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Evaluator

The evaluator classes for automatic evaluation.

Evaluator classes

The main entry point for using the evaluator:

evaluate.evaluator

< >

( task: str = None ) → Evaluator

Parameters

Returns

Evaluator

An evaluator suitable for the task.

Utility factory method to build an Evaluator. Evaluators encapsulate a task and a default metric name. They leverage pipeline functionalify from transformers to simplify the evaluation of multiple combinations of models, datasets and metrics for a given task.

Examples:

>>> from evaluate import evaluator
>>> # Sentiment analysis evaluator
>>> evaluator("sentiment-analysis")

The base class for all evaluator classes:

class evaluate.Evaluator

< >

( task: strdefault_metric_name: str = None )

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.

check_required_columns

< >

( data: typing.Union[str, datasets.arrow_dataset.Dataset]columns_names: typing.Dict[str, str] )

Parameters

  • data (str or Dataset) — Specifies the dataset we will run evaluation on.
  • columns_names (List[str]) —
  • List of column names to check in the dataset. The keys are the arguments to the compute() method, —
  • while the values are the column names to check. —

Ensure the columns required for the evaluation are present in the dataset.

compute_metric

< >

( metric: EvaluationModulemetric_inputs: typing.Dictstrategy: typing.Literal['simple', 'bootstrap'] = 'simple'confidence_level: float = 0.95n_resamples: int = 9999random_state: typing.Optional[int] = None )

Compute and return metrics.

get_dataset_split

< >

( datasubset = Nonesplit = None ) → split

Parameters

  • data (str) — Name of dataset
  • subset (str) — Name of config for datasets with multiple configurations (e.g. ‘glue/cola’)
  • split (str, defaults to None) — Split to use

Returns

split

str containing which split to use

Infers which split to use if None is given.

load_data

< >

( data: typing.Union[str, datasets.arrow_dataset.Dataset]subset: str = Nonesplit: str = None ) → data (Dataset)

Parameters

  • data (Dataset or str, 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. —
  • subset (str, defaults to None) — Specifies dataset subset to be passed to name in load_dataset. To be used with datasets with several configurations (e.g. glue/sst2).
  • split (str, defaults to None) — User-defined dataset split by name (e.g. train, validation, test). Supports slice-split (test[:n]). If not defined and data is a str type, will automatically select the best one via choose_split().

Returns

data (Dataset)

Loaded dataset which will be used for evaluation.

Load dataset with given subset and split.

predictions_processor

< >

( *args**kwargs )

A core method of the Evaluator class, which processes the pipeline outputs for compatibility with the metric.

prepare_data

< >

( data: Datasetinput_column: strlabel_column: str*args**kwargs ) → dict

Parameters

  • data (Dataset) — Specifies the dataset we will run evaluation on.
  • input_column (str, defaults to "text") — the name of the column containing the text feature in the dataset specified by data.
  • label_column (str, defaults to "label") — the name of the column containing the labels in the dataset specified by data.

Returns

dict

metric inputs. list: pipeline inputs.

Prepare data.

prepare_metric

< >

( metric: typing.Union[str, evaluate.module.EvaluationModule] )

Parameters

  • metric (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.

Prepare metric.

prepare_pipeline

< >

( model_or_pipeline: typing.Union[str, ForwardRef('Pipeline'), typing.Callable, ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')]tokenizer: typing.Union[ForwardRef('PreTrainedTokenizerBase'), ForwardRef('FeatureExtractionMixin')] = Nonefeature_extractor: typing.Union[ForwardRef('PreTrainedTokenizerBase'), ForwardRef('FeatureExtractionMixin')] = Nonedevice: int = None )

Parameters

  • model_or_pipeline (str or Pipeline or Callable or PreTrainedModel or TFPreTrainedModel, —
  • defaults to None) — If the argument in not specified, we initialize the default pipeline for the task. 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.
  • preprocessor (PreTrainedTokenizerBase or FeatureExtractionMixin, optional, defaults to None) — Argument can be used to overwrite a default preprocessor 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.

Prepare pipeline.

