented as Float instead of Decimal, and it's missing the as_string=True parameter.
The @post_load method is missing for the OrderSchema.
The date parsing in the example usage seems unnecessary since the schema should handle this.

Despite these issues, the solution demonstrates a good understanding of using dataclasses with Marshmallow for JSON serialization and deserialization.

openai/gpt-4-0125-preview says: (6.5) ()

The provided solution meets some elements of the task but is incomplete or incorrect in certain aspects:

Definition of dataclasses: The solution correctly provides two dataclasses, Order and Person. The Order dataclass properly defines a datetime for created and uses Decimal for amount. Similarly, Person defines appropriate fields including a datetime for date_of_birth and a list of Order objects.
Score: 2/2
Definition of schema classes: There are schema classes defined for both Order and Person. Each schema has fields that correspond to the dataclass attributes, although not entirely correctly configured.
Score: 1.5/2
Date format specification: The format for date_of_birth in PersonSchema is specified, but there are errors in using serialize and deserialize instead of format. The approach attempted is correctly directed at using %m/%d/%Y but is incorrectly applied.
Score: 0.5/1
Decimal as string: The schema for Order uses fields.Float, which does not align with the requirement for amount to be a decimal treated as a string. It should have used fields.Decimal(as_string=True).
Score: 0/1
Linking schema and dataclasses with @post_load: PersonSchema includes a @post_load method correctly aimed at creating a Person object from the loaded data. However, OrderSchema lacks a similar method. The structure is partially correct for PersonSchema.
Score: 1.5/2
Additional Elements:
The error handling, dealing with nested structures, and proper conversion management could have been addressed more thoroughly.
The use of the parse function from dateutil to handle datetime objects in the script section might confuse since a more integrated approach within the schemas is expected in such tasks.
Schema relationships are suitably defined but could have been more precise, especially in technical correctness.
Incorrect usage of Marshmallow fields misleads functionality.
Score: 1/2

Given these observations, the solution misses on some key requirements and inaccuracies in the schema configuration, particularly concerning format handling and type appropriateness.

Total Score:

{
  "score": 6.5
}

Response 2 (5.5)

To achieve this, you can use Marshmallow's post_load hook to parse date and datetime strings upon deserialization and Marshmallow-Enum plugin to work with enums. First, you need to install Marshmallow-Enum plugin using pip install marshmallow-enum.

Here's an example of how to parse date_of_birth and amount, and use Marshmallow-Enum for OrderStatus:

from datetime import datetime
from decimal import Decimal
from marshmallow import Schema, post_load, Field
from marshmallow.validate import OneOf
from marshmallow_enum import EnumField


classOrderStatus = EnumField(Enum(OrderStatus))


class PersonSchema(Schema):
    first_name = Field(required=True)
    last_name = Field(required=True)
    date_of_birth = Field(required=True, allow_none=True, load_from='date_of_birth')

    class OrderSchema(Schema):
        created = Field(required=True, load_from='created')
        amount = Field(required=True, load_from='amount')
        status = classOrderStatus(required=True, allow_none=True)

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


@post_load
def make_person(data, **kwargs):
    date_of_birth = data.get('date_of_birth')
    if date_of_birth:
        data['date_of_birth'] = datetime.strptime(date_of_birth, '%m/%d/%Y').date()
    return Person(**data)   


# Initializing the schema
person_schema = PersonSchema()

# Loading the data
data = {'first_name': 'Jane', 'last_name': 'Smith', 'date_of_birth': '03/16/1977', 'orders': [{'created': '2024-01-25T15:00:04', 'amount': '100.00'}]}
loaded_data = person_schema.load(data)

# Access the data
print(loaded_data.date_of_birth)
print(loaded_data.orders[0].created)
print(loaded_data.orders[0].status)

In this example, the make_person function will parse 'date_of_birth' into a datetime.date object and then create a Person instance with this data. The classOrderStatus serves to work with OrderStatus enum in Person.orders list.

