Source code for transformers.pipelines.text2text_generation

from ..file_utils import add_end_docstrings, is_tf_available, is_torch_available
from ..tokenization_utils import TruncationStrategy
from ..utils import logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
    import tensorflow as tf

    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

logger = logging.get_logger(__name__)


[docs]@add_end_docstrings(PIPELINE_INIT_ARGS) class Text2TextGenerationPipeline(Pipeline): """ Pipeline for text to text generation using seq2seq models. This Text2TextGenerationPipeline pipeline can currently be loaded from :func:`~transformers.pipeline` using the following task identifier: :obj:`"text2text-generation"`. The models that this pipeline can use are models that have been fine-tuned on a translation task. See the up-to-date list of available models on `huggingface.co/models <https://huggingface.co/models?filter=seq2seq>`__. Usage:: text2text_generator = pipeline("text2text-generation") text2text_generator("question: What is 42 ? context: 42 is the answer to life, the universe and everything") """ # Used in the return key of the pipeline. return_name = "generated" def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.check_model_type( TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING )
[docs] def check_inputs(self, input_length: int, min_length: int, max_length: int): """ Checks wether there might be something wrong with given input with regard to the model. """ return True
[docs] def __call__( self, *args, return_tensors=False, return_text=True, clean_up_tokenization_spaces=False, truncation=TruncationStrategy.DO_NOT_TRUNCATE, **generate_kwargs ): r""" Generate the output text(s) using text(s) given as inputs. Args: args (:obj:`str` or :obj:`List[str]`): Input text for the encoder. return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to include the tensors of predictions (as token indices) in the outputs. return_text (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to include the decoded texts in the outputs. clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to clean up the potential extra spaces in the text output. truncation (:obj:`TruncationStrategy`, `optional`, defaults to :obj:`TruncationStrategy.DO_NOT_TRUNCATE`): The truncation strategy for the tokenization within the pipeline. :obj:`TruncationStrategy.DO_NOT_TRUNCATE` (default) will never truncate, but it is sometimes desirable to truncate the input to fit the model's max_length instead of throwing an error down the line. generate_kwargs: Additional keyword arguments to pass along to the generate method of the model (see the generate method corresponding to your framework `here <./model.html#generative-models>`__). Return: A list or a list of list of :obj:`dict`: Each result comes as a dictionary with the following keys: - **generated_text** (:obj:`str`, present when ``return_text=True``) -- The generated text. - **generated_token_ids** (:obj:`torch.Tensor` or :obj:`tf.Tensor`, present when ``return_tensors=True``) -- The token ids of the generated text. """ assert return_tensors or return_text, "You must specify return_tensors=True or return_text=True" prefix = self.model.config.prefix if self.model.config.prefix is not None else "" if isinstance(args[0], list): assert ( self.tokenizer.pad_token_id is not None ), "Please make sure that the tokenizer has a pad_token_id when using a batch input" args = ([prefix + arg for arg in args[0]],) padding = True elif isinstance(args[0], str): args = (prefix + args[0],) padding = False else: raise ValueError( f" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`" ) with self.device_placement(): inputs = self._parse_and_tokenize(*args, padding=padding, truncation=truncation) if self.framework == "pt": inputs = self.ensure_tensor_on_device(**inputs) input_length = inputs["input_ids"].shape[-1] elif self.framework == "tf": input_length = tf.shape(inputs["input_ids"])[-1].numpy() min_length = generate_kwargs.get("min_length", self.model.config.min_length) max_length = generate_kwargs.get("max_length", self.model.config.max_length) self.check_inputs(input_length, min_length, max_length) generations = self.model.generate( inputs["input_ids"], attention_mask=inputs["attention_mask"], **generate_kwargs, ) results = [] for generation in generations: record = {} if return_tensors: record[f"{self.return_name}_token_ids"] = generation if return_text: record[f"{self.return_name}_text"] = self.tokenizer.decode( generation, skip_special_tokens=True, clean_up_tokenization_spaces=clean_up_tokenization_spaces, ) results.append(record) return results
