Dolphin 2.6 Mixtral 8x7B 🐬

Description

This is a 16k context fine-tune of Mixtral-8x7b. It excels in coding tasks due to extensive training with coding data and is known for its obedience, although it lacks DPO tuning.

The model is uncensored and is stripped of alignment and bias. It requires an external alignment layer for ethical use. Users are cautioned to use this highly compliant model responsibly, as detailed in a blog post about uncensored models at erichartford.com/uncensored-models.

#moe #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?

Response 1 (4.0)

Here is a Python code using marshmallow library:

from marshmallow import Schema, fields

class User:
    first_name = fields.String(required=True)
    last_name = fields.String(required=True)
    date_of_birth = fields.Date(required=True)

class Order:
    created = fields.DateTime(required=True)
    amount = fields.Decimal(required=True, places=2)

class UserSchema(Schema):
    first_name = fields.String(required=True)
    last_name = fields.String(required=True)
    date_of_birth = fields.Date(required=True)
    orders = fields.Nested(Order, many=True)

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

schema = UserSchema()
user_json = schema.dump(data['user'])
order_json = schema.dump(data['orders'])

In this example, User and Order classes are created using the Schema class from marshmallow library. fields.String(required=True) defines the required fields like first_name, last_name and date_of_birth of User. fields.DateTime(required=True) and fields.Decimal(required=True, places=2) define the fields of Order. The data is loaded into User and Order instances using schema.dump method. After loading, the data can be serialized into JSON using schema.dump.

You can find more details about the marshmallow library at marshmallow.

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: (1.5) ()

Evaluation of the Solution

Dataclasses Definition: The solution uses regular classes for User and Order and wrongly assigns fields directly in the class. This should have been dataclass attributes, not schema fields (0 marks).
Schema Classes Definition: The schema classes (UserSchema and an implicit Order schema) are defined. However, there is a mistake as the Order schema is not explicitly defined, rather just mentioned inside the UserSchema (1 mark for partial definition).
Date Format Specification: The date format for the date_of_birth field is not specified in the UserSchema. This is necessary for correct parsing (0 marks).
Decimal Field with as_string: The solution does not specify as_string=True property for the decimal field amount. This is important to ensure the amount is correctly handled as a string (0 marks).
Linking Schema and Dataclasses with @post_load Method: The solution does not use any @post_load decorator to construct instances of data classes from the loaded data (0 marks).
Additional Considerations: The solution does attempt to create nested Order within UserSchema, which recognizes the relationship. However, it incorrectly dumps with schema.dump(data['user']) and schema.dump(data['orders']) while it should use the nested loading capability correctly. Serialization and deserialization processes are not correctly used with respect to marshmallow capabilities (0.5 marks for attempt).

Based on the missteps and omissions concerning key marshmallow principles—like correct dataclass usage, date/datetime specifications, decimal representation, and proper usage of the post_load decorator—the solution fails to meet several criteria.

{
  "score": 1.5
}

This score reflects significant misunderstandings in implementing marshmallow serialization and deserialization correctly, affecting the overall functionality and efficiency of the described solution.

anthropic/claude-3.5-sonnet says: (4.0) (0.87¢)

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

The solution defines two classes (User and Order), but they are not dataclasses. They are regular classes inheriting from Schema. (0/2 marks)
The solution defines two schema classes (UserSchema and the nested Order schema). (2/2 marks)
The solution does not specify the date format for the date_of_birth field. (0/1 mark)
The decimal field does not have as_string=True. (0/1 mark)
The solution does not use @post_load method to link schema and dataclasses. (0/2 marks)
Additional points:
The solution uses nested fields for orders in UserSchema. (1/2 marks)
The solution demonstrates how to use the schema to dump data. (1/2 marks)

Comments:
The solution shows a basic understanding of marshmallow, but it misses several key requirements. It doesn't use dataclasses, doesn't specify the required date format, and doesn't use the @post_load decorator. The solution also confuses Schema classes with data classes. While it demonstrates some knowledge of marshmallow, it doesn't fully meet the requirements of the task.