The task specific evaluators

ImageClassificationEvaluator

class evaluate.ImageClassificationEvaluator

< >

( task = 'image-classification'default_metric_name = None )

Image classification evaluator. This image classification evaluator can currently be loaded from evaluator() using the default task name image-classification. Methods in this class assume a data format compatible with the ImageClassificationPipeline.

compute

< >

( model_or_pipeline: typing.Union[str, ForwardRef('Pipeline'), typing.Callable, ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] = Nonedata: typing.Union[str, datasets.arrow_dataset.Dataset] = Nonesubset: typing.Optional[str] = Nonesplit: typing.Optional[str] = Nonemetric: typing.Union[str, evaluate.module.EvaluationModule] = Nonetokenizer: typing.Union[str, ForwardRef('PreTrainedTokenizer'), NoneType] = Nonefeature_extractor: typing.Union[str, ForwardRef('FeatureExtractionMixin'), NoneType] = Nonestrategy: typing.Literal['simple', 'bootstrap'] = 'simple'confidence_level: float = 0.95n_resamples: int = 9999device: int = Nonerandom_state: typing.Optional[int] = Noneinput_column: str = 'image'label_column: str = 'label'label_mapping: typing.Union[typing.Dict[str, numbers.Number], NoneType] = None )

Parameters

  • model_or_pipeline (str or Pipeline or Callable or PreTrainedModel or TFPreTrainedModel, defaults to 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.
  • data (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.
  • subset (str, defaults to None) — Defines which dataset subset to load. If None is passed the default subset is loaded.
  • split (str, defaults to None) — Defines which dataset split to load. If None is passed, infers based on the choose_split function.
  • metric (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:
  • confidence_level (float, defaults to 0.95) — The confidence_level value passed to bootstrap if "bootstrap" strategy is chosen.
  • n_resamples (int, defaults to 9999) — The n_resamples value passed to bootstrap if "bootstrap" strategy is chosen.
  • device (int, defaults to None) — Device ordinal for CPU/GPU support of the pipeline. Setting this to -1 will leverage CPU, a positive integer will run the model on the associated CUDA device ID. If None is provided it will be inferred and CUDA:0 used if available, CPU otherwise.
  • random_state (int, optional, defaults to None) — The random_state value passed to bootstrap if "bootstrap" strategy is chosen. Useful for debugging.

Compute the metric for a given pipeline and dataset combination.

Examples:

>>> from evaluate import evaluator
>>> from datasets import load_dataset
>>> task_evaluator = evaluator("image-classification")
>>> data = load_dataset("beans", split="test[:40]")
>>> results = task_evaluator.compute(
>>>     model_or_pipeline="nateraw/vit-base-beans",
>>>     data=data,
>>>     label_column="labels",
>>>     metric="accuracy",
>>>     label_mapping={'angular_leaf_spot': 0, 'bean_rust': 1, 'healthy': 2},
>>>     strategy="bootstrap"
>>> )

QuestionAnsweringEvaluator

class evaluate.QuestionAnsweringEvaluator

< >

( task = 'question-answering'default_metric_name = None )

Question answering evaluator. This evaluator handles extractive question answering, where the answer to the question is extracted from a context.

This question answering evaluator can currently be loaded from evaluator() using the default task name question-answering.

Methods in this class assume a data format compatible with the QuestionAnsweringPipeline.

compute

< >

( model_or_pipeline: typing.Union[str, ForwardRef('Pipeline'), typing.Callable, ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] = Nonedata: typing.Union[str, datasets.arrow_dataset.Dataset] = Nonesubset: typing.Optional[str] = Nonesplit: typing.Optional[str] = Nonemetric: typing.Union[str, evaluate.module.EvaluationModule] = Nonetokenizer: typing.Union[str, ForwardRef('PreTrainedTokenizer'), NoneType] = Nonestrategy: typing.Literal['simple', 'bootstrap'] = 'simple'confidence_level: float = 0.95n_resamples: int = 9999device: int = Nonerandom_state: typing.Optional[int] = Nonequestion_column: str = 'question'context_column: str = 'context'id_column: str = 'id'label_column: str = 'answers'squad_v2_format: typing.Optional[bool] = None )

