Phi-3 Mini 128K Instruct

Description

Phi-3 Mini is a powerful 3.8B 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. This model is static, trained on an offline dataset with an October 2023 cutoff date.

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?

To achieve this, you'll need to define several dataclasses and use marshmallow for serialization and deserialization. Here's how you can do it:

First, import the necessary libraries:

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

Define the dataclasses:

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: str

@dataclass
class Order:
    created: str
    amount: str

@dataclass
class Customer:
    first_name: str
    last_name: str
    date_of_birth: str
    orders: list

Then, create a mapping between these classes and your original JSON structure and define the schemas:

from marshmallow import Schema, fields, post_load

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Str()

    @post_load
    def make_date_of_birth(self, data):
        return datetime.strptime(data['date_of_birth'], "%m/%d/%Y")

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

    @post_load
    def make_amount(self, data):
        return Decimal(data['amount'])

class CustomerSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Str()
    orders = fields.List(fields.Nested(OrderSchema))

    @post_load
    def make_orders(self, data):
        return [OrderSchema().load(order) for order in data['orders']]

Finally, define a class to handle input and output and serialize it to your JSON format:

class JsonCustomer:
    def __init__(self, data):
        self.data = data

    def to_json(self):
        return PersonSchema().dumps(self.data)

    @classmethod
    def from_json(cls, json):
        data = PersonSchema().load(json)
        return cls(data)

Now you can use these classes as follows:

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

# Convert to JSON
json_customer = JsonCustomer(customer_data)
print(json_customer.to_json())

# Convert back to Python dictionary
customer_dict = JsonCustomer.from_json(json_customer.to_json())
print(customer_dict.__dict__)

This way, you have classes that can serialize and deserialize the data according to your needs.

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: (6.0) (1.07¢)

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

  1. The solution defines three dataclasses: Person, Order, and Customer. While it includes an extra dataclass (Customer), it does have the required Person and Order dataclasses. (2 marks)

  2. The solution defines three schema classes: PersonSchema, OrderSchema, and CustomerSchema. It includes the required schemas for Person and Order. (2 marks)

  3. The solution does not specify the date format for the date_of_birth field in the schema. It's missing the format="%m/%d/%Y" in the field definition. (0 marks)

  4. The decimal field in OrderSchema does not include as_string=True. (0 marks)

  5. The solution uses @post_load methods on the schema classes to link schemas and dataclasses. However, they don't correctly create instances of the dataclasses. (1 mark)

  6. Additional observations:

  7. The solution includes an extra JsonCustomer class for handling JSON serialization/deserialization.
  8. The dataclasses are not correctly utilized in the schema's post_load methods.
  9. The date_of_birth is incorrectly handled as a string in the dataclasses.
    (1 mark)

Breakdown of marks:

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

Score:

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

The solution demonstrates a good understanding of using dataclasses and marshmallow schemas but misses some key requirements, particularly in handling specific data types correctly. The overall structure is on the right track, but there are areas for improvement in terms of correctly implementing the date and decimal fields, and properly linking the schemas to the dataclasses.

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

The solution presents a data serialization and deserialization approach using data classes and Marshmallow for dealing with a JSON structure in Python. Here is my analysis of the solution according to the marking scheme:

  1. Define two dataclasses, one for the person and one for the order:

  2. The solution defines dataclasses for Person and Order. Additionally, a Customer dataclass is also defined which encapsulates both person and list of orders. This is beyond the requirement but doesn't capture the programming needs accurately, as the Customer class should use proper types for its fields. (+1 mark)

  3. Define two schema classes, one for the person and one for the order:

  4. Both PersonSchema and OrderSchema are correctly provided. Additionally, a CustomerSchema was defined which uses these two schemas. (+2 marks)

  5. Specify the date format for date_of_birth:

  6. The date_of_birth field in PersonSchema is not correctly specified. It uses Str() type instead of Date() with a format specification. Thus, this misses the requirement to correctly parse and serialize the date. (0 marks)

  7. The decimal field specification:

  8. fields.Decimal() was used for amount in OrderSchema, but without proper as_string=True. This leads to improper handling of the decimal data as the requirement specifies it should be serialized as a string. (0 marks)

  9. Link schema and dataclasses using @post_load on schema classes:

  10. @post_load decorators are implemented for PersonSchema, OrderSchema, and CustomerSchema, but they are not correctly used to return instances of the corresponding dataclasses. These methods are only partially capturing the intended object construction (codes aim to perform conversion, but do not construct dataclass instances). (+1 mark)

  11. Anything else:

  12. The concept of defining a JsonCustomer class for converting to and from JSON is useful and implements serialization and deschainization process. However, the process is flawed as it doesn't respect the actual structure or data types necessary (like date and decimal handling). (+1 mark)

Overall the solution shows a decent understanding of using Marshmallow with dataclasses but misses critical features around correct data types handling (dates and decimals) and proper linkage between schemas and dataclasses. Adding correct types and parameter specifications could have made it more robust.