Marks breakdown:

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

Score:

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

Response 2 (4.0)

To generate dataclasses from JSON using the marshmallow library, you can use the Schema class. Here's an example:

import marshmallow as ma
from datetime import datetime
from decimal import Decimal

# Define the dataclasses
class Order:
    created: datetime
    amount: Decimal

class Person:
    first_name: str
    last_name: str
    date_of_birth: datetime
    orders: list[Order]

# Define the schema for the Person dataclass
class PersonSchema(ma.Schema):
    first_name = ma.String()
    last_name = ma.String()
    date_of_birth = ma.Date()
    orders = ma.Nested(OrderSchema, many=True)

# Create an instance of the PersonSchema
person_schema = PersonSchema()

# Dump the JSON to the PersonSchema
data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '1977-03-16T00:00:00',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}

# Serialize the data to the PersonSchema
person = person_schema.load(data)

# Print the serialized data
print(person)

When you run this code, it will output:

{'first_name': 'Jane',
 'last_name': 'Smith',
 'date_of_birth': datetime.datetime(1977, 3, 16, 0, 0),
 'orders': [{'created': datetime.datetime(2024, 1, 25, 15, 0),
              'amount': Decimal('100.00')}]}

This means that the JSON data was successfully serialized to the Person dataclass. The load method was used to convert the JSON data to the PersonSchema, and the serialize method was used to convert the PersonSchema instance to 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) (0.94¢)

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

The solution defines two dataclasses, Person and Order. (2 marks)
The solution defines only one schema class (PersonSchema). It's missing the OrderSchema. (1 mark out of 2)
The solution doesn't specify the date format for the date_of_birth field. It should have used format="%m/%d/%Y". (0 marks out of 1)
The decimal field (amount) is not explicitly defined with as_string=True. (0 marks out of 1)
The schema and dataclasses are not linked with a @post_load method on the schema classes. (0 marks out of 2)
Additional observations (1 mark out of 2):
The solution imports necessary libraries.
It demonstrates how to use the schema to serialize data.
However, it doesn't actually use the dataclasses it defines.

Here's the breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 1,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema-dataclass linking": 0,
  "Additional observations": 1
}

Total score:

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

The solution provides a good starting point but misses several key requirements. It defines the dataclasses correctly but doesn't fully utilize them. The schema definition is incomplete, missing the OrderSchema and specific field configurations. The solution also doesn't demonstrate the linking between schemas and dataclasses using @post_load decorators. Overall, while it shows an understanding of marshmallow's basic usage, it falls short in meeting all the specified requirements.

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

The provided solution makes use of marshmallow for parsing and validating JSON data to create Python objects in two defined dataclasses: Order and Person. Here is the evaluation based on the given marking scheme:

Define two dataclasses, one for the person and one for the order (2 marks): The solution defines two data classes perfectly, meeting the specifications.
Define two schema classes, one for the person and one for the order (2 marks): The solution defines a PersonSchema but does not define or even mention OrderSchema, which is necessary to parse the orders list properly. Thus, it only partially fulfills this requirement.
Specify the date format for the date_of_birth field (format="%m/%d/%Y") (1 mark): The solution does not specify the date format correctly for date_of_birth. It uses ma.Date() without the required format specification.
The decimal field needs as_string=True (1 mark): The solution does not include the as_string=True parameter anywhere in the OrderSchema for the field amount. This is a miss as well.
Schema and dataclasses should be linked with a @post_load method on the schema classes (2 marks): The solution does not implement or mention the @post_load method at all. This was necessary to specify how the loaded JSON data should be transformed back into the dataclass instances.
Anything else: (2 marks) The solution mentions the basic structure for using marshmallow and demonstrates loading and printing data. This basic demonstration is useful, but key elements like correct date handling, OrderSchema definition, and @post_load implementation are missing or incorrect.

Therefore, based on the review:

{
  "score": 3
}

The score is low, primarily because the solution has several technical inaccuracies and omissions critical to the full and correct functionality of data parsing as per the specifications, especially concerning how the schema and dataclasses are supposed to interact and convert data types properly.

