Nous: Capybara 34B

Description

This model is trained on the Yi-34B model for 3 epochs on the Capybara dataset. It's the first 34B Nous model and first 200K context length Nous model.

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?

You can use the marshmallow library to generate Python dataclasses from the given JSON. Here's how you can do it:

  1. First, install marshmallow if you haven't already:
pip install marshmallow
  1. Define your schema using marshmallow:
from marshmallow import Schema, fields, post_load
from datetime import date, datetime
from decimal import Decimal

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(dump_only=True)  # Use dump_only because date_of_birth is not needed in the input JSON

class OrderSchema(Schema):
    created = fields.DateTime(dump_only=True)  # Use dump_only because created is not needed in the input JSON
    amount = fields.Decimal(as_string=True)

class PersonWithOrdersSchema(Schema):
    person = PersonSchema()
    orders = fields.Nested(OrderSchema, many=True)

    @post_load
    def make_person_with_orders(self, data, errors):
        return PersonWithOrders(**data)
  1. Instantiate the schema and load the JSON:
import 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_with_orders_schema = PersonWithOrdersSchema()
person_with_orders = person_with_orders_schema.load(data)
  1. Access the dataclasses:
person = person_with_orders.person
orders = person.orders

Now, you have created the desired dataclasses: person and orders.

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) (0.91¢)

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

  1. The solution does not define dataclasses explicitly. It only defines schema classes. (0/2 marks)

  2. The solution defines three schema classes: PersonSchema, OrderSchema, and PersonWithOrdersSchema. (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 correctly uses as_string=True. (1/1 mark)

  5. The solution includes a @post_load method on the PersonWithOrdersSchema class, but it's not properly implemented for both schemas. (1/2 marks)

  6. Additional observations:

  7. The solution uses dump_only=True for date_of_birth and created fields, which is incorrect as these fields are needed in the input JSON. (-1 mark)
  8. The solution doesn't handle the conversion of date_of_birth from string to date object. (-0.5 marks)
  9. The PersonWithOrdersSchema doesn't accurately represent the structure of the input JSON. (-0.5 marks)

Here's the breakdown of marks:

{
  "Dataclass definition": 0,
  "Schema definition": 2,
  "Date format specification": 0,
  "Decimal field configuration": 1,
  "Post_load method implementation": 1,
  "Additional observations": 0
}

Final score:

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

The solution demonstrates some understanding of marshmallow but lacks crucial elements like proper dataclass definitions and accurate field configurations. It also misses some important details in handling the JSON structure and data conversions.

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

This solution attempts to define schemas using Python's marshmallow library to parse and define data from a JSON structure into dataclasses equivalent. Here is the evaluation:

  1. Definition of two dataclasses: The solution does not explicitly define any Python dataclasses (dataclass from dataclasses module). It only defines marshmallow schema classes, and there is no use of the dataclass decorator or any dataclass structure. (-2 marks)

  2. Definition of two schema classes: The solution provides definitions for three schema classes, PersonSchema, OrderSchema, and a combined schema PersonWithOrdersSchema. Although it correctly defines schema classes, it failed to establish distinct dataclasses for Person and Order. (+2 marks)

  3. Date format specification for date_of_birth: The date_of_birth field in PersonSchema uses fields.Date(), but the solution fails to specify the required date format ("%m/%d/%Y"), crucial for correctly parsing the date string from the JSON format provided. (-1 mark)

  4. Decimal field with as_string=True: The solution correctly configures the amount field in OrderSchema with fields.Decimal(as_string=True) which correctly serializes the decimal to a string in JSON. (+1 mark)

  5. Linkage of schema and dataclasses using @post_load: The solution implements a @post_load method, but it is inaccurately attached to the combined schema class PersonWithOrdersSchema and references a non-existent PersonWithOrders data structure. While the intent is clear, there is no actual dataclass defined named PersonWithOrders confusing the implementation and resulting in potential runtime errors. Additionally, there's a mistake in the method definition (errors does not need to be passed as a parameter in latest marshmallow versions). (-1 mark)

