Add missing tasks to `pipeline` docstring (#8428)
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Santiago Castro
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@ -513,7 +513,7 @@ Here, the model generates a random text with a total maximal length of *50* toke
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concerned, I will"*. The default arguments of ``PreTrainedModel.generate()`` can be directly overridden in the
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pipeline, as is shown above for the argument ``max_length``.
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Here is an example of text generation using ``XLNet`` and its tokenzier.
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Here is an example of text generation using ``XLNet`` and its tokenizer.
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.. code-block::
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@ -834,7 +834,7 @@ Here is an example of doing translation using a model and a tokenizer. The proce
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1. Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder
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model, such as ``Bart`` or ``T5``.
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2. Define the article that should be summarizaed.
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2. Define the article that should be summarized.
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3. Add the T5 specific prefix "translate English to German: "
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4. Use the ``PreTrainedModel.generate()`` method to perform the translation.
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@ -16,7 +16,7 @@ This model takes [facebook/bart-large-mnli](https://huggingface.co/facebook/bart
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You can play with an interactive demo of this zero-shot technique with this model, as well as the non-finetuned [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli), [here](https://huggingface.co/zero-shot/).
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## Inteded Usage
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## Intended Usage
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This model was fine-tuned on topic classification and will perform best at zero-shot topic classification. Use `hypothesis_template="This text is about {}."` as this is the template used during fine-tuning.
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@ -24,7 +24,7 @@ widget:
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This model takes [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) and fine-tunes it on a combination of NLI data in 15 languages. It is intended to be used for zero-shot text classification, such as with the Hugging Face [ZeroShotClassificationPipeline](https://huggingface.co/transformers/master/main_classes/pipelines.html#transformers.ZeroShotClassificationPipeline).
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## Inteded Usage
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## Intended Usage
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This model is intended to be used for zero-shot text classification, especially in languages other than English. It is fine-tuned on XNLI, which is a multilingual NLI dataset. The model can therefore be used with any of the languages in the XNLI corpus:
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@ -396,7 +396,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
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new_num_tokens (:obj:`int`, `optional`):
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The number of new tokens in the embedding matrix. Increasing the size will add newly initialized
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vectors at the end. Reducing the size will remove vectors from the end. If not provided or :obj:`None`,
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just returns a pointer to the input tokens :obj:`tf.Variable` module of the model wihtout doing
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just returns a pointer to the input tokens :obj:`tf.Variable` module of the model without doing
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anything.
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Return:
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@ -442,7 +442,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
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Increasing the size will add newly initialized vectors at the end. Reducing the size will remove
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vectors from the end. If not provided or :obj:`None`, just returns a pointer to the input tokens
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:obj:`tf.Variable`` module of the model wihtout doing anything.
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:obj:`tf.Variable`` module of the model without doing anything.
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Return:
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:obj:`tf.Variable`: Pointer to the resized Embedding Module or the old Embedding Module if
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@ -539,7 +539,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
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) != len(decoder_modules):
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# this can happen if the name corresponds to the position in a list module list of layers
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# in this case the decoder has added a cross-attention that the encoder does not have
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# thus skip this step and substract one layer pos from encoder
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# thus skip this step and subtract one layer pos from encoder
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encoder_layer_pos -= 1
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continue
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elif name not in encoder_modules:
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@ -598,7 +598,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
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new_num_tokens (:obj:`int`, `optional`):
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The number of new tokens in the embedding matrix. Increasing the size will add newly initialized
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vectors at the end. Reducing the size will remove vectors from the end. If not provided or :obj:`None`,
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just returns a pointer to the input tokens :obj:`torch.nn.Embedding` module of the model wihtout doing
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just returns a pointer to the input tokens :obj:`torch.nn.Embedding` module of the model without doing
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anything.
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Return:
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@ -639,7 +639,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
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Increasing the size will add newly initialized vectors at the end. Reducing the size will remove
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vectors from the end. If not provided or :obj:`None`, just returns a pointer to the input tokens
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:obj:`torch.nn.Embedding`` module of the model wihtout doing anything.
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:obj:`torch.nn.Embedding`` module of the model without doing anything.
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Return:
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:obj:`torch.nn.Embedding`: Pointer to the resized Embedding Module or the old Embedding Module if
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@ -1366,7 +1366,7 @@ class SQuADHead(nn.Module):
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Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
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should be masked.
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return_dict (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether or not to return a :class:`~transformers.file_utils.ModelOuput` instead of a plain tuple.
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Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
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Returns:
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"""
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@ -1652,7 +1652,7 @@ def apply_chunking_to_forward(
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The input tensors of ``forward_fn`` which will be chunked
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Returns:
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:obj:`torch.Tensor`: A tensor with the same shape as the :obj:`foward_fn` would have given if applied`.
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:obj:`torch.Tensor`: A tensor with the same shape as the :obj:`forward_fn` would have given if applied`.
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Examples::
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@ -1673,7 +1673,7 @@ def apply_chunking_to_forward(
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input_tensor.shape == tensor_shape for input_tensor in input_tensors
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), "All input tenors have to be of the same shape"
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# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compability
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# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
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num_args_in_forward_chunk_fn = len(inspect.signature(forward_fn).parameters)
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assert num_args_in_forward_chunk_fn == len(
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input_tensors
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@ -1057,12 +1057,12 @@ class ZeroShotClassificationPipeline(Pipeline):
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return -1
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def _parse_and_tokenize(
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self, sequences, candidal_labels, hypothesis_template, padding=True, add_special_tokens=True, **kwargs
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self, sequences, candidate_labels, hypothesis_template, padding=True, add_special_tokens=True, **kwargs
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):
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"""
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Parse arguments and tokenize only_first so that hypothesis (label) is not truncated
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"""
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sequence_pairs = self._args_parser(sequences, candidal_labels, hypothesis_template)
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sequence_pairs = self._args_parser(sequences, candidate_labels, hypothesis_template)
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inputs = self.tokenizer(
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sequence_pairs,
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add_special_tokens=add_special_tokens,
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@ -2758,7 +2758,9 @@ def pipeline(
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- :obj:`"fill-mask"`: will return a :class:`~transformers.FillMaskPipeline`.
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- :obj:`"summarization"`: will return a :class:`~transformers.SummarizationPipeline`.
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- :obj:`"translation_xx_to_yy"`: will return a :class:`~transformers.TranslationPipeline`.
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- :obj:`"text2text-generation"`: will return a :class:`~transformers.Text2TextGenerationPipeline`.
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- :obj:`"text-generation"`: will return a :class:`~transformers.TextGenerationPipeline`.
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- :obj:`"zero-shot-classification:`: will return a :class:`~transformers.ZeroShotClassificationPipeline`.
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- :obj:`"conversation"`: will return a :class:`~transformers.ConversationalPipeline`.
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model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`):
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The model that will be used by the pipeline to make predictions. This can be a model identifier or an
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