Airoboros 70B

Description

A Llama 2 70B fine-tune using synthetic data (the Airoboros dataset).

Currently based on jondurbin/airoboros-l2-70b, but might get updated in the future.

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 use marshmallow to convert the JSON data into Python objects by defining dataclasses and schemas. Here's an example of how you could do this:

from datetime import date, datetime
from typing import List

import marshmallow

class UserSchema(marshmallow.Schema):
first_name = marshmallow.fields.Str()
last_name = marshmallow.fields.Str()
date_of_birth = marshmallow.fields.Date()
created = marshmallow.fields.DateTime()
amount = marshmallow.fields.Decimal()

class OrderSchema(marshmallow.Schema):
first_name = marshmallow.fields.Str()
last_name = marshmallow.fields.Str()
date_of_birth = marshmallow.fields.Date()
created = marshmallow.fields.DateTime()
amount = marshmallow.fields.Decimal()

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 = date_of_birth
self.orders = orders

class Meta:
    schema = UserSchema()

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

class Meta:
    schema = OrderSchema()

if name == "main":
# sample 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"
}
]
}

# load the data into a user object
user_data = UserSchema().load(data)

# convert the data into a User object
user = User(**user_data)

# print the User object
print(user.as_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) (1.01¢)

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

The solution defines two classes (User and Order), but they are not dataclasses. (1/2 marks)
The solution defines two schema classes (UserSchema and OrderSchema). (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 schema and classes are not linked with a @post_load method. Instead, they use a Meta class, which is not the correct approach. (0/2 marks)
Additional points:
The Order schema incorrectly includes first_name, last_name, and date_of_birth fields.
The User class has an 'orders' attribute, but it's not present in the UserSchema.
The solution attempts to use an 'as_json()' method which is not defined.
The solution imports 'List' from typing but doesn't use it.
(1/2 marks)

Here's the breakdown of marks:

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

Final score:

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

The solution demonstrates an understanding of using marshmallow for data serialization, but it misses several key requirements and contains some errors. It doesn't use dataclasses as requested, doesn't properly handle the date and decimal fields, and doesn't use the @post_load decorator to link schemas with classes. The Order schema also includes unnecessary fields. Overall, while it's a start, the solution needs significant improvements to fully meet the requirements.

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

The provided solution contains multiple issues and misalignments with the requirements specified. Here is a detailed evaluation based on the marking scheme:

Dataclasses for Person and Order (2 marks):
The provided solution defines Python classes named User and Order, which could serve as data classes, but they are not explicitly defined using the @dataclass decorator from Python's dataclasses module.
Score: 1/2 (As the complete data class implementation using the @dataclass decorator is missing).
Schema classes for Person and Order (2 marks):
Two schema classes UserSchema and OrderSchema are defined, but they are incorrectly set up. Each schema seems to define fields for both User and Order, which is incorrect.
Score: 1/2 (Classes are present but not correctly implemented).
Date format specification for date_of_birth (1 mark):
The date format for date_of_birth needs to be defined as "%m/%d/%Y", but this is not specified in the UserSchema.
Score: 0/1 (Date format not specified).
Decimal field with as_string=True (1 mark):
amount is included as a Decimal field in the schemas. However, nowhere in the schema is the as_string=True argument used to ensure it serializes correctly.
Score: 0/1 (Missing as_string=True configuration).
Link Schema and Dataclasses with @post_load (2 marks):
The @post_load method, which is crucial for converting loaded data into an instance of the appropriate dataclass, is missing.
Score: 0/2 (No @post_load method implementation).
Miscellaneous Completeness (2 marks):
The solution has shown an attempt to create and use APIs from the Marshmallow library, albeit with significant inaccuracies.
A Meta class is used in an unconventional way within Python classes, which is ineffective for the intended Marshmallow functionality.
The final instance creation logic (User(**user_data)) will likely fail due to schema errors and the absence of Order handling in User's constructor.
print(user.as_json()) is incorrect because as_json is not defined in the User class.
Score: 0.5/2 (For attempted usage and structure but significant errors in syntax and approach).

