# Adding a Custom Task

To add a new task, first either open an issue, to determine whether it will be
integrated in the core evaluations of lighteval, in the extended tasks, or the
community tasks, and add its dataset on the hub.

- Core evaluations are evaluations that only require standard logic in their
  metrics and processing, and that we will add to our test suite to ensure non
  regression through time. They already see high usage in the community.
- Extended evaluations are evaluations that require custom logic in their
  metrics (complex normalisation, an LLM as a judge, ...), that we added to
  facilitate the life of users. They already see high usage in the community.
- Community evaluations are submissions by the community of new tasks.

A popular community evaluation can move to become an extended or core evaluation over time.

> [!TIP]
> You can find examples of custom tasks in the <a href="https://github.com/huggingface/lighteval/tree/main/community_tasks">community_task</a> directory.

## Step by step creation of a custom task

> [!WARNING]
> To contribute your custom metric to the lighteval repo, you would first need
> to install the required dev dependencies by running `pip install -e .[dev]`
> and then run `pre-commit install` to install the pre-commit hooks.

First, create a python file under the `community_tasks` directory.

You need to define a prompt function that will convert a line from your
dataset to a document to be used for evaluation.

```python
# Define as many as you need for your different tasks
def prompt_fn(line, task_name: str = None):
    """Defines how to go from a dataset line to a doc object.
    Follow examples in src/lighteval/tasks/default_prompts.py, or get more info
    about what this function should do in the README.
    """
    return Doc(
        task_name=task_name,
        query=line["question"],
        choices=[f" {c}" for c in line["choices"]],
        gold_index=line["gold"],
        instruction="",
    )
```

Then, you need to choose a metric: you can either use an existing one (defined
in [`lighteval.metrics.metrics.Metrics`]) or [create a custom one](adding-a-new-metric)).
[//]: # (TODO: Replace lighteval.metrics.metrics.Metrics with ~metrics.metrics.Metrics once its autodoc is added)

```python
custom_metric = SampleLevelMetric(
    metric_name="my_custom_metric_name",
    higher_is_better=True,
    category=MetricCategory.IGNORED,
    use_case=MetricUseCase.NONE,
    sample_level_fn=lambda x: x,  # how to compute score for one sample
    corpus_level_fn=np.mean,  # How to aggregate the samples metrics
)
```

Then, you need to define your task using [`~tasks.lighteval_task.LightevalTaskConfig`].
You can define a task with or without subsets.
To define a task with no subsets:

```python
# This is how you create a simple task (like hellaswag) which has one single subset
# attached to it, and one evaluation possible.
task = LightevalTaskConfig(
    name="myothertask",
    prompt_function=prompt_fn,  # must be defined in the file or imported from src/lighteval/tasks/tasks_prompt_formatting.py
    suite=["community"],
    hf_repo="",
    hf_subset="default",
    hf_avail_splits=[],
    evaluation_splits=[],
    few_shots_split=None,
    few_shots_select=None,
    metric=[],  # select your metric in Metrics
)
```

If you want to create a task with multiple subset, add them to the
`SAMPLE_SUBSETS` list and create a task for each subset.

```python
SAMPLE_SUBSETS = []  # list of all the subsets to use for this eval


class CustomSubsetTask(LightevalTaskConfig):
    def __init__(
        self,
        name,
        hf_subset,
    ):
        super().__init__(
            name=name,
            hf_subset=hf_subset,
            prompt_function=prompt_fn,  # must be defined in the file or imported from src/lighteval/tasks/tasks_prompt_formatting.py
            hf_repo="",
            metric=[custom_metric],  # select your metric in Metrics or use your custom_metric
            hf_avail_splits=[],
            evaluation_splits=[],
            few_shots_split=None,
            few_shots_select=None,
            suite=["community"],
            generation_size=-1,
            stop_sequence=None,
        )
SUBSET_TASKS = [CustomSubsetTask(name=f"mytask:{subset}", hf_subset=subset) for subset in SAMPLE_SUBSETS]
```

Here is a list of the parameters and their meaning:

- `name` (str), your evaluation name
- `suite` (list), the suite(s) to which your evaluation should belong. This
  field allows us to compare different task implementations and is used as a
  task selection to differentiate the versions to launch. At the moment, you'll
  find the keywords ["helm", "bigbench", "original", "lighteval", "community",
  "custom"]; for core evals, please choose `lighteval`.
- `prompt_function` (Callable), the prompt function you defined in the step
  above
- `hf_repo` (str), the path to your evaluation dataset on the hub
- `hf_subset` (str), the specific subset you want to use for your evaluation
  (note: when the dataset has no subset, fill this field with `"default"`, not
  with `None` or `""`)
- `hf_avail_splits` (list), all the splits available for your dataset (train,
  valid or validation, test, other...)
- `evaluation_splits` (list), the splits you want to use for evaluation
- `few_shots_split` (str, can be `null`), the specific split from which you
  want to select samples for your few-shot examples. It should be different
  from the sets included in `evaluation_splits`
- `few_shots_select` (str, can be `null`), the method that you will use to
  select items for your few-shot examples. Can be `null`, or one of:
    - `balanced` select examples from the `few_shots_split` with balanced
      labels, to avoid skewing the few shot examples (hence the model
      generations) toward one specific label
    - `random` selects examples at random from the `few_shots_split`
    - `random_sampling` selects new examples at random from the
      `few_shots_split` for every new item, but if a sampled item is equal to
      the current one, it is removed from the available samples
    - `random_sampling_from_train` selects new examples at random from the
      `few_shots_split` for every new item, but if a sampled item is equal to
      the current one, it is kept! Only use this if you know what you are
      doing.
    - `sequential` selects the first `n` examples of the `few_shots_split`
- `generation_size` (int), the maximum number of tokens allowed for a
  generative evaluation. If your evaluation is a log likelihood evaluation
  (multi-choice), this value should be -1
- `stop_sequence` (list), a list of strings acting as end of sentence tokens
  for your generation
- `metric` (list), the metrics you want to use for your evaluation (see next
  section for a detailed explanation)
- `trust_dataset` (bool), set to True if you trust the dataset.


Then you need to add your task to the `TASKS_TABLE` list.

```python
# STORE YOUR EVALS

# tasks with subset:
TASKS_TABLE = SUBSET_TASKS

# tasks without subset:
# TASKS_TABLE = [task]
```

Once your file is created you can then run the evaluation with the following command:

```bash
lighteval accelerate \
    "model_name=HuggingFaceH4/zephyr-7b-beta" \
    "community|{custom_task}|{fewshots}|{truncate_few_shot}" \
    --custom-tasks {path_to_your_custom_task_file}
```
