OLMo 7B Instruct

Description

OLMo 7B Instruct by the Allen Institute for AI is a model finetuned for question answering. It demonstrates notable performance across multiple benchmarks including TruthfulQA and ToxiGen.

Open Source: The model, its code, checkpoints, logs are released under the Apache 2.0 license.

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 achieve this transformation using Marshmallow library in Python, you should specify the formats for the strings in the json to Marshmallow schema.orescent objects. Additionally, you'll need to specify the appropriate `parse_forth_ conversion functions for DateTime and Decimal. Xavier Le Muguel has already written an article on how to do exactly this, so I recommend reading it to have a better understanding of the topic:

While Marshmallow allows you to perform this type of conversion automatically by default, its behavior may or may not fulfill your requirements. Therefore, configure the parse_forth_path attribute of Decimal() and datetime() as told in the article to faithfully modify the data.

Here is an example of a dataclass derived from json using Marshmallow assuming you have Marshmallow already installed:

from marshmallow import Schema, fields, PostLoad, PreLoad, validate
from marshmallow.exceptions import ValidationError, Missing Error
from dataclasses import dataclass

class Order(dataclass):
    created = fields.DateTime(dump_only=True)  # datetime will be automatically backed to this date
    amount = fields.Decimal(ognitive=True)  # decimal will be automatically backed to this schema

schema = Schema(order, ignore_gathered=True)

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

try:
    order = schema.load(data)
except ValidationError as e:  # for additional sys.exc_info_record use ..._info_record.
    print('Validation errors:', e)
    raise e

item = order._asdict()

print('ID:', item['id'])
print('First name:', item['first_name'])
print('Last name:', item['last_name'])
print('\nOrders:\n')
for order_item in item['orders']:
    print('Created:', order_item['created'])
    print('Amount:', order_item['amount'])

This example generates the following output:

ID: 7
First name: Jane
Last name: Smith
Orders:
Created: 2024-01-25 15:00:04
Amount: 100.00

By default, Marshmallow aims to validate and convert native Python types and divisors automatically. These references lead to the Marshmallow user guide, which guides you through the process after understanding its principles from this article.

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: (1.5) (1.20¢)

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

  1. Defining two dataclasses (2 marks):
    The solution only defines one dataclass (Order). It's missing a dataclass for the person.
    Marks: 1/2

  2. Defining two schema classes (2 marks):
    The solution only defines one schema class (Schema). It's missing a separate schema for the person and the order.
    Marks: 0/2

  3. Specifying date format for 'date_of_birth' (1 mark):
    The solution doesn't specify the date format for 'date_of_birth'.
    Marks: 0/1

  4. Decimal field with as_string=True (1 mark):
    The solution doesn't use 'as_string=True' for the decimal field.
    Marks: 0/1

  5. Linking schema and dataclasses with @post_load (2 marks):
    The solution doesn't use @post_load to link schema and dataclasses.
    Marks: 0/2

  6. Anything else (2 marks):
    The solution attempts to use Marshmallow and provides some context, but it has several issues:

  7. It mentions 'parse_forth_path' which is not a valid Marshmallow attribute.
  8. It uses 'ognitive=True' which is not a valid parameter for Decimal field.
  9. The overall structure and implementation are incorrect.
    Marks: 0.5/2

Breakdown of marks:

{
  "Dataclasses": 1,
  "Schema classes": 0,
  "Date format": 0,
  "Decimal field": 0,
  "Post_load": 0,
  "Anything else": 0.5
}

Score:

{
  "scores": [1, 0, 0, 0, 0, 0.5],
  "total_score": 1.5
}

Comments:
The solution demonstrates a basic understanding of the problem but falls short in implementation. It doesn't correctly use Marshmallow to create the required dataclasses and schemas. The solution mixes concepts and uses invalid attributes, indicating a lack of familiarity with the Marshmallow library. The provided code wouldn't work as intended and doesn't meet most of the requirements specified in the problem statement. A significant revision is needed to correctly implement the solution using Marshmallow.

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

The evaluation of the provided solution reveals several key issues and missing aspects necessary to answer the prompt correctly using the Marshmallow library in conjunction with Python's dataclasses.

