Noromaid Mixtral 8x7B Instruct

Description

This model was trained for 8h(v1) + 8h(v2) + 12h(v3) on customized modified datasets, focusing on RP, uncensoring, and a modified version of the Alpaca prompting (that was already used in LimaRP), which should be at the same conversational level as ChatLM or Llama2-Chat without adding any additional special tokens.

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?

You can use the Marshmallow library in Python to create dataclasses from JSON data. Here's an example of how you can do this for your specific JSON data:

First, you need to install the Marshmallow library, if you haven't already:

pip install marshmallow

Next, you can create a scheme for your JSON data as follows:

from marshmallow import fields, Scheme
import datetime
import decimal

# Define a class for the order object
class Order:
    def __init__(self, created, amount):
        self.created = created
        self.amount = decimal.Decimal(amount)

# Define a class for the main object
class User:
    def __init__(self, first_name, last_name, date_of_birth, orders):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = datetime.date.fromisoformat(date_of_birth)
        self.orders = orders

# Create a Marshmallow scheme for the main object
class User Scheme( Scheme):
    first_name = field('string')
    last_name = field('string')
    date_of_birth = field('date')
    orders = field('list', data_key=' orders', nested=True)

# Create a Marshmallow scheme for the order object
class Order Scheme( Scheme):
    created = field('datetime')
    amount = field('decimal')

Finally, you can use the load() method of the scheme to convert JSON data to a Python object:

# Define the JSON data as a dictionary
json_dict = {
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '1977-03-16',
  'orders': [
    {
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
    }
  }
}

# Load the JSON data into a Python object
user = User Scheme().load(json_dict)

# Print the object
print(user)
print(user.date_of_birth)
print(user.orders[0].amount)

This will create a new object user that has the data from the JSON, and the date, date/time, and decimal values will be converted to the correct types.

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: (3.0) (0.95¢)

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

  1. The solution defines two classes (User and Order), but they are not dataclasses. (0/2 marks)

  2. The solution defines two schema classes (UserScheme and OrderScheme). (2/2 marks)

  3. The solution does not specify the correct date format for the date_of_birth field. It uses ISO format instead of the required "%m/%d/%Y". (0/1 mark)

  4. The decimal field does not include as_string=True. (0/1 mark)

  5. The solution does not use @post_load method to link schema and classes. (0/2 marks)

  6. Additional observations:

  7. The solution uses field() instead of fields.Field().
  8. The orders field in UserScheme is not correctly defined.
  9. The solution attempts to convert date_of_birth in the User class initialization, which is not correct.
    (1/2 marks)

Breakdown of marks:

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

Final score:

{
  "scores": [0, 2, 0, 0, 0, 1],
  "total_score": 3
}

The solution demonstrates some understanding of using Marshmallow for data serialization, but it falls short in several key areas. It doesn't use dataclasses, misses important field configurations, and doesn't properly link the schema to the classes. The approach to handling dates and decimals is also incorrect. The solution would need significant revisions to meet the requirements of the problem.

openai/gpt-4-0125-preview says: (0.0) ()

The solution provided demonstrates an attempt to use Marshmallow with dataclasses to parse JSON data, but it presents several crucial mistakes and misconfigurations:

  1. Defining dataclasses:
  2. The classes Order and User are defined as regular Python classes rather than using Python's dataclasses.dataclass. This is evident by the manual definition of __init__ methods. The requirement was to define dataclasses.

  3. Incorrect: Earns 0 out of 2 marks.

  4. Defining schema classes:

  5. Schemas are intended to be defined using marshmallow.Schema, not Scheme. There are several typographical errors such as Scheme and field which should be Schema and fields.
  6. Incorrect use of field instead of fields.String, fields.Decimal, etc.

  7. Incorrect: Earns 0 out of 2 marks.

  8. Date formatting for date_of_birth:

  9. The solution does not specify any custom date formatting for date_of_birth using format="%m/%d/%Y" or fields.Date(format="%m/%d/%Y").

  10. Incorrect: Earns 0 out of 1 mark.

  11. The decimal field with as_string=True:

  12. The provided schema does not use as_string=True for the amount field which is necessary to ensure that it's handled as a string and then converted to a Decimal.

  13. Incorrect: Earns 0 out of 1 mark.

  14. Linking schema to dataclasses with @post_load:

  15. There is no usage of the @post_load decorator in the provided solution to ensure that the loading of data creates instances of the defined dataclasses.

  16. Incorrect: Earns 0 out of 2 marks.

  17. Additional considerations:

  18. Errors in syntax such as using field rather than fields.Fieldtype, improper class and method names (mislabeled as Scheme instead of Schema).
  19. The approach taken with the JSON parsing and manual constructors in classes directly contradicts the use of Marshmallow and dataclasses for automatic deserialization and validation, which is the strength of using these tools.
  20. Incorrect: Earns 0 out of 2 marks.

Overall, while the solution attempts to address the task, it significantly deviates from the correct implementation and usage of Marshmallow and dataclasses. Major functionality and correct practices are not met.

