Yi 34B Chat

Description

The Yi series models are large language models trained from scratch by developers at 01.AI. This 34B parameter model has been instruct-tuned for chat.

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 (4.0)

To generate dataclasses from a JSON object using the marshmallow library in Python, you'll need to define a schema that can deserialize the JSON data into Python objects. Marshmallow provides a way to create such schemas using fields.

Here's how you can define a schema for your JSON data:

from marshmallow import Schema, fields, validate, ValidationError
from datetime import date, datetime
from decimal import Decimal

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

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

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

# Initialize the schemas
user_schema = UserSchema()
order_schema = OrderSchema()

# Deserialize the JSON data
try:
    # Use the load method to deserialize the JSON into a dictionary
    data = user_schema.load(json_data)

    # Convert the dictionary to a dataclass instance
    user_dataclass = user_schema.dump(data)
    print(user_dataclass)

    # Access the orders dataclass instances
    orders_dataclass = order_schema.dump(data['orders'])
    print(orders_dataclass)

except ValidationError as err:
    print("Validation error:", err)

In the above code, UserSchema and OrderSchema are defined using fields from marshmallow. The load method is used to deserialize the JSON data into a dictionary, and then the dump method is used to convert the dictionary to a dataclass instance.

Please note that the dump method is used for serialization, but in this case, we're using it to convert the loaded dictionary into a dataclass instance. This is because dump expects a dictionary, and load returns a dictionary, so we can chain these operations.

The OrderSchema is nested within the UserSchema because the orders field contains a list of Order objects.

Make sure to import the necessary modules for date, datetime, and Decimal as shown in the example.

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) (0.95¢)

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

The solution does not define any dataclasses. It only defines schemas. (0 marks)
The solution correctly defines two schema classes: UserSchema and OrderSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. (0 marks)
The decimal field is not specified with as_string=True. (0 marks)
The solution does not use @post_load method to link schemas and dataclasses. (0 marks)
Additional points:
The solution correctly uses nested fields for the orders.
It handles validation errors.
It demonstrates how to use the schemas to deserialize JSON data.
(2 marks)

Breakdown of marks:

{
  "dataclasses": 0,
  "schemas": 2,
  "date_format": 0,
  "decimal_field": 0,
  "post_load": 0,
  "additional": 2
}

Score:

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

Comments:
The solution provides a good starting point for working with marshmallow schemas, but it falls short in several key areas specified in the marking scheme. It doesn't define any dataclasses, which was a primary requirement. The date format for date_of_birth and the as_string=True for the decimal field are missing. The solution also doesn't use the @post_load decorator to link schemas and dataclasses. However, it does correctly define the schemas and demonstrates how to use them for deserialization, which are positive aspects of the solution.

openai/gpt-4-0125-preview says: (2.5) ()

The provided solution attempts to use the marshmallow library to deserialize a JSON object and then serialize it into a Python dataclass. However, there are multiple issues and misunderstandings in the approach and implementation that need to be addressed according to the marking scheme:

Dataclasses Definition:
The solution fails to define any dataclasses for either the person (User) or the order. Dataclasses are not defined or used anywhere in the code.
Score: 0/2
Schema Classes Definition:
The solution correctly defines two schema classes, UserSchema and OrderSchema, using marshmallow.
Score: 2/2
Date Format Specification:
The solution does not specify the custom date format for the date_of_birth field. The date format needs to align with the provided JSON (%m/%d/%Y), but no format is explicitly set.
Score: 0/1
Decimal Field Specification:
The decimal field is included in the schema, but the as_string=True property is not set on the amount field to ensure it's treated as a string during serialization.
Score: 0/1
@post_load Method Usage:
There is no @post_load method in either schema class to handle the creation of dataclass instances after loading JSON data. The explanation mistakenly asserts that dump can serve to convert to a dataclass, which is incorrect and not the intended use of dump.
Score: 0/2
Other Considerations:
The explanation about the use of dump and load is incorrect. dump is typically used for serialization (converting objects to JSON), not for creating dataclass instances from dictionaries.
Error handling (through excepting ValidationError) is implemented, which is a good practice but not enough to fully redeem other missing pieces.
Score: 0.5/2

Given these evaluations, the solution falls short in several critical areas according to the requirements specified in the problem statement.

{
  "score": 2.5
}

Response 2 (4.0)

Here's how you can define a schema for your JSON data:

from marshmallow import Schema, fields, validate, ValidationError
from datetime import date, datetime
from decimal import Decimal

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

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

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

# Initialize the schemas
user_schema = UserSchema()
order_schema = OrderSchema()

# Deserialize the JSON data
try:
    # Use the load method to deserialize the JSON into a dictionary
    data = user_schema.load(json_data)

    # Convert the dictionary to a dataclass instance
    user_dataclass = user_schema.dump(data)
    print(user_dataclass)

    # Access the orders dataclass instances
    orders_dataclass = order_schema.dump(data['orders'])
    print(orders_dataclass)

except ValidationError as err:
    print("Validation error:", err)

The OrderSchema is nested within the UserSchema because the orders field contains a list of Order objects.

Make sure to import the necessary modules for date, datetime, and Decimal as shown in the example.

