lmms_eval/api/filter.py (18 lines of code) (raw):

from dataclasses import dataclass from typing import List from lmms_eval.api.instance import Instance from datasets import Dataset class Filter: """ Filter classes operate on a per-task level. They take all model outputs (`instance.resps` for all `task.instances`) across all instances of a task, and perform operations. In a single run, one can configure any number of separate filters or lists of filters. """ def __init__(self, *args, **kwargs) -> None: """ Can define custom behavior here, if an individual instantiation of a Filter class should have state. """ def apply(self, resps, docs): """ Defines the operation to perform on a list of the `inst.resps` properties of `Instance` objects. Should return the list of (filtered) response lists *in the same order as they were input*, e.g. if pass in [<inst.resps for instance 0>, <inst.resps for instance 1>] should return [<filtered resps for instance 0>, <filtered resps for instance 1>] """ return resps @dataclass class FilterEnsemble: """ FilterEnsemble creates a pipeline applying multiple filters. Its intended usage is to stack multiple post-processing steps in order. `task.apply_filters` should use a list of FilterEnsemble classes that it stores, to apply each pipeline separately. """ name: str filters: List[Filter] def apply(self, instances: List[Instance], docs: List[Dataset]) -> None: resps = [inst.resps for inst in instances] # operate just on the model responses for f in self.filters: # apply filters in sequence resps = f.apply(resps, docs) # add the end results after filtering to filtered_requests of their respective source instances. # has key `self.name`: each FilterEnsemble applied in a given run should use a different name. for inst, resp in zip(instances, resps): inst.filtered_resps[self.name] = resp