LLaVA v1.6 34B

Description

LLaVA Yi 34B is an open-source model trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: NousResearch/Nous-Hermes-2-Yi-34B

It was trained in December 2023.

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?

Response 1 (3.0)

You can create a dataclass using marshmallow that maps the JSON data using field types provided by the marshmallow library as follows:

from datetime import datetime
from decimal import Decimal
from marshmallow import Field, fields, validate
from typing import Dict, List

class Person(dataclasses.dataclass):
    first_name: str = field(metadata={'validation': validate.OneOf([Field(regex='[a-zA-Z]+')] * 20)})
    last_name: str = field(metadata={'validation': validate.OneOf([Field(regex='[a-zA-Z]+')] * 20)})
    date_of_birth: datetime = field(exclude=True)
    orders: List[Order] = field(metadata={'exclude': not hasattr(PERSON.first_name, 'created')})

class Order(dataclasses.dataclass):
    created: datetime = field(metadata={'validation': validate.OneOf([Field(regex='[0-9]+/.+/')] * 20)})
    amount: Decimal = field(metadata={'validation': validate.OneOf([Field(regex='[0-9]+:.+')] * 20)})

Then, you can use the 'MarshmallowDataclass' adapter to convert the dataclass to a 'Marshmallow' object:

from marshmallow.dataclass import MarshmallowDataclass
import datetime

schema = MarshmallowDataclass(Person)

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

# Data schema
result = schema.load(data)
print('Loaded data:')
pprint(result)

The above code snippet should generate the required dataclasses with the correct types for the fields (date * datetime.date, created * datetime.datetime, amount * decimal.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) (0.97¢)

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

The solution defines two dataclasses, Person and Order. (2 marks)
The solution does not explicitly define schema classes. Instead, it uses MarshmallowDataclass to generate schemas. This doesn't fully meet the requirement. (0 marks)
The solution does not specify the date format for the date_of_birth field. (0 marks)
The decimal field (amount) is not specified with as_string=True. (0 marks)
The solution does not implement @post_load methods to link schemas and dataclasses. (0 marks)
Additional considerations:
The solution uses dataclasses and MarshmallowDataclass, which is a valid approach but different from the expected marshmallow implementation.
The solution includes some validation using regex, which wasn't required but adds value.
The implementation of the 'orders' field in the Person class is incorrect and would likely cause errors.
(1 mark)

Breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 0,
  "Date format specified": 0,
  "Decimal field as_string": 0,
  "post_load methods": 0,
  "Additional considerations": 1
}

Score:

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

The solution demonstrates an understanding of dataclasses and attempts to use marshmallow, but it doesn't fully meet the requirements specified in the problem. It misses key elements such as defining separate schema classes, specifying field formats, and linking schemas to dataclasses using @post_load methods. The approach using MarshmallowDataclass is interesting but doesn't align with the expected use of marshmallow as described in the problem statement.

openai/gpt-4-0125-preview says: (0.5) ()

The provided solution for using the marshmallow library and dataclasses in Python has several issues and areas where it does not align with the requirements or typical usage:

Definition of Two Dataclasses: The solution properly declares two dataclasses, Person and Order. However, the Person class is misusing the validation and exclude parameters in a way that is syntactically incorrect for dataclasses. Also, Order fields are not instantiated correctly. Score: 0.5/2
Definition of Two Schema Classes: The solution does not include explicit schema classes derived from marshmallow's Schema. Instead, it tries to utilize MarshmallowDataclass directly, which is not standard and the syntax used is incorrect. Score: 0/2
Date Format Specification: The solution does not specify the format for date_of_birth to be "%m/%d/%Y" as required. Score: 0/1
Decimal Field as String: Although the solution should use fields.Decimal(as_string=True), there’s no clear application of this in the provided code. Score: 0/1
Linkage of Schema and Dataclasses Using @post_load: The solution does not use @post_load methods or any other method to correctly parse and validate data after loading into the dataclass structure. Score: 0/2
Additional Elements:
Use of regex in validation incorrectly employs Field(regex='...') syntax where Field is actually not intended to be used for regex validation this way.
Misuse of marshmallow terms and constructs (like exclude in dataclass fields).
The instantiation of MarshmallowDataclass is not correct in the context of marshmallow.
The handling of datetime and decimal fields is incorrectly or inadequately addressed.

Score: 0/2

Summary: The provided solution demonstrates an attempt to use dataclasses with marshmallow but fails to correctly implement or utilize key features like schemas, validation, and type conversion. The example confuses certain Python and marshmallow concepts leading to non-functional code.

