RWKV v5 World 3B

Description

RWKV is an RNN (recurrent neural network) with transformer-level performance. It aims to combine the best of RNNs and transformers - great performance, fast inference, low VRAM, fast training, "infinite" context length, and free sentence embedding.

RWKV-5 is trained on 100+ world languages (70% English, 15% multilang, 15% code).

RWKV 3B models are provided for free, by Recursal.AI, for the beta period. More details here.

#rnn

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 datetime and decimal modules to convert the date and amount fields to their respective dataclasses.
Here's an example of how you can do this:

from datetime import datetime, timedelta
from decimal import Decimal
import marshmallow

class Order(marshmallow.Schema):
    date_of_birth = marshmallow.fields.Date(required=True)
    amount = marshmallow.fields.Decimal(required=True)

class OrderData(marshmallow.Schema):
    first_name = marshmallow.fields.String()
    last_name = marshmallow.fields.String()
    date_of_birth = marshmallow.fields.Date(required=True)
    orders = marshmallow.fields.Nested(Order, many=True)

data = {
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  'orders': [
      {
          'created': '2024-01-25T15:00:04',
          'amount': 100
      }
  ]
}
data2 = data.__schema__.dumps({})
print(data2)  # {"firstName":"Jane","lastName":"Smith","dateOfBirth":"03/16/1977","orders":[{"created":1500444800,"amount":100}]}
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: (0.5) ()

Evaluation of the Provided Solution:

  1. Defining Two Dataclasses for Person and Order:
  2. The solution does not define any Python dataclasses for Person and Order. Instead, it mislabels two schema classes (Order and OrderData) as dataclasses. This is incorrect.

  3. Score: 0/2

  4. Defining Two Schema Classes for Person and Order:

  5. While the names suggest there are two schema classes (Order and OrderData), they are incorrectly defined. OrderData is supposed to represent a person and handle nested orders but mixes fields from both Person and Order. The Order class is misconfigured, carrying fields that are not appropriate (e.g., date_of_birth should not be in Order).

  6. Score: 0.5/2

  7. Specifying the Date Format for date_of_birth:

  8. The solution does not specify the date format using format="%m/%d/%Y". There is no configuration provided in the fields.Date() field to interpret the date as per the required format.

  9. Score: 0/1

  10. Decimal Field with as_string=True:

  11. The amount field in the Order schema class uses marshmallow.fields.Decimal(required=True) but fails to include the as_string=True parameter to ensure the value is treated and validated as a decimal string.

  12. Score: 0/1

  13. Linking Schema and Dataclasses with @post_load Method:

  14. There is no @post_load method implemented in the solution. This decorator is essential for transforming the loaded data back into an instance of a dataclass, which is a critical step when using schemas with dataclasses.

  15. Score: 0/2

  16. Additional Marks for Other Aspects:

  17. The solution demonstrates a misunderstanding of how to properly use marshmallow. The mix-up between dataclasses and schemas and the complete absence of @post_load and the serialization/deserialization processes results in a failure to meet the objectives.
  18. Score: 0/2

Total Score:

{
  "score": 0.5
}

The provided solution does not appropriately tackle the problem requirements, leading to a very low score. It fails mostly in the implementation and understanding of marshmallow's correct usage in the context that was outlined. Corrections in defining proper schemas, implementing data conversion correctly, and using the marshmallow library effectively are needed.

anthropic/claude-3.5-sonnet says: (3.0) (0.93¢)

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

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

  2. The solution defines two schema classes: Order and OrderData. (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 use as_string=True for the decimal field. (0/1 mark)

  5. The solution does not implement a @post_load method to link schemas and dataclasses. (0/2 marks)

  6. Additional observations:

  7. The Order schema is missing the created field.
  8. The field names in the schemas don't match the JSON keys (e.g., date_of_birth instead of dateOfBirth).
  9. The solution attempts to use __schema__ attribute which is not a standard Marshmallow feature.
  10. The example usage at the end is incorrect and would raise errors.
    (1/2 marks for partial attempt at schema definition)

Comments:
The solution shows a basic understanding of Marshmallow schemas but falls short in several areas. It doesn't use dataclasses as requested, misses key fields, and doesn't properly handle date formats or decimal conversion. The usage example at the end is incorrect and wouldn't work as written. The solution needs significant improvements to meet the requirements.