Parameters

  • model_or_pipeline (str or Pipeline or Callable or PreTrainedModel or TFPreTrainedModel, defaults to 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.
  • data (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.
  • subset (str, defaults to None) — Defines which dataset subset to load. If None is passed the default subset is loaded.
  • split (str, defaults to None) — Defines which dataset split to load. If None is passed, infers based on the choose_split function.
  • metric (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:
  • confidence_level (float, defaults to 0.95) — The confidence_level value passed to bootstrap if "bootstrap" strategy is chosen.
  • n_resamples (int, defaults to 9999) — The n_resamples value passed to bootstrap if "bootstrap" strategy is chosen.
  • device (int, defaults to None) — Device ordinal for CPU/GPU support of the pipeline. Setting this to -1 will leverage CPU, a positive integer will run the model on the associated CUDA device ID. If None is provided it will be inferred and CUDA:0 used if available, CPU otherwise.
  • random_state (int, optional, defaults to None) — The random_state value passed to bootstrap if "bootstrap" strategy is chosen. Useful for debugging.

Compute the metric for a given pipeline and dataset combination.

Examples:

>>> from evaluate import evaluator
>>> from datasets import load_dataset
>>> task_evaluator = evaluator("question-answering")
>>> data = load_dataset("squad", split="validation[:2]")
>>> results = task_evaluator.compute(
>>>     model_or_pipeline="sshleifer/tiny-distilbert-base-cased-distilled-squad",
>>>     data=data,
>>>     metric="squad",
>>> )

Datasets where the answer may be missing in the context are supported, for example SQuAD v2 dataset. In this case, it is safer to pass squad_v2_format=True to the compute() call.

>>> from evaluate import evaluator
>>> from datasets import load_dataset
>>> task_evaluator = evaluator("question-answering")
>>> data = load_dataset("squad_v2", split="validation[:2]")
>>> results = task_evaluator.compute(
>>>     model_or_pipeline="mrm8488/bert-tiny-finetuned-squadv2",
>>>     data=data,
>>>     metric="squad_v2",
>>>     squad_v2_format=True,
>>> )

TextClassificationEvaluator

class evaluate.TextClassificationEvaluator

< >

( 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.

compute

< >

( model_or_pipeline: typing.Union[str, ForwardRef('Pipeline'), typing.Callable, ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] = Nonedata: typing.Union[str, datasets.arrow_dataset.Dataset] = Nonesubset: typing.Optional[str] = Nonesplit: typing.Optional[str] = Nonemetric: typing.Union[str, evaluate.module.EvaluationModule] = Nonetokenizer: typing.Union[str, ForwardRef('PreTrainedTokenizer'), NoneType] = Nonefeature_extractor: typing.Union[str, ForwardRef('FeatureExtractionMixin'), NoneType] = Nonestrategy: typing.Literal['simple', 'bootstrap'] = 'simple'confidence_level: float = 0.95n_resamples: int = 9999device: int = Nonerandom_state: typing.Optional[int] = Noneinput_column: str = 'text'second_input_column: typing.Optional[str] = Nonelabel_column: str = 'label'label_mapping: typing.Union[typing.Dict[str, numbers.Number], NoneType] = None )

Parameters

  • model_or_pipeline (str or Pipeline or Callable or PreTrainedModel or TFPreTrainedModel, defaults to 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.
  • data (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.
  • subset (str, defaults to None) — Defines which dataset subset to load. If None is passed the default subset is loaded.
  • split (str, defaults to None) — Defines which dataset split to load. If None is passed, infers based on the choose_split function.
  • metric (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:
  • confidence_level (float, defaults to 0.95) — The confidence_level value passed to bootstrap if "bootstrap" strategy is chosen.
  • n_resamples (int, defaults to 9999) — The n_resamples value passed to bootstrap if "bootstrap" strategy is chosen.
  • device (int, defaults to None) — Device ordinal for CPU/GPU support of the pipeline. Setting this to -1 will leverage CPU, a positive integer will run the model on the associated CUDA device ID. If None is provided it will be inferred and CUDA:0 used if available, CPU otherwise.
  • random_state (int, optional, defaults to None) — The random_state value passed to bootstrap if "bootstrap" strategy is chosen. Useful for debugging.

