The evaluator classes for automatic evaluation.
The main entry point for using the evaluator:
( task: str = None ) → Evaluator
Parameters
str) —
The task defining which evaluator will be returned. Currently accepted tasks are:
"text-classification" (alias "sentiment-analysis" available): will return a TextClassificationEvaluator.Returns
An evaluator suitable for the task.
Utility factory method to build an Evaluator.
Evaluators encapsulate a task and a default metric name. They leverate pipeline functionalify from transformers
to simplify the evaluation of multiple combinations of models, datasets and metrics for a given task.
The base class for all evaluator classes:
The Evaluator class is the class from which all evaluators inherit. Refer to this class for methods shared across different evaluators.
Base class implementing evaluator operations.
( model_or_pipeline: typing.Union[str, ForwardRef('Pipeline'), typing.Callable, ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] = None data: typing.Union[str, datasets.arrow_dataset.Dataset] = None metric: typing.Union[str, evaluate.module.EvaluationModule] = None tokenizer: typing.Union[str, ForwardRef('PreTrainedTokenizer'), NoneType] = None strategy: typing.Literal['simple', 'bootstrap'] = 'simple' confidence_level: float = 0.95 n_resamples: int = 9999 **compute_parameters: typing.Dict )
A core method of the Evaluator class, computes the metric value for a pipeline and dataset compatible
with the task specified by the Evaluator.
The class for text classification evaluation:
( task = 'text-classification' default_metric_name = None )
Text classification evaluator.
This text classification evaluator can currently be loaded from evaluator() using the default task name
text-classification or with a "sentiment-analysis" alias.
Methods in this class assume a data format compatible with the TextClassificationPipeline - a single textual
feature as input and a categorical label as output.
( model_or_pipeline: typing.Union[str, ForwardRef('Pipeline'), typing.Callable, ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] = None data: typing.Union[str, datasets.arrow_dataset.Dataset] = None metric: typing.Union[str, evaluate.module.EvaluationModule] = None tokenizer: typing.Union[str, ForwardRef('PreTrainedTokenizer'), NoneType] = None strategy: typing.Literal['simple', 'bootstrap'] = 'simple' confidence_level: float = 0.95 n_resamples: int = 9999 random_state: typing.Optional[int] = None input_column: str = 'text' label_column: str = 'label' label_mapping: typing.Union[typing.Dict[str, numbers.Number], NoneType] = None )
Parameters
str or Pipeline or Callable or PreTrainedModel or TFPreTrainedModel, —
None) —
If the argument in not specified, we initialize the default pipeline for the task (in this case
text-classification or its alias - sentiment-analysis). If the argument is of the type str or
is a model instance, we use it to initialize a new Pipeline with the given model. Otherwise we assume the
argument specifies a pre-initialized pipeline.
str or Dataset, defaults to None) -- Specifies the dataset we will run evaluation on. If it is of type str`, we treat it as the dataset
name, and load it. Otherwise we assume it represents a pre-loaded dataset.
str or EvaluationModule, defaults to None” —
Specifies the metric we use in evaluator. If it is of type str, we treat it as the metric name, and
load it. Otherwise we assume it represents a pre-loaded metric.
tokenizer — (str or PreTrainedTokenizer, optional, defaults to None):
Argument can be used to overwrite a default tokenizer if model_or_pipeline represents a model for
which we build a pipeline. If model_or_pipeline is None or a pre-initialized pipeline, we ignore
this argument.
strategy — (Literal["simple", "bootstrap"], defaults to “simple”):
specifies the evaluation strategy. Possible values are:
"simple" - we evaluate the metric and return the scores."bootstrap" - on top of computing the metric scores, we calculate the confidence interval for each
of the returned metric keys, using `scipy’s bootstrap method
https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.bootstrap.html`.float, defaults to 0.95) —
The confidence_level value passed to bootstrap if "bootstrap" strategy is chosen.
int, defaults to 9999) —
The n_resamples value passed to bootstrap if "bootstrap" strategy is chosen.
int, optional, defaults to None) —
The random_state value passed to bootstrap if "bootstrap" strategy is chosen. Useful for
debugging.
str, defaults to "text") —
the name of the column containing the text feature in the dataset specified by data.
str, defaults to "label") —
the name of the column containing the labels in the dataset specified by data.
Dict[str, Number], optional, defaults to None) —
We want to map class labels defined by the model in the pipeline to values consistent with those
defined in the label_column of the data dataset.
Compute the metric for a given pipeline and dataset combination.
Examples:
>>> from evaluation import evaluator
>>> from datasets import Dataset, load_dataset
>>> e = evaluator("text-classification")
>>> data = Dataset.from_dict(load_dataset("imdb")["test"][:2])
>>> results = e.compute(
>>> model_or_pipeline="huggingface/prunebert-base-uncased-6-finepruned-w-distil-mnli",
>>> data=data,
>>> metric="accuracy",
>>> input_column="text",
>>> label_column="label",
>>> label_mapping={"LABEL_0": 0.0, "LABEL_1": 1.0},
>>> strategy="bootstrap",
>>> n_resamples=10,
>>> random_state=0
>>> )