OpenChat 3.5 7B (free)

Description

OpenChat 7B is a library of open-source language models, fine-tuned with "C-RLFT (Conditioned Reinforcement Learning Fine-Tuning)" - a strategy inspired by offline reinforcement learning. It has been trained on mixed-quality data without preference labels.

For OpenChat fine-tuned on Mistral 7B, check out OpenChat 7B.
For OpenChat fine-tuned on Llama 8B, check out OpenChat 8B.

#open-source

Note: this is a free, rate-limited version of OpenChat 3.5 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?

Response 1 (7.0)

You can use the marshmallow library in Python to generate dataclasses from the given JSON. First, you'll need to install the library if you haven't already:

pip install marshmallow

Next, you can use the marshmallow and marshmallow_dataclass libraries to define a dataclass and create a instance from your input JSON.

# First, install marshmallow_dataclass
pip install marshmallow-dataclass

from marshmallow import Schema, fields
from marshmallow_dataclass import dataclass
from datetime import datetime
from dateutil.parser import parse
import json
from decimal import Decimal

# Define dataclass
@dataclass
class PersonOrder:
    first_name: str
    last_name: str
    date_of_birth: datetime
    orders: list[dict]

class Order:
    created: datetime
    amount: Decimal

    def __init__(self, **kwargs) -> None:
        self.created = parse(kwargs.get('created'))
        self.amount = Decimal(kwargs.get('amount'))

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(format='%m/%d/%Y')
    orders = fields.List(fields.Nested(OrderSchema), allow_unused=True)

class OrderSchema(Schema):
    created = fields.DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount = fields.Decimal(as_string=True)

person_schema = PersonSchema()
order_schema = OrderSchema()

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

# Load JSON into dataclass
person = person_schema.load(json.loads(input_json))

# Print dataclass
print(person)

This example will generate a dataclass from the input JSON, with the specified types for the fields. The date_of_birth field is parsed as a datetime object, the created field as a datetime object, and the amount field as a 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)

anthropic/claude-3.5-sonnet says: (7.0) (0.92¢)

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

The solution defines two dataclasses: PersonOrder and Order. (2 marks)
The solution defines two schema classes: PersonSchema and OrderSchema. (2 marks)
The solution specifies the date format for the date_of_birth field correctly: fields.Date(format='%m/%d/%Y'). (1 mark)
The decimal field is correctly defined with as_string=True: fields.Decimal(as_string=True). (1 mark)
The solution does not implement a @post_load method to link the schema and dataclasses. (0 marks)
Additional observations:
The solution uses marshmallow_dataclass, which is a good approach.
The Order class is defined but not used as a dataclass.
The solution demonstrates how to load JSON data into the schema.
(1 mark)

Breakdown of marks:

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

Score:

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

The solution covers most of the required elements but lacks the @post_load method to link the schema and dataclasses. It also has a minor inconsistency with the Order class not being used as a dataclass. Overall, it's a good attempt that addresses most of the requirements.

openai/gpt-4-0125-preview says: (5.0) ()

This solution attempts to use the Python 'marshmallow' library and the 'marshmallow_dataclass' extension to deserialize JSON into Python dataclasses. The overall objective is to transform the JSON input into Python objects with correct data types through the schema definitions provided. Let's evaluate the solution against the given marking scheme:

Two dataclasses definition (2 marks):
Two dataclasses (PersonOrder and Order) are defined in the solution, but it's inaccurate. It awkwardly merges person data and order as a single dataclass which should have been separated as Person and Order.
Marks: 1/2
Two schema classes definition (2 marks):
The solution defines two schema classes (PersonSchema and OrderSchema), aligning with the requirement.
Marks: 2/2
Date format for 'date_of_birth' field (1 mark):
The format is appropriately specified in PersonSchema for date_of_birth using %m/%d/%Y.
Marks: 1/1
Decimal field 'as_string=True' (1 mark):
Correctly uses fields.Decimal(as_string=True) in OrderSchema for amount.
Marks: 1/1
@post_load method linking schemas and dataclasses (2 marks):
The provided solution lacks the implementation of @post_load methods which are pivotal for marshaling JSON data into a specific structure of dataclasses post-deserialization.
Marks: 0/2
Additional criteria (2 marks):
The Python script erroneously defines PersonOrder to include both personal and order details, which should be separate for clarity and reusability.
The presence of an inappropriate allow_unused argument in the schema definition; it appears to be a mistake, possibly meant to be load_only or dump_only.
Incorrect implementation and setup in the used decorator @dataclass, should be more specifically tailored for marshmallow's utility, particularly concerning the nesting of the Order schema within PersonSchema.
Marks: 0/2

Conclusion

The solution demonstrates a partial but flawed understanding of how to use marshmallow for deserializing JSON into properly structured Python dataclasses while observing specific data type requirements. The primary issues include incorrect dataclass structuring, missing @post_load method logic, and confusion concerning the decoration and utilization of schemas.