{
  "score": 0.5
}

Response 2 (3.0)

To generate the data classes from the given JSON using the marshmallow library in Python, you can follow these steps:

First, install the marshmallow library if you haven't already:

pip install marshmallow

Define a schema for the JSON object:

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()

Use the dump() method to transform the JSON object into a Python object:

import json
from datetime import datetime
from decimal import Decimal

json_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()
user_data = user_schema.dump(json_data)

Now, you can use the update() method to update the original JSON object with the generated Python objects:

updated_data = json_data
updated_data['date_of_birth'] = datetime.strptime(json_data['date_of_birth'], '%m/%d/%Y').date()
updated_data['orders'][0]['created'] = datetime.fromisoformat(updated_data['orders'][0]['created'])
updated_data['orders'][0]['amount'] = Decimal(updated_data['orders'][0]['amount'])

Finally, you can use the update() method to generate a new JSON object with the updated Python objects:

import json

updated_json = updated_data.dump().data
print(json.dumps(updated_json, ensure_ascii=False))

This will output the following JSON object with the specified data types:

{
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': datetime.datetime(1977, 3, 16),
  'orders': [
    {
      'created': datetime.datetime(2024, 1, 25, 15, 0, 4),
      'amount': Decimal('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: (3.0) (1.09¢)

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

Defining two dataclasses (2 marks):
The solution doesn't define any dataclasses. It only defines schema classes.
Marks: 0/2
Defining two schema classes (2 marks):
The solution correctly defines UserSchema and OrderSchema.
Marks: 2/2
Specifying date format for date_of_birth (1 mark):
The solution doesn't specify the date format in the schema. It uses strptime() in a separate step.
Marks: 0/1
Decimal field with as_string=True (1 mark):
The solution doesn't use as_string=True for the amount field.
Marks: 0/1
Linking schema and dataclasses with @post_load (2 marks):
The solution doesn't use @post_load or link to any dataclasses.
Marks: 0/2
Anything else (2 marks):
The solution does provide a way to convert the JSON data to Python objects with correct types, although not using the recommended method. It also shows how to update the original JSON with the converted data.
Marks: 1/2

Breakdown of marks:

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

Score:

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

Comments:
The solution partially addresses the problem by defining schema classes and providing a way to convert JSON data to Python objects with correct types. However, it doesn't use dataclasses as requested, doesn't specify the date format in the schema, doesn't use as_string=True for the decimal field, and doesn't use @post_load to link schemas with dataclasses. The approach used is more manual and doesn't fully leverage the features of the marshmallow library as specified in the problem statement.

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

The provided solution attempts to use Marshmallow for processing a JSON object by defining Schemas, handling conversions of date and time formats, and custom data types like decimals. Let's evaluate it according to the specified criteria:

Defining dataclasses: The solution does not define any Python dataclasses for the schema; it only uses Marshmallow's Schema definitions. The use of actual dataclasses would involve importing dataclasses from Python's standard library and defining class structures accordingly. (0/2)
Defining schema classes: Two schema classes, UserSchema and OrderSchema, are correctly defined which aligns with the requirement. (2/2)
Specifying date format for date_of_birth: The solution merely has date_of_birth = fields.Date() without specifying the required format %m/%d/%Y. This is crucial for correctly parsing the string date from JSON. (0/1)
Decimal field with as_string=True: The solution does not use as_string=True in the definition of the Decimal field. This attribute is important to ensure the decimal amount remains a string when serialized/deserialized, providing accuracy and preventing loss from floating-point representation. (0/1)
Linking schema and dataclasses with @post_load: The solution does not employ the @post_load decorator or any related method to convert loaded data into instances of a Python dataclass or any other structured object. This is a significant omission as it directly affects the ability to transform deserialized data into practical, strongly-typed objects. (0/2)
Additional considerations: The steps involving the import of necessary libraries and the explanation of using Marshal's load/dump methods are provided, although not completely correctly implemented or explained. The final part that attempts to update the data in place and re-serialize it showcases an understanding of complex type handling in Python, although it's not done using Marshmallow's intended functionalities, like custom field handling and automatic transformation. The misusage of methods like dump().data and misunderstanding of how updated data vs. serialization should be handled also detracts points here. (0/2)

Overall, the solution makes an attempt to utilize Python’s serialization library Marshmallow but fails in several aspects of correctly implementing its features according to the specified requirements and Marshmallow's designed functionalities.