Compute the metric for a given pipeline and dataset combination.

Examples:

>>> from evaluate import evaluator
>>> from datasets import load_dataset
>>> task_evaluator = evaluator("text-classification")
>>> data = load_dataset("imdb", split="test[:2]")
>>> results = task_evaluator.compute(
>>>     model_or_pipeline="huggingface/prunebert-base-uncased-6-finepruned-w-distil-mnli",
>>>     data=data,
>>>     metric="accuracy",
>>>     label_mapping={"LABEL_0": 0.0, "LABEL_1": 1.0},
>>>     strategy="bootstrap",
>>>     n_resamples=10,
>>>     random_state=0
>>> )

TokenClassificationEvaluator

class evaluate.TokenClassificationEvaluator

< >

( task = 'token-classification'default_metric_name = None )

Token classification evaluator.

This token classification evaluator can currently be loaded from evaluator() using the default task name token-classification.

Methods in this class assume a data format compatible with the TokenClassificationPipeline.

compute

< >

( model_or_pipeline: typing.Union[str, ForwardRef('Pipeline'), typing.Callable, ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] = Nonedata: typing.Union[str, datasets.arrow_dataset.Dataset] = Nonesubset: typing.Optional[str] = Nonesplit: str = Nonemetric: typing.Union[str, evaluate.module.EvaluationModule] = Nonetokenizer: typing.Union[str, ForwardRef('PreTrainedTokenizer'), NoneType] = Nonestrategy: typing.Literal['simple', 'bootstrap'] = 'simple'confidence_level: float = 0.95n_resamples: int = 9999device: typing.Optional[int] = Nonerandom_state: typing.Optional[int] = Noneinput_column: str = 'tokens'label_column: str = 'ner_tags'join_by: typing.Optional[str] = ' ' )

Parameters

  • model_or_pipeline (str or Pipeline or Callable or PreTrainedModel or TFPreTrainedModel, defaults to 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.
  • data (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.
  • subset (str, defaults to None) — Defines which dataset subset to load. If None is passed the default subset is loaded.
  • split (str, defaults to None) — Defines which dataset split to load. If None is passed, infers based on the choose_split function.
  • metric (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:
  • confidence_level (float, defaults to 0.95) — The confidence_level value passed to bootstrap if "bootstrap" strategy is chosen.
  • n_resamples (int, defaults to 9999) — The n_resamples value passed to bootstrap if "bootstrap" strategy is chosen.
  • device (int, defaults to None) — Device ordinal for CPU/GPU support of the pipeline. Setting this to -1 will leverage CPU, a positive integer will run the model on the associated CUDA device ID. If None is provided it will be inferred and CUDA:0 used if available, CPU otherwise.
  • random_state (int, optional, defaults to None) — The random_state value passed to bootstrap if "bootstrap" strategy is chosen. Useful for debugging.

Compute the metric for a given pipeline and dataset combination.

The dataset input and label columns are expected to be formatted as a list of words and a list of labels respectively, following conll2003 dataset. Datasets whose inputs are single strings, and labels are a list of offset are not supported.

Examples:

>>> from evaluate import evaluator
>>> from datasets import load_dataset
>>> task_evaluator = evaluator("token-classification")
>>> data = load_dataset("conll2003", split="validation[:2]")
>>> results = task_evaluator.compute(
>>>     model_or_pipeline="elastic/distilbert-base-uncased-finetuned-conll03-english",
>>>     data=data,
>>>     metric="seqeval",
>>> )

For example, the following dataset format is accepted by the evaluator:

dataset = Dataset.from_dict(
    mapping={
        "tokens": [["New", "York", "is", "a", "city", "and", "Felix", "a", "person", "."]],
        "ner_tags": [[1, 2, 0, 0, 0, 0, 3, 0, 0, 0]],
    },
    features=Features({
        "tokens": Sequence(feature=Value(dtype="string")),
        "ner_tags": Sequence(feature=ClassLabel(names=["O", "B-LOC", "I-LOC", "B-PER", "I-PER"])),
        }),
)

