We define 19 literals
, basic keywords or punctuation signs used when creating evaluation prompts in an automatic manner, such as yes
, no
, because
, etc.
We welcome translations in your language!
To contribute, you’ll need to
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", ...]
)
You should first read our guide on adding a custom task, to better understand the different parameters we use.
Then, you should take a look at the current multilingual tasks file, to understand how they are defined. For multilingual evaluations the prompt_function
should be implemented by language-adapted template. The template will take care of correct formatting, correct and consistent usage of language adjusted prompt anchors (e.g Question/Answer) and punctuation.
Browse the list of all templates here to see which are the most adapted to your own task.
Then, when ready, to define your own task, you should:
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()]
]
All LightevalTaskConfig parameters are strongly typed, including the inputs to the template function. Make sure to take advantage of your IDE’s functionality to make it easier to correctly fill these parameters.
Once everything is good, open a PR, and we’ll be happy to review it!
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