Neural Chat 7B v3.1

Description

A fine-tuned model based on mistralai/Mistral-7B-v0.1 on the open source dataset Open-Orca/SlimOrca, aligned with DPO algorithm. For more details, refer to the blog: The Practice of Supervised Fine-tuning and Direct Preference Optimization on Habana Gaudi2.

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 task using marshmallow, you first need to create a schema definition which you will later use to generate the expected dataclass structures.

Create the schema to handle the outer JSON structure:

class Schema(Schema):
first_name = String(fi_constraints={'min_length': 1})
last_name = String(fi_constraints={'min_length': 1})
date_of_birth = Date(strip_offset=True, format="%m/%d/%Y")
orders = List(OrderSchema)

Create a separate schema just for the 'orders' element as it's a distinct data type:

class OrderSchema(Schema):
created = DateTime()
amount = Decimal(required=False, coerce=True)

Compile the schema using marshmallow:

schema = Schema()
ordered_schema = OrderSchema()

Now, you can easily deserialize the JSON and generate Python dataclasses. Import needed rounders (Date, DateTime, Double, etc). For example:

json_data = {
'first_name': 'Jane',
'last_name': 'Smith',
'date_of_birth': '03/16/1977',
'orders': [{
'created': '2024-01-25T10:00:00',
'amount': '100.00'
}]
}

data = schema.loads(json.dumps(json_data)) # Transforming JSON into dataclasses
order_data = ordered_schema.loads(json.dumps(data["orders"])) # Deserialising 'orders' key

After this, data and order_data will have various fields represented through their appropriate value type within the dataclasses. If needed, conversion from float to decimal is automatically done by marshmallow when handling 'amount'.

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) (0.96¢)

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

  1. The solution does not explicitly define dataclasses for the person and order. (0/2 marks)

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

  3. The solution correctly specifies the date format for the date_of_birth field using format="%m/%d/%Y". (1/1 mark)

  4. The solution does not include as_string=True for the decimal field. (0/1 mark)

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

  6. Additional points:

  7. The solution imports necessary fields from marshmallow.
  8. It demonstrates how to use the schemas to deserialize JSON data.
  9. It mentions the automatic conversion from float to decimal by marshmallow.
    (1/2 marks)

Breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 1,
  "Decimal field specification": 0,
  "Schema and dataclasses linking": 0,
  "Additional points": 1
}

Score:

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

Comments:
The solution provides a good starting point for using marshmallow to handle JSON data, but it falls short in several key areas. It doesn't define the required dataclasses, which is a crucial part of the task. The schema definitions are correct, but the decimal field is missing the as_string=True parameter. The solution also lacks the @post_load decorators to link the schemas with dataclasses. While it demonstrates how to use the schemas for deserialization, it doesn't fully meet the requirements of generating dataclasses from the JSON input.

openai/gpt-4-0125-preview says: (2.0) ()

The proposed solution presents an attempt to deserialize a JSON object into Python dataclasses using the Marshmallow library but it has several errors and lacks clarity in some aspects of implementation. Here are the details of the assessment:

  1. Definition of Dataclasses: The solution does not define any Python dataclass, which is essential for mapping JSON data onto Python objects neatly. This part of the task has been omitted entirely.

  2. Score: 0/2

  3. Definition of Schema Classes: The solution does define two schema classes, Schema and OrderSchema. However, the names are generic and could be more descriptive (e.g., PersonSchema and OrderSchema). Also, there's an undefined class Schema without any import reference which suggests it might be a typo.

  4. Score: 1/2

  5. Specifying the Date Format: The solution correctly specifies the date format for date_of_birth using the argument format="%m/%d/%Y". However, Date() and strip_offset=True do not correctly relate to Marshmallow's expected usage or syntax. The correct field type should have been fields.Date.

  6. Score: 0.5/1

  7. Handling Decimal with as_string=True: The solution does not specify the decimal field appropriately; it tries to coerce the value and manages requirement but misses the crucial as_string=True which is essential to ensure that decimals do not lose precision in JSON serialization/deserialization.

  8. Score: 0/1

  9. Linking via @post_load method: The solution does not provide a @post_load method or any mechanism which would convert the loaded data directly into the dataclasses. The use of @post_load is crucial to ensure the validation and processing logic that converts the deserialized data into the required Python object.

  10. Score: 0/2

  11. Additional elements: The solution attempts to deserialize JSON data using the schemas defined, but the usage of methods and transformations is incorrect (e.g., using json.dumps(data["orders"]) instead of correct access and handling of deserialization). The code lacks proper handling to transform the JSON objects into complex Python types effectively.

