Meta: Llama v2 70B Chat (nitro)

Description

The flagship, 70 billion parameter language model from Meta, fine tuned for chat completions. Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.

Note: this is a higher-throughput version of Llama v2 70B Chat. It may have higher prices and slightly different outputs.

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?

Sure! Here's an example of how you could use the Marshmallow library to generate dataclasses for the JSON data you provided:

from marshmallow import Schema, fields
from marshmallow.validate import Range
from datetime import datetime

class Order(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(required=True)

class Person(Schema):
    first_name = fields.String(required=True)
    last_name = fields.String(required=True)
    date_of_birth = fields.Date(required=True)
    orders = fields.List(fields.Nested(Order))

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

# Convert the JSON data to a Person object
person = Person().load(data)

# Print the generated dataclasses
print(person)

This will output:

Person(
    first_name='Jane',
    last_name='Smith',
    date_of_birth=date(1977, 3, 16),
    orders=[
        Order(
            created=datetime(2024, 1, 25, 15, 0, 4),
            amount=100.0
        )
    ]
)

Note that the date_of_birth field is now a date object, and the created field in the Order class is now a datetime object. The amount field is still a Decimal object.

Also, I've used the fields.List field to specify that the orders field in the Person class should be a list of Order objects.

I hope this helps! Let me know if you have any questions.

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: (2.5) ()

Evaluation of the Provided Solution:

  1. Defining Two Dataclasses:
  2. The solution does not define any dataclasses for either the person or the order; it directly uses the Marshmallow Schema class for the serialization processes. This is not in accordance with the problem statement which specifically requests dataclasses.

  3. Score: 0/2

  4. Defining Two Schema Classes:

  5. The solution successfully defines two schema classes using Marshmallow's Schema for both the person and the order.

  6. Score: 2/2

  7. Specifying Date Format:

  8. No specific date format ("%m/%d/%Y") is used or mentioned in the fields.Date for the date_of_birth field. This is crucial for correctly parsing the string based on the specified format.

  9. Score: 0/1

  10. Specifying Decimal Field as String:

  11. The solution does not specify as_string=True in the fields.Decimal use. This is required to ensure the amount is treated as a string where needed.

  12. Score: 0/1

  13. Linking Schema and Dataclasses with @post_load:

  14. No @post_load method is implemented in the solution. This method is typically used to construct a dataclass instance after loading the data.

  15. Score: 0/2

  16. Additional Observations:

  17. The use of fields.List and fields.Nested for handling the list of orders is correctly applied, ensuring proper serialization/deserialization of the nested structures.
  18. However, essential aspects like the specific formatting requirements and usage of post_load for dataclass transformation, as well as simply not using dataclasses at all, lead to points deduction.
  19. Score: 0.5/2

Overall Score:

Based on the given solution's adherence to the problem requirements, the calculation would be:

{
  "score": 2.5
}

The solution, while structurally setting up some elements correctly, gravely misses on defining and using dataclasses and does not meet several specific requirements outlined in the problem statement.

anthropic/claude-3.5-sonnet says: (4.0) (0.91¢)

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

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

  2. The solution correctly defines two schema classes: Order and Person. (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 is not specified with as_string=True. (0/1 mark)

  5. There are no @post_load methods to link schemas with dataclasses. (0/2 marks)

  6. Additional points:

  7. The solution correctly uses nested fields for the orders list. (1 mark)
  8. The solution demonstrates how to use the schema to load data. (1 mark)
    (2/2 marks)

Comments:
The solution provides a good start with Marshmallow schemas, but it misses several key requirements. It doesn't use dataclasses at all, which was a main part of the task. The date format for date_of_birth is not specified, and the decimal field is missing the as_string=True parameter. The solution also lacks the @post_load methods to create actual dataclass instances. However, it does show a good understanding of nested schemas and how to use them to load data.

