Language.ENGLISH: TranslationLiterals(
        language=Language.ENGLISH,
        question_word="question", # Usage: "Question: How are you?"
        answer="answer", # Usage: "Answer: I am fine"
        confirmation_word="right", # Usage: "He is smart, right?"
        yes="yes", # Usage: "Yes, he is"
        no="no", # Usage: "No, he is not"
        also="also", # Usage: "Also, she is smart."
        cause_word="because", # Usage: "She is smart, because she is tall"
        effect_word="therefore", # Usage: "He is tall therefore he is smart"
        or_word="or", # Usage: "He is tall or small"
        true="true", # Usage: "He is smart, true, false or neither?"
        false="false", # Usage: "He is smart, true, false or neither?"
        neither="neither", # Usage: "He is smart, true, false or neither?"
        # Punctuation and spacing: only adjust if your language uses something different than in English
        full_stop=".",
        comma=",",
        question_mark="?",
        exclamation_mark="!",
        word_space=" ",
        sentence_space=" ",
        colon=":",
        # The first characters of your alphabet used in enumerations, if different from English
        indices=["A", "B", "C", ...]
    )

your_tasks = [
    LightevalTaskConfig(
        # Name of your evaluation
        name=f"evalname_{language.value}_{formulation.name.lower()}",
        # The evaluation is community contributed
        suite=["community"],
        # This will automatically get the correct metrics for your chosen formulation
        metric=get_metrics_for_formulation(
            formulation,
            [
                loglikelihood_acc_metric(normalization=None),
                loglikelihood_acc_metric(normalization=LogProbTokenNorm()),
                loglikelihood_acc_metric(normalization=LogProbCharNorm()),
            ],
        ),
        # In this function, you choose which template to follow and for which language and formulation
        prompt_function=get_template_prompt_function(
            language=language,
            # then use the adapter to define the mapping between the
            # keys of the template (left), and the keys of your dataset
            # (right)
            # To know which template keys are required and available,
            # consult the appropriate adapter type and doc-string.
            adapter=lambda line: {
                "key": line["relevant_key"],
                ...
            },
            formulation=formulation,
        ),
        # You can also add specific filters to remove irrelevant samples
        hf_filter=lambda line: line["label"] in <condition>,
        # You then select your huggingface dataset as well as
        # the splits available for evaluation
        hf_repo=<dataset>,
        hf_subset=<subset>,
        evaluation_splits=["train"],
        hf_avail_splits=["train"],
    )
    for language in [
        Language.YOUR_LANGUAGE, ...
    ]
    for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()]
]