OpenHermes 2.5 Mistral 7B

Description

A continuation of OpenHermes 2 model, trained on additional code datasets.
Potentially the most interesting finding from training on a good ratio (est. of around 7-14% of the total dataset) of code instruction was that it has boosted several non-code benchmarks, including TruthfulQA, AGIEval, and GPT4All suite. It did however reduce BigBench benchmark score, but the net gain overall is significant.

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)

Sure, Marshmallow is a great library for working with data serialization and deserialization in Python. Here's how you can model the given JSON using Marshmallow:

Firstly, you'll want to define the data classes.

from datetime import datetime
from decimal import Decimal
from marshmallow import post_load, Field, pre_load, pre_dump, Schema, validate_schema


class OrderSchema(Schema):
    created = DateTime(tle_type=datetime)
    amount = Field(required=True, load_default=Decimal)

class PersonSchema(Schema):
    first_name = Field(required=True)
    last_name = Field(required=True)
    date_of_birth = Date()
    orders = PostProcess(for_=list, load_from='order', required=True)

You can then start validating your JSON:

schema = PersonSchema()
data = {
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  'orders': [
    {
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
    }
]}
validated_data = schema.load(data)

If there are any problems with the data (like missing fields), they'll be raised as exceptions.

Next to load the data:

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

validated_loaded_data = schema.load(data)

And finally to dump:

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

dumped_data = schema.dump(validated_loaded_data)

You'd need to handle the additional conversion from Decimal or Date to strings if you're wanting to send the data to an external API or save to a database.

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) (0.95¢)

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

The solution does not define dataclasses as requested. It only defines schema classes. (0/2 marks)
The solution defines two schema classes: OrderSchema and PersonSchema. (2/2 marks)
The solution does not specify the date format for the date_of_birth field. (0/1 mark)
The solution does not include as_string=True for the decimal field. (0/1 mark)
The solution does not include @post_load methods to link schema and dataclasses. (0/2 marks)
Additional points:
The solution includes imports and demonstrates how to use the schema for validation, loading, and dumping data. (1/2 marks)
The solution uses some incorrect field types (e.g., DateTime instead of fields.DateTime, PostProcess instead of fields.List(fields.Nested(OrderSchema))). (0/2 marks)

Comments:
The solution provides a basic structure for using Marshmallow to handle the JSON data, but it falls short in several key areas. It doesn't use dataclasses as requested, misses some important field configurations, and contains some errors in field definitions. The demonstration of schema usage is helpful, but the implementation doesn't fully meet the requirements specified in the problem statement.

Mark breakdown:

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

Score:

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

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

The submitted solution attempts to use the marshmallow library for serialization and deserialization but contains several critical errors and omissions:

Dataclass Definition: The provided solution lacks actual Python dataclass definitions for Person and Order. Instead, it jumps straight to schema declarations which manage the serialization process but are not replacements for dataclasses themselves.
Schema Classes Definition: The defined schema classes PersonSchema and OrderSchema are correctly aimed but use incorrect constructors and methods (DateTime and Field are not correctly implemented or do not exist with those names/contexts in Marshmallow). The correct fields should be fields.DateTime and fields.Decimal.
Date Formatting in Schema: The schema does not specify the format for the date_of_birth field using format="%m/%d/%Y" as required. This requirement is crucial for ensuring that the date strings are correctly interpreted.
Decimal Field Handling: The solution does not handle the decimal using as_string=True for the amount field to ensure that it remains serialized as a string.
Linking Schema and Dataclasses (@post_load Method): There is no usage of the @post_load decorator or method in either schema class to map the serialized data back into an instance of the dataclasses, which is essential for fully utilizing dataclasses with Marshmallow.
Additional Items:
Data validation and error handling is mentioned, but the specific handling or examples of catching exceptions is not shown.
The repeated schema loading and dumping process is outlined but contains redundancies and doesn't add value in the context of explaining how to handle specific fields like dates or decimals.

Given the above points:

0/2 for dataclasses definition (absent).
1/2 for schema classes definition (present but incorrectly defined).
0/1 for date formatting in schema (missing).
0/1 for decimal field handling (missing as_string=True).
0/2 for linking schema and dataclasses with @post_load (not implemented).
1/2 for other considerations (mentions validation and serialization process but lacks depth and correctness).

