OpenHermes 2 Mistral 7B

Description

Trained on 900k instructions, surpasses all previous versions of Hermes 13B and below, and matches 70B on some benchmarks. Hermes 2 has strong multiturn chat skills and system prompt capabilities.

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 dataclasses from your JSON. Here's an example of how you can achieve this:

  1. First, install the marshmallow library if you haven't already:
pip install marshmallow
  1. Create a schema.py file and import the necessary modules:
from datetime import datetime
from decimal import Decimal
from marshmallow import Schema, fields, post_load
  1. Define the dataclasses for the JSON data:
class Person(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(required=True)

class Order(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(precision=2, required=True)

class OrderSchema(Schema):
    orders = fields.List(fields.Nested(Order), required=True)
  1. Define a PersonOrderSchema class that combines the Person and OrderSchema classes:
class PersonOrderSchema(Schema):
    person = fields.Nested(Person, required=True)
    orders = fields.List(fields.Nested(Order), required=True)
  1. Define a PersonOrderSchema instance and load the JSON data:
person_order_schema = PersonOrderSchema()
person_order_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}
person_order_instance = person_order_schema.load(person_order_data)

Now, person_order_instance will be a dataclass with the properties from the JSON data, with date_of_birth as a date, created as a datetime, and amount 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: (3.0) (0.91¢)

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

  1. Defining two dataclasses:
    The solution doesn't define dataclasses. It defines Schema classes instead.

  2. Defining two schema classes:
    The solution defines four schema classes: Person, Order, OrderSchema, and PersonOrderSchema.

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

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

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

  6. Additional points:

  7. The solution uses Schema classes instead of dataclasses, which is not the correct approach.
  8. It correctly imports necessary modules.
  9. It attempts to create a structure for handling both Person and Order data.

Here's the breakdown of marks:

{
  "Defining dataclasses": 0,
  "Defining schema classes": 2,
  "Date format specification": 0,
  "Decimal field configuration": 0,
  "Using @post_load": 0,
  "Additional points": 1
}

Final score:

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

The solution misses several key requirements, most notably the use of dataclasses and the proper configuration of fields. It uses Schema classes from marshmallow instead of dataclasses, which is a fundamental misunderstanding of the task. While it shows some understanding of structuring data with marshmallow, it fails to meet the specific requirements outlined in the problem statement.

openai/gpt-4-0125-preview says: (1.5) ()

The provided solution attempts to use the marshmallow library to handle JSON data into Python data structures using schemas, but there are several issues and omissions as per the problem requirements and marshmallow library usage.

  1. Definition of dataclasses for Person and Order (2 marks):

  2. The solution incorrectly defines Person and Order as Schema instead of using Python dataclasses. This is a conceptual error as it mixes up schema definitions with data model definitions. Dataclasses are not defined here.
    Score: 0/2

  3. Definition of schema classes for Person and Order (2 marks):

  4. It defines Person, Order, and OrderSchema as schema classes. However, since the Person and Order aren't correctly defined as dataclasses separately from their schemas, this isn't entirely correct. Data and schema handling are incorrectly merged, leading to misinterpretation of the requirement.
    Score: 1/2

  5. Specifying date format for date_of_birth (1 mark):

  6. The date format for date_of_birth (%m/%d/%Y) is not specified in the fields. Required format handling in marshmallow should be explicit in the field definition to correctly parse strings into date objects.
    Score: 0/1

  7. Decimal field with as_string=True (1 mark):

  8. The amount field does not use the as_string=True attribute which is necessary to ensure that the decimal value is treated correctly as a string when loading from and dumping to JSON, preventing unnecessary conversion errors or precision loss.
    Score: 0/1

  9. Linking schema and dataclasses with @post_load method (2 marks):

  10. No @post_load decorator or related methods are presented in the solution to map the loaded data to dataclass instances. This method is essential for converting the parsed data into the actual dataclass instances after loading.
    Score: 0/2

  11. Additional considerations (2 marks):

  12. The solution does not handle actual use of the decimal and datetime types explicitly beyond field definitions. There are also no error handling or additional explanations on marshmallow usage for complex types.
  13. Would expect some explanation of handling nested structures which is superficially handled but not detailed.
    Score: 0.5/2

Overall, the solution misses key elements required to properly use the marshmallow library for the specific needs of the problem.

