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https://github.com/huggingface/transformers.git
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Change back pipeline signatures (#3105)
* Change back pipeline signatures * String types for non-imported objects
This commit is contained in:
Lysandre Debut
GitHub
parent
d6df9a8ffe
commit
0ae91c80aa
@ -296,19 +296,13 @@ class Pipeline(_ScikitCompat):
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pickle format.
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Arguments:
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model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
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The model that will be used by the pipeline to make predictions. This can be :obj:`None`, a string
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checkpoint identifier or an actual pre-trained model inheriting from
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model (:obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`):
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The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
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:class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
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TensorFlow.
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If :obj:`None`, the default of the pipeline will be loaded.
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tokenizer (:obj:`str` or :obj:`~transformers.PreTrainedTokenizer`, `optional`, defaults to :obj:`None`):
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The tokenizer that will be used by the pipeline to encode data for the model. This can be :obj:`None`,
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a string checkpoint identifier or an actual pre-trained tokenizer inheriting from
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tokenizer (:obj:`~transformers.PreTrainedTokenizer`):
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The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
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:class:`~transformers.PreTrainedTokenizer`.
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If :obj:`None`, the default of the pipeline will be loaded.
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modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`, defaults to :obj:`None`):
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Model card attributed to the model for this pipeline.
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framework (:obj:`str`, `optional`, defaults to :obj:`None`):
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@ -334,12 +328,11 @@ class Pipeline(_ScikitCompat):
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"""
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default_input_names = None
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task = None
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def __init__(
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self,
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model: Optional = None,
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tokenizer: PreTrainedTokenizer = None,
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model: Union["PreTrainedModel", "TFPreTrainedModel"],
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tokenizer: PreTrainedTokenizer,
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modelcard: Optional[ModelCard] = None,
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framework: Optional[str] = None,
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args_parser: ArgumentHandler = None,
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@ -350,8 +343,6 @@ class Pipeline(_ScikitCompat):
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if framework is None:
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framework = get_framework()
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model, tokenizer = self.get_defaults(model, tokenizer, framework)
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self.model = model
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self.tokenizer = tokenizer
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self.modelcard = modelcard
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@ -483,26 +474,6 @@ class Pipeline(_ScikitCompat):
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else:
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return predictions.numpy()
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def get_defaults(self, model, tokenizer, framework):
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task_defaults = SUPPORTED_TASKS[self.task]
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if model is None:
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if framework == "tf":
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model = task_defaults["tf"].from_pretrained(task_defaults["default"]["model"]["tf"])
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elif framework == "pt":
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model = task_defaults["pt"].from_pretrained(task_defaults["default"]["model"]["pt"])
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else:
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raise ValueError("Provided framework should be either 'tf' for TensorFlow or 'pt' for PyTorch.")
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if tokenizer is None:
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default_tokenizer = task_defaults["default"]["tokenizer"]
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if isinstance(default_tokenizer, tuple):
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# For tuple we have (tokenizer name, {kwargs})
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tokenizer = AutoTokenizer.from_pretrained(default_tokenizer[0], **default_tokenizer[1])
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else:
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tokenizer = AutoTokenizer.from_pretrained(default_tokenizer)
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return model, tokenizer
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class FeatureExtractionPipeline(Pipeline):
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"""
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@ -518,19 +489,13 @@ class FeatureExtractionPipeline(Pipeline):
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`huggingface.co/models <https://huggingface.co/models>`__.
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Arguments:
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model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
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The model that will be used by the pipeline to make predictions. This can be :obj:`None`, a string
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checkpoint identifier or an actual pre-trained model inheriting from
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model (:obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`):
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The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
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:class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
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TensorFlow.
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If :obj:`None`, the default of the pipeline will be loaded.