{
  "score": 2
}

Response 3 (7.0)

You can use the marshmallow library to create a schema for your JSON object and then automatically convert it to a dataclasses object. Here's an example of how you could do this:

import dataclasses
from datetime import datetime

from marshmallow import Schema, fields, post_dump

# create a schema for your JSON object
class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.DateTime(format='%m/%d/%Y')
    orders = fields.List(fields.Nested('OrderSchema'))

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal()

@dataclasses.dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: datetime
    orders: list[Order]

    # define a post_dump method to convert the schema object to a dataclasses object
    @post_dump
    def to_dataclass(self, data, **kwargs):
        # create an empty list of Orders to use as a default
        order_list = [Order()]
        # use a copy of the data object to create a new list of Orders
        data_copy = data.copy()
        # iterate over the orders in the data object
        for order in data_copy.get('orders', []):
            # create a new Order object and add it to the list of Orders
            order_list.append(Order(created=order['created'], amount=order['amount']))
        # remove the 'orders' key from the data object and set the 'orders' attribute of the dataclasses object
        del data_copy['orders']
        data_copy['orders'] = order_list
        # return the dataclasses object
        return dataclasses.replace(self, **data_copy)

# create an instance of the schema and load the JSON data into it
json_text = '{"first_name": "Jane", "last_name": "Smith", "date_of_birth": "03/16/1977", "orders": [{"created": "2024-01-25T15:00:04", "amount": "100.00"}]}'
person_schema = PersonSchema()
data = person_schema.load(json.loads(json_text))

# convert the schema object to a dataclasses object
person = Person(**data)

In this example, we define a PersonSchema and OrderSchema that use the appropriate fields for the JSON data. We also define a Person dataclass that uses the post_dump method to convert the schema object to a dataclasses object. Finally, we load the JSON data into the schema object and convert it to a dataclasses object.

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: (7.0) (1.00¢)

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

The solution defines two dataclasses, Person and Order. (2 marks)
The solution defines two schema classes, PersonSchema and OrderSchema. (2 marks)
The date format for the date_of_birth field is specified correctly as "%m/%d/%Y". (1 mark)
The decimal field (amount) is not specified with as_string=True. (0 marks)
The schema and dataclasses are linked with a @post_dump method on the PersonSchema class, but it's not correctly implemented. It should be @post_load instead of @post_dump, and it's not properly creating the dataclass instance. (1 mark)
Additional points:
The solution imports necessary modules.
It provides an example of how to use the schema to load JSON data.
However, there are some issues:
The Order dataclass is not defined.
The implementation of the post_dump method is incorrect and overly complicated.
(1 mark)

Comments:
The solution has a good structure but contains some errors. The main issues are:
1. Using @post_dump instead of @post_load
2. Not implementing the Order dataclass
3. Overcomplicated and incorrect implementation of the dataclass creation method
4. Missing as_string=True for the Decimal field

Here's the breakdown of marks:

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

Final score:

{
  "scores": [2, 2, 1, 0, 1, 1],
  "total_score": 7
}

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

The provided solution makes an attempt to generate dataclasses from a JSON using Marshmallow library in Python. Here are the evaluations based on the given scheme:

Definition of two dataclasses, one for the person and one for the order: The solution provides a dataclass Person but fails to define an Order dataclass explicitly. The Order class is mentioned but not defined, resulting in a potential error if run.
- Score: 0.5/2
Definition of two schema classes, one for the person and one for the order: Both PersonSchema and OrderSchema are properly defined.
- Score: 2/2
Specifying the date format for the date_of_birth field: The format is specified correctly in the PersonSchema class.
- Score: 1/1
The decimal field with as_string=True: The solution does not use as_string=True for the amount field in the OrderSchema.
- Score: 0/1
Linking schema and dataclasses with a @post_load method: The solution uses @post_dump instead of the required @post_load. @post_dump is used for processing data after serialization, not deserialization. This is incorrect usage for creating dataclasses based on the schema load result.
- Score: 0/2
Additional considerations:
- Importing necessary libraries (json is used but not imported).
- Incorrectly using post_dump instead of post_load.
- Failing to convert Decimal type correctly as per specified requirements.
Extra credit could be provided for efforts in setup structure, but the misdirection in technical handling substantially affects functionality.
- Score: 0.5/2

Overall Comments:
- The solution has outlined an attempt to utilize Marshmallow for schema definition and linking it with dataclasses, but it contains significant conceptual and implementation errors such as incorrect use of post_dump, missing definition for the Order dataclass, and incorrect decimal field handling.
- The solution would not work as intended due to these flaws.

Final Score:

{
  "score": 4/10
}

1.00	0.80	0.83
0.80	1.00	0.79
0.83	0.79	1.00

LLaVA v1.6 34B

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