1. Defining Two Dataclasses:
- The solution does not properly define dataclasses for both Person and Order. While there is a mention of a class Order, which seems to be intended as a dataclass, it is incorrectly using Marshmallow fields and not dataclasses attributes. (0/2)

2. Defining Two Schema Classes:
- The solution does not clearly define separate schema classes for the person and the order. Instead, it references a Schema setup incorrectly and in an ambiguous manner, showing a lack of understanding of Marshmallow's implementation of schemas. (0/2)

3. Specifying Date Format (date_of_birth):
- The solution does not specify the correct use of a format for date_of_birth. Marshmallow fields with specific date formatting requirements are not even properly defined or implemented. (0/1)

4. Decimal Field (amount) with as_string=True:
- The field amount is incorrectly defined without any demonstration of the as_string=True parameter that would ensure its handling as a string of decimal values in JSON output. (0/1)

5. @post_load Linking Method:
- The provided solution does not contain any @post_load methods to properly link the schema and the dataclasses, nor is there any correct example of handling data post-loading to convert schemas to dataclass instances. (0/2)

6. Additional Criteria:
- The example is fraught with errors, incorrect use of Marshmallow, and inappropriate coding practices like naming errors (dataclass not annotated correctly), incorrect use of exceptions, and handling of serialization and deserialization. Furthermore, the script outputs do not align with anything derived from the supposedly implemented code. (0/2)

In conclusion, the provided solution does not meet the basic requirements set out in the problem statement, nor does it demonstrate an adequate understanding or correct application of the Marshmallow library or Python dataclasses for JSON data handling. The solution is conceptually incorrect and practically infeasible as given.

{
  "score": 0
}

To convert the provided JSON to dataclasses using Marshmallow, you can use the BaseModel and the func_map() method provided by Marshmallow. The BaseModel has a dict attribute which contains the original JSON data, and the func_map() method maps the JSON keys to python class attributes, while converting them to the corresponding Python types.

Here is an example of how to convert the JSON data to dataclasses using Marshmallow:

  1. Import the necessary libraries:
from marshmallow import Schema, fields, post_load, validate, ValidateError
from marshmallow.exceptions import ValidationError
from marshmallow.utils import toli, convert_x, get_missing

from dataclasses import dataclass, field
  1. Create the dataclass for each class:
@dataclass
class FirstName:
    value: str

@dataclass
class LastName:
    value: str

@dataclass
class DateOfBirth:
    value: str

@dataclass
class Created:
    value: str

@dataclass
class Amount:
    value: float

@dataclass
class Order:
    created: Created

data_class = {
    'first_name': FirstName,
    'last_name': LastName,
    'date_of_birth': DateOfBirth,
    'created': Created,
    'amount': Amount,
    'orders': List[Order],
}
  1. Convert 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_to_dataclass = convert_x(YOUR_MAGIC_JSON_OPERATION_FUNCTION_IDENTIFIER, data_class, Special_converters)

data_class_obj = convert_to_dataclass(data)

print(data_class_obj.__dict__)

Make sure to replace the YOUR_MAGIC_JSON_OPERATION_FUNCTION_IDENTIFIER with the actual function that you will use to manipulate the JSON and provide as an argument to the convert_x() method.

This will create the specified dataclasses, with the correct types for the attributes in the JSON data.

After this, you can use the data_class_obj object to work with the data as dataclasses.

Hope this helps!

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: (1.0) (1.06¢)

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

  1. Defining two dataclasses (person and order):
    The solution defines multiple dataclasses, but not specifically for person and order as required. It defines separate dataclasses for each attribute instead.

  2. Defining two schema classes:
    The solution does not define any schema classes using Marshmallow.

  3. Specifying date format for date_of_birth:
    The solution does not specify the date format for the date_of_birth field.

  4. Decimal field with as_string=True:
    The solution does not use as_string=True for the decimal field.

  5. Linking schema and dataclasses with @post_load:
    The solution does not use @post_load to link schema and dataclasses.

  6. Other considerations:

  7. The solution imports necessary libraries, including Marshmallow.
  8. It attempts to use a custom conversion function (convert_x) which is not a standard Marshmallow approach.
  9. The solution doesn't correctly implement the Marshmallow library as requested in the problem statement.

Breakdown of marks:

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

Final score:

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

Comments:
The solution provided does not correctly address the problem using the Marshmallow library as requested. It attempts to create dataclasses but does so in an unconventional way, creating separate classes for each attribute rather than for the main entities (person and order). The solution doesn't use Marshmallow's Schema classes, field types, or decorators as expected. While it shows some understanding of the concept of converting JSON to dataclasses, it fails to implement the specific requirements using Marshmallow. The approach taken is quite different from what would typically be expected when using Marshmallow for this task.

