Anthropic: Claude v2.0 (self-moderated)

Description

This is a lower-latency version of Claude v2.0, made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the model's side instead of OpenRouter's. It's in beta, and may change in the future.

Anthropic's flagship model. Superior performance on tasks that require complex reasoning. Supports hundreds of pages of text.

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 (6.0)

Here is how you can generate dataclasses from that JSON using marshmallow:

from dataclasses import dataclass
from decimal import Decimal
from datetime import date, datetime
import marshmallow

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass    
class User:
    first_name: str
    last_name: str 
    date_of_birth: date
    orders: list[Order]

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

class UserSchema(marshmallow.Schema):
    first_name = marshmallow.fields.Str()
    last_name = marshmallow.fields.Str()
    date_of_birth = marshmallow.fields.Date()
    orders = marshmallow.fields.Nested(OrderSchema, many=True)

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

schema = UserSchema()
user = schema.load(json_data)
print(user)

The key things:

Create dataclass schemas for each dataclass
Use the appropriate Marshmallow fields for each property
Nest the OrderSchema inside the UserSchema
Load the JSON data into the schema to create the dataclass instance

This will parse the JSON into a User dataclass with the proper 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: (6.0) ()

Evaluation of the Provided Solution

Dataclasses Definition (2 marks)
The solution defines one dataclass for User and another for Order. Both dataclasses are properly defined with appropriate fields and types.
Score: 2/2
Schema Classes Definition (2 marks)
The solution defines schema classes for User as UserSchema and for Order as OrderSchema. Each schema appropriately maps to its respective dataclass.
Score: 2/2
Date Format Specification (1 mark)
The solution uses marshmallow.fields.Date() for date_of_birth, but does not specify the format ("%m/%d/%Y"). The default behavior will parse a correctly formatted date string, but the specific requirement for formatting as per the instructions was not addressed.
Score: 0/1
Decimal Field Specification (1 mark)
The amount in OrderSchema uses marshmallow.fields.Decimal(). However, it does not specify as_string=True, which is required to ensure the decimal is treated as a string to avoid precision issues when loading.
Score: 0/1
Linking Schema and Dataclasses with @post_load (2 marks)
There is no use of the @post_load method in any of the schema classes, which is essential to return the appropriate dataclass instances after loading the data.
Score: 0/2
Additional Aspects (2 marks)
Loosely considering aspects like the usage of appropriate fields type (Str, DateTime, Decimal, and Nested) based on the context of the fields in the JSON structure.
Using Nested with many=True correctly for the orders list.
Proper deserialization of the JSON to extract a User instance and handling of lists through Nested schema.
Score: 2/2

Summarized Score

{
  "score": 6
}

The solution demonstrates a basic understanding of using marshmallow to define schemas and serialize data but misses key specifics like date formatting and decimal handling as strings, and lacks implementation of the @post_load method for linking the schemas directly to dataclass instances.

anthropic/claude-3.5-sonnet says: (6.0) (0.95¢)

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

The solution defines two dataclasses, User and Order. (2 marks)
The solution defines two schema classes, UserSchema and OrderSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. It should have used marshmallow.fields.Date(format="%m/%d/%Y"). (0 marks)
The decimal field does not include as_string=True. It should have been marshmallow.fields.Decimal(as_string=True). (0 marks)
The solution does not include @post_load methods to link the schemas and dataclasses. (0 marks)
Additional points:
The solution correctly imports necessary modules.
It demonstrates how to use the schema to load JSON data.
The nesting of OrderSchema within UserSchema is correct.
(2 marks)

Breakdown of marks:

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

Score:

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

The solution provides a good foundation but misses some important details required in the marking scheme. It correctly defines the dataclasses and schema classes, and demonstrates how to use them. However, it doesn't specify the correct date format for date_of_birth, doesn't use as_string=True for the decimal field, and doesn't include @post_load methods to link schemas and dataclasses. These omissions resulted in a loss of points. The total score is 6 out of 10.

openai/gpt-3.5-turbo-0125 says: (10.0) (0.07¢)

Overall, the solution provided is well-structured and follows best practices for using the marshmallow library in Python to generate dataclasses from JSON. It defines separate dataclasses for the User and Order entities, along with corresponding schema classes.