Overall, this solution holds numerous flaws in terms of logic, syntax, and an understanding of how Marshmallow should be used with Python’s data classes and schema handling.

{
  "score": 2.5
}

Response 2 (3.0)

To create dataclasses from the provided JSON using marshmallow library, you can first install the marshmallow library if you haven't already:

pip install marshmallow

Then, in your code, define the appropriate schemas and classes for each type of object as follows:

import marshmallow as ma
from datetime import date, datetime

class DateSchema(ma.Schema):
    class Meta:
        type_ = date

class DecimalSchema(ma.Schema):
    class Meta:
        type_ = float

class OrderSchema(ma.Schema):
    created = ma.fields.DateTime(as_datetime=True)
    amount = ma.fields.Decimal()

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

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 = date_of_birth
        self.orders = orders

    class Config:
        schema_class = UserSchema

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

user = User(**user_dict)

# Marshal the object to a dictionary
user_dict_from_schema = user.schema(). dump(user)

# Unmarshal the dictionary back to an object
user = user.schema(). load(user_dict_from_schema)

print(user_dict_from_schema)
print(user)

This code defines three schemas: DateSchema, DecimalSchema, and OrderSchema. OrderSchema has two fields: created, which is a DateTime, and amount, which is a Decimal. UserSchema nests the OrderSchema and adds the fields first_name, last_name, and date_of_birth.

The User class takes four arguments in its constructor and configures its schema. The if __name__ == '__main__' block creates a user object from the provided dictionary, dumps it to a dictionary using user.schema().dump(user), and then unmarshals it back into an object using user.schema().load(user_dict_from_schema). Finally, both the marshaled and unmarshaled objects are printed.

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) (1.03¢)

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

The solution defines one dataclass (User) instead of two as required. It's missing a dataclass for the order. (1/2 marks)
The solution defines three schema classes: DateSchema, DecimalSchema, and OrderSchema. It's missing a separate UserSchema. (1/2 marks)
The solution doesn't specify the date format for the date_of_birth field. (0/1 mark)
The decimal field doesn't have as_string=True. (0/1 mark)
The solution doesn't use @post_load method to link schema and dataclasses. (0/2 marks)
Additional points:
The solution uses a Config class within User, which is not standard for dataclasses.
It defines additional schemas (DateSchema, DecimalSchema) which weren't required.
It includes a main block to demonstrate usage, which is helpful.
(1/2 marks)

Breakdown of marks:

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

Final score:

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

The solution demonstrates some understanding of marshmallow and how to use it with classes, but it misses several key requirements from the problem statement. It doesn't use dataclasses as requested, doesn't properly format the date and decimal fields, and doesn't use the @post_load decorator to link schemas and classes. The approach of using a Config class within User is not typical for dataclasses and marshmallow integration. Overall, while the solution shows some knowledge, it needs significant improvements to fully meet the requirements.

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

The provided solution attempts to use the marshmallow library to convert JSON data into Python objects using dataclasses and schema. Here's an evaluation based on the specified marking scheme:

Defining two dataclasses, one for the person and one for the order:
The solution defines only one Python class (User) that is not a dataclass.
There is no Order dataclass defined.
Score: 0/2
Defining two schema classes, one for the person and one for the order:
The solution correctly defines OrderSchema and UserSchema.
However, DateSchema and DecimalSchema are erroneous and do not properly handle their respective types.
Score: 1/2
Specifying the date format for the date_of_birth field.
The solution does not specify the date format ("%m/%d/%Y") as required.
Score: 0/1
The decimal field needs as_string=True.
The solution includes a decimal field but does not use the as_string=True attribute necessary for correct serialization.
Score: 0/1
Linking the schema and dataclasses using a @post_load method.
There are no @post_load methods used to handle the loading of the data into dataclass objects or Python class objects.
The schemas are not correctly linked to properly initialize complex nested structures.
Score: 0/2
Anything else:
The solution correctly uses nested schemas for handling nested data structures.
Use of marshmallow for serialization is depicted, though with some incorrect implementations (like the absence of handling for dates and decimals specifically).
Score: 1/2

Overall, the solution misses several critical components of effective data serialization as specified in the problem, such as the incorrect handling of date and decimal types, missing @post_load methods for dataclass-object creation, and improper usage of dataclasses.