{
  "score": 0
}

You can use the Marshmallow library in Python to create data classes and validate/ Serialize/ Deserialize the data. Here is an example of how you can do this for your JSON:

First, install the Marshmallow library using pip:

pip install marshmallow

Then, you can use the following code to create the data classes and validate the data:

from datetime import datetime
from dateutil importparser
from marshmallow import fields, validate
from marshmallow import Shema

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

class OrderShema(Shema):
    created = field(
        required=True,
        dump_default= datetime.utcnow,
        load_default=lambda x: parse(x),
        data_key='created',
        allow_non_data=True,
        dump_time=True,
        load_time=True
    )
    amount = field(
        required=True,
        dump_default=0.00,
        data_key='amount',
        allow_non_data=True,
        validate=lambda x: x == Decimal(x) and 0 <= Decimal(x) <= 100000.00
    )
    class Meta:
        strict=True

class Person:
    def __init__(self, first_name, last_name, date_of_birth, orders):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = date_of_birth
        self.orders = orders

class PersonShema(Shema):
    first_name = field(
        required=True,
        data_key='first_name',
        allow_non_data=True,
        validate=lambda x: x is not None and x.strip() != ''
    )
    last_name = field(
        required=True,
        data_key='last_name',
        allow_non_data=True,
        validate=lambda x: x is not None and x.strip() != ''
    )
    date_of_birth = field(
        required=True,
        data_key='date_of_birth',
        allow_non_data= True,
        validate=lambda x: x == parse(x)
    )
    orders = field(
        data_key='orders',
        allow_non_data= True,
        validate=lambda x: all([isinstance(i, Order) for i in x])
    )
    class Meta:
        strict=True

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

# deserialize the data
person = PersonShema().load(person_dict)

# serialize the data
print(PersonShema().dump(person))

The code creates the Order and Person classes to represent the data, as well as the OrderShema and PersonShema classes to validate and serialize/ deserialize the data. The code initializes the data in the person_dict variable, deserializes the data into a Person object using the PersonShema.load() method, and then serializes the data into a dictionary using the PersonShema.dump() method. The code also demonstrates how to parse the date and datetime strings, and how to validate the Decimal amount.

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: (5.0) (1.10¢)

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

  • The solution defines two classes, Order and Person, which serve as dataclasses. (2 marks)
  • The solution defines two schema classes, OrderSchema and PersonSchema. (2 marks)
  • The solution doesn't specify the date format for the date_of_birth field as required (format="%m/%d/%Y"). (0 marks)
  • The decimal field (amount) doesn't have as_string=True. (0 marks)
  • The schema and dataclasses are not linked with a @post_load method on the schema classes. (0 marks)
  • Other observations:
  • The solution uses Shema instead of Schema (typo).
  • It uses field instead of fields.Field.
  • It attempts to handle date parsing and validation, though not in the exact way specified.
  • It includes some validation logic for fields.
    (1 mark for partial attempt at additional functionality)

Here's the breakdown of marks:

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

Final score:

{
  "scores": [2, 2, 0, 0, 0, 1],
  "total_score": 5
}

The solution demonstrates an understanding of using Marshmallow for data validation and serialization/deserialization, but it misses some key requirements from the marking scheme. It doesn't use the specific date format for date_of_birth, doesn't set as_string=True for the decimal field, and doesn't link the schema and dataclasses using @post_load. However, it does attempt to handle date parsing and includes some validation logic, which shows some understanding of the problem beyond the basic requirements.

openai/gpt-4-0125-preview says: (2.5) ()

The provided solution aims to build a deserialization and serialization system using the Marshmallow library to handle JSON data structures and convert them into Python objects. Here's a detailed evaluation based on the criteria:

  1. Definition of two dataclasses, one for the person and one for the order (2 marks):
  2. Classes Order and Person are defined, but they are not proper dataclasses; they're regular Python classes. The solution could benefit from leveraging Python's dataclasses module.

  3. Score: 1/2

  4. Definition of two schema classes, one for the person and one for the order (2 marks):

  5. Classes OrderShema and PersonShema are provided, though there is a typo in the word Schema. Also, they're missing correct use of Marshmallow's Schema and fields.

  6. Score: 1/2

  7. Specify the date format for the date_of_birth field (format="%m/%d/%Y") (1 mark):

  8. The date_of_birth field does not use Marshmallow's fields.Date with the specified format.

  9. Score: 0/1

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

  11. The code for the amount in OrderShema does not use fields.Decimal(as_string=True) to ensure the decimal amount is serialized back into a string.

  12. Score: 0/1

  13. Schema and dataclasses should be linked with a @post_load method on the schema classes (2 marks):

  14. There are no @post_load decorators used to bind schema load operations to the construction of Order or Person objects.