For example, the following dataset format is not accepted by the evaluator:

dataset = Dataset.from_dict(
    mapping={
        "tokens": [["New York is a city and Felix a person."]],
        "starts": [[0, 23]],
        "ends": [[7, 27]],
        "ner_tags": [["LOC", "PER"]],
    },
    features=Features({
        "tokens": Value(dtype="string"),
        "starts": Sequence(feature=Value(dtype="int32")),
        "ends": Sequence(feature=Value(dtype="int32")),
        "ner_tags": Sequence(feature=Value(dtype="string")),
    }),
)

TextGenerationEvaluator

class evaluate.TextGenerationEvaluator

< >

( task = 'text-generation'default_metric_name = Nonepredictions_prefix: str = 'generated' )

Text generation evaluator. This Text generation evaluator can currently be loaded from evaluator() using the default task name text-generation. Methods in this class assume a data format compatible with the TextGenerationPipeline.

compute

< >

( model_or_pipeline: typing.Union[str, ForwardRef('Pipeline'), typing.Callable, ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] = Nonedata: typing.Union[str, datasets.arrow_dataset.Dataset] = Nonesubset: typing.Optional[str] = Nonesplit: typing.Optional[str] = Nonemetric: typing.Union[str, evaluate.module.EvaluationModule] = Nonetokenizer: typing.Union[str, ForwardRef('PreTrainedTokenizer'), NoneType] = Nonefeature_extractor: typing.Union[str, ForwardRef('FeatureExtractionMixin'), NoneType] = Nonestrategy: typing.Literal['simple', 'bootstrap'] = 'simple'confidence_level: float = 0.95n_resamples: int = 9999device: int = Nonerandom_state: typing.Optional[int] = Noneinput_column: str = 'text'label_column: str = 'label'label_mapping: typing.Union[typing.Dict[str, numbers.Number], NoneType] = None )

Text2TextGenerationEvaluator

class evaluate.Text2TextGenerationEvaluator

< >

( task = 'text2text-generation'default_metric_name = None )

Text2Text generation evaluator. This Text2Text generation evaluator can currently be loaded from evaluator() using the default task name text2text-generation. Methods in this class assume a data format compatible with the Text2TextGenerationPipeline.

compute

< >

( model_or_pipeline: typing.Union[str, ForwardRef('Pipeline'), typing.Callable, ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] = Nonedata: typing.Union[str, datasets.arrow_dataset.Dataset] = Nonesubset: typing.Optional[str] = Nonesplit: typing.Optional[str] = Nonemetric: typing.Union[str, evaluate.module.EvaluationModule] = Nonetokenizer: typing.Union[str, ForwardRef('PreTrainedTokenizer'), NoneType] = Nonestrategy: typing.Literal['simple', 'bootstrap'] = 'simple'confidence_level: float = 0.95n_resamples: int = 9999device: int = Nonerandom_state: typing.Optional[int] = Noneinput_column: str = 'text'label_column: str = 'label'generation_kwargs: dict = None )

Parameters

  • model_or_pipeline (str or Pipeline or Callable or PreTrainedModel or TFPreTrainedModel, defaults to 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.
  • data (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.
  • subset (str, defaults to None) — Defines which dataset subset to load. If None is passed the default subset is loaded.
  • split (str, defaults to None) — Defines which dataset split to load. If None is passed, infers based on the choose_split function.
  • metric (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:
  • confidence_level (float, defaults to 0.95) — The confidence_level value passed to bootstrap if "bootstrap" strategy is chosen.
  • n_resamples (int, defaults to 9999) — The n_resamples value passed to bootstrap if "bootstrap" strategy is chosen.
  • device (int, defaults to None) — Device ordinal for CPU/GPU support of the pipeline. Setting this to -1 will leverage CPU, a positive integer will run the model on the associated CUDA device ID. If None is provided it will be inferred and CUDA:0 used if available, CPU otherwise.
  • random_state (int, optional, defaults to None) — The random_state value passed to bootstrap if "bootstrap" strategy is chosen. Useful for debugging.
  • input_column (str, defaults to "text") — the name of the column containing the input text in the dataset specified by data.
  • label_column (str, defaults to "label") — the name of the column containing the labels in the dataset specified by data.
  • generation_kwargs (Dict, optional, defaults to None) — The generation kwargs are passed to the pipeline and set the text generation strategy.