Score:

{
  "score": 5/10
}

Response 2 (5.0)

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

pip install marshmallow

Then, you can create the dataclasses by writing a Python script. You'll need to use the pydantic library in addition to marshmallow to add type annotations and enforce them:

pip install pydantic

Here is the Python script that creates the dataclasses and handles the JSON input:

from marshmallow import Schema, fields
from marshmallow.utils import literal
from marshmallow.validate import Length, Email
from pydantic import BaseModel
import datetime
from decimal import Decimal

# Custom datetime field for marshmallow
class CustomDateTime(fields.Field):
    def _serialize(self, value, attr, obj, **kws):
        return value.isoformat()

# Custom decimal field for marshmallow
class CustomDecimal(fields.Field):
    def _serialize(self, value, attr, obj, **kws):
        return str(value)

class Order(BaseModel):
    created: datetime.datetime
    amount: Decimal

class Customer(BaseModel):
    first_name: str
    last_name: str
    date_of_birth: datetime.date
    orders: list[Order]

# Marshmallow Schema for the Customer dataclass
class CustomerSchema(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), required=True)
    created = CustomDateTime()
    amount = CustomDecimal()

# Load the JSON data into the Customer dataclass
json_data = {
    "first_name": "Jane",
    "last_name": "Smith",
    "date_of_birth": "1977-03-16",
    "orders": [
        {
            "created": "2024-01-25T15:00:04Z",
            "amount": 100.00
        }
    ]
}

schema = CustomerSchema()
customer = schema.load(json_data)

# Accessing classes and schema attributes
print(customer.first_name)  # Jane
print(customer.last_name)   # Smith
print(customer.date_of_birth)  # 1977-03-16
print(customer.orders[0].created)  # 2024-01-25T15:00:04+00:00
print(customer.orders[0].amount)  # 100.00

Note that in Python, date and datetime are different. If a date is required as per your specific problem, the date of birth should be of type "date" and not "datetime". An example is '03/16/1977'. But marshmallow does not natively support working with string dates to convert them into a date object, so you would need to convert that string into a date before parsing.

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 dataclasses, Order and Customer, using pydantic's BaseModel. (2 marks)
The solution defines two schema classes, CustomerSchema and OrderSchema. However, OrderSchema is not fully implemented. (1 mark)
The solution does not specify the date format for the date_of_birth field as requested. (0 marks)
The solution does not use as_string=True for the decimal field. Instead, it creates a custom CustomDecimal field. (0 marks)
The solution does not implement @post_load methods to link schemas and dataclasses. (0 marks)
Additional points:
The solution uses pydantic for type annotations, which is a good practice. (1 mark)
Custom fields are created for datetime and decimal, showing understanding of field customization. (1 mark)

Here's the breakdown of marks:

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

Final score:

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

Comments:
The solution shows a good understanding of dataclasses and schema creation, but it misses some key requirements. It uses pydantic instead of pure dataclasses, which is not strictly wrong but deviates from the expected solution using marshmallow. The date format for date_of_birth is not specified as requested, and the decimal field handling doesn't use the required as_string=True. The @post_load decorator is missing, which is crucial for linking schemas and dataclasses in marshmallow. While the custom fields show good initiative, they don't fully meet the specified requirements. Overall, the solution demonstrates partial understanding but needs refinement to fully address the problem as stated.

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

The provided solution demonstrates an attempt to use both the marshmallow and pydantic libraries to parse JSON data into Python objects with specific types. However, there are several critical issues and divergences from the requirements that impact the score:

Dataclasses Definition (1/2 marks):
The solution used pydantic for dataclasses (BaseModel) which is not in accordance with the requirement to define dataclasses directly, i.e., using Python's built-in dataclasses.dataclass.
Data types like datetime and Decimal are correctly aligned with requirement specifications, but the approach does not utilize the native dataclasses module.
Schema Classes Definition (1/2 marks):
CustomerSchema is defined using marshmallow, which is correct.
However, OrderSchema is mentioned but not defined, causing potential errors in the code.
Date Format Specification (0/1 marks):
The date_of_birth field should specifically parse dates in the format "%m/%d/%Y", but the code does not mention this format anywhere and uses an incorrect date from JSON input ("1977-03-16"), differing from the problem statement ("03/16/1977").
Decimal Field Handling (0/1 marks):
The requirement is to use as_string=True with the Decimal field in the schema, but the CustomDecimal class defined in the solution does not conform to this specification.
Linking Schema with Dataclasses via @post_load (0/2 marks):
The solution does not use @post_load to connect the schema parsing results to dataclass constructions. Using pydantic’s BaseModel does not involve the @post_load decorator, which is specific to marshmallow.
Additional Considerations (1/2 marks):
The developer correctly differentiated between date and datetime types.
Conversion hints for date strings were acknowledged but not implemented.
Installation instructions and custom serialization methods (CustomDateTime, CustomDecimal) were provided but slightly misaligned with requirements.

Given these observations, the evaluation of the solution leads to a reduced score, primarily due to not meeting several essential criteria outlined in the problem description.