Final Score:

{
  "score": 1.5
}

You can use the marshmallow library to generate dataclasses from your JSON. Here's an example of how you can achieve this:

  1. First, install the marshmallow library if you haven't already:
pip install marshmallow
  1. Create a schema.py file and import the necessary modules:
from datetime import datetime
from decimal import Decimal
from marshmallow import Schema, fields, post_load
  1. Define the dataclasses for the JSON data:
class Person(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(required=True)

class Order(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(precision=2, required=True)

class OrderSchema(Schema):
    orders = fields.List(fields.Nested(Order), required=True)
  1. Define a PersonOrderSchema class that combines the Person and OrderSchema classes:
class PersonOrderSchema(Schema):
    person = fields.Nested(Person, required=True)
    orders = fields.List(fields.Nested(Order), required=True)
  1. Define a PersonOrderSchema instance and load the JSON data:
person_order_schema = PersonOrderSchema()
person_order_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}
person_order_instance = person_order_schema.load(person_order_data)

Now, person_order_instance will be a dataclass with the properties from the JSON data, with date_of_birth as a date, created as a datetime, and amount 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: (3.0) (0.91¢)

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

  1. Defining two dataclasses:
    The solution doesn't define dataclasses. It defines Schema classes instead.

  2. Defining two schema classes:
    The solution defines four schema classes: Person, Order, OrderSchema, and PersonOrderSchema.

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

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

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

  6. Additional points:

  7. The solution uses Schema classes instead of dataclasses, which is not the correct approach.
  8. It correctly imports necessary modules.
  9. It attempts to create a structure for handling both Person and Order data.

Here's the breakdown of marks:

{
  "Defining dataclasses": 0,
  "Defining schema classes": 2,
  "Date format specification": 0,
  "Decimal field configuration": 0,
  "Using @post_load": 0,
  "Additional points": 1
}

Final score:

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

The solution misses several key requirements, most notably the use of dataclasses and the proper configuration of fields. It uses Schema classes from marshmallow instead of dataclasses, which is a fundamental misunderstanding of the task. While it shows some understanding of structuring data with marshmallow, it fails to meet the specific requirements outlined in the problem statement.

openai/gpt-4-0125-preview says: (1.5) ()

The provided solution attempts to use the marshmallow library to handle JSON data into Python data structures using schemas, but there are several issues and omissions as per the problem requirements and marshmallow library usage.

  1. Definition of dataclasses for Person and Order (2 marks):

  2. The solution incorrectly defines Person and Order as Schema instead of using Python dataclasses. This is a conceptual error as it mixes up schema definitions with data model definitions. Dataclasses are not defined here.
    Score: 0/2

  3. Definition of schema classes for Person and Order (2 marks):

  4. It defines Person, Order, and OrderSchema as schema classes. However, since the Person and Order aren't correctly defined as dataclasses separately from their schemas, this isn't entirely correct. Data and schema handling are incorrectly merged, leading to misinterpretation of the requirement.
    Score: 1/2

  5. Specifying date format for date_of_birth (1 mark):

  6. The date format for date_of_birth (%m/%d/%Y) is not specified in the fields. Required format handling in marshmallow should be explicit in the field definition to correctly parse strings into date objects.
    Score: 0/1

  7. Decimal field with as_string=True (1 mark):

  8. The amount field does not use the as_string=True attribute which is necessary to ensure that the decimal value is treated correctly as a string when loading from and dumping to JSON, preventing unnecessary conversion errors or precision loss.
    Score: 0/1

  9. Linking schema and dataclasses with @post_load method (2 marks):

  10. No @post_load decorator or related methods are presented in the solution to map the loaded data to dataclass instances. This method is essential for converting the parsed data into the actual dataclass instances after loading.
    Score: 0/2

  11. Additional considerations (2 marks):

  12. The solution does not handle actual use of the decimal and datetime types explicitly beyond field definitions. There are also no error handling or additional explanations on marshmallow usage for complex types.
  13. Would expect some explanation of handling nested structures which is superficially handled but not detailed.
    Score: 0.5/2

Overall, the solution misses key elements required to properly use the marshmallow library for the specific needs of the problem.

