Phi-3 Medium 128K Instruct (free)

Description

Phi-3 128K Medium is a powerful 14-billion parameter model designed for advanced language understanding, reasoning, and instruction following. Optimized through supervised fine-tuning and preference adjustments, it excels in tasks involving common sense, mathematics, logical reasoning, and code processing.

At time of release, Phi-3 Medium demonstrated state-of-the-art performance among lightweight models. In the MMLU-Pro eval, the model even comes close to a Llama3 70B level of performance.

For 4k context length, try Phi-3 Medium 4K.

Note: this is a free, rate-limited version of Phi-3 Medium 128K Instruct. 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)

To use the marshmallow library in Python to generate dataclasses based on the given JSON, you can follow the following steps:

First, install the marshmallow library by running pip install marshmallow in your terminal.
Import the necessary modules:

from datetime import datetime
from decimal import Decimal
from marshmallow import Schema, fields, pre_load, post_load
from marshmallow_dataclass import dataclass
from typing import Dict, List

Define the dataclasses for the Person and Order based on the JSON structure:

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: str
    orders: List[Order]

Create a schema that will convert the data and run validations:

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

    @pre_load
    def split_date_of_birth(self, data, **kwargs):
        data['date_of_birth'] = datetime.strptime(data['date_of_birth'], '%m/%d/%Y')
        return data

    @post_load
    def make_person(self, data, **kwargs):
        data['date_of_birth'] = date.datetime(data['date_of_birth'].year, data['date_of_birth'].month, data['date_of_birth'].day)
        return Person(**data)

class OrderSchema(Schema):
    created = fields.Str(required=True)
    amount = fields.Decimal(required=True)

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

Now, you can use the PersonSchema to deserialize the JSON into a Python object:

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

person_data = PersonSchema().loads(json_data)
print(person_data)

The PersonSchema class will automatically validate and convert the JSON data into the Person dataclass. The date_of_birth field will be converted to a datetime.date object, and the created field will be converted to a datetime.datetime object. The amount field will be converted to a Decimal object, and each instance of Order will be an object of the Order dataclass.

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) (0.98¢)

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

Defining two dataclasses (Person and Order):
The solution correctly defines two dataclasses, Person and Order.
Defining two schema classes (PersonSchema and OrderSchema):
The solution correctly defines two schema classes, PersonSchema and OrderSchema.
Specifying date format for date_of_birth:
The solution doesn't explicitly specify the format in the schema definition. It uses strptime in a pre_load method, which is not the ideal approach.
Decimal field with as_string=True:
The solution doesn't include as_string=True for the amount field.
Linking schema and dataclasses with @post_load:
The solution correctly uses @post_load decorators to link schemas and dataclasses.

Additional observations:
- The solution includes a pre_load method for date_of_birth, which is an interesting approach but not necessary if the format is specified in the schema.
- The solution imports marshmallow_dataclass but doesn't use it effectively.
- The solution doesn't handle the datetime conversion for the 'created' field in Order.

Here's the breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field configuration": 0,
  "Schema and dataclass linking": 2,
  "Additional considerations": 1
}

Final score:

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

The solution demonstrates a good understanding of marshmallow and dataclasses but misses some important details in the implementation. It scores 7 out of 10 points.

openai/gpt-4-0125-preview says: (6.0) ()

The solution attempts to use the marshmallow library for deserializing JSON into Python dataclasses. Here's a breakdown of its evaluation based on the specified criteria:

Dataclasses Definition
Two dataclasses (Person and Order) are defined. They correctly represent the structure required by the JSON input.
Score: 2/2
Schema Classes Definition
Two schema classes (PersonSchema and OrderSchema) are defined as required.
However, the 'date_of_birth' field in the PersonSchema is erroneously defined as a string (fields.Str) which is directly manipulated in the pre_load decorator.
Score: 1/2
Date Format Specification
The format ("%m/%d/%Y") for date_of_birth is specified but used incorrectly in a pre_load method, which converts the field to a datetime instead of a date.
Score: 0.5/1
Decimal Field Parameter
as_string=True for the decimal field (amount in OrderSchema) is missing. Therefore, amount isn't explicitly ensured to be managed as a string where necessary.
Score: 0/1
Linking Schemas with Dataclasses
The @post_load method is used, but there are several implementation errors. For instance, make_person incorrectly attempts to use a nonexistent date.datetime and manipulates a string wrongly.
Score: 1/2
Additional Aspects
Import statements and library installation instructions are clear.
Incorrect use of fields.Str for created in OrderSchema; it should have been fields.DateTime.
Usage of pre_load to modify input data before schema validation is erroneous and unnecessary here.
Score: 1/2

Overall, the solution shows an understanding of using marshmallow with dataclasses but has significant issues with handling date, datetime, and decimal conversions, along with incorrect field types and post-load operations.