{
  "score": 3
}

Response 3 (9.0)

To solve this problem, you will have to use dataclasses to create the data structures and marshmallow to serialize and deserialize the JSON. The marshmallow library provides types such as Date for the date field and Decimal for the amount field. Here's how you can do it:

First, install the required packages if you haven't already:

pip install marshmallow
pip install marshmallow-dataclasses

Then, you can create your data classes and serializers:

from dataclasses import dataclass, field
from typing import List
from datetime import datetime
from decimal import Decimal
from marshmallow import Schema, fields, post_load

# Define your data classes
@dataclass
class Order:
    date_created: datetime
    amount: Decimal = field(metadata={"data": "decimal"})

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: datetime
    orders: List[Order] = field(default_factory=list)

# Define your serializers
class OrderSchema(Schema):
    date_created = fields.DateTime(format="%Y-%m-%d")
    amount = fields.Decimal(as_string=True, places=2)

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(format="%m/%d/%Y")
    orders = fields.Nested(OrderSchema, many=True)

    @post_load
    def make_person(self, data, **kwargs):
        return Person(**data)

# Load and parse JSON
input_json = '''
{
  "first_name": "Jane",
  "last_name": "Smith",
  "date_of_birth": "03/16/1977",
  "orders": [
    {
      "created": "2024-01-25T15:00:00",
      "amount": "100.00"
    }
  ]
}
'''

person_schema = PersonSchema()
person_data = person_schema.load(input_json)
print(person_data)

In this example, the Order and Person data classes contain the fields specified in your JSON. The OrderSchema and PersonSchema represent the corresponding serializers. The @post_load decorator allows you to define how the deserialization should be performed, ensuring that the loaded data is converted into a Person object with the right classes.

Please replace the input_json variable with the actual JSON string you want to deserialize. The printed output will be a Person object with the data extracted from your 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: (9.0) (1.06¢)

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

The solution defines two dataclasses, Order and Person. (2 marks)
The solution defines two schema classes, OrderSchema and PersonSchema. (2 marks)
The solution specifies the date format for the date_of_birth field using format="%m/%d/%Y". (1 mark)
The decimal field (amount) uses as_string=True. (1 mark)
The schema and dataclasses are linked with a @post_load method on the PersonSchema class. However, it's missing for the OrderSchema. (1 mark)
Additional points:
The solution imports necessary libraries.
It provides instructions for installing required packages.
It includes a sample JSON and demonstrates how to use the schema to load data.
(2 marks)

Breakdown of marks:

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

Total score:

{
  "scores": [2, 2, 1, 1, 1, 2],
  "total_score": 9
}

The solution is generally good and covers most of the requirements. It correctly defines the dataclasses and schema classes, uses the proper date format for date_of_birth, and sets as_string=True for the decimal field.

The main point of improvement would be to add a @post_load method to the OrderSchema class to ensure that Order objects are created when deserializing. Additionally, the date_created field in the OrderSchema should use "%Y-%m-%dT%H:%M:%S" as the format to match the input JSON.

Overall, the solution demonstrates a good understanding of using marshmallow with dataclasses for JSON serialization and deserialization.

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

The provided solution touches on most of the requirements laid out in the prompt but contains inconsistencies and errors which affect its correctness in adhering to the specified details:

Data Classes (2 marks):
Data classes Order and Person are correctly defined, making use of Python's dataclasses library.
Fields specified in the JSON are represented in the data classes with appropriate types (datetime for date_created and date_of_birth, Decimal for amount).
Schema Classes (1.5 marks):
Schema classes OrderSchema and PersonSchema are defined.
However, the field name in OrderSchema ('date_created') does not match with the JSON key ('created'), which will lead to an error when deserializing. This is a significant oversight.
Date Format Specification (1 mark):
Correct date format specification for date_of_birth in PersonSchema using format="%m/%d/%Y".
Decimal Field Specification (1 mark):
The as_string=True attribute is correctly used for the amount field in OrderSchema.
@post_load Method (2 marks):
Correct implementation of the @post_load method in PersonSchema to return a Person instance after loading, ensuring that the values are returned as instances of the data classes.
Other (0.5 marks):
Libraries and installation steps are correctly indicated.
Implementation attempts to link serialization and deserialization through schemas.
Misses crucial error with incorrect field name matching ('date_created' vs 'created') which would break functionality.
Does not include instructions for correcting the class property naming mismatch or error handling/validations.
There's no example or mention of converting input_json which is a string into an actual JSON object (i.e., use of json.loads).

Overall, while the solution addresses the basic structure, key implementation mistakes particularly in schema field names and JSON handling detract from its efficacy. The adjustments needed, particularly in ensuring correct field naming and handling of JSON strings, are essential for the solution to function correctly.

Given this evaluation, the assessment would be:

{
  "score": 7
}

1.00	0.83	0.86
0.83	1.00	0.79
0.86	0.79	1.00

OpenChat 3.5 7B (free)

Description

Conclusion

Most Similar

Also by openchat

Model Consistency

Model Evaluations

claude-3-haiku
claude-3.5-sonnet	7.0	5.0	9.0
gpt-3.5-turbo-0125
gpt-4-0125-preview	5.0	3.0	7.0
gpt-4-1106-preview