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) (0.95¢)

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

The solution does not define any dataclasses. It only defines schemas. (0 marks)
The solution correctly defines two schema classes: UserSchema and OrderSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. (0 marks)
The decimal field is not specified with as_string=True. (0 marks)
The solution does not use @post_load method to link schemas and dataclasses. (0 marks)
Additional points:
The solution correctly uses nested fields for the orders.
It handles validation errors.
It demonstrates how to use the schemas to deserialize JSON data.
(2 marks)

Breakdown of marks:

{
  "dataclasses": 0,
  "schemas": 2,
  "date_format": 0,
  "decimal_field": 0,
  "post_load": 0,
  "additional": 2
}

Score:

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

openai/gpt-4-0125-preview says: (2.5) ()

Dataclasses Definition:
The solution fails to define any dataclasses for either the person (User) or the order. Dataclasses are not defined or used anywhere in the code.
Score: 0/2
Schema Classes Definition:
The solution correctly defines two schema classes, UserSchema and OrderSchema, using marshmallow.
Score: 2/2
Date Format Specification:
The solution does not specify the custom date format for the date_of_birth field. The date format needs to align with the provided JSON (%m/%d/%Y), but no format is explicitly set.
Score: 0/1
Decimal Field Specification:
The decimal field is included in the schema, but the as_string=True property is not set on the amount field to ensure it's treated as a string during serialization.
Score: 0/1
@post_load Method Usage:
There is no @post_load method in either schema class to handle the creation of dataclass instances after loading JSON data. The explanation mistakenly asserts that dump can serve to convert to a dataclass, which is incorrect and not the intended use of dump.
Score: 0/2
Other Considerations:
The explanation about the use of dump and load is incorrect. dump is typically used for serialization (converting objects to JSON), not for creating dataclass instances from dictionaries.
Error handling (through excepting ValidationError) is implemented, which is a good practice but not enough to fully redeem other missing pieces.
Score: 0.5/2

Given these evaluations, the solution falls short in several critical areas according to the requirements specified in the problem statement.

{
  "score": 2.5
}

Response 3 (4.0)

Here's how you can define a schema for your JSON data:

from marshmallow import Schema, fields, validate, ValidationError
from datetime import date, datetime
from decimal import Decimal

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

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

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

# Initialize the schemas
user_schema = UserSchema()
order_schema = OrderSchema()

# Deserialize the JSON data
try:
    # Use the load method to deserialize the JSON into a dictionary
    data = user_schema.load(json_data)

    # Convert the dictionary to a dataclass instance
    user_dataclass = user_schema.dump(data)
    print(user_dataclass)

    # Access the orders dataclass instances
    orders_dataclass = order_schema.dump(data['orders'])
    print(orders_dataclass)

except ValidationError as err:
    print("Validation error:", err)

The OrderSchema is nested within the UserSchema because the orders field contains a list of Order objects.

Make sure to import the necessary modules for date, datetime, and Decimal as shown in the example.

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) (0.95¢)

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

The solution does not define any dataclasses. It only defines schemas. (0 marks)
The solution correctly defines two schema classes: UserSchema and OrderSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. (0 marks)
The decimal field is not specified with as_string=True. (0 marks)
The solution does not use @post_load method to link schemas and dataclasses. (0 marks)
Additional points:
The solution correctly uses nested fields for the orders.
It handles validation errors.
It demonstrates how to use the schemas to deserialize JSON data.
(2 marks)

Breakdown of marks:

{
  "dataclasses": 0,
  "schemas": 2,
  "date_format": 0,
  "decimal_field": 0,
  "post_load": 0,
  "additional": 2
}

Score:

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

openai/gpt-4-0125-preview says: (2.5) ()

Dataclasses Definition:
The solution fails to define any dataclasses for either the person (User) or the order. Dataclasses are not defined or used anywhere in the code.
Score: 0/2
Schema Classes Definition:
The solution correctly defines two schema classes, UserSchema and OrderSchema, using marshmallow.
Score: 2/2
Date Format Specification:
The solution does not specify the custom date format for the date_of_birth field. The date format needs to align with the provided JSON (%m/%d/%Y), but no format is explicitly set.
Score: 0/1
Decimal Field Specification:
The decimal field is included in the schema, but the as_string=True property is not set on the amount field to ensure it's treated as a string during serialization.
Score: 0/1
@post_load Method Usage:
There is no @post_load method in either schema class to handle the creation of dataclass instances after loading JSON data. The explanation mistakenly asserts that dump can serve to convert to a dataclass, which is incorrect and not the intended use of dump.
Score: 0/2
Other Considerations:
The explanation about the use of dump and load is incorrect. dump is typically used for serialization (converting objects to JSON), not for creating dataclass instances from dictionaries.
Error handling (through excepting ValidationError) is implemented, which is a good practice but not enough to fully redeem other missing pieces.
Score: 0.5/2

Given these evaluations, the solution falls short in several critical areas according to the requirements specified in the problem statement.

{
  "score": 2.5
}

1.00	1.00	1.00
1.00	1.00	1.00
1.00	1.00	1.00

Yi 34B Chat

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