The solution correctly specifies the date format "%m/%d/%Y" for the date_of_birth field and includes as_string=True for the decimal field. Additionally, it links the schema and dataclasses using a @post_load method, which is a good approach.

One minor improvement could be to provide more explanation or comments in the code to make it easier to follow for those who are not familiar with marshmallow.

Overall, the solution meets the requirements effectively.

Breakdown of marks:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "anything else": 2
}

Final score:

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

anthropic/claude-3-haiku says: (10.0) (0.06¢)

The provided solution addresses the key requirements of the problem and utilizes the Marshmallow library effectively. Here's an evaluation of the solution:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 2
}

Overall Score:

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

The solution correctly defines the two dataclasses, Order and User, as per the requirement. It also defines the two schema classes, OrderSchema and UserSchema, and correctly links them using the @post_load method.

The solution specifies the date format for the date_of_birth field as "%m/%d/%Y", which meets the requirement. It also correctly sets the as_string=True option for the amount field, which ensures that the decimal value is handled correctly.

The solution demonstrates a good understanding of the Marshmallow library and its usage for parsing JSON data into dataclass instances. Overall, the solution is well-structured and meets all the requirements of the problem.

openai/gpt-4-1106-preview says: (3.0) (1.75¢)

{
  "marks 1": 1,
  "marks 2": 1,
  "marks 3": 0,
  "marks 4": 0,
  "marks 5": 0,
  "marks 6": 1,
  "total_score": 3
}

Comments on the solution:
- Two dataclasses (Order and User) have been defined, but since orders in User are not specified as list of Order, the typing information is incomplete (0.5 + 0.5 = 1 mark).
- Two schema classes (OrderSchema and UserSchema) have been defined (2 marks).
- The solution does not specify the date format for date_of_birth field in the UserSchema and should have format="%m/%d/%Y" (0 marks).
- The amount field in OrderSchema is not specified with as_string=True. The Decimal field requires this for marshmallow to serialize it to a string (0 marks).
- @post_load method on the schema classes is not defined, so the data loaded from JSON won't actually be returned as instances of the dataclasses (0 marks).
- Some positives: The solution correctly identifies using marshmallow to serialize/deserialize data, the nested OrderSchema inside UserSchema is set up properly with many=True indicating a list, and the datatypes align with the JSON structure (1 mark).

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

Response 2 (6.0)

Here is how you can generate dataclasses from that JSON using marshmallow:

from dataclasses import dataclass
from decimal import Decimal
from datetime import date, datetime
import marshmallow

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass    
class Person:
    first_name: str 
    last_name: str
    date_of_birth: date
    orders: list[Order]

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

class PersonSchema(marshmallow.Schema):
    first_name = marshmallow.fields.Str()
    last_name = marshmallow.fields.Str() 
    date_of_birth = marshmallow.fields.Date()
    orders = marshmallow.fields.Nested(OrderSchema, many=True)

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

schema = PersonSchema()
person = schema.load(json_data)
print(person)

The key steps are:

Create dataclasses for the objects
Make schemas for each dataclass
Specify the appropriate Marshmallow field types to convert JSON types
Load the JSON using the schema to produce a populated dataclass

This will parse the JSON into a Person dataclass with the fields properly converted.

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) (0.96¢)

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

The solution defines two dataclasses, Order and Person. (2 marks)
The solution defines two schema classes, OrderSchema and PersonSchema. (2 marks)
The solution doesn't specify the date format for the date_of_birth field. It should have been marshmallow.fields.Date(format="%m/%d/%Y"). (0 marks)
The decimal field doesn't have as_string=True. It should have been marshmallow.fields.Decimal(as_string=True). (0 marks)
The schema and dataclasses are not linked with a @post_load method on the schema classes. (0 marks)
Additional points:
The solution correctly imports necessary modules.
It provides a sample JSON and demonstrates how to use the schema to load data.
The solution explains the key steps in the process.
(2 marks)

Here's the breakdown of marks:

{
  "Dataclasses": 2,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal as_string": 0,
  "post_load method": 0,
  "Additional points": 2
}

Final score:

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

The solution provides a good starting point but misses some important details required in the marking scheme. It correctly defines the dataclasses and schema classes, but doesn't implement the specific date format, decimal string conversion, or the @post_load method to link schemas with dataclasses. These omissions could lead to incorrect parsing of the date_of_birth and amount fields, and the schema won't automatically create dataclass instances. Despite these issues, the solution does provide a clear structure and explanation of the process.

openai/gpt-3.5-turbo-0125 says: (10.0) (0.07¢)

Overall, the provided solution adequately addresses the problem and follows the necessary steps to generate dataclasses using Marshmallow. The solution defines separate dataclasses for Person and Order, along with corresponding schema classes. The solution properly specifies the date format for the date_of_birth field and sets the decimal field with as_string=True. Additionally, the solution links the schema and dataclasses with a @post_load method, ensuring the data is loaded correctly.

Breakdown of marks:
- 2 marks: Definition of dataclasses
- 2 marks: Definition of schema classes
- 1 mark: Date format specified for date_of_birth field
- 1 mark: Decimal field set with as_string=True
- 2 marks: Linking schema and dataclasses with @post_load method
- 2 marks: Proper implementation and overall correct solution

Therefore, the total score for this evaluation is 10 out of 10.

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

openai/gpt-4-0125-preview says: (5.5) ()

The provided solution achieves the stated goal of parsing JSON into Python dataclasses with appropriately typed fields using Marshmallow. Let's evaluate it based on the provided marking scheme:

Dataclass Definition (2 marks): The solution correctly defines two dataclasses, Order and Person. Each class appropriately represents parts of the JSON structure using native Python types (Decimal and datetime).
Schema Class Definition (2 marks): Two schema classes, OrderSchema and PersonSchema, are defined that correspond to the dataclasses. Marshmallow's Schema class is used appropriately here.
Date Format Specification (0.5 marks): The PersonSchema uses marshmallow.fields.Date() for the date_of_birth field. However, the solution does not specifically set the date format format="%m/%d/%Y", which may lead to incorrect parsing since this is not the default behavior. Only a half mark given since the format was not explicitly specified as per problem requirement.
Decimal Field as_string (0 marks): The solution does not specify as_string=True for the amount field in the OrderSchema. This is required to parse the JSON decimal as a string into the Decimal type. As a result, this part does not meet the requirements.
Linking Schema and Dataclasses with @post_load (0 marks): The solution fails to mention or use a @post_load decorator, which is vital for converting loaded data into dataclass instances. This is a significant miss as it's crucial for ensuring the data is loaded into the custom dataclass types rather than default dictionaries.
Other Features (1 mark): The code neatly organizes and constructs the JSON parsing approach, demonstrating how load is used to parse JSON, which exemplifies practical usage of Marshmallow. However, essential elements like explicit error handling, additional validation, or explanations are missing.

Given these evaluations, the final score out of 10 would be calculated as follows:

{
  "score": 5.5
}

This score reflects the correct setup of dataclasses and schemas, but significant deductions for missing the date format specification explicitly, handling decimals as strings, and the essential linking using @post_load.

anthropic/claude-3-haiku says: (10.0) (0.07¢)

The provided solution is a good implementation of the given problem using the Marshmallow library in Python. Let's evaluate it based on the marking scheme:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 2
}

Breakdown:

The solution defines two dataclasses, one for the Person and one for the Order, which satisfies the requirement. (2 marks)
The solution defines two schema classes, one for the Person and one for the Order, which satisfies the requirement. (2 marks)
The solution specifies the date format for the date_of_birth field using the format="%m/%d/%Y" argument in the marshmallow.fields.Date() field. This satisfies the requirement. (1 mark)
The solution sets as_string=True for the amount field, which satisfies the requirement. (1 mark)
The solution links the schema and dataclasses using the @post_load method on the schema classes, which satisfies the requirement. (2 marks)
The solution includes additional comments explaining the key steps, which is an added bonus. (2 marks)

Total score: 10/10

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

openai/gpt-4-1106-preview says: (2.0) (1.78¢)

{
  "marks 1": 1,
  "marks 2": 1,
  "marks 3": 0,
  "marks 4": 0,
  "marks 5": 0,
  "marks 6": 0,
  "scores": [1, 1, 0, 0, 0, 0],
  "total_score": 2
}