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tokenizer (:obj:`str` or :obj:`~transformers.PreTrainedTokenizer`, `optional`, defaults to :obj:`None`):
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The tokenizer that will be used by the pipeline to encode data for the model. This can be :obj:`None`,
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a string checkpoint identifier or an actual pre-trained tokenizer inheriting from
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tokenizer (:obj:`~transformers.PreTrainedTokenizer`):
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The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
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:class:`~transformers.PreTrainedTokenizer`.
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If :obj:`None`, the default of the pipeline will be loaded.
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modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`, defaults to :obj:`None`):
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Model card attributed to the model for this pipeline.
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framework (:obj:`str`, `optional`, defaults to :obj:`None`):
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@ -546,12 +511,10 @@ class FeatureExtractionPipeline(Pipeline):
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on the associated CUDA device id.
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"""
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task = "feature-extraction"
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def __init__(
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self,
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model: Optional = None,
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tokenizer: PreTrainedTokenizer = None,
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model: Union["PreTrainedModel", "TFPreTrainedModel"],
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tokenizer: PreTrainedTokenizer,
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modelcard: Optional[ModelCard] = None,
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framework: Optional[str] = None,
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args_parser: ArgumentHandler = None,
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@ -586,19 +549,13 @@ class TextClassificationPipeline(Pipeline):
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`huggingface.co/models <https://huggingface.co/models?search=&filter=text-classification>`__.
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Arguments:
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model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
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The model that will be used by the pipeline to make predictions. This can be :obj:`None`, a string
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checkpoint identifier or an actual pre-trained model inheriting from
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model (:obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`):
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The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
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:class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
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TensorFlow.
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If :obj:`None`, the default of the pipeline will be loaded.
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tokenizer (:obj:`str` or :obj:`~transformers.PreTrainedTokenizer`, `optional`, defaults to :obj:`None`):
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The tokenizer that will be used by the pipeline to encode data for the model. This can be :obj:`None`,
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a string checkpoint identifier or an actual pre-trained tokenizer inheriting from
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tokenizer (:obj:`~transformers.PreTrainedTokenizer`):
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The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
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:class:`~transformers.PreTrainedTokenizer`.
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If :obj:`None`, the default of the pipeline will be loaded.
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modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`, defaults to :obj:`None`):
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Model card attributed to the model for this pipeline.
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framework (:obj:`str`, `optional`, defaults to :obj:`None`):
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@ -614,8 +571,6 @@ class TextClassificationPipeline(Pipeline):
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on the associated CUDA device id.
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"""
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task = "sentiment-analysis"
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def __call__(self, *args, **kwargs):
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outputs = super().__call__(*args, **kwargs)
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scores = np.exp(outputs) / np.exp(outputs).sum(-1)
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@ -638,19 +593,13 @@ class FillMaskPipeline(Pipeline):
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`huggingface.co/models <https://huggingface.co/models?search=&filter=lm-head>`__.
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Arguments:
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model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
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The model that will be used by the pipeline to make predictions. This can be :obj:`None`, a string
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checkpoint identifier or an actual pre-trained model inheriting from
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model (:obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`):
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The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
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:class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
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TensorFlow.
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If :obj:`None`, the default of the pipeline will be loaded.
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tokenizer (:obj:`str` or :obj:`~transformers.PreTrainedTokenizer`, `optional`, defaults to :obj:`None`):
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The tokenizer that will be used by the pipeline to encode data for the model. This can be :obj:`None`,
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a string checkpoint identifier or an actual pre-trained tokenizer inheriting from
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tokenizer (:obj:`~transformers.PreTrainedTokenizer`):
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The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
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:class:`~transformers.PreTrainedTokenizer`.
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If :obj:`None`, the default of the pipeline will be loaded.
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modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`, defaults to :obj:`None`):
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Model card attributed to the model for this pipeline.
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framework (:obj:`str`, `optional`, defaults to :obj:`None`):
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@ -666,12 +615,10 @@ class FillMaskPipeline(Pipeline):
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on the associated CUDA device id.