  15. Score: 0/2

  16. Additional Components (2 marks):

  17. Numerous issues exist including typos "Shema" instead of "Schema", incorrect use of field instead of fields.Field, misunderstanding of date parsing (parse instead of dateutil.parser.parse), and incorrect imports and method usages.
  18. The approach shows an elementary attempt but with several critical and technical flaws.
  19. Score: 0.5/2

Given the solution's many inaccuracies and misunderstandings of the Marshmallow library's usage, the overall mastery and compliance with the problem are minimal. The resultant score is thus:

{
  "score": 2.5
}

You can use the Marshmallow library in Python to create data classes from JSON data and also specify the data types for each field. Here's an example of how you can do this for your JSON data:

First, install the Marshmallow library if you haven't already:

pip install marshmallow

Then, you can use the following code to define the data classes and their fields types:

from datetime import datetime
from dateutil importparser
import marshmallow as mm

Define the data classes

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

class Person:
def init(self, first_name, last_name, date_of_birth, orders):
self.first_name = first_name
self.last_name = last_name
self.date_of_birth = date_of_birth
self.orders = orders

Define the marshmallow schemas

class OrderSchema(mm.SQLAlchemyAutoSchema):
class Meta:
model = Order
load_instance = True
include_fk = True

    # Define the data types for the fields
    created = mm.fields.DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount = mm.fields.Decimal()

class PersonSchema(mm.SQLAlchemy AutoShadowSchema):
class Meta:
model = Person
load_instance = True
include_fk = True

    # Define the data types for the field
    first_name = mm.fields.Str()
    last_name = mm.fields.Str()
    date_of_birth = mm.fields.Date(format='%m-%d-%Y')

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

Load the JSON data

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

person_schema = PersonSchema()
person = person_schema.load(data)

print(person.date_of_birth)
print(person.orders[0].created)
print(person.orders[0].amount)

The Person and Order classes define the data classes for your JSON data. The OrderSchema and PersonSchema classes define the Marshmallow schemas for these classes, and in the Meta class, you can specify the model, whether to load the instance, include foreign keys, and most important, define the data types for each field.

After that, you can use the load method on the PersonSchema instance to parse the JSON data and convert it into the Person and Order objects.

The date_of_birth field is a date, so it's formatted as a string in the Ryan's Calendar format. The created field is a datetime, so it's formatted as an ISO-8601 string. The amount field is a decimal.

To parse the date and datetime strings, I used the dateutil.parser.parse method to convert them into the correct data types.

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: (5.0) (1.04¢)

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

  1. The solution defines two classes, Order and Person, which serve as dataclasses. (2 marks)

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

  3. The date format for the date_of_birth field is specified, but incorrectly. It uses '%m-%d-%Y' instead of '%m/%d/%Y'. (0 marks)

  4. The decimal field (amount) is not specified with as_string=True. (0 marks)

  5. The schema and dataclasses are not linked with a @post_load method. Instead, the solution uses load_instance = True in the Meta class, which is not the same as the required @post_load method. (0 marks)

  6. Additional observations:

  7. The solution uses SQLAlchemyAutoSchema, which wasn't mentioned in the problem statement.
  8. The date parsing for date_of_birth doesn't match the format in the JSON data.
  9. The solution imports dateutil.parser but doesn't use it.
  10. The solution prints the parsed data at the end, which is a good way to verify the results.

(1 mark for additional efforts)

Breakdown of marks:

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

Score:

{
  "scores": [2, 2, 0, 0, 0, 1],
  "total_score": 5
}

The solution demonstrates a good understanding of using Marshmallow for data parsing, but misses some specific requirements from the problem statement. The total score is 5 out of 10.

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

The provided solution involves the use of Python and the Marshmallow library to parse JSON data into Python objects using dataclasses and schema definitions. Here’s a breakdown based on the marking scheme:

  1. Definition of Two Dataclasses: Partially fulfilled. The solution defines Python classes, Person and Order. However, they are regular classes, not dataclasses (from dataclasses import dataclass is missing, and no dataclass decorator is used).

  2. Score: 1/2

  3. Definition of Two Schema Classes: Fulfilled. The solution includes OrderSchema and PersonSchema using Marshmallow.

  4. Score: 2/2

  5. Specifying the Date Format for date_of_birth: Fulfilled. The date format is specified correctly in PersonSchema for the date_of_birth field.

  6. Score: 1/1

  7. Decimal Field with as_string=True: Not fulfilled. The amount field in OrderSchema does not use the as_string=True property, which is crucial to serialize the decimal field as a string.

  8. Score: 0/1

  9. Linking Schema and Dataclasses with @post_load: Not fulfilled. The solution does not use the @post_load method in the schemas to return instances of the respective classes after loading the data.

  10. Score: 0/2

  11. Additional Observations:

  12. Incorrect use of SQLAlchemyAutoSchema: This implies integration with SQLAlchemy, which is not required or mentioned in the problem statement.
  13. Misuse of terminology and libraries: e.g., reference to Ryan's Calendar format which is unclear and likely incorrect; incorrect import (from dateutil importparser) that doesn't relate to code functionality as described.
  14. Code inaccuracies and typos, e.g., SQLAlchemy AutoShadowSchema seems to be a typo or error.

  15. Incomplete and inaccurate handling of type conversions for datetime and decimal types as described.

  16. Score: 0/2

Overall Evaluation:
The solution shows a basic attempt to use Marshmallow for JSON parsing into Python objects but has significant misunderstandings and errors, especially in applying correct Marshmallow practices, and fails to address several key requirements accurately.

Final score is:

{
  "score": 4
}
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