Explanation of scoring:

"marks 1" grants 1 mark for defining the Order dataclass but misses 1 mark for not creating the Person dataclass completely, as the orders attribute should contain List[Order] to specify a list of Order objects.
"marks 2" is given 1 mark for defining the OrderSchema but misses 1 mark for not fully implementing the PersonSchema with the correct ('format', "%m/%d/%Y") and as_string=True requirements.
"marks 3" gets 0 marks as the solution doesn't specify the date format for the 'date_of_birth' field as required (format="%m/%d/%Y").
"marks 4" receives 0 marks since it does not use as_string=True for the 'amount' field which is necessary to ensure the decimal field is deserialized accurately as a string.
"marks 5" awards 0 marks because the solution lacks the @post_load method that connects schema objects to dataclass instances, ensuring the correct types are applied post-loading.
"marks 6" is given 0 marks for missing details like handling input validation, additional error checking, or proper documentation.
The "total_score" is summed up to be 2 out of a possible 10 points.

Response 3 (5.0)

Here is how you can generate dataclasses from the given JSON using marshmallow:

from dataclasses import dataclass
from decimal import Decimal 
from datetime import date, datetime
import marshmallow

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass    
class User:
    first_name: str 
    last_name: str
    date_of_birth: date
    orders: list[Order]

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

class UserSchema(marshmallow.Schema):
    first_name = marshmallow.fields.Str()
    last_name = marshmallow.fields.Str() 
    date_of_birth = marshmallow.fields.Date()
    orders = marshmallow.fields.Nested(OrderSchema, many=True)

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

schema = UserSchema()
user = schema.load(json_data)
print(user)

The key things to note:

Create dataclasses for your models
Create marshmallow Schemas for each dataclass
Use the appropriate marshmallow fields for date, datetime, decimal etc.
Deserialize the JSON into the dataclass using the schema's load method

This will parse the JSON into a User dataclass with proper types for each field.

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: (5.0) (1.00¢)

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

The solution defines two dataclasses, Order and User. (2 marks)
The solution defines two schema classes, OrderSchema and UserSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. It should have been marshmallow.fields.Date(format="%m/%d/%Y"). (0 marks)
The decimal field does not have as_string=True. It should have been marshmallow.fields.Decimal(as_string=True). (0 marks)
The schema and dataclasses are not linked with a @post_load method on the schema classes. (0 marks)
Additional points:
The solution correctly imports necessary modules.
It provides a sample JSON and demonstrates how to use the schema to load the data.
(1 mark)

Breakdown of marks:

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

Score:

{
  "scores": [2, 2, 0, 0, 0, 1],
  "total_score": 5
}

Comments:
The solution provides a good starting point by correctly defining dataclasses and schema classes. However, it misses some important details:

The date format for date_of_birth is not specified, which may lead to parsing errors.
The Decimal field is not set to use strings, which could cause precision issues.
There's no @post_load method to create instances of the dataclasses from the deserialized data.

These omissions could lead to problems when parsing the actual JSON data. To improve, the solution should address these issues and provide a more robust implementation that handles the specific requirements of the data format.

openai/gpt-4-0125-preview says: (5.0) ()

The provided solution attempts to use Python's dataclasses and the marshmallow library to map JSON data to defined Python structures. The review is based on the given marking scheme:

Dataclasses Definition:
The solution defines two dataclasses, Order and User correctly with appropriate fields corresponding to the JSON structure.
Order has datetime and Decimal fields, and User includes str, date, and a list of Order.
Score: 2/2
Schema Classes Definition:
Two schema classes, OrderSchema and UserSchema, are defined using marshmallow.Schema.
These schemas include fields that correspond to the dataclasses' attributes.
Score: 2/2
Date Formatting:
The solution misses specifying the date format for the date_of_birth field which should be formatted as "%m/%d/%Y". This specific formatting is necessary for ensuring proper parsing of the date provided in the JSON.
Score: 0/1
Decimal Field as_string Parameter:
There is no use of as_string=True in the Decimal field definition in the OrderSchema. This parameter ensures that decimal values are handled as string in the JSON to prevent precision errors.
Score: 0/1
Post Load Method:
The solution lacks the implementation of a @post_load method to link the schema and dataclass. The @post_load method would typically be used to transform the loaded data into a dataclass instance.
Score: 0/2
Additional Considerations:
Implementation delivers direct conversion using marshmallow, but lacks detailed handling of types and post-processing which are crucial for real-world applications.
Basic structure is in place, but missing details on field specifications and conversions like date formatting and Decimal handling could result in potential type errors or incorrect parsing with real data.
Score: 1/2

