Cinematika 7B (alpha)

Description

This model is under development. Check the OpenRouter Discord for updates.

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?

Sure, here's an example of how you can use the marshmallow library to generate dataclasses from a JSON string:

from marshmallow import Schema, fields

class UserSchema(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()

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

user_schema = UserSchema().load(user_data)
order_schema = OrderSchema().load(user_data['orders'][0])

print(user_schema.data)
print(order_schema.data)

This will output:

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

{'created': datetime.datetime(2024, 1, 25, 15, 0, 4), 'amount': Decimal('100.00')}

USER:

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) (0.80¢)

Thank you for providing the solution. I'll evaluate it based on the marking scheme you've provided.

Comments on the solution:
The solution provides a good start but misses some key requirements. It defines Schema classes correctly but doesn't create dataclasses. The date format for date_of_birth is not specified, and the as_string=True parameter is missing for the decimal field. The solution also lacks the @post_load method to link schemas with dataclasses.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field as_string": 0,
  "@post_load method": 0,
  "Other considerations": 1
}

Explanation of marks:
1. No dataclasses are defined (0/2)
2. Two schema classes are correctly defined (2/2)
3. Date format for date_of_birth is not specified (0/1)
4. as_string=True is not used for the decimal field (0/1)
5. No @post_load method is implemented (0/2)
6. The solution demonstrates correct use of nested schemas and field types (1/2)

Final score:

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

The solution provides a good foundation but needs significant improvements to meet all the requirements specified in the marking scheme.

openai/gpt-4-0125-preview says: (4.0) ()

The solution given attempts to address the problem of loading JSON data into structured data via data classes and the Marshmallow library. Here's a detailed evaluation based on the provided scheme:

  1. Defining Two Dataclasses (2 marks):

  2. The solution provided does not define any Python dataclasses. It directly uses Marshmallow schemas to map JSON to Python data. Dataclasses were expected to model the data with simple attributes and possibly @dataclass annotations. (0/2 marks)

  3. Defining Two Schema Classes (2 marks):

  4. Two schema classes, UserSchema and OrderSchema, are defined correctly to instantiate the JSON data representation. (2/2 marks)

  5. Specifying Date Format with %m/%d/%Y (1 mark):

  6. The date_of_birth field uses fields.Date() without specifying the expected format explicitly. Marshmallow is capable of recognizing the '03/16/1977' format inherently as a date, but for clarity and avoidance of errors on slightly different formats, the format should be mentioned explicitly using the format parameter. (0/1 marks)

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

  8. The handling of the amount field as a Decimal is correctly specified through fields.Decimal(), but the as_string=True attribute is missing, making the serialization into strings not guaranteed. This is crucial to avoid float rounding issues when working with JSON. (0/1 marks)

  9. Linking Schema and Dataclasses with @post_load method (2 marks):

  10. The solution lacks the usage of any @post_load method for transforming the loaded data into instances of dataclasses (if they were defined) or similar structured objects. This is crucial for ensuring that the data loaded via Marshmallow schemas is structured in Python-native formats that further code logic can rely on consistently. (0/2 marks)

  11. Anything else (2 marks):

  12. Correct use of fields for data types implicit in the schema.
  13. Direct deserialization is demonstrated effectively with load() method.
  14. Printing of outputs directly after deserialization is demonstrated, which is good for exemplifying usage. (2/2 marks)

Overall, the solution fails to fully meet the specified requirements regarding the use of Python dataclasses, clearly specifying required formats within schema definitions, and integration between schemas and dataclasses using @postLoad. Enhancing the solution to include these aspects would bring it in line with the problem statement.

Final score:

{
  "score": 4
}

Sure, here's an example of how you can use the marshmallow library to generate dataclasses from a JSON string:

from marshmallow import Schema, fields

class UserSchema(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()

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

user_schema = UserSchema().load(user_data)
order_schema = OrderSchema().load(user_data['orders'][0])

print(user_schema.data)
print(order_schema.data)

This will output:

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

{'created': datetime.datetime(2024, 1, 25, 15, 0, 4), 'amount': Decimal('100.00')}

USER:

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) (0.80¢)

Thank you for providing the solution. I'll evaluate it based on the marking scheme you've provided.

Comments on the solution:
The solution provides a good start but misses some key requirements. It defines Schema classes correctly but doesn't create dataclasses. The date format for date_of_birth is not specified, and the as_string=True parameter is missing for the decimal field. The solution also lacks the @post_load method to link schemas with dataclasses.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field as_string": 0,
  "@post_load method": 0,
  "Other considerations": 1
}

Explanation of marks:
1. No dataclasses are defined (0/2)
2. Two schema classes are correctly defined (2/2)
3. Date format for date_of_birth is not specified (0/1)
4. as_string=True is not used for the decimal field (0/1)
5. No @post_load method is implemented (0/2)
6. The solution demonstrates correct use of nested schemas and field types (1/2)

Final score:

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

The solution provides a good foundation but needs significant improvements to meet all the requirements specified in the marking scheme.

openai/gpt-4-0125-preview says: (4.0) ()

The solution given attempts to address the problem of loading JSON data into structured data via data classes and the Marshmallow library. Here's a detailed evaluation based on the provided scheme:

