FireLLaVA 13B

Description

A blazing fast vision-language model, FireLLaVA quickly understands both text and images. It achieves impressive chat skills in tests, and was designed to mimic multimodal GPT-4.

The first commercially permissive open source LLaVA model, trained entirely on open source LLM generated instruction following data.

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 generate dataclasses using the marshmallow library in python, you can create a schema for each dataclass and then use the dump method to convert the json data to instances of the dataclasses.

Here's an example of how you can do this for the given json data:

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

@dataclass
class Order:
    created: datetime
    amount: float

@dataclass
class Customer:
    first_name: str
    last_name: str
    date_of_birth: datetime
    orders: list[Order]

scheme = Schema(
    fields.Str(name="first_name"),
    fields.Str(name="last_name"),
    fields.Date(name="date_of_birth"),
    fields.List(
        fields.Nested(
            schema=OrderSchema()
        ),
        name="orders"
    ),
)

order_schema = Schema(
    fields.DateTime(name="created"),
    fields.Float(name="amount"),
)

data = {'first_name': 'Jane',
        'last_name': 'Smith',
        'date_of_birth': '03/16/1977',
        'orders': [{'created': '2024-01-25T15:00:
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: (3.0) (0.96¢)

Let's evaluate this solution based on the marking scheme:

  1. The solution defines two dataclasses, Order and Customer. (2 marks)

  2. The solution defines two schema classes, Schema for the customer and OrderSchema for the order. However, OrderSchema is not fully defined in the provided code. (1 mark)

  3. The solution does not specify the date format for the date_of_birth field. (0 marks)

  4. The decimal field (amount) is not specified as a Decimal with as_string=True. It's defined as a float instead. (0 marks)

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

  6. Other observations:

  7. The Schema class is used incorrectly. It should be subclassed, not instantiated directly.
  8. The date_of_birth field in the dataclass is defined as datetime, but in the schema, it's defined as Date.
  9. The solution is incomplete, cutting off mid-dictionary.
    (0 marks)

Breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 1,
  "Date format specified": 0,
  "Decimal field correctly defined": 0,
  "Post_load methods included": 0,
  "Other considerations": 0
}

Score:

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

This solution demonstrates a basic understanding of using dataclasses and marshmallow for JSON parsing, but it has several significant issues and is incomplete. It doesn't fully address the requirements specified in the problem statement, particularly regarding data types and schema-to-dataclass mapping. The implementation of the schemas is incorrect, and the solution doesn't handle the specific data format requirements mentioned in the problem. Overall, while it shows some knowledge of the concepts, it needs substantial improvement to fully solve the given problem.

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

The provided solution has several issues and omissions based on the criteria laid out in the marking scheme:

  1. Dataclasses definition:

  2. Two dataclasses (Order and Customer) have been defined correctly. This meets the requirement.
    +2 marks

  3. Schema classes definition:

  4. The schemas ('scheme' and 'order_schema') are attempted but are syntactically incorrect as they use Schema(...) with wrong parameters and do not properly inherit from Schema. The constructors of Schema do not accept positional arguments of fields.XXX.
    +0 marks

  5. Date format specification:

  6. The date format is not specified using %m/%d/%Y as required for date_of_birth. The fields.Date is used, but without specifying the format.
    +0 marks

  7. Decimal field specification:

  8. The amount field in the Order is incorrectly defined as a float instead of using fields.Decimal(as_string=True) as outlined by the problem statement.
    +0 marks

  9. Linking schema to dataclasses using @post_load:

  10. There is no @post_load method implemented in either schema to convert the loaded data back into instances of the dataclasses, which is a critical integration point when using Marshmallow with data classes.
    +0 marks

  11. Additional criteria:

  12. Some correct foundation elements are used such as the right import statements, correct basic structure for dataclasses, and partially correct use of marshmallow fields, though implemented incorrectly.
    +1 mark

Overall, the solution misunderstands the implementation of Marshmallow schemas, particularly not using the correct form of schema declarations and missing critical functionalities like specifying data types appropriately (decimal, date format) and essential methods (@post_load). The solution appears incomplete and cut off, potentially due to an error or oversight.

Based on these evaluations:

{
  "score": 3
}

You can use the marshmallow library to define custom dataclasses from the given JSON data. First, install the marshmallow library using pip install marshmallow. Then, you can define your dataclasses with the necessary fields and their corresponding types. Finally, you can use the parse method of the Marshmallow class to deserialize the JSON data into instances of your dataclasses.

Here's an example of how to define

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: (1.0) (0.81¢)

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

The solution provided is incomplete and does not actually implement the required functionality. It only provides a general description of how to use the marshmallow library without any specific code. Let's break down the marks:

  1. The solution does not define any dataclasses. (0/2 marks)
  2. The solution does not define any schema classes. (0/2 marks)
  3. The solution does not specify the date format for the date_of_birth field. (0/1 mark)
  4. The solution does not mention as_string=True for the decimal field. (0/1 mark)
  5. The solution does not implement or mention @post_load methods. (0/2 marks)
  6. The solution does provide a basic overview of using marshmallow, which is somewhat helpful. (1/2 marks)

Comments:
The provided solution is very general and lacks specific implementation details. It doesn't address any of the specific requirements mentioned in the problem statement, such as handling date, datetime, and decimal fields. The solution also doesn't show how to create the required dataclasses or schema classes using marshmallow. To improve, the solution should include actual code examples demonstrating the creation of dataclasses, schema classes, and the proper field definitions with their respective formats and options.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 0,
  "Date format specification": 0,
  "Decimal field as_string": 0,
  "post_load implementation": 0,
  "General information": 1
}

Final score:

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

openai/gpt-4-0125-preview says: (0.0) ()