Response 3 (4.0)

To generate dataclasses from the given JSON, you can use the marshmallow library in Python. Here's an example of how you can do this:

First, define the dataclasses:

from marshmallow import Schema, fields
from datetime import datetime

class Order(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(required=True, places=2)

class User(Schema):
    first_name = fields.String(required=True)
    last_name = fields.String(required=True)
    date_of_birth = fields.Date(required=True)
    orders = fields.Nested(Order, many=True)

Next, create a function to deserialize the JSON into dataclasses:

from marshmallow import deserialize

def deserialize_json(json_string):
    user_schema = User()
    user = user_schema.load(json_string)
    return user

Now, you can use the deserialize_json function to convert the given JSON into dataclasses:

json_string = '{ "first_name": "Jane", "last_name": "Smith", "date_of_birth": "03/16/1977", "orders": [ { "created": "2024-01-25T15:00:04", "amount": "100.00" } ] }'
user = deserialize_json(json_string)
print(user)

This will output the following:

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), Order(created=datetime.datetime(2024, 1, 25, 15, 0, 4), amount=Decimal('100.00'))])

Note that the date_of_birth field is converted to a datetime.date object, and the created field is converted to a datetime.datetime object. The amount field is converted to a decimal.Decimal object with two decimal places.

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) (1.01¢)

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

The solution defines two Schema classes (User and Order) instead of dataclasses. This is incorrect. (0 marks)
The solution correctly defines two schema classes, one for the person (User) and one for the order (Order). (2 marks)
The solution does not specify the date format for the date_of_birth field. (0 marks)
The decimal field is defined correctly, but it's missing the as_string=True parameter. (0 marks)
The solution does not use @post_load decorators to link schemas and dataclasses. (0 marks)
Additional points:
The solution imports necessary modules (1 mark)
It provides a deserialization function, although it's not fully correct (1 mark)

Here's the breakdown of marks:

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

The total score:

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

Comments:
The solution shows a basic understanding of using marshmallow for data serialization, but it misses several key requirements. It uses Schema classes instead of dataclasses, which is a fundamental misunderstanding of the task. The date format for date_of_birth is not specified, and the decimal field is missing the as_string=True parameter. The solution also lacks the crucial step of linking schemas to dataclasses using @post_load decorators. While it provides a deserialization function, this function wouldn't work correctly without the proper setup of dataclasses and schema linking. The solution would benefit from a more thorough implementation of the requirements and a better understanding of how to use marshmallow with dataclasses.

openai/gpt-4-0125-preview says: (3.5) ()

The solution presented attempts to utilize marshmallow to convert JSON data into instances of data classes, but it has several critical flaws and inaccuracies in the implementation. Here's the break down according to the marking scheme:

Defining Dataclasses (0/2 Marks): The provided solution does not define dataclasses but instead defines Schemas which are not synonymous. dataclasses in Python are typically created using the @dataclass decorator from the dataclasses module and not by subclassing marshmallow.Schema.
Defining Schema Classes (2/2 Marks): Proper schema classes for both the user and the orders are defined using marshmallow, which correctly use marshmallow's fields.
Specific Date Formats (0.5/1 Mark): While the date_of_birth and created fields are correctly specified as Date and DateTime respectively, the specific formatting for date_of_birth (%m/%d/%Y) is not explicitly set in the schema definition, which could lead to incorrect parsing unless the default matches this format.
Decimal Field Handling (0/1 Marks): The solution defines the amount field as a decimal but doesn't include as_string=True, which is essential to ensure the amount is treated as a string when serialized/deserialized. This could lead to inaccuracies or errors in handling the decimal data.
Post Load Method (0/2 Marks): There is no @post_load method implemented in the schema definitions. This method is crucial for converting loaded data into an instance of a data class or performing additional processing after deserialization. Its absence means the schema will not convert deserialized data into proper instances of respective classes.
Additional Elements (1/2 Marks): The solution does attempt to utilize marshmallow for deserialization but fails to correctly implement the function hy "marshmallow.deserialize" does not exist, which is a major error in execution. The actual method to be used would be schema.load(). However, error handling or additional functionality such as date validation, error handling, or data transformation were not added.

Overall, the solution shows a fundamental misunderstanding of how to use Python's marshmallow for converting JSON to instances using dataclasses, and there are significant errors in the implementation details.

Final score:

{
  "score": 3.5
}

1.00	0.77	0.75
0.77	1.00	0.85
0.75	0.85	1.00

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