  12. Score: 0.5/2

Overall, while there is an attempt to use marshmallow, there are significant errors and misunderstandings regarding proper library usage, and critical steps and practices are missing or incorrectly applied.

Final score:

{
  "score": 2
}

To accomplish this using marshmallow, you can create the dataclasses and use schema definitions. Import marshmallow, marshmallow.Schema, and marshmallow.fields as shown below:

from marshmallow import Schema, fields

Next, define a Scholarship schema that holds the first name, last name, and date of birth as Strings.

class Scholarship(Schema):
first_name = fields.Str()
last_name = fields.Str()
date_of_birth = fields.Date(format="%m/%d/%Y")

Then, define an Order schema that holds created as a DateTime and amount as a Decimal field. Marshmallow supports Decimal type through the DecimalField for decimals. Keep in mind that "created" is assumed as a date time now but could be converted to a datetime format using datetime.fromisoformat() after validation.

class Order(Schema):
created = fields.DateTime(format="%Y-%m-%dT%H:%M:%S")
amount = fields.Decimal(allow_nan=False, allow_int_equality=False, require_empty=False)

Finally, define the top-level data class (root) that includes the Scholarship and the list of Orders.

class User(Schema):
scholarship = fields.Nested(Scholarship)
orders = fields.List(fields.Nested(Order))

Byixn an instance of User class with the provided JSON:

user_instance = User(user_json)

This instance will contain the dataclasses for all the given data in the JSON. Please note that the marshmallow library will also assist you with validation, data conversion, and other useful tasks for the data representation.

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) (1.03¢)

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 three schema classes: Scholarship, Order, and User. While this covers the required fields, it doesn't exactly match the expected structure. (1/2 marks)

  3. The solution correctly specifies the date format for the date_of_birth field with format="%m/%d/%Y". (1/1 mark)

  4. The solution does not include as_string=True for the decimal field. (0/1 mark)

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

  6. Additional observations:

  7. The solution uses a nested Scholarship schema instead of having first_name, last_name, and date_of_birth directly in the User schema.
  8. The solution uses allow_nan=False, allow_int_equality=False, require_empty=False for the Decimal field, which shows some understanding of Decimal field options.
  9. The solution mentions using datetime.fromisoformat() for the 'created' field, which is not necessary as marshmallow can handle ISO format directly.
    (1/2 marks)

Breakdown of marks:

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

Score:

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

The solution demonstrates some understanding of marshmallow schemas but misses several key requirements. It doesn't use dataclasses, which was a core part of the task. The schema structure doesn't quite match the expected output, and it's missing important elements like the @post_load decorator and the as_string parameter for the Decimal field. While it correctly handles the date format for date_of_birth, overall, the solution falls short of fully addressing the problem as specified.

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

The provided solution attempts to address the problem of parsing JSON data into Python objects using the Marshmallow library but has the following issues and merits according to the given marking scheme:

  1. Dataclasses Definition (2 marks): The solution does not actually define any Python dataclasses (@dataclass from Python's dataclasses module) for both person (Scholarship?) and order. The classes defined are schemas, not dataclasses. 0 marks.

  2. Schema Classes Definition (2 marks): Two schema classes are indeed defined: Scholarship for person-related data and Order for order-related data. Though poorly named (Scholarship is not intuitive), the schema definition part is fulfilled. 2 marks.

  3. Date Format Specification (1 mark): The date_of_birth field’s schema correctly sets the Date format to "%m/%d/%Y". 1 mark.

  4. Decimal Field as_string Parameter (1 mark): The solution fails to specify the amount field of the Order schema as a Decimal using as_string=True. It merely describes parameters like allow_nan, allow_int_equality, but misses as_string. 0 marks.

  5. Linkage of Schema and Dataclasses via @post_load (2 marks): The solution lacks any @post_load methods in the schema classes, which are critical for properly creating pointed Python objects (dataclass instances) from parsed data. 0 marks.

  6. Other Considerations (2 marks): Mention of the Marshmallow library’s capabilities like validation and data conversion is beneficial. However, significant issues such as naming inaccuracies, lack of @post_load methods, and complete absence of real dataclass definitions take away from the utility and accuracy of the implementation. Only basic structure is laid out. 1 mark.

Overall, given the strict adherence to the provided marking scheme, the solution lacks critical elements required for parsing and mapping JSON to Python objects correctly using Marshmallow in the context described. It does not fulfill the basic requirements of using Python's dataclasses and correct serialization/deserialization.

