venv/lib/python3.10/site-packages/evaluate/evaluator/text2text_generation.py

# Copyright 2022 The HuggingFace Evaluate Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple, Union

from datasets import Dataset
from typing_extensions import Literal

from ..module import EvaluationModule
from ..utils.file_utils import add_start_docstrings
from .base import EVALUATOR_COMPUTE_RETURN_DOCSTRING, EVALUTOR_COMPUTE_START_DOCSTRING, Evaluator


if TYPE_CHECKING:
    from transformers import Pipeline, PreTrainedModel, PreTrainedTokenizer, TFPreTrainedModel


TASK_DOCUMENTATION_KWARGS = r"""
        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.
"""

TEXT2TEXT_TASK_DOCSTRING_EXAMPLE = r"""
    Examples:
    ```python
    >>> from evaluate import evaluator
    >>> from datasets import load_dataset
    >>> task_evaluator = evaluator("text2text-generation")
    >>> data = load_dataset("cnn_dailymail", "3.0.0", split="validation[:40]")
    >>> results = task_evaluator.compute(
    >>>     model_or_pipeline="facebook/bart-large-cnn",
    >>>     data=data,
    >>>     input_column="article",
    >>>     label_column="highlights",
    >>>     metric="rouge",
    >>> )
    ```
"""

SUMMARIZATION_TASK_DOCSTRING_EXAMPLE = r"""
    Examples:
    ```python
    >>> from evaluate import evaluator
    >>> from datasets import load_dataset
    >>> task_evaluator = evaluator("summarization")
    >>> data = load_dataset("cnn_dailymail", "3.0.0", split="validation[:40]")
    >>> results = task_evaluator.compute(
    >>>     model_or_pipeline="facebook/bart-large-cnn",
    >>>     data=data,
    >>>     input_column="article",
    >>>     label_column="highlights",
    >>> )
    ```
"""


TRANSLATION_TASK_DOCSTRING_EXAMPLE = r"""
    Examples:
    ```python
    >>> from evaluate import evaluator
    >>> from datasets import load_dataset
    >>> task_evaluator = evaluator("translation")
    >>> data = load_dataset("wmt19", "fr-de", split="validation[:40]")
    >>> data = data.map(lambda x: {"text": x["translation"]["de"], "label": x["translation"]["fr"]})
    >>> results = task_evaluator.compute(
    >>>     model_or_pipeline="Helsinki-NLP/opus-mt-de-fr",
    >>>     data=data,
    >>> )
    ```
"""


class Text2TextGenerationEvaluator(Evaluator):
    """
    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 [`~transformers.Text2TextGenerationPipeline`].
    """

    PREDICTION_PREFIX = "generated"
    PIPELINE_KWARGS = {"truncation": True}

    def __init__(self, task="text2text-generation", default_metric_name=None):
        super().__init__(task, default_metric_name=default_metric_name)

    def predictions_processor(self, predictions, label_mapping):
        return {"predictions": [pred[f"{self.PREDICTION_PREFIX}_text"] for pred in predictions]}

    @add_start_docstrings(
        EVALUTOR_COMPUTE_START_DOCSTRING,
        TASK_DOCUMENTATION_KWARGS,
        EVALUATOR_COMPUTE_RETURN_DOCSTRING,
        TEXT2TEXT_TASK_DOCSTRING_EXAMPLE,
    )
    def compute(
        self,
        model_or_pipeline: Union[
            str, "Pipeline", Callable, "PreTrainedModel", "TFPreTrainedModel"  # noqa: F821
        ] = None,
        data: Union[str, Dataset] = None,
        subset: Optional[str] = None,
        split: Optional[str] = None,
        metric: Union[str, EvaluationModule] = None,
        tokenizer: Optional[Union[str, "PreTrainedTokenizer"]] = None,  # noqa: F821
        strategy: Literal["simple", "bootstrap"] = "simple",
        confidence_level: float = 0.95,
        n_resamples: int = 9999,
        device: int = None,
        random_state: Optional[int] = None,
        input_column: str = "text",
        label_column: str = "label",
        generation_kwargs: dict = None,
    ) -> Tuple[Dict[str, float], Any]:
        if generation_kwargs is not None:
            self.PIPELINE_KWARGS.update(generation_kwargs)

        result = super().compute(
            model_or_pipeline=model_or_pipeline,
            data=data,
            subset=subset,
            split=split,
            metric=metric,
            tokenizer=tokenizer,
            strategy=strategy,
            confidence_level=confidence_level,
            n_resamples=n_resamples,
            device=device,
            random_state=random_state,
            input_column=input_column,
            label_column=label_column,
        )

        return result


class SummarizationEvaluator(Text2TextGenerationEvaluator):
    """
    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`].
    """