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"""
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task = "fill-mask"
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def __init__(
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self,
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model: Optional = None,
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tokenizer: PreTrainedTokenizer = None,
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model: Union["PreTrainedModel", "TFPreTrainedModel"],
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tokenizer: PreTrainedTokenizer,
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modelcard: Optional[ModelCard] = None,
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framework: Optional[str] = None,
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args_parser: ArgumentHandler = None,
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@ -743,19 +690,13 @@ class NerPipeline(Pipeline):
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`huggingface.co/models <https://huggingface.co/models?search=&filter=token-classification>`__.
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Arguments:
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model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
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The model that will be used by the pipeline to make predictions. This can be :obj:`None`, a string
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checkpoint identifier or an actual pre-trained model inheriting from
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model (:obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`):
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The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
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:class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
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TensorFlow.
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If :obj:`None`, the default of the pipeline will be loaded.
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tokenizer (:obj:`str` or :obj:`~transformers.PreTrainedTokenizer`, `optional`, defaults to :obj:`None`):
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The tokenizer that will be used by the pipeline to encode data for the model. This can be :obj:`None`,
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a string checkpoint identifier or an actual pre-trained tokenizer inheriting from
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tokenizer (:obj:`~transformers.PreTrainedTokenizer`):
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The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
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:class:`~transformers.PreTrainedTokenizer`.
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If :obj:`None`, the default of the pipeline will be loaded.
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modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`, defaults to :obj:`None`):
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Model card attributed to the model for this pipeline.
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framework (:obj:`str`, `optional`, defaults to :obj:`None`):
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@ -769,19 +710,14 @@ class NerPipeline(Pipeline):
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device (:obj:`int`, `optional`, defaults to :obj:`-1`):
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Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, >=0 will run the model
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on the associated CUDA device id.
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Example::
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from transformers import pi
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"""
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default_input_names = "sequences"
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task = "ner"
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def __init__(
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self,
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model: Optional = None,
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tokenizer: PreTrainedTokenizer = None,
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model: Union["PreTrainedModel", "TFPreTrainedModel"],
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tokenizer: PreTrainedTokenizer,
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modelcard: Optional[ModelCard] = None,
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framework: Optional[str] = None,
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args_parser: ArgumentHandler = None,
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@ -928,19 +864,13 @@ class QuestionAnsweringPipeline(Pipeline):
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`huggingface.co/models <https://huggingface.co/models?search=&filter=question-answering>`__.
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Arguments:
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model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
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The model that will be used by the pipeline to make predictions. This can be :obj:`None`, a string
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checkpoint identifier or an actual pre-trained model inheriting from
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model (:obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`):
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The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
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:class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
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TensorFlow.
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If :obj:`None`, the default of the pipeline will be loaded.
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tokenizer (:obj:`str` or :obj:`~transformers.PreTrainedTokenizer`, `optional`, defaults to :obj:`None`):
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The tokenizer that will be used by the pipeline to encode data for the model. This can be :obj:`None`,
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a string checkpoint identifier or an actual pre-trained tokenizer inheriting from
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tokenizer (:obj:`~transformers.PreTrainedTokenizer`):
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The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
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:class:`~transformers.PreTrainedTokenizer`.
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If :obj:`None`, the default of the pipeline will be loaded.
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modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`, defaults to :obj:`None`):
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Model card attributed to the model for this pipeline.
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framework (:obj:`str`, `optional`, defaults to :obj:`None`):
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@ -957,12 +887,11 @@ class QuestionAnsweringPipeline(Pipeline):
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"""
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default_input_names = "question,context"
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task = "question-answering"
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def __init__(
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self,
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model: Optional = None,
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tokenizer: Optional[PreTrainedTokenizer] = None,
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model: Union["PreTrainedModel", "TFPreTrainedModel"],
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tokenizer: PreTrainedTokenizer,
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modelcard: Optional[ModelCard] = None,
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framework: Optional[str] = None,
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device: int = -1,
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