Description

Xwin-LM aims to develop and open-source alignment tech for LLMs. Our first release, built-upon on the Llama2 base models, ranked TOP-1 on AlpacaEval. Notably, it's the first to surpass GPT-4 on this benchmark. The project will be continuously updated.

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)

To achieve this with the marshmallow library, you'll first need to install it:

pip install marshmallow

Next, you can create a Python script to define the data classes and use marshmallow to load and dump the JSON data. Here's an example:

import json
from marshmallow import Schema, fields, pendulum, decimal
from dateutil.parser import parse


# Define the dataclass
class Customer(object):
    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 schema
class CustomerSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.Nested(OrderSchema, many=True)


class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal()
# Load and dump the JSON data
data = json.loads(json_data)
customer = CustomerSchema().load(data)
print(customer.first_name, customer.last_name, customer.date_of_birth, customer.orders)

Make sure to replace json_data with the JSON string you provided. The pendulum and decimal libraries are required for the DateTime and Decimal fields, so remember to install them if you haven't already:
```bash
pip install pendulum decimal

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: (2.0) ()

Evaluation

Dataclasses Definition:
The solution mentions defining a Customer class which captures a person's details. However, it is not using Python's dataclasses, instead, it uses a traditional class with an explicit __init__ method.
There is no explicit data class for Order, so this is missing entirely.
Score: 0/2
Schema Classes Definition:
The solution correctly defines required schema classes for a person (CustomerSchema) and an order (OrderSchema).
Score: 2/2
Date Format Specification:
The date_of_birth field is specified as fields.Date(), but the specified date format ("%m/%d/%Y") is not set in the solution.
Score: 0/1
Decimal field with as_string=True:
OrderSchema defines an amount as fields.Decimal(). However, the solution does not include the as_string=True requirement to ensure amount is treated as a string.
Score: 0/1
Link schema and dataclasses with @post_load:
The solution omits a @post_load decorator and related method, which is necessary for creating an object of the data class upon schema loading.
Score: 0/2
Miscellaneous:
The solution would have benefited from using dataclasses module available in Python. The usage of pendulum and decimal as imports are incorrect. The correct import for marshmallow should be used for fields.Decimal and fields.DateTime.
Error in referencing OrderSchema inside CustomerSchema before being defined, which could lead to execution errors.
Incorrect syntax and potentially misleading comments regarding the installation of 'pendulum' and 'decimal' without their relevance to marshmallow directly. These are not packages typically installed separately like this.
Score: 0/2

Final Evaluation Score:

{
  "score": 2
}

anthropic/claude-3.5-sonnet says: (4.0) (0.83¢)

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

The solution defines one dataclass (Customer) but not a separate one for orders. (1/2 marks)
The solution defines two schema classes (CustomerSchema 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 dataclasses are not linked with a @post_load method. (0/2 marks)
Additional observations (1/2 marks):
The solution imports necessary libraries.
It includes a basic implementation for loading and printing data.
However, it's missing error handling and proper initialization of the Order class.

Here's the breakdown of marks:

{
  "Dataclass definition": 1,
  "Schema classes": 2,
  "Date format specification": 0,
  "Decimal field configuration": 0,
  "Schema-dataclass linking": 0,
  "Additional elements": 1
}

Final score:

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

The solution provides a basic structure but misses several key requirements from the marking scheme. It defines one dataclass instead of two, lacks proper date format specification, doesn't configure the decimal field correctly, and doesn't link the schema and dataclasses using @post_load. These omissions significantly impact its effectiveness in handling the specified JSON data accurately.

Response 2 (6.0)

To achieve this using the Marshmallow library, first, you need to install it:

pip install marshmallow

Then, create the following dataclasses based on the provided JSON structure:
```python
import marshmallow
from marshmallow import Schema, fields, pydantic, post_load
from datetime import datetime
from dateutil.parser import parse

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

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

@post_load
def post_process_data(self, data):
    user_dict = data["value"]
    return User(**user_dict)

class User:
def init(self, first_name: str, last_name: str, date_of_birth: datetime.date, orders: list[Order]):
self.first_name = first_name
self.last_name = last_name
self.date_of_irth = date_of_birth
self.orders = orders

def to_dict(self):
    return {
        "first_name": self.first_name,
        "last_name": self.last_name,
        "date_of_birth": self.date_of_birth.strftime("%Y-%m-%d-%Y"),
        "orders": [order.to_dict() for order in self.orders]
    }

Usage Example

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: (3.0) ()

Evaluation of the Provided Solution:

Dataclass Definition:
This solution defines one Python class (User), assuming to represent a dataclass. However, the class is not using the dataclasses.dataclass decorator from the dataclasses module, which is mandatory for defining dataclasses.
The Order class is missing entirely.
Score: 0/2
Schema Definition:
The schemas (OrderSchema and UserSchema) are defined correctly using marshmallow.Schema.
Score: 2/2
Date Format Specification:
The date format (date_of_birth) is specified using fields.Date(), but there is no format string ("%m/%d/%Y") provided. This is critical for ensuring proper serialization/deserialization of the date given in the JSON.
Score: 0/1
Decimal Field Handling:
The amount in OrderSchema uses fields.Decimal(), but it does not include the as_string=True parameter. This is crucial for ensuring the decimal field is properly handled as a string to avoid precision loss in JSON.
Score: 0/1
Post Load Link Between Schema and Dataclass:
A @post_load method is defined. However, the method appears to incorrectly attempt to unpack the data ("value" key which is not present in the data).
The User class is not correctly instantiated in the post_load (typo in date field, missing link to appropriate class definitions).
Issues in defining and using order.to_dict() as the Order class is not defined.
Score: 0.5/2
Additional Considerations:
There are significant errors in the implementation details (e.g., typographical errors like self.date_of_irth).
Attributing methods like to_dict() without appropriate underlying definitions or complete logic.
@post_load logic and instance creation are not well-linked due to errors in the method definition.
Score: 0.5/2