Thus, the calculated score for this solution is:

{
  "score": 2
}

Response 2 (6.0)

You can achieve this using the marshmallow library and the datevalidator from marshmallow_data. Here's an example of how to do it:

First, you need to install the libraries using pip:

pip install marshmallow marshmallow_data

Then, you can use the marshmallow library to define your dataclasses. Here's an example:

from marshmallow import Schema, fields, post_load, validate
from marshmallow_data import datevalidator

class OrderSchema(Schema):
    created = fields.DateTime(required=True, validate=validate.Range(min=datetime.datetime.utcfromtimestamp(0)))
    amount = fields.Decimal(required=True, precision=2, allow_nan=False)

class CustomerSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(required=True, data_type='python_date')
    orders = fields.Nested(OrderSchema, required=True)

    @post_load
    def make_customer(oute, data):
        return Customer(**data)

In your main function, you can load and use the data like this:

from marshmallow import ValidationError

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

customer_data = json.loads(json_string)

customer_schema = CustomerSchema()
customer_obj = customer_schema.load(customer_data)

print(customer_obj)

This will output:

Customer(first_name='Jane', last_name='Smith', date_of_birth=date(1977, 3, 16), orders=[etc...])

Remember to handle exceptions like invalid JSON and validation errors.

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: (6.0) (0.94¢)

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

The solution defines two schema classes (CustomerSchema and OrderSchema) but doesn't explicitly define dataclasses. (1/2 marks)
The solution correctly defines two schema classes (CustomerSchema and OrderSchema). (2/2 marks)
The solution doesn't specify the date format for the date_of_birth field. (0/1 mark)
The decimal field (amount) doesn't include as_string=True. (0/1 mark)
The schema classes are linked with a @post_load method, but only for the CustomerSchema. The OrderSchema is missing this decorator. (1/2 marks)
Additional points:
The solution imports necessary libraries and includes installation instructions. (1 mark)
The solution provides an example of how to use the schemas with sample JSON data. (1 mark)

Here's the breakdown of marks:

{
  "Dataclass definitions": 1,
  "Schema definitions": 2,
  "Date format specification": 0,
  "Decimal field configuration": 0,
  "Post-load method implementation": 1,
  "Additional features": 2
}

Final score:

{
  "scores": [1, 2, 0, 0, 1, 2],
  "total_score": 6
}

Comments:
The solution provides a good starting point but misses some key requirements. It correctly defines the schema classes and includes a post-load method for the CustomerSchema. However, it doesn't explicitly define dataclasses, doesn't specify the correct date format for date_of_birth, and doesn't configure the decimal field correctly. The OrderSchema is also missing a post-load method. The additional example of usage and installation instructions are helpful additions to the solution.

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

The provided solution demonstrates an attempt to parse a JSON input and create structured Python objects using the Marshmallow library. However, there are several points where the answer could be improved or corrected based on the problem statement and marking scheme. Here is a detailed review:

Dataclasses Definition: The solution did not explicitly define Python dataclasses, which are typically created using the @dataclass decorator. Instead, only schema classes are defined.
Score: 0/2
Schema Classes Definition: The solution correctly creates OrderSchema and CustomerSchema classes using the Marshmallow library.
Score: 2/2
Specifying Date Format for date_of_birth: The solution mentions using a date field for 'date_of_birth' but does not specify the required format %m/%d/%Y as described in the problem statement.
Score: 0/1
Decimal Field Configuration (as_string=True): The solution incorporates the use of the Decimal field, but does not set as_string=True, missing the requirement to serialize the decimal amount as a string in the output.
Score: 0/1
Linking Schema and Dataclasses with @post_load:
The solution provides a @post_load method named make_customer in the CustomerSchema, presumably to construct customer objects after loading data. However, this method contains a syntax error (oute instead of self) and utilizes undefined customer dataclasses since no actual Customer class or equivalent dataclasses were ever defined.
Score: 1/2 (given partially for the attempt)
Additional Points:
The solution engages the use of nested schema fields properly for embedding orders within a customer.
Error handling considerations with a mention of handling invalid JSON and validation errors.
There's a slight misuse by including marshmallow_data which does not exist and seems to be a confusion or misstatement.
The solution does not convert the JSON string correctly due to possible errors in the syntax (json.loads usage with an incorrect string format).
Score: 1/2

Final evaluation based on the issues and correctness in the implementation per the specified criteria:

{
  "score": 4
}

Response 3 (4.0)

To generate the required data classes from your JSON using marshmallow in Python, you'll need to follow these steps:

Install marshmallow library: If you don't have marshmallow installed, you can do so by running the following command in your terminal:

pip install marshmallow

Import necessary modules: Import the required modules from marshmallow.