    PREDICTION_PREFIX = "summary"
    PIPELINE_KWARGS = {"truncation": True}

    def __init__(self, task="summarization", default_metric_name=None):
        super().__init__(task, default_metric_name=default_metric_name)

    @add_start_docstrings(
        EVALUTOR_COMPUTE_START_DOCSTRING,
        TASK_DOCUMENTATION_KWARGS,
        EVALUATOR_COMPUTE_RETURN_DOCSTRING,
        SUMMARIZATION_TASK_DOCSTRING_EXAMPLE,
    )
    def compute(
        self,
        model_or_pipeline: Union[
            str, "Pipeline", Callable, "PreTrainedModel", "TFPreTrainedModel"  # noqa: F821
        ] = None,
        data: Union[str, Dataset] = None,
        subset: Optional[str] = None,
        split: Optional[str] = None,
        metric: Union[str, EvaluationModule] = None,
        tokenizer: Optional[Union[str, "PreTrainedTokenizer"]] = None,  # noqa: F821
        strategy: Literal["simple", "bootstrap"] = "simple",
        confidence_level: float = 0.95,
        n_resamples: int = 9999,
        device: int = None,
        random_state: Optional[int] = None,
        input_column: str = "text",
        label_column: str = "label",
        generation_kwargs: dict = None,
    ) -> Tuple[Dict[str, float], Any]:
        result = super().compute(
            model_or_pipeline=model_or_pipeline,
            data=data,
            subset=subset,
            split=split,
            metric=metric,
            tokenizer=tokenizer,
            strategy=strategy,
            confidence_level=confidence_level,
            n_resamples=n_resamples,
            device=device,
            random_state=random_state,
            input_column=input_column,
            label_column=label_column,
            generation_kwargs=generation_kwargs,
        )

        return result


class TranslationEvaluator(Text2TextGenerationEvaluator):
    """
    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 [`~transformers.TranslationPipeline`].
    """

    PREDICTION_PREFIX = "translation"
    PIPELINE_KWARGS = {"truncation": True}

    def __init__(self, task="translation", default_metric_name=None):
        super().__init__(task, default_metric_name=default_metric_name)

    @add_start_docstrings(
        EVALUTOR_COMPUTE_START_DOCSTRING,
        TASK_DOCUMENTATION_KWARGS,
        EVALUATOR_COMPUTE_RETURN_DOCSTRING,
        TRANSLATION_TASK_DOCSTRING_EXAMPLE,
    )
    def compute(
        self,
        model_or_pipeline: Union[
            str, "Pipeline", Callable, "PreTrainedModel", "TFPreTrainedModel"  # noqa: F821
        ] = None,
        data: Union[str, Dataset] = None,
        subset: Optional[str] = None,
        split: Optional[str] = None,
        metric: Union[str, EvaluationModule] = None,
        tokenizer: Optional[Union[str, "PreTrainedTokenizer"]] = None,  # noqa: F821
        strategy: Literal["simple", "bootstrap"] = "simple",
        confidence_level: float = 0.95,
        n_resamples: int = 9999,
        device: int = None,
        random_state: Optional[int] = None,
        input_column: str = "text",
        label_column: str = "label",
        generation_kwargs: dict = None,
    ) -> Tuple[Dict[str, float], Any]:
        result = super().compute(
            model_or_pipeline=model_or_pipeline,
            data=data,
            subset=subset,
            split=split,
            metric=metric,
            tokenizer=tokenizer,
            strategy=strategy,
            confidence_level=confidence_level,
            n_resamples=n_resamples,
            device=device,
            random_state=random_state,
            input_column=input_column,
            label_column=label_column,
            generation_kwargs=generation_kwargs,
        )

        return result