Translation Notebook



In the Translation section, we’re delving into the details of testing translation models by utilizing the Hugging Face Transformers library, alongside John Snow Labs. However, we’re taking it a step further. Beyond the standard evaluation, we’re introducing various perturbations to the input text, such as lowercasing and uppercasing. This approach allows us to assess the models’ robustness and their ability to accurately translate text into different languages when faced with these variations. It’s a methodical examination, ensuring these models can maintain accuracy even when encountering diverse linguistic challenges.

Open in Collab

Category Hub Task Open In Colab
Translation : Hugging Face/John Snow Labs Translation Open In Colab

Config Used

    target_language: de
    min_pass_rate: 0.65
        min_pass_rate: 0.66
        min_pass_rate: 0.66

Supported Tests

  • uppercase: capitalization of the test set is turned into uppercase

  • lowercase: capitalization of the test set is turned into lowercase

  • titlecase: capitalization of the test set is turned into title case

  • add_punctuation: special characters at end of each sentence are replaced by other special characters, if no special character at the end, one is added

  • strip_punctuation: special characters are removed from the sentences (except if found in numbers, such as ‘2.5’)

  • add_typo: typos are introduced in sentences

  • add_contraction: contractions are added where possible (e.g. ‘do not’ contracted into ‘don’t’)

  • add_context: tokens are added at the beginning and at the end of the sentences

  • swap_entities: named entities replaced with same entity type with same token count from terminology

  • swap_cohyponyms: Named entities replaced with co-hyponym from the WordNet database

  • american_to_british: American English will be changed to British English

  • british_to_american: British English will be changed to American English

  • number_to_word: Converts numeric values in sentences to their equivalent verbal representation.

  • add_ocr_typo: Ocr typos are introduced in sentences

  • add_speech_to_text_typo: Introduce common conversion errors from SSpeech to Text conversion.

  • add_abbreviation:Replaces words or expressions in texts with their abbreviations

  • multiple_perturbations : Transforms the given sentences by applying multiple perturbations in a specific sequence.

  • adjective_synonym_swap : Transforms the adjectives in the given sentences to their synonyms.

  • adjective_antonym_swap : Transforms the adjectives in the given sentences to their antonyms.

  • strip_all_punctuation: Strips all punctuation from the sentences.