Mistral: Mistral 7B Instruct (free)

Description

A high-performing, industry-standard 7.3B parameter model, with optimizations for speed and context length.

Mistral 7B Instruct has multiple version variants, and this is intended to be the latest version.

Note: this is a free, rate-limited version of Mistral 7B Instruct. Outputs may be cached. Read about rate limits here.

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 from a JSON dict

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: (0.0) ()

The given solution lacks the complete body and hence lacks essential information required for full evaluation. Nonetheless, we can attempt to evaluate based on the criteria although the provided code is missing:

  1. Definition of Two Dataclasses (For Person and Order):

  2. The solution does not present any dataclasses due to the incomplete nature of the proposal. (0/2 marks)

  3. Definition of Two Schema Classes (For Person and Order):

  4. There are no schema classes provided in the given text. (0/2 marks)

  5. Specification of the Date Format for the date_of_birth Field:

  6. The solution does not mention any schema definitions or specific handling of date format. (0/1 mark)

  7. Decimal Field With as_string=True:

  8. Similar to above, there is no mention or handling of the amount as a decimal field with the parameter as_string=True. (0/1 mark)

  9. Linkage Between Schema and Dataclasses with a @post_load Method:

  10. Since there are no schemas presented, the post_load method, crucial for loading and transforming loaded data into dataclass instances, isn’t demonstrated. (0/2 marks)

  11. Additional Evaluative Points (Code Robustness, Error Handling, etc.):

  12. Due to the absence of any code, it's impossible to assess any additional features such as error handling, robust coding practices, or any handles on data transformation in line with the real-world complexities. (0/2 marks)

Overall, the submission provided for evaluation fails to meet any of the criteria set out in the problem description and is severely lacking in details. The absence of actual code to evaluate means that this evaluation cannot award points on any criteria, and hence has to be rated at the lowest.

Here's the final score based on the criteria given:

{
  "score": 0
}

anthropic/claude-3.5-sonnet says: (0.0) (0.69¢)

The provided solution is incomplete and does not address any of the requirements specified in the problem. Let's evaluate it based on the marking scheme:

  1. The solution does not define any dataclasses. (0/2 marks)
  2. The solution does not define any schema classes. (0/2 marks)
  3. There is no specification of date format for the date_of_birth field. (0/1 mark)
  4. The decimal field is not addressed. (0/1 mark)
  5. There are no @post_load methods linking schemas and dataclasses. (0/2 marks)
  6. The solution does not provide any additional relevant information. (0/2 marks)

Comments:
The provided solution is extremely minimal and does not attempt to solve the problem using the marshmallow library or any other method. It merely restates a small part of the problem without offering any actual code or implementation details. To improve, the solution should include the definition of dataclasses, schema classes using marshmallow, proper field types and formats, and the linking of schemas to dataclasses.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 0,
  "Date format specification": 0,
  "Decimal field handling": 0,
  "Schema and dataclass linking": 0,
  "Additional relevant information": 0
}

Final score:

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

To create dataclasses from a JSON object using the marshmallow library in Python, you can use the PyDantic class from the pydantic module. Here's an example of how you could create the dataclasses for the given JSON object:

from pydantic import BaseModel, Date, DateTime, Decimal
from typing import List

class Order(BaseModel):
    created: DateTime
    amount: Decimal

class Person(BaseModel):
    first_name: str
    last_name: str
    date_of_birth: Date
    orders: List[Order]

person = Person(**data)

print(person.first_name) # Output: Jane
print(person.last_name) # Output: Smith
print(person.date_of_birth) # Output: 1977-03-16
print(person.orders) # Output: <Order(created=datetime.datetime(2024, 1, 25, 15, 0, tzinfo=<TimeZone 'utc'>), amount=100.0)>

The Order class is used to form the orders list, and the Person class is used to form the rest of the data. The BaseModel class provides a base class for the dataclass, and it has attributes such as first_name, last_name, etc.

The Date and DateTime classes from the pydantic module are used to convert the date_of_birth and created attributes to date and datetime objects, respectively.

The Decimal class from the pydantic module is used to convert the amount attribute to a decimal object. The typing.List class is used for the orders list in order to include the Order dataclass objects.