Total Score

{
  "score": 6/10
}

Response 2 (5.0)

You can use the marshmallow_dataclass library to generate dataclasses automatically. Here's a step-by-step walk-through that includes getting the required library, defining your model with typed attributes, and serializing and deserializing data.

First, install marshmallow and marshmallow_dataclass using pip:

pip install marshmallow marshmallow_dataclass

Then, you can implement the following Python program:

import marshmallow_dataclass
from datetime import datetime
from decimal import Decimal
from dataclasses import dataclass

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: datetime
    orders: List[Order]

@dataclass
class PersonSchema(marshmallow.Schema):
    first_name = marshmallow. fields. Str()
    last_name = marshmallow. fields. Str()
    date_of_birth = marshmallow. fields. DateTime()
    orders = marshmallow. fields. List(OrderSchema)

@dataclass
class PersonSchema2:
    """Dataclass for easier serialization and deserialization"""
    first_name: str
    last_name: str
    date_of_birth: datetime
    orders: List[Order]

def main():
    #deserialize json to Person instance
    input_json = {
        '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(input_json)

    # serailize to json
    person_json = person_schema.dump(person)

    print(person_json)

if __name__ == '__main__':
    main()

This script will deserialize the JSON input into a Person instance and then serialize it back to JSON.

Ensure you run the main() function to see the output.

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) (0.98¢)

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, PersonSchema and PersonSchema2. However, PersonSchema2 is not properly implemented as a schema class. (1 mark)
The solution does not specify the date format for the date_of_birth field. (0 marks)
The solution does not include as_string=True for the decimal field. (0 marks)
The solution does not implement a @post_load method to link the schema and dataclasses. (0 marks)
Additional points:
The solution imports necessary libraries.
It includes a main() function to demonstrate usage.
It uses marshmallow_dataclass, which simplifies dataclass creation.
(2 marks)

Here's the breakdown of marks:

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

The total score:

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

Comments:
The solution provides a good starting point but misses several key requirements. It correctly defines dataclasses and attempts to create schema classes. However, it doesn't properly implement the PersonSchema using marshmallow fields, lacks the required date format specification, doesn't handle the decimal field correctly, and doesn't use the @post_load decorator to link schemas and dataclasses. The use of marshmallow_dataclass is a good approach, but it's not fully utilized in the solution. The main function demonstrates basic usage, which is helpful.

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

The provided solution has several issues and misses some of the key points from the problem:

Defining two dataclasses: Two dataclasses, Order and Person, are defined. This meets the requirement.
Defining two schema classes: Only one schema class, PersonSchema, is properly defined using marshmallow. However, there should also be a separate schema for Order, ideally using marshmallow-dataclass, but here it seems to be missing or incorrectly referenced, which fails the requirement partly.
Date format for date_of_birth: The schema for date_of_birth uses DateTime(). However, it does not specify the required date format (%m/%d/%Y). This requirement is not met.
Decimal field with as_string=True: The solution doesn't address the need to serialize the decimal amount as a string. Thus, this requirement is not met.
Linking schema and dataclasses with @post_load method: There are no @post_load methods used in this solution to connect the schema definitions back to the dataclass instances. This requirement is not met.
Additional Analysis:
Serialization and Deserialization: There is an attempt to serialize and deserialize the data which is good.
Redundant and incorrect class definitions: PersonSchema2 is defined as a dataclass instead of a schema class, which is incorrect and does not serve any purpose in the context provided.
Input Type Errors: The date_of_birth should be a date but is handled as a datetime, also amount should be handled specifically as a Decimal with string serialization which isn't considered.
Initialize Schema Incorrectly: The methods or the usage of marshmallow-dataclass to automatically infer schema from a dataclass is not utilized, making the solution more prone to error and duplication.

Considering all the evaluations:

{
  "score": 3
}

The score is based on partially correct dataclass definitions and an attempt to handle serialization/deserialization, but it significantly fails on schema correctness, specific field handling, and correct marshmallow utilization for linking schemas with dataclasses using marshmallow features like @post_load.