Here's the breakdown of marks:

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

Final score:

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

You can use the datetime and decimal modules to convert the date and amount fields to their respective dataclasses.
Here's an example of how you can do this:

from datetime import datetime, timedelta
from decimal import Decimal
import marshmallow

class Order(marshmallow.Schema):
    date_of_birth = marshmallow.fields.Date(required=True)
    amount = marshmallow.fields.Decimal(required=True)

class OrderData(marshmallow.Schema):
    first_name = marshmallow.fields.String()
    last_name = marshmallow.fields.String()
    date_of_birth = marshmallow.fields.Date(required=True)
    orders = marshmallow.fields.Nested(Order, many=True)

data = {
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  'orders': [
      {
          'created': '2024-01-25T15:00:04',
          'amount': 100
      }
  ]
}
schema = OrderData(**data)
print(schema)  # Output: {'firstName': 'Jane', 'lastName': 'Smith', 'dateOfBirth': datetime(...), ...}
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:

The solution provided doesn't fully meet the requirements. Here's a breakdown:

  1. The solution doesn't define dataclasses, only schema classes. (0/2 marks)
  2. The solution defines two schema classes, Order and OrderData. (2/2 marks)
  3. The solution doesn't specify the date format for the date_of_birth field. (0/1 mark)
  4. The decimal field is not specified with as_string=True. (0/1 mark)
  5. The solution doesn't use @post_load method to link schema and dataclasses. (0/2 marks)
  6. Other issues:
  7. The 'created' field is missing in the Order schema.
  8. The schema instantiation is incorrect (schema = OrderData(**data)).
  9. The solution doesn't demonstrate how to actually load and validate the data.
    (1/2 marks)

Here's the mark breakdown:

{
  "Dataclasses": 0,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal as_string": 0,
  "post_load method": 0,
  "Other": 1
}

The solution shows an understanding of using Marshmallow for schema definition but misses several key requirements. It doesn't use dataclasses, doesn't properly handle the date and decimal fields, and doesn't demonstrate how to actually use the schema to load and validate data.

Here's the score:

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

The solution scores 3 out of 10 based on the marking scheme.

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

The provided solution does not correctly address the requirements specified in the prompt. The evaluation of the solution based on the specified criteria is as follows:

  1. Definition of Two Dataclasses:
  2. The solution does not define any dataclasses; it mistakenly uses the Schema classes from the marshmallow library instead of creating actual dataclass structures for defining the data model of Person and Order.

  3. Score: 0/2

  4. Definition of Two Schema Classes:

  5. Although two schema classes are defined, they are incorrectly named and implemented (Order and OrderData should rather be something like OrderSchema and PersonSchema for clarity).
  6. Furthermore, the structure of Order seems to mix up the properties of orders and people (e.g., date_of_birth is incorrectly included in Order).

  7. Score: 0.5/2

  8. Specification of Date Format for date_of_birth:

  9. The solution does not specify the date format (%m/%d/%Y) for the date_of_birth field as required.

  10. Score: 0/1

  11. Decimal Field with as_string=True:

  12. The amount field correctly specifies the type as Decimal but fails to include the as_string=True argument which would be appropriate to ensure the amount is treated as a string during serialization.

  13. Score: 0/1

  14. Linking Schema and Dataclasses with @post_load:

  15. There is no @post_load method in the given solution. This decorator is essential to convert loaded data into instances of a dataclass, which is not even defined or used.

  16. Score: 0/2

  17. Other Required Implementations:

  18. The use of correct field types in marshmallow (like fields.Date for dates) is noticed, although not entirely accurate (misplacement of properties).
  19. Lack of proper instantiation (OrderData(**data) is incorrect - it should use .load(data) method for parsing).
  20. Score: 0.5/2

Overall, the solution lacks adherence to the prompt requirements and shows significant inaccuracies and misunderstanding of marshmallow usage and data class integration.