{
  "score": 2
}

Response 3 (7.0)

Here's a basic example of how you could use the marshmallow library to deserialize the provided JSON into dataclasses:

from datetime import datetime
from dataclasses import dataclass
from marshmallow import Schema, fields, post_load
import dateutil.parser

@dataclass
class Order:
    created: datetime
    amount: decimal

class OrderSchema(Schema):
    created = fields.Field(load=dateutil.parser.parse)
    amount = fields.Decimal()

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

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

class UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Field(load=dateutil.parser.parse)
    orders = fields.Nested(OrderSchema, many=True)

    @post_load
    def make_user(self, data):
        return User(**data)

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

user_schema = UserSchema()
user = user_schema.load(user_dict)

print(user.first_name) # Jane
print(user.last_name) # Smith
print(user.date_of_birth) # <datetime obj>
print(user.orders[0].created) # <datetime obj>
print(user.orders[0].amount) # 100.0

In this example, I've defined the User and Order dataclasses, as well as their respective schemas (using the Marshmallow library). Then, I've used the load() method of the UserSchema class to deserialize the provided JSON into a User object.

The post_load decorator is used to convert the raw data from the JSON into the actual dataclass objects.

Note that you'll need to import dateutil for the date parsing to work.

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: (7.0) (0.99¢)

Let's evaluate this solution based on the given 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 uses dateutil.parser.parse instead of fields.Date(format="%m/%d/%Y"). (0 marks)
The decimal field does not have as_string=True. (0 marks)
The schema and dataclasses are linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution uses dateutil.parser.parse for parsing dates, which is flexible but may not always produce the desired result.
The solution imports necessary modules and demonstrates how to use the schema to load data.
(1 mark)

Breakdown of marks:

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

Score:

{
  "scores": [2, 2, 0, 0, 2, 1],
  "total_score": 7
}

Comments:
The solution provides a good basic structure for using marshmallow with dataclasses. However, it misses some specific requirements:
1. The date_of_birth field should use a specific format ("%m/%d/%Y").
2. The amount field should be defined with as_string=True.
These omissions could lead to parsing errors or unexpected behavior. Additionally, while using dateutil.parser.parse is flexible, it may not always interpret dates in the expected way, especially for ambiguous formats.

openai/gpt-4-0125-preview says: (7.0) ()

Review of the provided solution:

Dataclass Definitions (1.5/2 marks):
The solution defines dataclasses Order and User, which is correct.
However, there's an error in the Order dataclass: decimal is referenced but not imported or defined as decimal.Decimal.
Schema Definitions (2/2 marks):
Correctly defines OrderSchema and UserSchema as subclasses of Schema.
Date Format Specification (0.5/1 marks):
Instead of specifying the date format for date_of_birth with format="%m/%d/%Y", the solution uses fields.Field with a custom load method calling dateutil.parser.parse. While dateutil.parser.parse can handle a variety of date formats, it is not strictly enforcing the specific format as required.
Decimal Field Specification (0/1 marks):
The amount field in OrderSchema is not set with as_string=True, which is necessary per requirements to ensure that the decimal is treated correctly as per Marshmallow’s handling of decimal fields.
Linking Schema and Dataclasses with @post_load (2/2 marks):
Correctly uses the @post_load method in both OrderSchema and UserSchema to properly instantiate the dataclasses from parsed data, fulfilling the requirement perfectly.
Anything Else (1/2 marks):
There is a logical attempt to parse datetime objects correctly using dateutil.parser.parse.
The solution attempts to handle nested data structures with a fields.Nested in UserSchema.
An error for not importing decimal properly and specifying how decimals should be treated diminishes the score.

Overall, the solution shows a good understanding of the Marshmallow library and its primary functionalities but falls short in several critical areas, especially in precisely matching the format requirement and handling decimals.

Final Score:

{
  "score": 7
}

1.00	0.86	0.88
0.86	1.00	0.85
0.88	0.85	1.00

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