o 5tiw @sLddlmZmZddlmZddlmZddlmZdZ GdddeZ d S) )DictTuple)Dataset) Evaluator) DatasetColumna= input_column (`str`, defaults to `"text"`): the name of the column containing the input text 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. csNeZdZdZddZddeffdd Zd ed ed ee e ffd dZ Z S)TextGenerationEvaluatora 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 [`~transformers.TextGenerationPipeline`]. csdfdd|DiS)z Args: predictions: A list of lists of dicts Returns: `dict`: All the generated texts are flattened and stored under the "data" key. datacs&g|]}|D] }|jdqqS)_text)predictions_prefix).0 pred_listpredselfV/home/ubuntu/.local/lib/python3.10/site-packages/evaluate/evaluator/text_generation.py /s&zATextGenerationEvaluator.predictions_processor..r)r predictionsargskwargsrrrpredictions_processor'sz-TextGenerationEvaluator.predictions_processortext-generationN generatedr cstj||d||_dS)N)taskdefault_metric_name)super__init__r )rrrr  __class__rrr1s z TextGenerationEvaluator.__init__r input_columnreturncOs||d|iit||fS)a Prepare data. Args: 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`. Returns: `dict`: metric inputs. `list`: pipeline inputs. r )check_required_columnsr)rr r rrrrr prepare_data5sz$TextGenerationEvaluator.prepare_data)rNr) __name__ __module__ __qualname____doc__rstrrrrrrr# __classcell__rrrrrs  &rN) typingrrdatasetsrbaserutilsrTASK_DOCUMENTATION_KWARGSrrrrrs