Compute the metric for a given pipeline and dataset combination.

SummarizationEvaluator

class evaluate.SummarizationEvaluator

< >

( task = 'summarization'default_metric_name = None )

Text summarization evaluator. This text summarization evaluator can currently be loaded from evaluator() using the default task name summarization. Methods in this class assume a data format compatible with the SummarizationEvaluator.

compute

< >

( model_or_pipeline: typing.Union[str, ForwardRef('Pipeline'), typing.Callable, ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] = Nonedata: typing.Union[str, datasets.arrow_dataset.Dataset] = Nonesubset: typing.Optional[str] = Nonesplit: typing.Optional[str] = Nonemetric: typing.Union[str, evaluate.module.EvaluationModule] = Nonetokenizer: typing.Union[str, ForwardRef('PreTrainedTokenizer'), NoneType] = Nonestrategy: typing.Literal['simple', 'bootstrap'] = 'simple'confidence_level: float = 0.95n_resamples: int = 9999device: int = Nonerandom_state: typing.Optional[int] = Noneinput_column: str = 'text'label_column: str = 'label'generation_kwargs: dict = None )

Parameters

  • model_or_pipeline (str or Pipeline or Callable or PreTrainedModel or TFPreTrainedModel, defaults to 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.
  • data (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.
  • subset (str, defaults to None) — Defines which dataset subset to load. If None is passed the default subset is loaded.
  • split (str, defaults to None) — Defines which dataset split to load. If None is passed, infers based on the choose_split function.
  • metric (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:
  • confidence_level (float, defaults to 0.95) — The confidence_level value passed to bootstrap if "bootstrap" strategy is chosen.
  • n_resamples (int, defaults to 9999) — The n_resamples value passed to bootstrap if "bootstrap" strategy is chosen.
  • device (int, defaults to None) — Device ordinal for CPU/GPU support of the pipeline. Setting this to -1 will leverage CPU, a positive integer will run the model on the associated CUDA device ID. If None is provided it will be inferred and CUDA:0 used if available, CPU otherwise.
  • random_state (int, optional, defaults to None) — The random_state value passed to bootstrap if "bootstrap" strategy is chosen. Useful for debugging.
  • input_column (str, defaults to "text") — the name of the column containing the input text in the dataset specified by data.
  • label_column (str, defaults to "label") — the name of the column containing the labels in the dataset specified by data.
  • generation_kwargs (Dict, optional, defaults to None) — The generation kwargs are passed to the pipeline and set the text generation strategy.

Compute the metric for a given pipeline and dataset combination.

TranslationEvaluator

class evaluate.TranslationEvaluator

< >

( task = 'translation'default_metric_name = None )

Translation evaluator. This translation generation evaluator can currently be loaded from evaluator() using the default task name translation. Methods in this class assume a data format compatible with the TranslationPipeline.

compute

< >

( model_or_pipeline: typing.Union[str, ForwardRef('Pipeline'), typing.Callable, ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] = Nonedata: typing.Union[str, datasets.arrow_dataset.Dataset] = Nonesubset: typing.Optional[str] = Nonesplit: typing.Optional[str] = Nonemetric: typing.Union[str, evaluate.module.EvaluationModule] = Nonetokenizer: typing.Union[str, ForwardRef('PreTrainedTokenizer'), NoneType] = Nonestrategy: typing.Literal['simple', 'bootstrap'] = 'simple'confidence_level: float = 0.95n_resamples: int = 9999device: int = Nonerandom_state: typing.Optional[int] = Noneinput_column: str = 'text'label_column: str = 'label'generation_kwargs: dict = None )