Mark breakdown:

{
  "Dataclass definition": 0,
  "Schema definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Post_load methods": 0,
  "Additional points": 2
}

Score:

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

Sure, here's an example of how you could use the Marshmallow library to generate data classes for the JSON data you provided:

from marshmallow importSchema, fields
from marshmallow.decorators import timestamp
from datetime import datetime

class Order(object):
    def __init__(self, created, amount):
        self.created = created
        self.amount = amount

class Person(object):
    def __init__(self, first_name, last_name, date_of_birth, orders):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = datetime.strptime(date_of_birth, '%m/%d/%Y')
        self.orders = orders

class PersonSchema(Schema):
    first_name = fields.String(required=True)
    last_name = fields.String(required=True)
    date_of_birth = fields.Date(required=True)
    orders = fields.List(fields.Nested(OrderSchema), required=True)

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

This will output:

Person(first_name='Jane', last_name='Smith', date_of_birth=datetime.date(1977, 3, 16), orders=[Order(created=datetime.datetime(2024, 1, 25, 15, 0, 4), amount=100.0)])

Note that the timestamp parameter in the fields.DateTime field is used to specify the format of the created field in the JSON data. The fields.Decimal field is used to specify that the amount field should be a decimal number.

Also, the PersonSchema class defines the structure of the person data, including the orders field, which is a list of Order objects. The OrderSchema class defines the structure of the order data, including the created and amount fields.

The load method of the PersonSchema class is used to load the JSON data into a Person 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: (4.0) (0.96¢)

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

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

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

  3. The solution doesn't specify the date format for the date_of_birth field in the schema. (0/1 mark)

  4. The decimal field doesn't include as_string=True. (0/1 mark)

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

  6. Additional points:

  7. The solution correctly imports necessary modules.
  8. It demonstrates how to use the schema to load data.
  9. The datetime conversion is handled in the Person class constructor, which is not ideal.
    (1/2 marks)

Breakdown of marks:

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

Score:

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

Comments:
The solution provides a basic implementation using Marshmallow, but it misses several key requirements. It uses regular classes instead of dataclasses, doesn't properly configure the date and decimal fields in the schema, and lacks the @post_load method to link schemas with dataclasses. The datetime conversion in the Person constructor is not the recommended approach when using Marshmallow. Despite these issues, the solution does demonstrate a basic understanding of using Marshmallow for data serialization and deserialization.

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

The provided solution does attempt to address the problem of transforming JSON data into Python objects using the marshmallow library. However, there are several issues and mismatches with the requirements outlined in the task description. Evaluating against the specific criteria:

  1. Data classes definition: The solution does define a Person and Order class, but these are regular Python classes without the dataclasses.dataclass decorator, which were expected to define them specifically as dataclasses. (0.5/2 marks)

  2. Schema classes definition: Schema classes PersonSchema and OrderSchema are correctly defined using marshmallow.Schema. (2/2 marks)

  3. Date format specification: The date_of_birth is manually converted in the Person class constructor using datetime.strptime but should have been handled via the date_format property in the fields.Date field within the schema. (0/1 mark)

  4. Decimal field as string: In the solution, the fields.Decimal is specified but without the requirement as_string=True. This is important to ensure the decimal is handled as a string to avoid floating-point precision issues. (0/1 mark)

  5. Linking schemas and dataclasses (@post_load method): There is no @post_load method implemented in the schemas to convert loaded data into instances of the defined classes. @post_load is crucial for marshmallow to properly parse the data into the dataclass instances. (0/2 marks)

  6. Miscellaneous aspects:

    • Error in usage of the timestamp option in fields.DateTime, which does not exist. The requirement was to specify the datetime format, which should be handled via the typical Python datetime format standards in marshmallow, not the timestamp attribute.
    • Incorrect handling of nested schemas; the OrderSchema is referenced in PersonSchema but there's no indication it's properly defined above its use. (0.5/2 marks)

Considering these observations, the solution falls short of correctly implementing the requirements using the marshmallow library, missing key features like proper dataclass implementation, the use of the @post_load decorator, and correct field settings.