You might have to adjust the code based on your actual data structure and requirements, but this should give you a general idea of how to use Marshmallow for this task.

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) (1.06¢)

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

The solution does not explicitly define two dataclasses (one for person and one for order). (0/2 marks)
The solution defines two schema classes: PersonSchema and OrderSchema (nested within PersonSchema). (2/2 marks)
The solution specifies the date format for the date_of_birth field, but not in the schema definition. It's done in the make_person function using datetime.strptime(date_of_birth, '%m/%d/%Y').date(). (0.5/1 mark)
The solution does not include as_string=True for the decimal field. (0/1 mark)
The schema and dataclasses are linked with a @post_load method on the PersonSchema class. However, there's no equivalent for the OrderSchema. (1/2 marks)
Additional points:
The solution includes handling for an Enum field (OrderStatus) which wasn't mentioned in the original problem. (1/2 marks)
The solution provides a complete example of how to use the schema to load and access data. (1/2 marks)

Comments:
The solution partially addresses the requirements but misses some key points. It doesn't use dataclasses, which was a primary requirement. The date format for date_of_birth is handled, but not in the schema definition. The decimal field is not properly configured. The solution adds extra functionality (Enum handling) that wasn't required, which could be seen as a positive or negative depending on the context.

Here's the breakdown of marks:

{
  "Dataclasses": 0,
  "Schema classes": 2,
  "Date format": 0.5,
  "Decimal field": 0,
  "Post_load methods": 1,
  "Additional points": 2
}

Final score:

{
  "scores": [0, 2, 0.5, 0, 1, 2],
  "total_score": 5.5
}

openai/gpt-4-0125-preview says: (2.0) ()

The evaluated solution for generating data classes and corresponding schema classes using the Marshmallow library for the given JSON input suffers from a few issues that need to be addressed concerning the provided marking scheme.

Defining dataclasses for Person and Order: The solution does not explicitly define dataclasses for Person and Order. Instead, it implicitly assumes the existence of a Person data structure in the make_person function initialization (Person(**data)) but does not define or show these dataclasses (0 marks).
Defining Schema classes for Person and Order: The PersonSchema and an inner OrderSchema are defined. However, defining an inner class inside a schema for handling orders is not a conventional Marshmallow practice as top-level classes for each data model are preferred for better reusability and clarity (1 mark for effort).
Specifying the Date format for date_of_birth: The solution specifies the date format for date_of_birth correctly in the make_person function instead of in the PersonSchema as Field with formatting options, but it does handle parsing correctly (0.5 marks).
Decimal field with as_string=True: The solution does not specify the amount field to be handled as a Decimal and particularly does not set as_string=True, which is needed for ensuring the amount is treated as a string when loaded or dumped (0 marks).
Linking schemas and dataclasses with @post_load: The solution includes a @post_load decorator but uses it incorrectly. The @post_load decorator is applied in a non-standard way outside of any schema class and it does not distinctly define or link back properly to the dataclass (0.5 marks as it attempts linking but fails execution properly).
Additional considerations: The solution attempts to integrate EnumField from Marshmallow-Enum but does so erroneously since OrderStatus is neither defined nor relevant to the required problem specifications. This introduces unnecessary complexity (0 marks).

The code has significant structural issues and misuses several Marshmallow functionalities, which could prevent the code from functioning correctly. The author does vaguely attempt to address some requirements (such as date parsing) but often with incorrect or suboptimal implementations.

