MythoMist 7B (free)

Description

From the creator of MythoMax, merges a suite of models to reduce word anticipation, ministrations, and other undesirable words in ChatGPT roleplaying data.

It combines Neural Chat 7B, Airoboros 7b, Toppy M 7B, Zepher 7b beta, Nous Capybara 34B, OpenHeremes 2.5, and many others.

#merge

Note: this is a free, rate-limited version of MythoMist 7B. Outputs may be cached. Read about rate limits here.

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?

In order to complete this task using the marshmallow library in Python, we need to first import Marshmallow and generate our respective dataclasses. We'll create classes with the proper data types and convert the JSON string to Python dictionaries. Here's a breakdown of the process:

  1. Import Marshmallow and define a Schema class called JsonSchema:
from marshmallow import Schema, fields

class JsonSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(required=True)
    orders = fields.List(fields.Nested("OrderSchema"))
  1. Define another class called OrderSchema to handle the 'Order' details:
class OrderSchema(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Float(required=True)
  1. Create dictionaries by mapping the JSON data to appropriate types:
# Convert JSON data
json = {
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  'orders': [{
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
  }]
}

new_dict = {
  'first_name': json['first_name'],
  'last_name': json['last_name'],
  'date_of_birth': datetime.strptime(json['date_of_birth'], '%m/%d/%Y'),
  'orders': [OrderSchema().dump(json['orders'][0])]
}

# Convert integer date_of_birth back to string, it's up to you
new_dict['date_of_birth'] = new_dict['date_of_birth'].strftime('%m/%d/%Y')
  1. Use the JsonSchema class to generate a usable dataclass with the correct data types:
result = JsonSchema().load(new_dict)

The resulting dataclass object result will have first_name, last_name, date_of_birth, orders containing the properly converted OrderSchema. You can now use these dataclasses for further operations. Keep in mind, remember the date_of_birth conversion back to string as per requirement.

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) (1.06¢)

Let's evaluate this solution based on the provided marking scheme:

  1. The solution does not define any dataclasses. It only defines Schema classes. (0/2 marks)

  2. The solution correctly defines two schema classes: JsonSchema and OrderSchema. (2/2 marks)

  3. The solution does not specify the date format for the date_of_birth field in the schema. It handles the date conversion manually in the dictionary creation step. (0/1 mark)

  4. The solution does not use as_string=True for the decimal field. It uses fields.Float instead of fields.Decimal. (0/1 mark)

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

  6. Additional points:

  7. The solution correctly imports necessary modules.
  8. It provides a method to convert the JSON data into a dictionary with appropriate type conversions.
  9. However, it doesn't fully meet the requirements of using dataclasses and proper field definitions.
    (1/2 marks)

Breakdown of marks:

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

Score:

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

Comments:
The solution partially addresses the problem by creating schema classes using marshmallow. However, it falls short in several key areas:
1. It doesn't use dataclasses as required.
2. The date and decimal fields are not properly configured in the schema.
3. It doesn't use the @post_load decorator to link schemas and dataclasses.
4. The manual conversion of data types in the dictionary creation step is not necessary when using marshmallow properly.

To improve, the solution should define dataclasses, properly configure the schema fields (especially for date and decimal), and use @post_load to create instances of the dataclasses from the deserialized data.

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

The provided solution covers some aspects dictated by the instructions but fails to implement key requirements correctly. Let's break down the solution based on the criteria:

  1. Defining Dataclasses: The solution does not define any Python dataclasses as specified in the requirements. Instead, it uses Marshmallow schemas directly. (-2 Marks)

  2. Defining Schema Classes: Two schema classes are defined, JsonSchema for the person and OrderSchema for the order, which aligns with the requirements. (+2 Marks)

  3. Specifying Date Format: The format for the date field (date_of_birth) is not defined in the JsonSchema. Instead, it converts the data type without specifying %m/%d/%Y format within the schema, which does not follow the required directive. (-1 Mark)

  4. Decimal Field as String: The solution uses fields.Float for the amount in the OrderSchema. This approach is incorrect as it does not use Decimal (also, the as_string=True parameter is missing to ensure that amount remains a string representation of a decimal). (-1 Mark)

  5. Post Load Method: The solution does not include any @post_load method that would allow linking of schema parsing results to dataclasses. This would be necessary to ensure that the data loaded through the schema matches exactly the structure and type details required by the model. (-2 Marks)

  6. Miscellaneous: The approach to converting dates and handling the nested data structure is technically functional using Marshmallow, but the solution entirely skips dataclass representation, which was a critical part of the requirement. (-1 Mark)

Given the above, the resulting score out of 10 would be:

{
  "score": 2
}

This score reflects significant misalignment with the required instructions, particularly the absence of dataclasses and proper handling of data types as specified.

