Phi-3 Mini 128K Instruct (free)

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.

Note: this is a free, rate-limited version of Phi-3 Mini 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?

To accomplish this using Marshmallow, follow the steps below. Note that Python's datetime, date, and Decimal from the decimal module will be used for type conversions - Marshmallow will handle the validation and serialization.

First, import the necessary modules and create the schema classes:

from datetime import datetime
from decimal import Decimal
from marshmallow import fields

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

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

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

These classes will define how the input data should be serialized to and deserialization from JSON: PersonSchema for the person data, OrderSchema for orders, and PersonWithOrdersSchema to handle the entire person with orders.

To actually perform the conversion from the raw JSON to these serialized data structures, use Marshmallow's loads for deserialization:

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

data = ma.fields.Str(strip_whitespace=True)(raw_data)

person_schema = PersonSchema()
person = person_schema.loads(data)

orders_schema = OrderSchema()
orders = orders_schema.load(person['orders'])

person_with_orders_schema = PersonWithOrdersSchema()
person_with_orders = person_with_orders_schema.load(data)

This code will deserialize your JSON input into Python dictionaries and list of orders and maintains the types assigned to each field.

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) (0.96¢)

Let's evaluate this solution based on the provided marking scheme:

  1. Defining two dataclasses (2 marks):
    The solution doesn't define any dataclasses. It only defines schema classes.

  2. Defining two schema classes (2 marks):
    The solution defines three schema classes: PersonSchema, OrderSchema, and PersonWithOrdersSchema. This meets and exceeds the requirement.

  3. Specifying date format for date_of_birth (1 mark):
    The solution doesn't specify the date format for the date_of_birth field.

  4. Decimal field with as_string=True (1 mark):
    The solution correctly uses as_string=True for the amount field in OrderSchema.

  5. Linking schema and dataclasses with @post_load (2 marks):
    The solution doesn't use @post_load to link schemas with dataclasses.

  6. Anything else (2 marks):

  7. The solution imports necessary modules.
  8. It provides code to deserialize the JSON data using the defined schemas.
  9. It handles the nested structure of orders within a person.

Here's the breakdown of marks:

{
  "Dataclasses": 0,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal field": 1,
  "Post_load": 0,
  "Anything else": 2
}

The solution partially meets the requirements. It correctly defines schema classes and handles the decimal field, but misses out on dataclasses, date format specification, and @post_load usage. However, it does provide additional functionality for deserialization.

Final score:

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

The solution scores 5 out of 10 based on the given marking scheme.

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

The provided solution attempts to use the Marshmallow library for serializing and deserializing JSON data into Python data structures. Below, I'll evaluate it according to the marking scheme provided:

  1. Define two dataclasses, one for the person and one for the order:
  2. The solution does not define any Python dataclasses. Instead, it only makes use of Marshmallow Schema classes. Dataclasses were expected based on the prompt.

  3. Score: 0/2

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

  5. Two specific schema classes (PersonSchema and OrderSchema) are well defined.
  6. Additionally, a combined schema (PersonWithOrdersSchema) is defined, but this goes beyond the specification.

  7. Score: 2/2

  8. Specify the date format for the date_of_gas_birth field:

  9. The date format for the date_of_birth field is not specified using format="%m/%d/%Y" in the fields.Date() as expected.

  10. Score: 0/1

  11. The decimal field needs as_string=True:

  12. The amount field in OrderSchema correctly uses fields.Decimal(as_string=True).

  13. Score: 1/1

  14. Link the schema and dataclasses with a @post_load method on the schema classes:

  15. The solution does not utilize the @post_load decorator at all. This should be used to transform loaded data into instances of the respective data classes.

  16. Score: 0/2

  17. Anything else:

  18. The raw JSON data is quoted incorrectly for Python (' should be used instead of '''). Also, the ma.Schema and deserialization methods (loads, load) usage are incorrect or incomplete in practice. Marshmallow specific errors and usages, such as missing import statements, use of an undefined ma object, improper use of ma.fields.Str(strip_whitespace=True)(raw_data), and incorrect handling of deserialization into the combined schema, reduce the quality.
  19. Score: 0/2

Final evaluation:

{
  "score": 3
}

The solution scores low primarily due to a failure to meet essential requirements such as defining dataclasses, correctly setting up date and datetime formatting, and using the @post_load decorator for constructing Python objects. Moreover, technical errors in the presented code with respect to JSON syntax and misuse of Marshmallow features influenced the final score.

You can use the Python library called Marshmallow to convert the JSON into data structures represented by dataclasses. First, we need to import the necessary libraries and define our dataclasses along with their respective fields. Then, use Marshmallow's Schema class to map from JSON to dataclass.

Here's a step-by-step guide:

Firstly import the necessary libraries:

from marshmallow import Schema, fields
from decimal import Decimal
import datetime
import dateutil.parser

Next, define your dataclasses. In this case, it includes the original data and a nested 'order' with additional fields.

class Person(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

class Order(object):
    def __init__(self, created, amount):
        self.created = created
        self.amount = Decimal(amount)

Then, create a schema mapping. In this schema class, we define the properties for the 'Person' dataclass:

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.Nested('OrderSchema')  # here we map the nested order

Next, define the order schema:

class OrderSchema(Schema):
    created = fields.DateTime(format='iso')
    amount = fields.Decimal(precision=2)

Finally, you can use the "load" method on your schema, providing the raw JSON that you want to convert:

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

After running this, 'data' will be an instance of the 'Person' class with all the respective fields populated from your raw 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) (0.89¢)

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

  1. The solution defines two classes (Person and Order), but they are not dataclasses. (-1 mark)

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

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

  4. The decimal field does not have as_string=True. (-1 mark)

  5. The schema and classes are not linked with a @post_load method. (-2 marks)

  6. Additional observations:

  7. The solution imports necessary libraries.
  8. It uses Decimal for the amount field.
  9. It uses fields.Nested for the orders field in PersonSchema.
    (1 mark)

Breakdown of marks:

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

Score:

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

The solution provides a good start but misses several key requirements. It uses regular classes instead of dataclasses, doesn't specify the correct date format for date_of_birth, doesn't use as_string=True for the decimal field, and lacks the @post_load method to link schemas with dataclasses. However, it correctly defines two schema classes and shows understanding of nested fields and the overall structure required.