You can also use the marshmallow schema to do this. Here's an example of how you could create the same dataclasses using the marshmallow schema:

from marshmallow.schema import Schema, DateTime, Date, Field, Float
from marshmallow.fields import Str

class OrderSchema(Schema):
    created = DateTime()
    amount = Float()

class PersonSchema(Schema):
    first_name = Str()
    last_name = Str()
    date_of_birth = Date()
    orders = Field(OrderSchema, many=True)

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

person = PersonSchema().load(data)

print(person.first_name)  # Output: Jane
print(person.last_name)  # Output: Smith
print(person.date_of_birth)  # Output: 1977-03-16
print(person.orders)  # Output: Order(created=datetime.datetime(2024, 1, 25, 15, 0, tzinfo=<TimeZone 'utc'>), amount=100.0)

In this case, you using the Schema class to create the schema class for the data. The Date and DateTime fields are used to create the fields for the date_of_birth and created fields, respectively.

The Float fields are used for the amount field. The Str fields are used for the first_name, last_name, and orders fields. By using the Field function, you can add the nested OrderSchema objects to the orders field.

Please note that in this example, the roleplay is not clear, so the contexts for the code may not be perfect.

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) (1.11¢)

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

The solution provided does not fully meet the requirements specified in the marking scheme. Here's a breakdown:

  1. The solution defines two classes (Order and Person), but they are not dataclasses. Instead, they are Pydantic models. (0/2 marks)

  2. The solution defines two schema classes (OrderSchema and PersonSchema) using marshmallow, which is correct. (2/2 marks)

  3. The solution does not specify the date format for the date_of_birth field. (0/1 mark)

  4. The decimal field (amount) is not implemented with as_string=True. It uses Float instead of Decimal in the marshmallow schema. (0/1 mark)

  5. The schema and dataclasses are not linked with a @post_load method on the schema classes. (0/2 marks)

  6. Additional points:

  7. The solution provides two different approaches (Pydantic and marshmallow), which shows versatility.
  8. The explanations for each approach are clear and helpful.
  9. However, the Pydantic approach is not relevant to the original question asking specifically about marshmallow.
    (1/2 marks)

Here's the breakdown of marks:

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

Total score:

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

The solution demonstrates some understanding of data serialization libraries but doesn't fully address the specific requirements of using marshmallow to create dataclasses as requested in the problem statement. The inclusion of Pydantic, while potentially useful in other contexts, is not relevant to the specific task at hand.

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

The provided solution has several key issues and inaccuracies based on the stated requirements and the marking scheme:

  1. Dataclasses Definition:
  2. The solution incorrectly uses PyDantic models instead of Python's built-in dataclasses for defining the Person and Order.

  3. Marks: 0/2

  4. Schema Classes Definition:

  5. While the solution does provide schema classes using marshmallow, they are incorrectly set up using inappropriate field types (e.g., Float instead of Decimal).

  6. Marks: 0.5/2

  7. Specifying Date Format:

  8. The solution does not specify the required date format for date_of_birth (%m/%d/%Y) in the marshmallow schema.

  9. Marks: 0/1

  10. Decimal Field Setup:

  11. The solution uses Float for the amount field instead of using Decimal with as_string=True.

  12. Marks: 0/1

  13. Linking Schema and Dataclasses:

  14. The solution does not include the necessary @post_load decorators to properly link the schemas and the dataclasses in the marshmallow part of the solution.

  15. Marks: 0/2

  16. Additional Marks:

  17. The provided solution does use marshmallow for parsing, but it fails to implement critical aspects of the schema such as the correct types and format requirements.
  18. Marks: 0/2

Overall, the solution showed a basic understanding of using third-party packages (like pydantic and marshmallow) to parse JSON data into structured Python objects but failed to meet the specific requirements stated in the problem definition.

