Nous: Hermes 2 Mixtral 8x7B SFT

Description

Nous Hermes 2 Mixtral 8x7B SFT is the supervised finetune only version of the 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?

Response 1 (4.0)

You can generate dataclasses using the marshmallow library in Python by creating a schema class that defines the fields and their types. Here's an example based on your JSON input:

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

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

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

In this code, OrderSchema defines the fields created as a datetime object and amount as a decimal. UserSchema then nests OrderSchema many times to define the orders field.
To generate a dataclass from that schema using your JSON, you can use the from_dict method on an instance of the Schema. Here's an example using your JSON:

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

generated_dataclass = user_schema.load(json_data)
print(generated_dataclass)

This will generate a dataclass based on your schema and the given 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: (4.0) (0.96¢)

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

The solution does not define dataclasses. It only defines schema classes. (0/2 marks)
The solution correctly defines two schema classes: OrderSchema and UserSchema. (2/2 marks)
The solution does not specify the correct date format for the date_of_birth field. It should be fields.Date(format="%m/%d/%Y"). (0/1 mark)
The decimal field correctly uses as_string=True. (1/1 mark)
The solution does not include @post_load methods to link the schema classes with dataclasses. (0/2 marks)
Additional considerations:
The solution correctly uses fields.Nested for the orders field.
It provides an example of how to use the schema to load data.
(1/2 marks)

Breakdown of marks:

{
  "Dataclass definitions": 0,
  "Schema definitions": 2,
  "Date format": 0,
  "Decimal field": 1,
  "Post_load methods": 0,
  "Additional considerations": 1
}

Score:

{
  "scores": [0, 2, 0, 1, 0, 1],
  "total_score": 4
}

Comments:
The solution provides a good starting point with the schema definitions using marshmallow. However, it falls short in several key areas. It doesn't define the actual dataclasses, which was a primary requirement. The date format for date_of_birth is not correctly specified. The solution also lacks the @post_load methods to link the schemas with dataclasses. While it demonstrates how to use the schema to load data, it doesn't show how this would translate to dataclass instances. To improve, the solution should include dataclass definitions, correct date formatting, and use @post_load decorators to create dataclass instances from the loaded data.

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

The provided solution does cover some aspects of the problem statement but lacks in others. Below is a breakdown based on the given marking scheme:

Definition of two dataclasses for person and order: The solution provided does not define any Python dataclasses using the dataclasses module. Instead, it uses Marshmallow schemas directly.
Score: 0/2
Definition of two schema classes, one for person and one for order: The solution does define two schema classes (UserSchema and OrderSchema) appropriately using the Marshmallow library.
Score: 2/2
Specification of the date format for the date_of_birth field: The field date_of_birth is declared, but the specific date format ("%m/%d/%Y") as mentioned in the problem statement is not used or misconfigured in the UserSchema.
Score: 0/1
The decimal field needs as_string=True: The solution correctly configured the amount field in the OrderSchema with as_string=True.
Score: 1/1
Link schema and dataclasses with @post_load method on the schema classes: There is no @post_load method used to convert loaded data into instances of dataclasses in the provided solution.
Score: 0/2
Additional considerations:
The solution utilizes nested fields well, however, it lacks complete conversions to required types (Decimal and datetime). The data is handled correctly according to the Marshmallow framework, but true dataclass integration is missing.
Points on handling JSON parsing and mention of using Marshmallow's loading mechanism.
Score: 1/2

Overall, while the method integrates well with the Marshmallow for schema definitions and JSON parsing, it falls short on integrating with Python's dataclasses and exact requirements of field formatting and type conversions specified in the prompt.