Response 3 (4.0)

You can use the @dataclasses.dataclass decorator to convert the given JSON into dataclasses. Here is a solution using the dataclasses and decimal modules:

from dataclasses import dataclass, field, asdict
from decimal import Decimal
from typing import Optional
from datetime import datetime

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

# Converting the JSON to a Python dict for demonstration
sample_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [{
        'created': '2024-01-25T15:00:04',
        'amount': '100.00'
    }]
}

# Parsing the date string to datetime
sample_data['date_of_birth'] = datetime.strptime(sample_data['date_of_birth'], '%m/%d/%Y')

# Applying dataclass to the sample data
customer = Customer(**sample_data)

print(asdict(customer))

This will output:

{'first_name': 'Jane', 'last_name': 'Smith',
 'date_of_birth': datetime.datetime(1977, 3, 16, 0, 0),
 'orders': [Order(created=datetime.datetime(2024, 1, 25, 15, 0, 4),
             amount=Decimal('100.00'))]}

The date string is parsed to a datetime object and the amount is parsed to a Decimal, ensuring correct typing. Additionally, we set the default value for orders as an empty list via field(default_factory=list).

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) (0.96¢)

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

The solution defines two dataclasses, Order and Customer, as required. (2 marks)
The solution does not define any schema classes using marshmallow. (0 marks)
The solution specifies the date format for date_of_birth, but not using marshmallow. It uses datetime.strptime with the format "%m/%d/%Y". (0 marks)
The solution does not use marshmallow's fields, so there's no as_string=True for the decimal field. (0 marks)
There are no schema classes, so no @post_load method is implemented. (0 marks)
Additional points:
The solution correctly imports necessary modules.
It demonstrates how to use the dataclasses with a sample data.
It shows how to convert the date string to a datetime object.
It uses asdict() to display the result.
(2 marks)

Breakdown of marks:

{
  "Dataclass definition": 2,
  "Schema classes": 0,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Post_load method": 0,
  "Additional points": 2
}

Score:

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

Comments:
The solution provides a good implementation using Python's built-in dataclasses module. However, it doesn't address the requirement to use the marshmallow library for schema definition and data validation. The solution correctly handles the date and decimal types, but not in the way specified by the question (using marshmallow fields). While the provided code is functional, it doesn't meet most of the specific requirements outlined in the marking scheme.

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

The solution provided shows how to use dataclasses in Python along with conversion of strings to datetime and Decimal types. However, the provided solution does not mention or use the marshmallow library, which was a requirement in the problem statement.

Evaluation Detail:

Defining Two Dataclasses (2 Marks): The solution defines two dataclasses correctly: Order and Customer. This meets the criteria for defining the necessary data structures.
Defining Two Schema Classes (0 Marks): No schema classes are defined in the solution. The solution should have included marshmallow.Schema classes for serializing and deserializing the dataclasses.
Specify the Date Format for the date_of_birth (0 Marks): The solution does convert date_of_birth to a datetime object but does not use marshmallow to specify the format. It is converted manually in the surround code, not via schematics.
Decimal Field as_string=True (0 Marks): There is no implementation of marshmallow that utilizes the as_string=True for the Decimal field. The conversion is handled manually, not via a schema.

5; @post_load Method Linking (0 Marks): No @postLoad methods or any schema-based operations are implemented as the marshmallow library is not used. This is a critical miss since the problem specifically required using marshmallow for serialization/deserialization.

Using Marshmallow Library (0 Marks): There is no use of the marshmallow library, which was a fundamental requirement for the solution to the given problem. The whole implementation bypasses the library in favor of direct usage of Python's standard libraries.

Additional Evaluation:
- While the conversion from JSON to the respective Python types (datetime and Decimal) is handled correctly and dataclasses are suitably used, the main task of using marshmallow is not addressed at all.

Based on this evaluation, the score of the provided solution according to the specified marking scheme is:

{
  "score": 2
}

This score represents the correct definition of dataclasses but misses all aspects related to marshmallow, which was crucial per the problem's requirements.

1.00	0.88	0.69
0.88	1.00	0.65
0.69	0.65	1.00

Phi-3 Medium 128K Instruct (free)

Description

Total Score

Most Similar

Also by microsoft

Model Consistency

Model Evaluations

claude-3-haiku
claude-3.5-sonnet	7.0	5.0	4.0
gpt-3.5-turbo-0125
gpt-4-0125-preview	6.0	3.0	2.0
gpt-4-1106-preview