High Quality Translation - Validator Details

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High Quality Translation

🏅Guardrails Certified

Last updated 1 year ago

🇬🇧 EN

🧵 STRING

🤖 ML

🧐 ETIQUETTE

📰 FACTUALITY

🤖 CHATBOTS

📞 CUSTOMER SUPPORT

Overview

Developed by	Guardrails AI
Date of development	Feb 15, 2024
Validator type	Format
Blog	-
License	Apache 2
Input/Output	Output

Description

This validator evaluates whether a translation is of high quality. It is useful for validating the output of language models that generate translations.

Requirements

Dependencies: unbabel-comet
IMPORTANT: Steps to follow before installing the validator:
- Please accept the gated model license from: https://huggingface.co/Unbabel/wmt22-cometkiwi-da
- Get your Huggingface token from: https://huggingface.co/settings/tokens (Either create a new token or use an existing one)
- Download Huggingface CLI: pip install -U "huggingface_hub[cli]"
- Login into Huggingface Hub using the token: huggingface-cli login --token $HUGGINGFACE_TOKEN

Installation

guardrails hub install hub://brainlogic/high_quality_translation

Usage Examples

Validating string output via Python

In this example, we use the high_quality_translation validator on any LLM generated text.

# Import Guard and Validator
from guardrails.hub import HighQualityTranslation
from guardrails import Guard

if __name__ == "__main__":
    # Use the Guard with the validator
    guard = Guard().use(HighQualityTranslation, threshold=0.75, on_fail="exception")

    # Test passing response
    guard.validate(
        "The capital of France is Paris.",
        metadata={"translation_source": "Die Hauptstadt von Frankreich ist Paris."},
    )

    try:
        # Test failing response
        guard.validate(
            "France capital Paris is of The.",
            metadata={"translation_source": "Die Hauptstadt von Frankreich ist Paris."},
        )
    except Exception as e:
        print(e)

Output:

Validation failed for field with errors: France capital Paris is of The. is a low quality translation.

API Reference

__init__(self, threshold=0.75, on_fail="noop")

Initializes a new instance of the Validator class.

Parameters:

threshold (float): The minimum score required for a translation to be considered high quality. The score is a float between 0 and 1, where 1 is the highest quality. The default is 0.75.
on_fail (str, Callable): The policy to enact when a validator fails. If str, must be one of reask, fix, filter, refrain, noop, exception or fix_reask. Otherwise, must be a function that is called when the validator fails.

validate(self, value, metadata={}) -> ValidationResult

Validates the given value using the rules defined in this validator, relying on the metadata provided to customize the validation process. This method is automatically invoked by guard.parse(...), ensuring the validation logic is applied to the input data.

Note:

This method should not be called directly by the user. Instead, invoke guard.parse(...) where this method will be called internally for each associated Validator.
When invoking guard.parse(...), ensure to pass the appropriate metadata dictionary that includes keys and values required by this validator. If guard is associated with multiple validators, combine all necessary metadata into a single dictionary.

Parameters:

value (Any): The input value to validate.
metadata (dict): A dictionary containing metadata required for validation. Keys and values must match the expectations of this validator.

Key Type Description Default
translation_source str The original source text that was translated. None

Key	Type	Description	Default
`translation_source`	`str`	The original source text that was translated.	None

Guardrails API

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