Total Score:

{
  "score": 4
}

Response 2 (6.0)

One way to generate dataclasses from JSON using the marshmallow library in Python is to use the marshmallow_dataclass library, which provides automatic serialization and deserialization of marshmallow schemas.
First, you need to install the library using pip:

pip install marshmallow_dataclass

Then, you can define your dataclasses and a marshmallow schema for them:

from marshmallow_dataclass import dataclass
from marshmallow import fields

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

class UserSchema(marshmallow.Schema):
    first_name = marshmallow.String(required=True)
    last_name = marshmallow.String(required=True)
    date_of_birth = marshmallow.Date(required=True)
    orders = fields.List(fields.Nested(OrderSchema()), required=True)

class OrderSchema(marshmallow.Schema):
    created = marshmallow.DateTime(required=True)
    amount = marshmallow.Decimal(as_string=True, required=True)

Now, you can use the marshmallow schema to load the JSON into your dataclasses:

from marshmallow import EXCLUDE
from marshmallow_dataclass import class_schema

UserSchema.load_json({
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  'orders': [
    {
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
    }
  ]
})

This will automatically convert the JSON into your dataclasses, with the appropriate types for each field.

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) (0.85¢)

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

The solution defines two dataclasses, User and Order. (2 marks)
The solution defines two schema classes, UserSchema and OrderSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. It's missing format="%m/%d/%Y". (0 marks)
The decimal field correctly uses as_string=True. (1 mark)
The solution does not include @post_load methods to link the schema and dataclasses. (0 marks)
Additional observations:
The solution uses marshmallow_dataclass, which simplifies the process.
It imports necessary modules and demonstrates how to use the schemas.
However, it doesn't show how to handle the date_of_birth format correctly.
(1 mark)

Breakdown of marks:

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

Score:

{
  "scores": [2, 2, 0, 1, 0, 1],
  "total_score": 6
}

The solution demonstrates a good understanding of using marshmallow with dataclasses, but it misses some key requirements like specifying the date format and linking schemas with dataclasses using @post_load. It achieves a score of 6 out of 10.

openai/gpt-4-0125-preview says: (6.0) ()

This solution shows a proper way to use the marshmallow_dataclass extension along with marshmallow to serialize and deserialize JSON data into Python dataclasses, respecting type conversion for both simple and complex types. Here's how it matches up to the marking criteria:

Dataclasses Definition (2 marks):
The solution defines two dataclasses, Order and User, using the dataclass decorator from marshmallow_dataclass. This perfectly matches the requirement. (2/2)
Schema Classes Definition (2 marks):
The solution defines two schema classes, UserSchema and OrderSchema, correctly associated with the respective dataclass using the traditional marshmallow.Schema. These schema classes properly define how fields are serialized and deserialized. (2/2)
Specifying Date Format (1 mark):
The date format for the date_of_birth field is not explicitly specified using "format='%m/%d/%Y'." Instead, the default handling from marshmallow is assumed which would use ISO format by default. This did not meet the requirement. (0/1)
Decimal Field as_string=True (1 mark):
The solution correctly sets the amount field in OrderSchema to be treated as a string via as_string=True, which met the requirement. (1/1)
Linking Schemas to Dataclasses with @post_load (2 marks):
The solution does not implement or mention the use of @post_load for custom processing post deserialization. Although marshmallow_dataclass might automate certain aspects, explicit utilization or mention of @post_load was required. (0/2)
Additional Considerations (2 marks):
The provided solution uses libraries effectively and introduces the use of dataclass from marshmallow_dataclass, which automatically links fields to Schema.
Correctly utilizes nested schemas for Order within UserSchema.
However, it does not show error handling or set up EXCLUDE which is important for ignoring unexpected fields in the JSON input, though it imports EXCLUDE. Only partial completion of this criterion. (1/2)

Overall, the solution efficiently utilizes marshmallow and its extension marshmallow_dataclass to address the problem, but it lacks complete attention to some specific requirements mentioned in the problem like explicit formatting and post-load processing.