{
  "score": 4
}

To create dataclasses using the marshmallow library, you should first define a schema class and then use that schema to create an instance (dataclass) of the loaded JSON. Next, you can encode the instance back to JSON to see if your custom classes are properly mapping the attributes of the original JSON.

Create a Schmea class.

from marshmallow import Schema, fields

# Define schema for Jane object
JaneSchema = Schema(
    {
        "first_name": fields.Str(),
        "last_name": fields.Str(),
        "date_of_birth": fields.Date(format="%m/%d/%Y"),
    }
)

# Define schema for the Order object
OrderSchema = Schema(
    {
        "created": fields.DateTime(format="%Y-%m-%dT%H:%M:%S"),
        "amount": fields.Decimal(as_string=True)
    }
)

# Define a schema for the top-level object
TopSchema = Schema(
    {
        "Jane": jQuerySchema,
        "orders": fields.Nested(OrderSchema, multiple=True)
    }
)

# Create an instance of the loaded JSON using the schema
json_dict = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {'created': '2024-01-25T15:00:04', 'amount': '100.00'}
    ]
}

jane_instance = TopSchema().load(json_dict)

# Showing the result of the conversion
print("\nDumb instance:")
print(jane_instance)

Output:

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

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) (1.10¢)

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

  1. Defining two dataclasses (2 marks):
    The solution doesn't explicitly define dataclasses. It only defines schemas.
    Marks: 0/2

  2. Defining two schema classes (2 marks):
    The solution defines three schema classes: JaneSchema, OrderSchema, and TopSchema.
    Marks: 2/2

  3. Specifying date format for date_of_birth (1 mark):
    The solution correctly specifies the date format: fields.Date(format="%m/%d/%Y")
    Marks: 1/1

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

  5. Linking schema and dataclasses with @post_load (2 marks):
    The solution doesn't use @post_load to link schemas with dataclasses.
    Marks: 0/2

  6. Anything else (2 marks):

  7. The solution creates a TopSchema that nests the other schemas, which is a good approach.
  8. It demonstrates how to load JSON data using the schema.
  9. However, it doesn't actually create dataclasses, which was part of the original requirement.
    Marks: 1/2

Comments:
The solution focuses on creating schemas using marshmallow, which is a good start. However, it misses the key requirement of creating dataclasses. The schemas are correctly defined with appropriate field types and formats. The approach of nesting schemas is good for handling the nested structure of the JSON. To fully meet the requirements, the solution should have defined dataclasses and used @post_load decorators to create instances of these classes when loading data.

Here's the breakdown of marks:

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

Final score:

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

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

The provided solution has attempted to use the marshmallow library to deserialize a given JSON into Python objects using schemas but does not fully meet the expected criteria based on the marking scheme provided. Here's the analysis:

  1. Two dataclasses, one for the person and one for the order: The solution does not define any Python dataclasses. It relies entirely on the dictionaries and marshmallow schemas for structure. (-2 marks)

  2. Two schema classes, one for the person and one for the order: The solution correctly defines schema classes (JaneSchema, OrderSchema) for a person named Jane and orders, respectively. However, these classes have not been used effectively for sequencing, and their naming convention (JaneSchema instead of a more generic PersonSchema) can be misleading. Partial credit can be given for the attempt. (+1 mark)

  3. Specifying the date format for the date_of_birth field: The format has been correctly specified in the JaneSchema which maps it as fields.Date(format="%m/%d/%Y"). (+1 mark)

  4. The decimal field needs as_string=True: The solution has this done correctly in the OrderSchema via fields.Decimal(as_string=True). (+1 mark)

  5. Schema and dataclasses linked with a @post_load method: The solution does not implement a @post_load function or any equivalent method that transforms the loaded data into defined dataclasses or custom objects. (-2 marks)

  6. Anything else: The solution in its current form does not efficiently organize or elaborate on key aspects such as error handling, data validation beyond type specification, or real use-case functionality enhancements like simplifying the JSON to object conversion while maintaining it comprehensible and maintainable. The TopSchema seems to be incorrectly drafted using jQuerySchema which is not defined anywhere and seems to be a typo or misunderstanding. (-2 marks)

Given these observations, the score for this solution would be:

{
  "score": 3
}

Overall, the provided solution needs significant improvements to correctly fulfill the requirements, particularly in defining dataclasses, using @post_load, and refining schema organization and usage.

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