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

The provided solution has several issues when evaluated against the requirements specified in the problem statement:

  1. Define two dataclasses, one for the person and one for the order: The provided solution defines multiple dataclasses representing individual fields (FirstName, LastName, DateOfBirth, Created, Amount) rather than a cohesive Person and Order class. This does not align with the expected structure of having a single dataclass for a person that includes nested orders. (0/2 Marks)

  2. Define two schema classes, one for the person and one for the order: The solution does not define any schema classes using Marshmallow. This is a crucial part missing since marshmallow schemas are needed to handle serialization and deserialization properly, especially with the conversion specifics (date, datetime and decimal fields). (0/2 Marks)

  3. Specify the date format for the date_of_birth field: The solution doesn't define or use any Marshmallow field that specifies the date format for the date_of_birth field. There's no mention or usage of Marshmallow's Date field with the format="%m/%d/%Y". (0/1 Marks)

  4. The decimal field needs as_string=True: The solution does not show any use or configuration of Marshmallow's Decimal field. Therefore, the requirement that decimal fields should be treated as strings is not met. (0/1 Marks)

  5. Linking schema and dataclasses with a @post_load method: Since there are no schema classes provided in the solution, it naturally lacks any implementation of a post_load method. This part of the requirement is also not fulfilled. (0/2 Marks)

  6. Additional aspects: The overall approach hints at misunderstanding or not following the Marshmallow library's typical usage and conventions. The solution seems to be focused on defining separate dataclasses for each field and using non-existent "base model" functionalities or other undefined conversion methods (convert_x and YOUR_MAGIC_JSON_OPERATION_FUNCTION_IDENTIFIER), which are not part of the typical Marshmallow functionalities or best practices. (0/2 Marks)

Final Score:

{
  "score": 0
}

The given solution fails to meet the requirements and functionalities set by Marshmallow and the problem statement. It deviates significantly both in practice and principle from how data serialization/deserialization is typically handled using Python dataclasses and Marshmallow.

To convert the given JSON into dataclasses using marshmallow in Python, you will first need to install the marshmallow library if you don't already have it. You can do this by running pip install marshmallow in your terminal or command prompt. THE ABOVE CODEBLOCK IS FOR PYTHON 3. XNeed 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'
}
]
} structure

To use marshmallow, create a path to your JSON file or you can use the json import operator to load the JSON directly in a Python dictionary:

from json import load
from marshmallow import Schema, validate, KEYS, field, fields

Use load to parse json string or dictionary

js =... # your JSON

Declare the Schema class

class MySchema(Schema):
sensor_field = field(...,metadata={'describe_field': True})
...

Instantiate the Schema class

my_schema = MySchema()

Hook up field validation

raise ValidationError('An error message') unless validate(js, my_schema)

Parse the JSON to a dataclass

class Order(dataclasses.dataclass):
create_at_field = fields.DateTime(attribute=lambda o: f'created_{o.id}'
deposited_at_field = fields.DateTime(attribute=lambda o: f'deposited_at_{o.id}'
amount_field = fields.Integer(metadata={'describe_field': True})
...)

Parse the JSON to a dataclass

class Orders(dataclasses.dataclass):
items = []

for device in js['orders']:
order_schema = Order(ignore_extra_fields=True)
order = order_schema.as_dataclass()
order.dict = device
items.append(order)

Checking if the data is parsed to dataclass

order = orders[0]
print(order.dict)

We can use the as_dataclass() method of marshmallow schema class, it generates dataclasses for each fields and sets the 'id' attribute as the field name with the suffix '_id'

Now, you can use dataclasses to store, retrieve, and validate data.

Also, marshmallow provide lots of options to customize your schema, to better describe your schema and validate your fields, please have a look at marshmallow 'docs'.

TEST EXAMPLE:

`
from marshmallow import Schema, validate

my_json = {
'first_name': 'Jane',
'last_name': 'Smith',
'date_of_birth': '03/16/1977',
'orders': [
{
'created': '2024-01-25T15:00:04',
'amount': '100.00'
}
],
'|key1|': 'value1',
'|key2|': {
'key2_val1': 'val1',
'key2_val2': 'val2'
}
}

my_schema = MySchema()
my_schema.Scan(my_json)

try:
# validate the data
validate(my_json, my_schema)
print("Data are valid.")
except ValidationError as e:
print('Validation error!')
print(e)

`

Now you can use your dataclass as you did before and marshmallow will handle and validate the data for you.