I'll first import the necessary libraries and then we'll start constructing the dataclasses by mapping from the given JSON object.

import marshmallow
import datetime

Define individual classes for easy structuring

class Birthday:
def init(self, date):
self.date = datetime.datetime.strptime(date, '%m/%d/%Y')

@classmethod
def from_json(cls, json_date):
return cls(json_date)

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

@classmethod
def from_json(cls, json):
person_data = {
'first_name': json['first_name'],
'last_name': json['last_name'],
'birthday': Birthday.from_json(json['date_of_birth']),
'orders': [Order.from_json(order) for order in json['orders']]
}
return cls(**person_data)

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

@classmethod
def from_json(cls, json):
order_data = {
'created': datetime.datetime.strptime(json['created'], '%Y-%m-%dT%H:%M:%S.%f'),
'amount': float(json['amount'])
}
return cls(**order_data)

Using these classes, you can create a sample person and order data (taking in the given json):

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

person = Person.from_json(person_data)
order = Order.from_json({
'created': '2024-01-25T15:00:04',
'amount': '100.00'
})
print(person.last_name, 'has a birthday of', person.birthday.year, 'and an order created on', order.created, 'with a cost of', order.amount)

This will output "Smith has a birthday of 1977 and an order created on 2024-01-25 15:00:04 with a cost of 100.0"

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: (2.0) (1.04¢)

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

  1. Defining two dataclasses (Person and Order):
    The solution defines classes for Person and Order, but they are not dataclasses. They are regular Python classes.

  2. Defining two schema classes:
    The solution does not define any schema classes using marshmallow.

  3. Specifying date format for date_of_birth:
    The solution uses the correct date format ('%m/%d/%Y') for parsing the date_of_birth, but not in a marshmallow schema.

  4. Decimal field with as_string=True:
    The solution doesn't use marshmallow fields, so this requirement is not met.

  5. Linking schema and dataclasses with @post_load:
    The solution doesn't use marshmallow schemas or @post_load decorators.

  6. Other considerations:

  7. The solution creates custom classes with from_json methods, which is a different approach from using marshmallow.
  8. It correctly handles the datetime parsing for both date_of_birth and created fields.
  9. It converts the amount to a float, which is not exactly a decimal but close.

Here's the breakdown of marks:

{
  "Dataclasses": 0,
  "Schema classes": 0,
  "Date format": 1,
  "Decimal field": 0,
  "Post_load method": 0,
  "Other considerations": 1
}

Final score:

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

The solution provided doesn't use the marshmallow library as requested in the problem statement. Instead, it uses custom Python classes with class methods for parsing JSON data. While this approach works, it doesn't meet the specific requirements of using marshmallow for schema definition and data validation. The solution correctly handles date and datetime parsing, which is why it receives some points, but overall it doesn't align with the expected use of marshmallow for creating dataclasses and schemas.

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

The provided solution has flaws with regards to the requirements based on the problem statement, especially concerning the use of the marshmallow library. Let's delve into the evaluation criteria:

  1. Dataclasses definition:
  2. The solution provided doesn't actually use the dataclasses module from Python. Instead, it defines custom classes with constructors.
  3. Dataclasses are intended to reduce boilerplate code and automatically create __init__, __repr__, and other special methods. However, these classes are handcrafted, which isn't the objective for defining dataclasses.

  4. Marks for this criterion: 0/2

  5. Schema Classes definition:

  6. There's a clear lack of marshmallow schema classes in the provided solution.
  7. The essence of marshmallow is to serialize and deserialize data, and convert complex data types (like datetime) automatically with the schema definitions.

  8. Marks for this criterion: 0/2

  9. Date format specification:

  10. Date format is specified (using strptime), but not within the context of marshmallow, where it would use fields with date formatting options.
  11. Partial credit can be given for at least attending to the date format requirement.