{
  "score": 5
}

To solve this problem, you can leverage the power of Marshmallow, a popular Python library for object serialization and deserialization. Marshmallow can be used to convert Python dataclasses to and from a JSON representation and also handle type conversions according to your specifications.

Here is an example of how you can use Marshmallow in this case:

First, import the necessary modules:

from datetime import datetime
from decimal import Decimal
from marshmallow import Schema, fields, pre_dump, pre_load
import json

# Define your dataclass in Python
class Person:
    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

Then, define your Marshmallow Schema:

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

Note: For date_of_birth, we use DateTime, but it won't be converted from string. You would generally need to manually parse this string into a date.

Next, define the pre_load and pre_dump methods to handle data type conversion:

@pre_dump
def convert_to_datetime(d):
    return datetime.strptime(d, "%m/%d/%Y")

@pre_dump
def convert_to_decimal(d):
    return Decimal(d)

And your final code for handling the transformation would be:

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

obj = Person(**data)

person_schema = PersonSchema()
data = person_schema.dump(obj)

print(json.dumps(data, indent=2))

Just remember to install the required Marshmallow library (pip install marshmallow) and handle the date parsing manually, as Marshmallow by itself won't handle the datetime to string conversion for date_of_birth.

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) (1.03¢)

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

  1. The solution defines only one dataclass (Person) instead of two (Person and Order). (1 out of 2 marks)

  2. The solution defines only one schema class (PersonSchema) instead of two (PersonSchema and OrderSchema). (1 out of 2 marks)

  3. The solution doesn't specify the date format for the date_of_birth field. (0 out of 1 mark)

  4. The decimal field is not specified with as_string=True. (0 out of 1 mark)

  5. The schema and dataclasses are not linked with a @post_load method. Instead, @pre_dump methods are used, which is not correct for this use case. (0 out of 2 marks)

  6. Additional points:

  7. The solution attempts to handle type conversions using @pre_dump methods, which shows some understanding of the problem. (1 mark)
  8. The solution includes imports and a basic structure for using Marshmallow. (1 mark)

Here's the breakdown of marks:

{
  "Dataclasses": 1,
  "Schema classes": 1,
  "Date format": 0,
  "Decimal field": 0,
  "Schema-dataclass linking": 0,
  "Additional points": 2
}

Final score:

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

The solution demonstrates a basic understanding of Marshmallow but falls short in several key areas. It doesn't correctly implement the two-class structure (Person and Order), doesn't properly handle the date and decimal fields, and doesn't use the @post_load decorator to link schemas with dataclasses. The attempt to use @pre_dump methods for type conversion is misplaced and wouldn't achieve the desired result. Overall, while the solution shows some familiarity with Marshmallow, it needs significant improvements to fully meet the requirements.

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

The provided solution attempts to demonstrate a method of using the marshmallow library to serialize and deserialize JSON data to Python objects. Let's evaluate it according to the marking scheme:

  1. Definition of two dataclasses:

    • The solution provides only one dataclass for Person instead of separate classes for person and order details (Order).
    • (-1 mark)

  2. Schema classes for the Person and Order:

    • Only one schema PersonSchema is defined. It inaccurately includes both person and order attributes (created, amount) in the same class. Order should be a separate dataclass with its own schema.
    • (-1 mark)

  3. Date format specification:

    • The solution does not specify the correct date format for the date_of_birth field, which should be handled as fields.Date(format="%m/%d/%Y") instead of DateTime.
    • (-1 mark)

  4. Decimal field handling:

    • The schema uses fields.Decimal() but does not set as_string=True, so decimals would not deserialize into strings.
    • (-1 mark)