[docs]@add_end_docstrings(PIPELINE_INIT_ARGS) class SummarizationPipeline(Text2TextGenerationPipeline): """ Summarize news articles and other documents. This summarizing pipeline can currently be loaded from :func:`~transformers.pipeline` using the following task identifier: :obj:`"summarization"`. The models that this pipeline can use are models that have been fine-tuned on a summarization task, which is currently, '`bart-large-cnn`', '`t5-small`', '`t5-base`', '`t5-large`', '`t5-3b`', '`t5-11b`'. See the up-to-date list of available models on `huggingface.co/models <https://huggingface.co/models?filter=summarization>`__. Usage:: # use bart in pytorch summarizer = pipeline("summarization") summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20) # use t5 in tf summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base", framework="tf") summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20) """ # Used in the return key of the pipeline. return_name = "summary"
[docs] def __call__(self, *args, **kwargs): r""" Summarize the text(s) given as inputs. Args: documents (`str` or :obj:`List[str]`): One or several articles (or one list of articles) to summarize. return_text (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to include the decoded texts in the outputs return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to include the tensors of predictions (as token indices) in the outputs. clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to clean up the potential extra spaces in the text output. generate_kwargs: Additional keyword arguments to pass along to the generate method of the model (see the generate method corresponding to your framework `here <./model.html#generative-models>`__). Return: A list or a list of list of :obj:`dict`: Each result comes as a dictionary with the following keys: - **summary_text** (:obj:`str`, present when ``return_text=True``) -- The summary of the corresponding input. - **summary_token_ids** (:obj:`torch.Tensor` or :obj:`tf.Tensor`, present when ``return_tensors=True``) -- The token ids of the summary. """ return super().__call__(*args, **kwargs)
[docs] def check_inputs(self, input_length: int, min_length: int, max_length: int) -> bool: """ Checks wether there might be something wrong with given input with regard to the model. """ if input_length < min_length // 2: logger.warning( f"Your min_length is set to {min_length}, but you input_length is only {input_length}. You might " "consider decreasing min_length manually, e.g. summarizer('...', min_length=10)" ) if input_length < max_length: logger.warning( f"Your max_length is set to {max_length}, but you input_length is only {input_length}. You might " "consider decreasing max_length manually, e.g. summarizer('...', max_length=50)" )
[docs]@add_end_docstrings(PIPELINE_INIT_ARGS) class TranslationPipeline(Text2TextGenerationPipeline): """ Translates from one language to another. This translation pipeline can currently be loaded from :func:`~transformers.pipeline` using the following task identifier: :obj:`"translation_xx_to_yy"`. The models that this pipeline can use are models that have been fine-tuned on a translation task. See the up-to-date list of available models on `huggingface.co/models <https://huggingface.co/models?filter=translation>`__. Usage:: en_fr_translator = pipeline("translation_en_to_fr") en_fr_translator("How old are you?") """ # Used in the return key of the pipeline. return_name = "translation"
[docs] def check_inputs(self, input_length: int, min_length: int, max_length: int): if input_length > 0.9 * max_length: logger.warning( f"Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider " "increasing your max_length manually, e.g. translator('...', max_length=400)" )
[docs] def __call__(self, *args, **kwargs): r""" Translate the text(s) given as inputs. Args: args (:obj:`str` or :obj:`List[str]`): Texts to be translated. return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to include the tensors of predictions (as token indices) in the outputs. return_text (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to include the decoded texts in the outputs. clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to clean up the potential extra spaces in the text output. generate_kwargs: Additional keyword arguments to pass along to the generate method of the model (see the generate method corresponding to your framework `here <./model.html#generative-models>`__). Return: A list or a list of list of :obj:`dict`: Each result comes as a dictionary with the following keys: - **translation_text** (:obj:`str`, present when ``return_text=True``) -- The translation. - **translation_token_ids** (:obj:`torch.Tensor` or :obj:`tf.Tensor`, present when ``return_tensors=True``) -- The token ids of the translation. """ return super().__call__(*args, **kwargs)