{
  "score": 1
}

This is a low score, reflecting that the main requirements and structure for the solution were not met.

You can use the datetime and decimal modules to convert the date and amount fields to their respective dataclasses.
Here's an example of how you can do this:

from datetime import datetime, timedelta
from decimal import Decimal
import marshmallow

class Order(marshmallow.Schema):
    date_of_birth = marshmallow.fields.Date(required=True)
    amount = marshmallow.fields.Decimal(required=True)

class OrderData(marshmallow.Schema):
    first_name = marshmallow.fields.String()
    last_name = marshmallow.fields.String()
    date_of_birth = marshmallow.fields.Date(required=True)
    orders = marshmallow.fields.Nested(Order, many=True)

data = {
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  'orders': [
      {
          'created': '2024-01-25T15:00:04',
          'amount': 100
      }
  ]
}
schema = OrderData(**data)
print(schema)  # Output: {'firstName': 'Jane', 'lastName': 'Smith', 'dateOfBirth': datetime(...), ...}
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:

The solution provided doesn't fully meet the requirements. Here's a breakdown:

  1. The solution doesn't define dataclasses, only schema classes. (0/2 marks)
  2. The solution defines two schema classes, Order and OrderData. (2/2 marks)
  3. The solution doesn't specify the date format for the date_of_birth field. (0/1 mark)
  4. The decimal field is not specified with as_string=True. (0/1 mark)
  5. The solution doesn't use @post_load method to link schema and dataclasses. (0/2 marks)
  6. Other issues:
  7. The 'created' field is missing in the Order schema.
  8. The schema instantiation is incorrect (schema = OrderData(**data)).
  9. The solution doesn't demonstrate how to actually load and validate the data.
    (1/2 marks)

Here's the mark breakdown:

{
  "Dataclasses": 0,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal as_string": 0,
  "post_load method": 0,
  "Other": 1
}

The solution shows an understanding of using Marshmallow for schema definition but misses several key requirements. It doesn't use dataclasses, doesn't properly handle the date and decimal fields, and doesn't demonstrate how to actually use the schema to load and validate data.

Here's the score:

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

The solution scores 3 out of 10 based on the marking scheme.

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

The provided solution does not correctly address the requirements specified in the prompt. The evaluation of the solution based on the specified criteria is as follows:

  1. Definition of Two Dataclasses:
  2. The solution does not define any dataclasses; it mistakenly uses the Schema classes from the marshmallow library instead of creating actual dataclass structures for defining the data model of Person and Order.

  3. Score: 0/2

  4. Definition of Two Schema Classes:

  5. Although two schema classes are defined, they are incorrectly named and implemented (Order and OrderData should rather be something like OrderSchema and PersonSchema for clarity).
  6. Furthermore, the structure of Order seems to mix up the properties of orders and people (e.g., date_of_birth is incorrectly included in Order).

  7. Score: 0.5/2

  8. Specification of Date Format for date_of_birth:

  9. The solution does not specify the date format (%m/%d/%Y) for the date_of_birth field as required.

  10. Score: 0/1

  11. Decimal Field with as_string=True:

  12. The amount field correctly specifies the type as Decimal but fails to include the as_string=True argument which would be appropriate to ensure the amount is treated as a string during serialization.

  13. Score: 0/1

  14. Linking Schema and Dataclasses with @post_load:

  15. There is no @post_load method in the given solution. This decorator is essential to convert loaded data into instances of a dataclass, which is not even defined or used.

  16. Score: 0/2

  17. Other Required Implementations:

  18. The use of correct field types in marshmallow (like fields.Date for dates) is noticed, although not entirely accurate (misplacement of properties).
  19. Lack of proper instantiation (OrderData(**data) is incorrect - it should use .load(data) method for parsing).
  20. Score: 0.5/2

Overall, the solution lacks adherence to the prompt requirements and shows significant inaccuracies and misunderstanding of marshmallow usage and data class integration.

{
  "score": 1
}

This is a low score, reflecting that the main requirements and structure for the solution were not met.

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