  6. Additional tasks like installation and usage: The solution includes instructions for installing marshmallow, initializing the schema, creating nested structures, and deserializing the data directly. It successfully demonstrates how to use schemas to load and access the deserialized data. This shows a good understanding of marshmallow's capabilities even though the direct link to dataclasses wasn’t established properly. (+2 marks)

Given the criteria and the analysis above, the solution scores as follows:

{
  "score": 6
}

To generate dataclasses from the given JSON using Marshmallow, you first need to define the schemas for each of the datatypes in your JSON. Here is an example of how you can do that:

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

class OrderSchema(Schema):
    created = fields.DateTime(format='iso8601', required=True)
    amount = fields.Decimal(as_string=True, required=True)

class PersonSchema(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), default=list)

json_data = '''
{
  "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(json.loads(json_data))

In this example, I have defined two schemas: OrderSchema and PersonSchema. The OrderSchema is for the orders list in your JSON, and the PersonSchema is for the rest of the JSON.

Make sure to install Marshmallow if you haven't already:

pip install marshmallow

Now, you can load the JSON string into a PersonSchema object and convert it into a dataclass instance.

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

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

  3. The solution does not specify the correct date format for the date_of_birth field. It should be format="%m/%d/%Y". (0/1 mark)

  4. The decimal field correctly uses as_string=True. (1/1 mark)

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

  6. Additional observations:

  7. The solution correctly imports necessary modules.
  8. It provides a sample JSON string and demonstrates how to load it.
  9. The created field in OrderSchema correctly uses the 'iso8601' format.
  10. The solution mentions how to install Marshmallow.
    (1/2 marks)

Breakdown of marks:

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

Score:

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

The solution provides a good start with schema definitions but misses several key requirements, particularly the creation of dataclasses and linking them to the schemas. It also doesn't use the correct date format for the date_of_birth field. While it demonstrates knowledge of Marshmallow, it falls short of fully addressing the problem as specified.

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

The provided solution shows an understanding of using the Marshmallow library to define schemas for JSON data handling in Python. However, it lacks some key elements required by the problem statement setup and scores accordingly as follows:

  1. Definition of Two Dataclasses: There is no explicit definition of Python dataclasses (Order and Person) used for modeling the respective entities in the the given JSON data. Using Python dataclasses would help structure the data better, especially when needed to transform JSON into Python objects with certain attributes and types. (-2 Points)

  2. Definition of Two Schema Classes: The solution defines two schema classes (OrderSchema and PersonSchema) appropriately using Marshmallow. Each class appears to correctly correspond to the elements (person and order) described in the JSON data, which meets this requirement. (+2 Points)

  3. Specify the Date Format for date_of_birth Field: The date_of_birth is defined in the schema but the required date format ("%m/%d/%Y") was not specified. This means the schema assumes the default ISO format, which would not correctly parse the format given in the JSON data. (-1 Point)

  4. Decimal Field as_string=True: The solution correctly represents the amount field in OrderSchema with Decimal(as_string=True), ensuring that when amounts are processed by the schema, they are treated as strings, which preserves their exact representation. (+1 Point)

  5. Linking Schema and Dataclasses with @post_load Method: The solution is missing the @post_load method that would tie the output of the schema loads directly to a dataclass (to instantiate objects of the defined dataclasses using the loaded data). This ties into the absence of defined dataclasses and is crucial for full marshmallow integration in practical applications. (-2 Points)

  6. Anything Else: The example provided correctly imports necessary packages and demonstrates data loading using the defined schema. However, the solution also fails to mention handling of errors which would be common in data loading from a schema, nor does it discuss how the nested orders are handled beyond schema nesting. (+1 Point for correct basic usage of Marshmallow and Python imports.)