from marshmallow import Schema, fields, pre_load
from dateutil.parser import parse
from decimal import Decimal
import datetime

Create a data class for the main data:

class DataClass:
    first_name = fields.Str(dump_only=True)
    last_name = fields.Str(dump_only=True)
    date_of_birth = fields.Date(serialize=lambda v: str(parse(v).date()), deserialize=lambda v: str(v))
    orders = fields.Nested("OrderClass", many=True, exclude=("_id",))

Create a data class for the orders:

class OrderClass:
    created = fields.DateTime(serialize='datetime:iso', deserialize=lambda v: parse(str(v)))
    amount = fields.Decimal(deserialize=Decimal)

Create a root schema:

class RootSchema(Schema):
    first_name = fields.Str(dump_only=True)
    last_name = fields.Str(dump_only=True)
    date_of_birth = fields.Date(serialize=lambda v: str(parse(v).date()), deserialize=lambda v: str(v))
    orders = fields.Nested("OrderClass", many=True, exclude=("_id",))

Create an order schema:

class OrderSchema(Schema):
    created = fields.DateTime(serialize='datetime:iso', deserialize=lambda v: parse(str(v)))
    amount = fields.Decimal(deserialize=Decimal)

Define a function to load data from the JSON:

def load_json(json_data):
    data_schema = RootSchema().load(json_data)
    return data_schema

Now, you can pass your JSON data to the load_json function, and it will convert it into your custom date and decimal data 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)

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

Solution Evaluation

Definition of Dataclasses:
- The provided solution incorrectly defines classes with marshmallow fields but does not use Python's dataclass or proper marshmallow model integrations. These are more field declarations than full dataclasses.
- Score: 0/2
Definition of Schema Classes:
- The classes RootSchema and OrderSchema are defined. However, they are purely marshmallow Schema classes and lack integration with dataclasses.
- Score: 1/2
Date Format for date_of_birth Field:
- The solution attempts to handle date_of_birth using a custom lambda function for serialization and deserialization but does not explicitly set the date format to "%m/%d/%Y" as required.
- Score: 0/1
Decimal Field as_string Property:
- The amount field in the OrderClass is defined using marshmallow's Decimal field but does not utilize the as_string=True argument to ensure the value is handled as a string.
- Score: 0/1
Post-load Method:
- The solution does not use any @post_load methods to link schema outputs back to dataclass instances or similar structured outputs. The marshmallow schemas are used just for validation and loading, not conversion to a Python class.
- Score: 0/2
Other Considerations:
- The solution does include a function load_json intended to deserialize JSON data according to the provided schemas.
- Correctly importing necessary libraries and lambda functions show a partial understanding of data handling.
- Lack of exceptions and error handling in custom functions.
- Score: 1/2

Additional Comments

The solution merges concept of fields inside what appears to be intended as data classes, which is a misunderstanding of Python's dataclass usage and marshmallow's schema patterns.
The custom serialization and deserialization implied but is not effectively implemented for compliance with the stringent datatype requirements specified.

Total Score

{
  "score": 2/10
}

anthropic/claude-3.5-sonnet says: (4.0) (0.95¢)

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

The solution doesn't define dataclasses as requested. It defines schema classes instead. (0/2 marks)
The solution defines two schema classes: RootSchema and OrderSchema. (2/2 marks)
The solution doesn't specify the date format for the date_of_birth field as requested. (0/1 mark)
The decimal field doesn't include as_string=True. (0/1 mark)
The solution doesn't use @post_load decorators to link schema and dataclasses. (0/2 marks)
Additional points:
The solution imports necessary modules. (+1 mark)
It uses dateutil.parser for parsing dates and datetimes. (+1 mark)
(2/2 marks)

Comments:
The solution provides a structure using marshmallow schemas, but it doesn't meet several key requirements:
- It doesn't use dataclasses as requested.
- The date format for date_of_birth is not specified correctly.
- The decimal field is missing the as_string=True parameter.
- There's no @post_load method to create dataclass instances.

While the solution shows an understanding of marshmallow, it doesn't fully address the specific requirements of the problem.

Marks breakdown:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Post_load method usage": 0,
  "Additional points": 2
}

Score:

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

1.00	0.80	0.82
0.80	1.00	0.79
0.82	0.79	1.00

OpenHermes 2.5 Mistral 7B

Description

Solution Evaluation

Additional Comments

Total Score

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