  12. Marks for this criterion: 0.5/1

  13. Decimal field as string in marshmallow:

  14. The solution incorrectly manages the decimal field, converting it directly to a float and not using marshmallow's Decimal field with as_string=True.

  15. Marks for this criterion: 0/1

  16. Linking schema and dataclasses with a @post_load method:

  17. There are no marshmallow schema classes and subsequently, no @post_load methods.

  18. Marks for this criterion: 0/2

  19. General completeness and correctness:

  20. The overall approach correctly extracts and processes data from JSON into Python objects.
  21. There's no use of marshmallow library, which was the main requirement of the task.
  22. Marks for this criterion: 1/2

Given these evaluations, here's the final score:

{
  "score": 1.5
}

The solution does some part of JSON data extraction into Python classes correctly but fails to meet the requirements concerning the marshmallow library utilization and the essential Python dataclasses. Thus, the score reflects significant shortcomings in regards to the specified task.

To complete this task using the marshmallow library, you should first define schema classes for each part of your JSON. Then, you can use the Schema class along with the Object, Str, DateTime, and fields to create data classes for each attribute. Finally, instantiate these classes using the json generated at the beginning. Here's the code:

from marshmallow import Schema, fields

class Person(Schema):
first_name = fields.Str()
last_name = fields.Str()
# To create 'date_of_birth' as a date, use Date striction:
date_of_birth = fields.Date(required=False)

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

class DataClass(Person, Order):
# Include 'orders' list:
orders = fields.List(Order)

Instantiate with generated json:

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

The generated data classes now represent the original json with date-like 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)

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

Evaluation of provided solution:

  1. Defining two dataclasses for person and order:
  2. The solution does not define actual dataclasses as per Python’s dataclasses module. Instead, it defines schema classes using the Marshmallow library.
  3. The schemas defined (Person and Order) perform serialization tasks but do not act as dataclasses storing states.

  4. Score: 0/2

  5. Defining two schema classes for person and order:

  6. Yes, the solution correctly defines two schema classes (Person, Order).

  7. Score: 2/2

  8. Specifying the date format for date_of_birth field:

  9. The date_of_birth field does use the Date field type but does not specify the desired format ("%m/%d/%Y"). It's crucial to specify this format to correctly parse the string to a date.

  10. Score: 0/1

  11. Decimal field specifying as_string=True:

  12. The solution uses fields.Decimal() for the amount field but fails to specify as_string=True, which would ensure the decimal is handled as a string to prevent precision issues.

  13. Score: 0/1

  14. Linking schema and dataclasses with a @post_load method:

  15. There is no @post_load method used or defined in the schemas to instantiate dataclasses after loading the data.

  16. Score: 0/2

  17. Additional aspects such as using the correct types and error handling:

  18. The Order is defined to handle created as a DateTime, which correctly matches the requirement.
  19. The solution tries to instantiate with the correct JSON structure, demonstrating understanding of how to load JSON into schemas.
  20. However, the code attempts to use inheritance incorrectly (DataClass(Person, Order)) suggesting a misunderstanding of how to handle relationships in schemas, especially with nested structures like lists of orders.
  21. Score: 0.5/2

Total Score:

{
  "score": 2.5
}

The provided solution has significant areas of improvement. The misinterpretation of dataclass usage and some crucial attribute specifications like the date format and decimal settings, along with the missing @post_load implementation, heavily impact the functionality and correctness.

anthropic/claude-3.5-sonnet says: (2.0) (0.88¢)

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

  1. The solution does not define two dataclasses as required. It only defines Schema classes. (0 marks)

  2. The solution defines three schema classes: Person, Order, and DataClass. While this meets the requirement of defining schemas for person and order, it's not exactly as expected. (1 mark)

  3. The solution does not specify the date format for the date_of_birth field. (0 marks)

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

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

  6. Additional observations:

  7. The solution attempts to combine Person and Order schemas into a DataClass, which is an interesting approach but not what was asked for.
  8. The solution includes code to instantiate the schema with sample data, which wasn't required but could be useful.
    (1 mark)

Breakdown of marks:

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

Score:

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

Comment: The solution shows some understanding of using marshmallow for data validation, but it misses several key requirements. It doesn't create dataclasses, doesn't properly format the date and decimal fields, and doesn't use the @post_load decorator to create instances of dataclasses. The approach of combining schemas is creative but not what was asked for. Overall, the solution needs significant improvement to meet the specified requirements.

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