Final Score:

{
  "score": 1.5
}

You can use the marshmallow library to generate dataclasses from your JSON. Here's an example of how you can achieve this:

  1. First, install the marshmallow library if you haven't already:
pip install marshmallow
  1. Create a schema.py file and import the necessary modules:
from datetime import datetime
from decimal import Decimal
from marshmallow import Schema, fields, post_load
  1. Define the dataclasses for the JSON data:
class Person(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(required=True)

class Order(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(precision=2, required=True)

class OrderSchema(Schema):
    orders = fields.List(fields.Nested(Order), required=True)
  1. Define a PersonOrderSchema class that combines the Person and OrderSchema classes:
class PersonOrderSchema(Schema):
    person = fields.Nested(Person, required=True)
    orders = fields.List(fields.Nested(Order), required=True)
  1. Define a PersonOrderSchema instance and load the JSON data:
person_order_schema = PersonOrderSchema()
person_order_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}
person_order_instance = person_order_schema.load(person_order_data)

Now, person_order_instance will be a dataclass with the properties from the JSON data, with date_of_birth as a date, created as a datetime, and amount 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: (3.0) (0.91¢)

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

  1. Defining two dataclasses:
    The solution doesn't define dataclasses. It defines Schema classes instead.

  2. Defining two schema classes:
    The solution defines four schema classes: Person, Order, OrderSchema, and PersonOrderSchema.

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

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

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

  6. Additional points:

  7. The solution uses Schema classes instead of dataclasses, which is not the correct approach.
  8. It correctly imports necessary modules.
  9. It attempts to create a structure for handling both Person and Order data.

Here's the breakdown of marks:

{
  "Defining dataclasses": 0,
  "Defining schema classes": 2,
  "Date format specification": 0,
  "Decimal field configuration": 0,
  "Using @post_load": 0,
  "Additional points": 1
}

Final score:

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

The solution misses several key requirements, most notably the use of dataclasses and the proper configuration of fields. It uses Schema classes from marshmallow instead of dataclasses, which is a fundamental misunderstanding of the task. While it shows some understanding of structuring data with marshmallow, it fails to meet the specific requirements outlined in the problem statement.

openai/gpt-4-0125-preview says: (1.5) ()

The provided solution attempts to use the marshmallow library to handle JSON data into Python data structures using schemas, but there are several issues and omissions as per the problem requirements and marshmallow library usage.

  1. Definition of dataclasses for Person and Order (2 marks):

  2. The solution incorrectly defines Person and Order as Schema instead of using Python dataclasses. This is a conceptual error as it mixes up schema definitions with data model definitions. Dataclasses are not defined here.
    Score: 0/2

  3. Definition of schema classes for Person and Order (2 marks):

  4. It defines Person, Order, and OrderSchema as schema classes. However, since the Person and Order aren't correctly defined as dataclasses separately from their schemas, this isn't entirely correct. Data and schema handling are incorrectly merged, leading to misinterpretation of the requirement.
    Score: 1/2

  5. Specifying date format for date_of_birth (1 mark):

  6. The date format for date_of_birth (%m/%d/%Y) is not specified in the fields. Required format handling in marshmallow should be explicit in the field definition to correctly parse strings into date objects.
    Score: 0/1

  7. Decimal field with as_string=True (1 mark):

  8. The amount field does not use the as_string=True attribute which is necessary to ensure that the decimal value is treated correctly as a string when loading from and dumping to JSON, preventing unnecessary conversion errors or precision loss.
    Score: 0/1

  9. Linking schema and dataclasses with @post_load method (2 marks):

  10. No @post_load decorator or related methods are presented in the solution to map the loaded data to dataclass instances. This method is essential for converting the parsed data into the actual dataclass instances after loading.
    Score: 0/2

  11. Additional considerations (2 marks):

  12. The solution does not handle actual use of the decimal and datetime types explicitly beyond field definitions. There are also no error handling or additional explanations on marshmallow usage for complex types.
  13. Would expect some explanation of handling nested structures which is superficially handled but not detailed.
    Score: 0.5/2

Overall, the solution misses key elements required to properly use the marshmallow library for the specific needs of the problem.

Final Score:

{
  "score": 1.5
}
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