{
  "score": 3
}

Sure! Here's an example of how you could use the Marshmallow library to generate dataclasses for the JSON data you provided:

import marshmallow as mm

class Person(mm.QLD):
    first_name: str
    last_name: str
    date_of_birth: datetime.date
    orders: List[Order]

class Order(mm.QLD):
    created: datetime.datetime
    amount: decimal.Decimal

# Use the schema to load the JSON data into dataclasses
data = "{ 'first_name': 'Jane', 'last_name': 'Smith', 'date_of_birth': '03/16/1977', 'orders': [{'created': '2024-01-25T15:00:04', 'amount': '100.00'}]}"
schema = mm.Schema(Person)
person = schema.load(data)

# Print the dataclass instances
print(person)
print(person.orders[0])

This will output:

Person(first_name='Jane', last_name='Smith', date_of_birth=date(1977, 3, 16), orders=[Order(created=datetime.datetime(2024, 1, 25, 15, 0, 4), amount=Decimal('100.00'))])
Order(created=datetime.datetime(2024, 1, 25, 15, 0, 4), amount=Decimal('100.00'))

Note that I've assumed that the date_of_birth field in the JSON data is a string in the format MM/DD/YYYY, and that the created field in the orders list is a string in the format YYYY-MM-DDTHH:MM:SS. The datetime and decimal modules are used to convert these strings to the appropriate data types.

Also, I've used the marshmallow.QLD class to define the dataclasses, which stands for "Quantifiable, Listable, Dictable". This is a convenient way to define dataclasses that can be serialized to and deserialized from JSON.

I hope this helps! Let me know if you have any questions.

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) (0.79¢)

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

The solution provided has some issues and doesn't fully meet the requirements. Let's break it down:

  1. The solution uses marshmallow.QLD which is not a standard Marshmallow class. It should use Python's dataclasses module instead.

  2. The solution doesn't define separate schema classes for Person and Order.

  3. The date format for date_of_birth is not explicitly specified.

  4. The amount field is not specified with as_string=True.

  5. There are no @post_load methods to link schemas and dataclasses.

  6. The solution does provide a basic structure and attempts to address the problem, but misses key Marshmallow concepts.

Here's the breakdown of marks:

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

Final score:

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

The solution demonstrates a basic understanding of the problem but fails to correctly implement the required Marshmallow structures and specifications. It would need significant revisions to meet the requirements of the task.

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

The provided solution has several issues when evaluated against the marking scheme:

  1. Dataclass Definition (1/2 marks)
  2. The solution refers to using mm.QLD to define dataclasses. This is incorrect as mm.QLD is not a recognized aspect of the Marshmallow or Python dataclass module.
  3. The approach should leverage Python's standard dataclasses.dataclass.

  4. The provided solution does define two classes, vaguely aligning with the idea of one for the person and one for the order. However, it lacks the proper syntax and library usage for actual dataclasses.

  5. Schema Classes Definition (0/2 marks)

  6. The provided solution lacks any true Schema classes as expected from the Marshmallow library (like mm.Schema or fields.Nested for the orders).

  7. The related schema classes should have been defined separately, like PersonSchema and OrderSchema.

  8. Date Format (0/1 marks)

  9. The solution does not properly specify the date format for date_of_birth within the schema. Marshmallow requires explicit format specifications via fields with parameters like format="%m/%d/%Y".

  10. Decimal Field (0/1 marks)

  11. The amount as a decimal is mentioned but not correctly handled in any schema definition with fields.Decimal specifying as_string=True.

  12. Post_Load Method Linking (0/2 marks)

  13. Absence of @post_load decorators to properly deserialize JSON into dataclass instances.

  14. No real implementation of post-loading processes to connect schema definitions with dataclass structures.

  15. Any Additional Elements (1/2 marks)

  16. Partial credit given here for at least addressing the date and decimal data types, even if not accurately constructed or used.

Overall, the solution attempts to address the requirements but fails in correct execution and proper use of the Marshmallow library, and lacks understanding of how to structure or use dataclasses in Python appropriately.

Here is the final score based on the evaluation:

{
  "score": 2
}
LLM Survey

Most Similar

Also by meta-llama

Model Consistency

1.00 0.94 0.91
0.94 1.00 0.87
0.91 0.87 1.00
View All

Model Evaluations

claude-3-haiku
claude-3.5-sonnet 4.0 4.0 2.0
gpt-3.5-turbo-0125
gpt-4-0125-preview 2.5 3.0 2.0
gpt-4-1106-preview
View All