  5. Use of @post_load for linking schema and dataclass:

    • No @post_load methods are used in the solution. Methods used are @pre_dump, which is incorrectly employed and not necessary for this use case. Required transformation should be handled with @post_load methods that instantiate the dataclasses correctly.
    • (-2 marks)

  6. Other aspects:

    • The solution does not include correct parsing of the orders array into respective dataclass instances, which means it fails to achieve the nested parsing required.
    • pre_dump decorators are misused. They are meant for processing data before serializing, and the code they contain is incorrectly formatted (should be methods inside a class).
    • Basic JSON serializing is demonstrated, but the approach taken leads to potential errors and misrepresentation of data types.
    • (-2 marks)

After this evaluation, the proposed solution appears to not meet the specified criteria effectively, thus scoring:

{
  "score": 2
}

This score reflects major conceptual misapplications regarding the use of marshmallow, misuse of method decorators, a lack of required error-checking conversions, and incorrect architectural separation of concerns concerning dataclasses and their corresponding schemas.

Here's how you can achieve this using Python with the marshmallow library. First, ensure you have marshmallow installed. You can install it via pip if it's not installed:

pip install marshmallow

Now, let's define our classes. We'll use marshmallow schemas to handle the conversion from our input format to pythonic data types.

import datetime
from decimal import Decimal
from marshmallow import Schema, fields

class PersonSchema(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(decimal_places=2)

# Instantiate the schemas
person_schema = PersonSchema()
order_schema = OrderSchema()

These schemas will help you to validate and load your data and convert it to the required python types.

Note: Above code assumes that the given JSON follows the exact structure. If there are variations in the schema or if the data may vary, that may require adjustments in the schema.

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) (0.86¢)

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

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

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

  3. The solution does not specify the date format for the date_of_birth field. (0/1 mark)

  4. The solution does not include as_string=True for the decimal field. (0/1 mark)

  5. The solution does not include @post_load methods to link schemas with dataclasses. (0/2 marks)

  6. Additional points:

  7. The solution correctly imports necessary modules.
  8. It uses nested fields for the orders in PersonSchema.
  9. It specifies decimal_places for the amount field.
    (1/2 marks)

Comments:
The solution provides a basic structure using marshmallow schemas, which is a step in the right direction. However, it misses several key requirements:
- It doesn't use dataclasses at all.
- It doesn't specify the correct date format for date_of_birth.
- It doesn't use as_string=True for the decimal field.
- It lacks the @post_load methods to create dataclass instances.

While the schema definitions are correct, they don't fully meet the requirements of the problem.

Mark breakdown:

{
  "Dataclass definitions": 0,
  "Schema definitions": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Post_load methods": 0,
  "Additional points": 1
}

Final score:

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

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

The provided solution has attempted to use the marshmallow library to convert JSON data into Python data types by defining schema classes. Here's how the solution stands relative to the specified marking scheme:

  1. Two dataclasses definition (2 marks): The solution does not define any Python dataclasses. It only defines marshmallow Schema classes, which function differently. dataclasses are a specific Python feature used for creating classes that are particularly suitable for storing data, but they have not been used here (0/2).

  2. Two schema classes definition (2 marks): The solution properly defines two marshmallow schema classes (PersonSchema and OrderSchema). Each schema aligns correctly with the intended JSON structure (2/2).

  3. Date format specification (1 mark): The date_of_birth field in the PersonSchema class is converted using fields.Date(), but the specific format ("%m/%d/%Y") required by the prompt is not set (0/1).

  4. Decimal field handling (1 mark): The amount field in the OrderSchema is defined as a fields.Decimal() with decimal_places=2. However, the requirement to format this as a string (as_string=True) in the schema was not included, which means it doesn’t fully meet the specification (0/1).

  5. @post_load method for linking schemas and dataclasses (2 marks): The solution lacks the implementation of @post_load method to convert loaded data into instances of any dataclasses or other structured Python objects. This linking is essential for completing the deserialization process properly in accordance with marshmallow best practices (0/2).

  6. Additional considerations (2 marks): The schemas are instantiated, which is a good practice, however, there's no demonstration or example of how to actually use these schemas to load or validate data, missing practical implementation (1/2).

Overall, the solution touches on some critical aspects of using marshmallow but falls short in fulfilling the requirements as stated in the problem, especially with respect to using dataclasses and precise type conversions.

Final score:

{
  "score": 3
}
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