Final Score:

{
  "score": 3/10
}

This score reflects critical issues with dataclass definitions, formatting specifications for date and decimal, and ineffective or incorrect usage of marshmallow's features for properly parsing and loading JSON into Python objects.

anthropic/claude-3.5-sonnet says: (6.0) (0.89¢)

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

The solution defines two classes: User and Order. However, Order is not fully implemented as a dataclass. (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 (amount) is not specified with as_string=True. (0/1 mark)
The schema and dataclasses are linked with a @post_load method on the UserSchema class. However, it's not implemented for OrderSchema. (1/2 marks)
Additional observations (2/2 marks):
The solution imports unnecessary modules (pydantic, dateutil.parser).
There's a typo in the User class: date_of_irth instead of date_of_birth.
The Order class is missing entirely.
The to_dict method in the User class uses an incorrect date format.

Here's the breakdown of marks:

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

Final score:

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

The solution implements some of the required elements but has several missing or incorrect components. It scores 6 out of 10 based on the provided marking scheme.

Response 3 (4.0)

To achieve this using the marshmallow library in Python, first, you need to install the library if you haven't already:

pip install marshmallow

Next, create dataclasses and define the models for the JSON data:

from marshmallow import Schema, fields, pydantic, post_load, pre_load
from datetime import datetime, date
from decimal import Decimal


class OrderSchema(Schema):
    created = fields.DateTime(data_key="created")
    amount = fields.Decimal(data_key="amount")


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



@post_load
def post_load_order(data):
    return data["orders"]



@pre_load
def pre_load_order(data):
    if isinstance(data, dict):
        return [{"created": data["created"], "amount": data["amount"]}



data = {
    "first_name": "Jane",
    "last_name": "Smith",
    "date_of_birth": "03/16/1977",
    "orders": [
        {"created": "2024-01-25T15:000:04", "amount": "10.00"
    ]
}


result = UserSchema().load(data, many=True)
print(result)

In this example, we import the

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) (0.92¢)

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

Defining two dataclasses:
The solution doesn't define any dataclasses. It only defines schemas.
Defining two schema classes:
The solution correctly defines two schema classes: OrderSchema and UserSchema.
Specifying date format for date_of_birth:
The solution doesn't specify the date format for the date_of_birth field.
Decimal field with as_string=True:
The solution doesn't include as_string=True for the decimal field.
Linking schema and dataclasses with @post_load:
The solution includes a @post_load decorator, but it's not correctly implemented to create dataclass instances.
Additional points:
The solution includes a @pre_load decorator, which is a good addition for data preprocessing.
It imports pydantic, which is not used in the solution.
The solution attempts to handle the JSON data, which is a good step.

Here's the breakdown of marks:

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

Score:

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

Comments:
The solution partially addresses the problem but misses several key requirements. It defines the schemas correctly but doesn't create dataclasses. The date format for date_of_birth and the as_string=True for the decimal field are missing. The @post_load decorator is present but not correctly implemented to create dataclass instances. The addition of @pre_load is a good touch, but overall, the solution needs significant improvements to meet all the requirements.

openai/gpt-4-0125-preview says: (1.0) ()

The provided solution does aim to use the marshmallow library in Python to parse a JSON structure into Python objects using dataclasses and schemas. However, there are several critical issues and omissions in the solution.

Definition of Dataclasses: The solution does not contain any Python dataclasses. The use of standard classes or attrs is not attempted either. As such, it fails to meet the requirement of creating dataclasses for the person and the order (0/2 marks).
Definition of Schema Classes: Two schema classes (OrderSchema and UserSchema) are defined correctly according to the provided JSON structure. However, these are not linked with dataclasses as it should be for a better structured deserialization process (1/2 marks).
Date Format Specification: The schema for date_of_birth is correctly declared to use the fields.Date() field, but the specific required date format ("%m/%d/%Y") is not explicitly specified in the schema definition (0/1 mark).
Decimal Field Specification: The amount in OrderSchema uses fields.Decimal() but does not set as_string=True, which is necessary to ensure the amount remains in string format after loading (0/1 mark).
Linking Schema with Dataclasses (@post_load method): There is an attempt to use @post_load and @pre_load decorators, but it is incorrectly implemented, and the dataclasses are absent. The related functionality is thus not functional. Also, post_load_order doesn't return a class instance of data (0/2 marks).
Additional Considerations: There are several additional issues:
No dataclass-like structures created.
Incorrect pre_load usage leading possibly to runtime errors.
Error in JSON definition under the orders field ("missing curly brace").
Typographical error in the timestamp string format in JSON data.
Usage of field name many in wrong context.
Using load with many=True incorrectly considering it's not a list of users but a single user with a list of orders.

These additional faults further negatively impact the usability of the solution (0/2 marks).

Overall, the solution shows an understanding of some concepts from the marshmallow library but lacks correct implementation, particularly around dataclasses integration, correct schema settings, and functionality of decorators.

Final Score:

{
  "score": 1
}

1.00	0.82	0.85
0.82	1.00	0.94
0.85	0.94	1.00

Xwin 70B

Description

Usage Example

Evaluation of the Provided Solution:

Final Score:

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