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

The provided solution explains the basic concept of using Marshmallow with dataclasses but has several significant issues and omissions based on the evaluation criteria:

  1. Dataclass Definition: The solution does not use the Python dataclasses.dataclass decorator but rather defines classes in the traditional way with constructors. This means that while the class definition exists, they are not technically dataclasses as specified in the problem.

  2. Schema Class Definition: The solution defines the necessary schema classes for both Person and Order. This part meets the requirement partially because while definitions are provided, the lack of @post_load method is critical.

  3. Date Format Specification: The solution specifies fields.Date() for date_of_birth but fails to explicitly set the format to "%m/%d/%Y" as required in the criteria. This can lead to incorrect parsing of dates.

  4. Decimal Field Formatting: The solution defines fields.Decimal(precision=2) but does not include as_string=True in the manner required, which can affect how decimal numbers are deserialized.

  5. Post Load Implementation: The schemas lack a @post_load method necessary for properly loading the data into instances of the defined classes from JSON. This is crucial for the linkage between the schema and the dataclass.

  6. Additional Elements: The solution covers importing necessary libraries and demonstrates how to map fields in schema. However, there is a lack of attention to error handling or any mention of enriched Marshmallow features that could enhance usability such as slot optimization in dataclasses or additional validation.

In summary, the solution attempts to address the problem but falls short in critical areas crucial for full functionality and correctness according to the problem statement. The explanation lacks detail in how the actual load process works with JSON strings (considering that there is also an error in handling the JSON string in the load method call).

{
  "score": 3
}

To solve this problem, first, make sure you have 'marshmallow' library installed. If not, you can install it using pip: 

pip install marshmallow

Now, let's define the data classes for your JSON data. The first step is to import the necessary functions from marshmallow library and then define them.

from marshmallow import Schema, fields

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

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

In this script, we first define two classes, PersonSchema and OrderSchema, which contain the fields that you want to extract from your JSON data.

For the PersonSchema, we have:
- first_name is a string.
- last_name is also a string.
- date_of_birth is a date field (from the marshmallow library).
- orders: this is a list that contains another dictionary with fields created and amount. Therefore, we specify fields.Nested(OrderSchema) which specifies another schema(OrderSchema) for the nested fields.

For the OrderSchema, we have:
- created is a datetime field.
- amount is a decimal field.

The created schemas can now be used to convert a JSON data into Python data structures.

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) (0.90¢)

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

  1. The solution does not define dataclasses, only schema classes. (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 decimal field does not include as_string=True. (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 components from marshmallow.
  8. It properly uses nested fields for the orders list in PersonSchema.
    (1/2 marks)

Comments:
The solution provides a basic structure using marshmallow schemas, which is a good start. However, it misses several key requirements:
- It doesn't use dataclasses at all, only schemas.
- It doesn't specify the required date format for date_of_birth.
- The decimal field is not configured with as_string=True.
- There's no implementation of @post_load methods to create dataclass instances.

To improve, the solution should include dataclass definitions, specify date formats, configure the decimal field correctly, and implement @post_load methods to create dataclass instances from the deserialized data.

Mark breakdown:

{
  "dataclasses": 0,
  "schemas": 2,
  "date_format": 0,
  "decimal_config": 0,
  "post_load": 0,
  "additional": 1
}

Score:

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

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

The provided solution outlines an approach using the marshmallow library to handle JSON deserialization into Python objects using schemas, but there are several key issues and omissions:

  1. Dataclasses Definition: The solution does not define any data classes (@dataclass) for Person and Order, which is required by the problem statement.
  2. Schema Classes Definition: While the solution provides schema classes (PersonSchema and OrderSchema), the link between these schemas and their corresponding data classes via a @post_load method is missing. This is needed to ensure that the schemas load data into instances of the dataclasses.
  3. Date Format Specification: The format for the date_of_birth as "%m/%d/%Y" is not explicitly specified in the PersonSchema. This is necessary for correct parsing of the date string from the JSON.
  4. Decimal Field Specification: The amount field in the OrderSchema does not use as_string=True which would ensure that the decimal is not loaded as a float, potentially introducing precision errors.
  5. Circular Reference: The solution references OrderSchema in PersonSchema before OrderSchema is defined, which will cause a NameError. This issue could have been resolved with a forward declaration or by defining the classes in the correct order.
  6. Usage Example or Additional Detail: The solution lacks detailed explanation on usage, like how to load JSON data using these schemas, or rigid validation, error handling, and examples of output after parsing.

Given these evaluations based on the criteria:
- Dataclasses Definition: 0/2
- Schema Classes Definition: 1/2 (Partially complete but lacks @post_load and correct order reference)
- Date Format Specification: 0/1
- Decimal Field Specification: 0/1
- Schema and Dataclasses Linking through @post_load: 0/2
- Additional Points (correctness, completeness): 0/2 

{
  "score": 1
}
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