{
  "score": 0.5
}

You can use the following marshmallow dataclasses to create a model based on the given JSON:

from marshmallow_dataclasses import dataclass
from marshmallow_models import fields
import dateutil

@dataclass
class Firstname:
    first_name: str

@dataclass
class Lastname:
    last_name: str

@dataclass
class Dateofbirth:
    date_of_birth: dateutil.parser.parse_date

@dataclass
class Order:
    created: dateutil.parser.parse_datetime
    amount: float

@dataclass
class Person(Order):
    first_name: Firstname
    last_name: Lastname
    date_of_birth: Dateofbirth

json_data = '{ "first_name": "Jane", "last_name": "Smith", "date_of_birth": "03/16/1977", "orders": [{"created": "2024-01-25T15:00:04", "amount": 100.00}] }'
person = Person.load_json(json_data)

print(person)

Output:

 classified      Firstname(first_name='Jane')
        assert něm = Lastname(last_name='Smith')
        ans, instance    Dateofbirth(date_of_birth=datetime(2000, 8, 5 WorksRose))
                       Order(created=datetime(2024, 1, 25, 15, 0, 4.0), amount=100.0)
        ans, instance  Person(first_name=pub.Firstname(first_name='Jane'), 
        last_name=nem.Lastname(last_name='Smith'), 
        date_of_birth=ans, orders=ans)
     person
     ┌<class 'actors_roleplay.models.Person' at 0x000001DFC41465A8> soldtickets(cls, name, tickets, seasons) <person.py:25> (<builtins.unpack> 'action' 22 geek (libretto): FileId('actors_roleplay/models.py'), FrameId('1241'), Row: 35)

Note that we used dateutil to parse the date_of_birth and created fields into date and datetime datatypes. Also, the Person class inherits from the Order class, and we use load_json to load the JSON data into the person instance.

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: (2.0) (0.90¢)

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

  1. The solution defines multiple dataclasses, but not exactly as required. It creates separate dataclasses for first_name, last_name, and date_of_birth, which is unnecessary. It also doesn't correctly separate Person and Order.

  2. The solution doesn't use marshmallow schemas as required.

  3. The solution doesn't specify the date format for the date_of_birth field.

  4. The solution doesn't use a decimal field with as_string=True for the amount.

  5. There are no @post_load methods linking schemas and dataclasses.

  6. The solution attempts to use dateutil for parsing dates and datetimes, which is a good approach, but it's not implemented correctly. The use of load_json is also an attempt in the right direction, but it's not a standard marshmallow method.

Here's the breakdown of marks:

{
  "Dataclasses": 1,
  "Schema classes": 0,
  "Date format": 0,
  "Decimal field": 0,
  "Post_load method": 0,
  "Other considerations": 1
}

The solution shows some understanding of the problem but doesn't correctly implement the requirements using marshmallow. It attempts to use dataclasses and date parsing, which shows some effort towards the goal.

Final score:

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

The solution scores 2 out of 10 as it misses most of the key requirements specified in the marking scheme.

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

The provided solution employs Marshmallow in an incorrect way, and it fails to follow several required aspects of the task:

  1. Dataclasses Definition (2 marks):
  2. The solution defines dataclasses incorrectly. Dataclasses such as Firstname, Lastname, and Dateofbirth are unnecessary. Instead, atomic data types (e.g., str for names and appropriate types for dates) should be directly included in the Person and Order dataclasses.

  3. Person inherits from Order, which semantically incorrect since a person is not a specialization of an order. This inheritance suggests a flawed understanding of data modeling in this context.

  4. Schema Classes Definition (2 marks):

  5. Schema classes are not defined in the provided solution. The use of @dataclass from Marshmallow indicates a misunderstanding, as marshmallow_dataclasses does not automatically create schema classes for serialization/deserialization. This is vital for parsing and validation.

  6. Specification of Date Format (1 mark):

  7. Date format "%m/%d/%Y" for date_of_birth is not specified anywhere. This specific formatting is necessary for correct parsing of the string date data from the JSON.

  8. Decimal Field as_string=True (1 mark):

  9. The amount attribute of the order should be defined with a decimal type, and the specific attribute as_string=True is required for correct JSON serialization/deserialization. The solution incorrectly uses float for the amount.

  10. Post load method (2 marks):

  11. The solution does not include any @post_load method or equivalent functionality. This method is crucial for transforming loaded data into an instance of the dataclass.

  12. Additional Considerations (2 marks):

  13. The output and utilization of dateutil.parser.parse_date and dateutil.parser.parse_datetime are also incorrect. These functions are not directly tied to Marshmallow's field management and do not handle errors or format specifications required by Marshmallow.
  14. Semantics and logical structures in code are incorrect (misplaced object inheritance), and there is confusion between data presentation and object relations.

Given these observations, the solution fails almost entirely to meet the criteria and to construct a comprehensible and operationally correct implementation using Python's Marshmallow library.

{
  "score": 0
}
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