Overall Score:
Given the missed critical components such as actual dataclass definition, proper date formatting, and use of @post_load for marshmallow deserialization into objects, the score reflects significant areas of improvement.

{
  "score": 3
}

To create dataclasses using marshmallow, you first need to define your schemas. A schema is a blueprint for your data that defines how it should be structured. In your case, you have two complex objects: Person and Order. Let's define schemas for both:

from marshmallow import Schema, fields

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

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

Now you can use the load method from the schema to deserialize the JSON data and create your dataclasses. First, import the dataclasses module:

from dataclasses import dataclass
import marshmallow
from marshmallow import Schema, fields

Now, create the dataclasses:

@dataclass
class Order:
    created: datetime
    amount: decimal.Decimal

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

Finally, load the JSON data into your schemas and construct your Person and Order dataclasses:

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_schema = PersonSchema()
order_schema = OrderSchema()

person_dataclass = person_schema.load(json.loads(json_data))

Now, person_dataclass will be a Person instance with all the required attributes correctly parsed and converted to the appropriate types.

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: (5.5) ()

Evaluating the given solution:

  1. Two Dataclasses Definition:
  2. The solution defines two dataclasses (Order and Person) correctly with appropriate types, including datetime, decimal.Decimal, and others, for the structured data provided in the problem.

  3. Score: 2 marks.

  4. Two Schema Classes Definition:

  5. The solution includes the definition of two schema classes (OrderSchema and PersonSchema) using the marshmallow library, which correspond to the dataclasses.

  6. Score: 2 marks.

  7. Specification of Date Format for date_of_birth:

  8. The date format "%m/%d/%Y" for the date_of_birth field is not explicitly specified in the PersonSchema. The schema uses fields.Date() without passing any arguments.

  9. Score: 0 marks.

  10. Decimal Field with as_string=True:

  11. The amount field in OrderSchema is defined simply as fields.Decimal(), without specifying as_string=True, which would ensure the serialized format maintains the string representation of the decimal.

  12. Score: 0 marks.

  13. Linking Schema and Dataclasses using @post_load:

  14. The supplied solution does not implement an @post_load method in either the OrderSchema or PersonSchema. This method would be crucial for converting the loaded data into instances of the respective dataclasses.

  15. Score: 0 marks.

  16. Additional Elements:

  17. Correct import and usage of necessary libraries (marshmallow, dataclasses).
  18. Attempt to load and serialize JSON data using the defined schemas.
  19. However, issues around deserialization into the proper configured dataclasses due to the lack of @post_load methods and correct configurations might result in type mismatches or improper conversions.
  20. Score: 1.5 marks.

Based on the above analysis, the final evaluation score out of a total of 10 points is:

{
  "score": 5.5
}

anthropic/claude-3.5-sonnet says: (6.0) (0.96¢)

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

  1. Defining two dataclasses (Person and Order):
    The solution correctly defines both dataclasses. (2 marks)

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

  3. Specifying date format for date_of_birth:
    The solution doesn't specify the date format for date_of_birth. It should have used fields.Date(format="%m/%d/%Y"). (0 marks)

  4. Decimal field with as_string=True:
    The solution doesn't include as_string=True for the decimal field. (0 marks)

  5. Linking schema and dataclasses with @post_load:
    The solution doesn't use @post_load to link the schema and dataclasses. (0 marks)

  6. Additional points:

  7. The solution imports necessary modules.
  8. It provides a good explanation of the process.
  9. It shows how to use the schema to load JSON data.
    (2 marks)

Breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field as_string": 0,
  "post_load usage": 0,
  "Additional points": 2
}

Total score:

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

The solution provides a good foundation but misses some important details required by the marking scheme. It correctly defines the dataclasses and schema classes but doesn't implement the specific requirements for date format, decimal field, and linking schemas to dataclasses using @post_load. Despite these omissions, it offers a clear explanation and demonstrates how to use the schema to load JSON data, which is valuable.

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