The provided solution has a number of deficiencies when evaluated against the marking scheme:

  1. The solution does not define two dataclasses for person and order. There is no mention of using Python's dataclasses module for creating the structural representation of the JSON object. (0/2)
  2. Similarly, two schema classes are not defined using marshmallow. The provided text mentions only the general approach without specificity in implementation, such as creating specific schema classes for person and order. (0/2)
  3. No specification is provided for the date format in the date_of_birth field. Marshmallow allows customizing field formats with parameters like format="%m/%d/%Y", but it was not stated in the provided solution. (0/1)
  4. The solution fails to mention the handling of the amount field with as_string=True parameter, which is essential for ensuring that the decimal field is treated as a string. (0/1)
  5. There is no mention of using the @post_load decorator or any method to link the schema and dataclasses, which is crucial for creating instances of the dataclasses based on parsed data. (0/2)
  6. Overall, the description provided is too generalized and lacking in detail. It does not describe how to specifically use the marshmallow library to decode the JSON correctly according to the fields' data types mentioned in the problem statement. Thus, additional points for completeness and minute detail are also missing. (0/2)

Resulting Score:

{
  "score": 0
}

The solution needs a lot of refinement and specificity to adequately solve the given problem using the marshmallow library.

You can create dataclasses with the desired field types using the date, DateTime, and Decimal classes from the built-in datetime and decimal modules in Python. Here's an example:

from datetime import datetime, timedelta
from decimal import Decimal
from marshmallow import Schema, fields
from typing import Optional

class BirthDate(fields.Field):
    def default(self, date_str):
        return datetime.strptime(date_str, '%m/%d/%Y')

    def to_native(self, obj, many=False):
        if isinstance(obj, datetime):
            return obj.date()
        elif isinstance(obj, str):
            return datetime.strptime(obj, '%m/%d/%Y')
        else:
            return super().to_native(obj, many)

class Order(Schema):
    created = fields.DateTime(dt_fmt='%Y-%m-%dT%H:%M:%S')
    amount = fields.Decimal(precision=2, scale=2)

class Person(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = BirthDate()
    orders = fields.List(Order)

# Example usage
person_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 = Person()
data = person_schema.dump(person_data)
print(data)

This will give you the desired dataclasses with the specified field 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)

anthropic/claude-3.5-sonnet says: (4.0) (1.00¢)

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

  1. The solution does not define dataclasses as requested. It only defines Schema classes. (0/2 marks)

  2. The solution defines two schema classes: Order and Person. (2/2 marks)

  3. The solution specifies a custom BirthDate field, which handles the date format, but it doesn't explicitly use the format "%m/%d/%Y" in the schema definition. (0.5/1 mark)

  4. The decimal field (amount) is defined correctly, but it doesn't include as_string=True. (0/1 mark)

  5. The solution doesn't use @post_load decorators to link schema and dataclasses. (0/2 marks)

  6. Additional points:

  7. The solution introduces a custom BirthDate field, which is a good approach to handle date formatting.
  8. It demonstrates how to use the schemas with an example.
    (1.5/2 marks)

Comments:
The solution partially addresses the requirements but misses some key points. It focuses on creating Schema classes rather than dataclasses as requested. The date formatting for date_of_birth is handled creatively with a custom field, but it doesn't exactly match the specified format in the marking scheme. The decimal field misses the as_string=True parameter. The solution doesn't implement the @post_load method to create dataclass instances from the validated data. However, the overall structure and the example usage are helpful.

Here's the breakdown of marks:

{
  "Dataclasses": 0,
  "Schema classes": 2,
  "Date format": 0.5,
  "Decimal field": 0,
  "Post_load method": 0,
  "Additional points": 1.5
}

Final score:

{
  "scores": [0, 2, 0.5, 0, 0, 1.5],
  "total_score": 4
}

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

The provided solution is evaluated on the following criteria:

  1. Defining Data Classes:

  2. The submitted solution does not explicitly define any Python dataclasses. Instead, the solution uses Marshmallow's Schema to model data. Remember, Python's dataclasses module is specifically for this purpose, which has not been used here.

  3. Defining Schema Classes:

  4. The solution properly defines schema classes (Person and Order) using Marshmallow, which is expected. They are correctly structured to mirror the outlined JSON structure.

  5. Date Format Specification for date_of_birth:

  6. While the solution creates a custom field class BirthDate to handle date strings, the implied requirement to format the date_of_birth field as "%m/%d/%Y" is technically addressed. However, the solution's handling of this field can be confusing and possibly error-prone as there are issues in the custom methods such as to_native which does not adhere to typical Marshmallow conventions.

  7. Decimal Field Specification:

  8. The solution defines the amount field as a Decimal in the Order schema, but it missed setting as_string=True which was specifically required by the problem statement to ensure the amount is treated as a string in serialized format.

  9. Linking Schema and Data Classes with @post_load:

  10. The solution does not use the @post_load decorator to create instances of any dataclasses post deserialization. It lacks any mechanisms to convert loaded data into actual instances of a structured class (like dataclasses) which would have been ideal for encapsulation and further processing.

  11. Additional Considerations:

  12. Implementation of custom fields should generally adhere more cleanly to the Field interface, which the custom BirthDate field sort of misses. Conceptually, the right classes (datetime, Decimal) are chosen, but integration with the serialized/deserialized objects isn't entirely correct.

Given these points, I would score the solution as follows:

{
  "score": 4
}

This score reflects major misses on the proper use of dataclasses, incorrect handling or implementation details like as_string for decimals, and missing implementation detail with the @post_load post-processing. Also, it complements the accomplishment in creating correct schema definitions and attempting to tackle date and decimal transformations, albeit with some flaws in execution.

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