Parameters

  • model_or_pipeline (str or Pipeline or Callable or PreTrainedModel or TFPreTrainedModel, defaults to 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.
  • data (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.
  • subset (str, defaults to None) — Defines which dataset subset to load. If None is passed the default subset is loaded.
  • split (str, defaults to None) — Defines which dataset split to load. If None is passed, infers based on the choose_split function.
  • metric (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:
  • confidence_level (float, defaults to 0.95) — The confidence_level value passed to bootstrap if "bootstrap" strategy is chosen.
  • n_resamples (int, defaults to 9999) — The n_resamples value passed to bootstrap if "bootstrap" strategy is chosen.
  • device (int, defaults to None) — Device ordinal for CPU/GPU support of the pipeline. Setting this to -1 will leverage CPU, a positive integer will run the model on the associated CUDA device ID. If None is provided it will be inferred and CUDA:0 used if available, CPU otherwise.
  • random_state (int, optional, defaults to None) — The random_state value passed to bootstrap if "bootstrap" strategy is chosen. Useful for debugging.
  • input_column (str, defaults to "text") — the name of the column containing the input text in the dataset specified by data.
  • label_column (str, defaults to "label") — the name of the column containing the labels in the dataset specified by data.
  • generation_kwargs (Dict, optional, defaults to None) — The generation kwargs are passed to the pipeline and set the text generation strategy.

Compute the metric for a given pipeline and dataset combination.

AutomaticSpeechRecognitionEvaluator

class evaluate.AutomaticSpeechRecognitionEvaluator

< >

( task = 'automatic-speech-recognition'default_metric_name = None )

Automatic speech recognition evaluator. This automatic speech recognition evaluator can currently be loaded from evaluator() using the default task name automatic-speech-recognition. Methods in this class assume a data format compatible with the AutomaticSpeechRecognitionPipeline.

compute

< >

( model_or_pipeline: typing.Union[str, ForwardRef('Pipeline'), typing.Callable, ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] = Nonedata: typing.Union[str, datasets.arrow_dataset.Dataset] = Nonesubset: typing.Optional[str] = Nonesplit: typing.Optional[str] = Nonemetric: typing.Union[str, evaluate.module.EvaluationModule] = Nonetokenizer: typing.Union[str, ForwardRef('PreTrainedTokenizer'), NoneType] = Nonestrategy: typing.Literal['simple', 'bootstrap'] = 'simple'confidence_level: float = 0.95n_resamples: int = 9999device: int = Nonerandom_state: typing.Optional[int] = Noneinput_column: str = 'path'label_column: str = 'sentence'generation_kwargs: dict = None )

Parameters

  • model_or_pipeline (str or Pipeline or Callable or PreTrainedModel or TFPreTrainedModel, defaults to 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.
  • data (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.
  • subset (str, defaults to None) — Defines which dataset subset to load. If None is passed the default subset is loaded.
  • split (str, defaults to None) — Defines which dataset split to load. If None is passed, infers based on the choose_split function.
  • metric (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:
  • confidence_level (float, defaults to 0.95) — The confidence_level value passed to bootstrap if "bootstrap" strategy is chosen.
  • n_resamples (int, defaults to 9999) — The n_resamples value passed to bootstrap if "bootstrap" strategy is chosen.
  • device (int, defaults to None) — Device ordinal for CPU/GPU support of the pipeline. Setting this to -1 will leverage CPU, a positive integer will run the model on the associated CUDA device ID. If None is provided it will be inferred and CUDA:0 used if available, CPU otherwise.
  • random_state (int, optional, defaults to None) — The random_state value passed to bootstrap if "bootstrap" strategy is chosen. Useful for debugging.

Compute the metric for a given pipeline and dataset combination.

Examples:

>>> from evaluate import evaluator
>>> from datasets import load_dataset
>>> task_evaluator = evaluator("automatic-speech-recognition")
>>> data = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="validation[:40]")
>>> results = task_evaluator.compute(
>>>     model_or_pipeline="https://huggingface.co/openai/whisper-tiny.en",
>>>     data=data,
>>>     input_column="path",
>>>     label_column="sentence",
>>>     metric="wer",
>>> )
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