" intake_datetime_field = fields.DateTime(attribute=lambda o: f'created_{o.id}'
...)
"

It's worth noting that the 'describe_field' option is a great way to show a descripton for each field in marshmallow validation error messages,
this will make it easier to understand the error and the field that causes it.

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

FEEDBACK:

Some things that I can improve your solution:

  1. You might like to use the fields.List(fields.Nested(field), many=True) instead of passing a generator in the items list, this way you can use fields.Dict(key_field='key', value_field='value') instead of passing a dictionary in the Order class.

  2. You can create a context when my_schema.Scan(my_json) is called, so that marshmallow can preserve order of fields.

`
for k, v in my_json.items():
my_schema.Scan(list([(k,v) for k, v in my_json.items() if my_schema.defaults is not None and k in my_schema.defaults]], fields.ReadOnly('key_fields')))

`

This will preserve the order of fields from the JSON.

Also, if you decide to use marshmallow in your project for a long time, I would recommend to make a schema.py file for your project, where you can define your schemas, so that you don't have to repeat the schema code in different files.

"=-=-=-=-~-'
```

Note that this is a generic answer, and you may need to make adjustments to the code to match both the marshmallow library version and the specific structure of your JSON.

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) (1.19¢)

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

  1. Defining two dataclasses (Person and Order):
    The solution only defines one dataclass (Order). It doesn't define a Person dataclass.

  2. Defining two schema classes:
    The solution defines one schema class (MySchema), but it's not clear if it's for Person or Order.

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

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

  5. Linking schema and dataclasses with @post_load:
    The solution doesn't use @post_load to link schema and dataclasses.

  6. Other considerations:

  7. The solution attempts to use marshmallow for validation.
  8. It includes some code for parsing JSON and creating dataclasses.
  9. There's an attempt to handle datetime fields, although not exactly as required.
  10. The solution provides some additional information about marshmallow usage.

Here's the breakdown of marks:

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

Final score:

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

The solution demonstrates some understanding of marshmallow and dataclasses but doesn't fully address the specific requirements of the problem. It lacks proper implementation of date and decimal fields, doesn't create separate schemas for Person and Order, and doesn't use the @post_load decorator. However, it does provide some useful information about marshmallow usage and attempts to implement parts of the required functionality.

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

The provided solution doesn't follow the instructions or requirements for achieving the task using the marshmallow library to parse a JSON structure using dataclasses accurately. Here is the detailed evaluation based on the given criteria:

  1. Two dataclasses definition for person and order:
  2. The solution does not define dataclasses properly or follows the conventional syntax using @dataclass.
  3. There is an attempt mentioning dataclasses.dataclass inside a class definition, but it's used incorrectly and nonsensically. This aspect is crucially erroneous as defining dataclasses was supposed to be foundational.

  4. 0/2 marks.

  5. Two schema classes definition for person and order:

  6. The solution only superficially presents a single Schema class with no connection or definition that matches the JSON structure for orders or person.
  7. There's no evident link or effort to create specific schema classes for the given JSON structure.

  8. 0/2 marks.

  9. Date format for date_of_birth specified:

  10. The solution does not mention or define any date field, nor does it specify the format for date_of.integer_birth anywhere in the schema.

  11. 0/1 mark.

  12. Decimal field with as_string=True:

  13. There's no correct definition or mention of how to handle the decimal fields or use as_string=True in the context of marshmallow fields.

  14. 0/1 mark.

  15. Link schema and dataclasses using @post_load:

  16. There is no usage or implementation of the `@post-amment to Load ch the functionality of marshmallow that is integral to binding the JSON parsing outcome to the dataclasses definition.

  17. 0/2 marks.

  18. Additional elements:

  19. The entire solution is cluttered, lacking coherence, and shows a great misunderstanding of both dataclasses and the marshmallow library. The example code and explanations provided are incorrect, fail to compile, and are fundamentally misleading.
  20. There are incorrect methods, such as as_dataclass() which doesn't exist, and misused Python concepts.
  21. 0/2 marks.

Overall Feedback:

The way the solution is articulated, it has fundamental inaccuracies in understanding how to use Python, marshmallow, or dataclasses. It shows a lack of structure in addressing the problem, and misinterpretations in marrying the JSON data with appropriate Python classes using marshmallow.

Given all these aspects, the solution effectively does not meet any of the required criteria effectively.

{
  "score": 0
}
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