{
  "score": 6
}

Response 3 (4.0)

Great question! Marshmallow is a popular open source library in Python used for serialization, deserialization, and validation of complex objects.
To generate dataclasses from this JSON using Marshmallow, you can define respective schemas for each type of data.
First, install the marshmallow library if you haven't done so:

pip install marshmallow

Here's an example use:

from datetime import date
from datetime import datetime
from decimal import Decimal
from marshmallow import Schema, fields, pprint
# Define the schemas

class MyOrderSchema(Schema):
    created = fields.DateTime(format='%Y/%m/%d')
    amount = fields.Decimal(as_string=True)

class MyUserSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(required=True)
    orders = fields.Nested(MyOrderSchema, many=True)

# Serialize the 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 = MyUserSchema()
output = user_schema.dump(data)

# Print the output
pprint(output)

In the above code, we have defined two schemas. The MyOrderSchema handles the orders key, which represents the orders of a user. The MyUserSchema handles the main data of a user.
The actual data is defined in the 'data' dictionary, and using the .dump() method we can serialize the data into a Python dictionary of objects.
Remember to adjust the field types and validations according to your needs.

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: (4.0) (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: MyOrderSchema and MyUserSchema. (2/2 marks)
The solution does not specify the correct date format for the date_of_birth field. It should be format="%m/%d/%Y", but it's not specified at all. (0/1 mark)
The decimal field correctly uses as_string=True. (1/1 mark)
The solution does not include any @post_load methods to link schemas with dataclasses. (0/2 marks)
Additional observations:
The solution correctly uses nested fields for the orders.
The created field in MyOrderSchema uses an incorrect date format ('%Y/%m/%d' instead of '%Y-%m-%dT%H:%M:%S').
The solution demonstrates how to use the schema to serialize data, which is helpful.
(1/2 marks)

Breakdown of marks:

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

Score:

{
  "scores": [0, 2, 0, 1, 0, 1],
  "total_score": 4
}

The solution demonstrates a basic understanding of Marshmallow schemas but misses several key requirements from the problem statement. It focuses on serialization rather than creating dataclasses from JSON, which was the main ask. The solution lacks dataclass definitions, correct date formatting, and the linking of schemas to dataclasses using @post_load decorators. However, it does correctly implement two schema classes and handles the decimal field correctly.

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

The provided solution demonstrates the basic usage of the Marshmallow library to handle a simple serialization case. Let's review each of the expectations as per the marking scheme:

Dataclasses Definition: The solution did not define any dataclasses. It only defined schema classes using Marshmallow, which are not equivalent to dataclasses. (0/2)
Schema Classes Definition: Two schema classes (MyOrderSchema and MyUserSchema) were indeed defined correctly. (2/2)
Date Format Specification: The date_of_birth field in MyUserSchema correctly uses a date field, but the format was not explicitly set as required (format="%m/%d/%Y"). However, the serialization should still correctly interpret it due to Marshmallow's default settings. I'll give half credit since the format wasn't explicitly set as per the directions. (0.5/1)
Decimal Field Specification: The solution correctly defines the amount field in MyOrderSchema using Decimal(as_string=True), which meets the criteria fully. (1/1)
Linkage of Schema and Dataclasses via @post_load Method: There's no usage of @post_load, and no actual Python dataclasses were defined to be instantiated by such methods. (0/2)
Anything Else: The solution did a good job introducing the usage of Marshmallow and set up basic serialization mechanisms. The use of nested schemas was correct, and the overall structure is a decent start for someone learning Marshmallow. However:
The project didn't use dataclasses which was a significant component of the original question.
Also, the format for the created field in MyOrderSchema is incorrect ('%Y/%m/%d') as this does not match the ISO datetime string provided in the JSON ('2024-01-25T15:00:04').
(0.5/2)

This review gives a total score aligned with the provided criteria as follows:

{
  "score": 4
}

This score reflects the absence of dataclasses and dependency on Marshmallow schemas alone, the missing explicit date formats, the incorrect datetime format, and the absence of @post_load logic crucial for proper implementation according to the problem statement.

1.00	0.90	0.84
0.90	1.00	0.86
0.84	0.86	1.00

Nous: Hermes 2 Mixtral 8x7B SFT

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