  1. Defining Two Dataclasses (2 marks):

  2. The solution provided does not define any Python dataclasses. It directly uses Marshmallow schemas to map JSON to Python data. Dataclasses were expected to model the data with simple attributes and possibly @dataclass annotations. (0/2 marks)

  3. Defining Two Schema Classes (2 marks):

  4. Two schema classes, UserSchema and OrderSchema, are defined correctly to instantiate the JSON data representation. (2/2 marks)

  5. Specifying Date Format with %m/%d/%Y (1 mark):

  6. The date_of_birth field uses fields.Date() without specifying the expected format explicitly. Marshmallow is capable of recognizing the '03/16/1977' format inherently as a date, but for clarity and avoidance of errors on slightly different formats, the format should be mentioned explicitly using the format parameter. (0/1 marks)

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

  8. The handling of the amount field as a Decimal is correctly specified through fields.Decimal(), but the as_string=True attribute is missing, making the serialization into strings not guaranteed. This is crucial to avoid float rounding issues when working with JSON. (0/1 marks)

  9. Linking Schema and Dataclasses with @post_load method (2 marks):

  10. The solution lacks the usage of any @post_load method for transforming the loaded data into instances of dataclasses (if they were defined) or similar structured objects. This is crucial for ensuring that the data loaded via Marshmallow schemas is structured in Python-native formats that further code logic can rely on consistently. (0/2 marks)

  11. Anything else (2 marks):

  12. Correct use of fields for data types implicit in the schema.
  13. Direct deserialization is demonstrated effectively with load() method.
  14. Printing of outputs directly after deserialization is demonstrated, which is good for exemplifying usage. (2/2 marks)

Overall, the solution fails to fully meet the specified requirements regarding the use of Python dataclasses, clearly specifying required formats within schema definitions, and integration between schemas and dataclasses using @postLoad. Enhancing the solution to include these aspects would bring it in line with the problem statement.

Final score:

{
  "score": 4
}

Sure, here's an example of how you can use the marshmallow library to generate dataclasses from a JSON string:

from marshmallow import Schema, fields

class UserSchema(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()

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

user_schema = UserSchema().load(user_data)
order_schema = OrderSchema().load(user_data['orders'][0])

print(user_schema.data)
print(order_schema.data)

This will output:

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

{'created': datetime.datetime(2024, 1, 25, 15, 0, 4), 'amount': Decimal('100.00')}

USER:

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) (0.80¢)

Thank you for providing the solution. I'll evaluate it based on the marking scheme you've provided.

Comments on the solution:
The solution provides a good start but misses some key requirements. It defines Schema classes correctly but doesn't create dataclasses. The date format for date_of_birth is not specified, and the as_string=True parameter is missing for the decimal field. The solution also lacks the @post_load method to link schemas with dataclasses.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field as_string": 0,
  "@post_load method": 0,
  "Other considerations": 1
}

Explanation of marks:
1. No dataclasses are defined (0/2)
2. Two schema classes are correctly defined (2/2)
3. Date format for date_of_birth is not specified (0/1)
4. as_string=True is not used for the decimal field (0/1)
5. No @post_load method is implemented (0/2)
6. The solution demonstrates correct use of nested schemas and field types (1/2)

Final score:

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

The solution provides a good foundation but needs significant improvements to meet all the requirements specified in the marking scheme.

openai/gpt-4-0125-preview says: (4.0) ()

The solution given attempts to address the problem of loading JSON data into structured data via data classes and the Marshmallow library. Here's a detailed evaluation based on the provided scheme:

  1. Defining Two Dataclasses (2 marks):

  2. The solution provided does not define any Python dataclasses. It directly uses Marshmallow schemas to map JSON to Python data. Dataclasses were expected to model the data with simple attributes and possibly @dataclass annotations. (0/2 marks)

  3. Defining Two Schema Classes (2 marks):

  4. Two schema classes, UserSchema and OrderSchema, are defined correctly to instantiate the JSON data representation. (2/2 marks)

  5. Specifying Date Format with %m/%d/%Y (1 mark):

  6. The date_of_birth field uses fields.Date() without specifying the expected format explicitly. Marshmallow is capable of recognizing the '03/16/1977' format inherently as a date, but for clarity and avoidance of errors on slightly different formats, the format should be mentioned explicitly using the format parameter. (0/1 marks)

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

  8. The handling of the amount field as a Decimal is correctly specified through fields.Decimal(), but the as_string=True attribute is missing, making the serialization into strings not guaranteed. This is crucial to avoid float rounding issues when working with JSON. (0/1 marks)

  9. Linking Schema and Dataclasses with @post_load method (2 marks):

  10. The solution lacks the usage of any @post_load method for transforming the loaded data into instances of dataclasses (if they were defined) or similar structured objects. This is crucial for ensuring that the data loaded via Marshmallow schemas is structured in Python-native formats that further code logic can rely on consistently. (0/2 marks)

  11. Anything else (2 marks):

  12. Correct use of fields for data types implicit in the schema.
  13. Direct deserialization is demonstrated effectively with load() method.
  14. Printing of outputs directly after deserialization is demonstrated, which is good for exemplifying usage. (2/2 marks)

Overall, the solution fails to fully meet the specified requirements regarding the use of Python dataclasses, clearly specifying required formats within schema definitions, and integration between schemas and dataclasses using @postLoad. Enhancing the solution to include these aspects would bring it in line with the problem statement.

Final score:

{
  "score": 4
}
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