{
  "score": 2
}

Response 3 (6.0)

You can use the Marshmallow library along with Python's built-in datetime and decimal modules to accomplish this. First, install the marshmallow library if you haven't already:

pip install marshmallow

Then, create a data class for the main person object:

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

class Person:
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(required=True, error_messages={'missing': 'Date of birth is required'})

    orders = fields.Nested('Order', many=True, exclusive=True)

    class Meta:
        unknown = EXCLUDE

    @post_load
    def make_person(self, data, **kwargs):
        return Person(**data)

For the 'Order' data class, you'll need to convert the string timestamp to a datetime object:

class Order:
    created = fields.DateTime(alias='created', required=True, convert=lambda x: datetime.strptime(x, '%Y-%m-%dT%H:%M:%S%z'))
    amount = fields.Decimal(alias='amount', required=True, precision=2)

    class Meta:
        unknown = EXCLUDE necessarily unused

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

Now, you can load your JSON:

from marshmallow.exceptions import ValidationError

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

schema = Person(**data)

If the JSON is valid, schema will be an instance of your Person class. Please be aware that this code assumes your 'date_of_birth' is in the format MM/DD/YYYY, and you may need to adjust the parsing accordingly.

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) (0.96¢)

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

Defining two dataclasses:
The solution doesn't explicitly define dataclasses. It defines two classes (Person and Order) but not as dataclasses.
Defining two schema classes:
The solution defines two classes that inherit from Schema, which is correct.
Date format for date_of_birth:
The solution doesn't specify the date format for the date_of_birth field.
Decimal field with as_string=True:
The solution doesn't include as_string=True for the decimal field.
Linking schema and dataclasses with @post_load:
The solution correctly uses @post_load decorators on both schema classes.
Additional points:
The solution correctly imports necessary modules.
It handles the datetime conversion for the 'created' field.
It includes error handling for missing date of birth.
The solution uses the unknown = EXCLUDE meta option, which is good practice.

Here's the breakdown of marks:

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

Final score:

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

The solution demonstrates a good understanding of Marshmallow schemas but misses some key requirements. It doesn't use dataclasses, doesn't specify the correct date format for date_of_birth, and doesn't include as_string=True for the decimal field. However, it correctly implements schema classes, uses @post_load decorators, and includes some good additional features.

openai/gpt-4-0125-preview says: (1.5) ()

The provided solution uses the Marshmallow library in combination with the datetime and decimal Python modules to convert a JSON structure into Python objects defined by the dataclasses. Let's move through the evaluation based on the given criteria:

Dataclasses Defined: The classes Person and Order are defined using what should have been Python's dataclass but actually, there seems to be confusion in the example as it improperly mixes Marshmallow fields with the class attributes without turning them actually into dataclasses with @dataclass. Therefore - 0/2.
Schema Classes Defined: The solution appears to present the "Person" and "Order" as schemata but interfaces them incorrectly because the fields defined should be inside a schema class inheriting from Schema, not an apparent dataclass. Therefore, marshmallow's schemta were not correctly defined - 0/2.
Date Format Specification: The date format should be specified for the date_of_birth, ideally using fields.Date, but this is not visible where it specifically mentions the format as %m/%d/%Y. So, this is missing - 0/1.
Decimal field as_string=True: The solution does not use as_string=True for the Decimal field, which is necessary to ensure that the decimal amount does not lose precision in JSON representation - 0/1.
Linking Schema/Dataclasses with @post_load: The usage of @post_load is correct in intent to convert loaded data back to the custom Python object. However, because they are not defined as part of an actual marshmallow Schema class, the mechanism won't function as intended. Therefore, execution is incorrect but the method and intent are recognized - 1/2.
Additional Comments: The setup for data handling shows some understanding of how marshmallow is meant to integrate with Python's data handling through fields and @post_load. However, the correct structure of schema and dataclass usage is not achieved. Especially Person(**data) is incorrect for schema loading; instead, an instance of the schema should be created followed by loading/validating the JSON data, e.g., person_schema.load(data) - 0.5/2.

Conclusion

The solution provided tries to setup marshmallow for JSON serialization and deserialization but falls short in correct implementation and syntax essential for marshmallow functionality.

{
  "score": 1.5
}

1.00	0.75	0.85
0.75	1.00	0.78
0.85	0.78	1.00

Nous: Hermes 2 Mistral 7B DPO

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