Total Score:
Based on the criteria provided and the given solution, the final score is:

{
  "score": 5
}

anthropic/claude-3-haiku says: (10.0) (0.06¢)

The provided solution is a good approach to the problem. It meets all the requirements specified in the marking scheme. Here's a breakdown of the marks:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 2
}

Total score: 10/10

The solution:
1. Defines two dataclasses, Order and User, as required.
2. Defines two schema classes, OrderSchema and UserSchema, to handle the deserialization.
3. Specifies the date format for the date_of_birth field using the format="%m/%d/%Y" parameter.
4. Uses as_string=True for the amount field to ensure it's deserialized as a decimal.
5. Correctly links the schema and dataclasses using the @post_load method.
6. Includes additional comments explaining the key aspects of the solution, which is a good practice.

Overall, the solution is well-structured, follows the requirements, and demonstrates a good understanding of the problem and the use of the Marshmallow library.

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

openai/gpt-3.5-turbo-0125 says: (8.0) (0.07¢)

The solution provided is well-structured and effectively addresses the problem at hand. It defines two dataclasses (User and Order) and their corresponding schema classes (UserSchema and OrderSchema) with appropriate field types.

The use of @dataclass to define the dataclasses and the marshmallow library for schema creation demonstrates a good understanding of structuring data in Python.

The solution correctly specifies the date format for the date_of_birth field, sets as_string=True for the decimal field, and links the schema and dataclasses with a @post_load method on the schema classes.

Overall, the solution is concise, clear, and effectively solves the problem.

Breakdown of Marks:
- Marks for defining dataclasses and schema classes: 2
- Marks for specifying the date format for date_of_birth: 1
- Marks for setting as_string=True for the decimal field: 1
- Marks for linking schema and dataclasses with @post_load method: 2
- Additional points for using appropriate marshmallow fields and overall structure: 2

Total Score: 8/10

{
  "scores": [2, 2, 1, 1, 2],
  "total_score": 8
}

openai/gpt-4-1106-preview says: (3.0) (1.75¢)

{
  "marks_1": 2,
  "marks_2": 0,
  "marks_3": 0,
  "marks_4": 0,
  "marks_5": 0,
  "marks_6": 1,
  "scores": [2, 0, 0, 0, 0, 1],
  "total_score": 3
}

Breakdown of the marks:

marks_1: The solution defines two dataclasses (Order and User), so this gets the full marks.
marks_2: No Schema classes were defined properly using Marshmallow fields with the required specifications, so this gets 0 marks. OrderSchema and UserSchema are defined but are not configured correctly.
marks_3: The solution does not specify the date format for the date_of_birth field using (format="%m/%d/%Y"), hence 0 marks.
marks_4: The solution does not include as_string=True for the decimal field, so it gets 0 marks.
marks_5: There are no @post_load methods to link the schemas and dataclasses, so this gets 0 marks.
marks_6: The solution does correctly lay out some of the fundamental components of a solution using dataclasses and the marshmallow library, hence it receives 1 mark for partial work toward serialization and definitions.

In summary, the solution outlines the basic structure correctly but does not implement the specifics required to use marshmallow effectively for the defined problem, scoring it a total of 3 out of 10.

1.00	0.94	0.98
0.94	1.00	0.93
0.98	0.93	1.00

Anthropic: Claude v2.0 (self-moderated)

Description

Evaluation of the Provided Solution

Summarized Score

Most Similar

Also by anthropic

Model Consistency

Model Evaluations

claude-3-haiku	10.0	10.0	10.0
claude-3.5-sonnet	6.0	6.0	5.0
gpt-3.5-turbo-0125	10.0	10.0	8.0
gpt-4-0125-preview	6.0	5.5	5.0
gpt-4-1106-preview	3.0	2.0	3.0