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* Document model outputs * Update docs/source/main_classes/output.rst Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
142 lines
4.2 KiB
ReStructuredText
142 lines
4.2 KiB
ReStructuredText
Model outputs
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-------------
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PyTorch models have outputs that are instances of subclasses of :class:`~transformers.file_utils.ModelOutput`. Those
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are data structures containing all the information returned by the model, but that can also be used as tuples or
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dictionaries.
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Let's see of this looks on an example:
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.. code-block::
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from transformers import BertTokenizer, BertForSequenceClassification
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import torch
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
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inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
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labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
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outputs = model(**inputs, labels=labels)
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The ``outputs`` object is a :class:`~transformers.modeling_outputs.SequenceClassifierOutput`, as we can see in the
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documentation of that class below, it means it has an optional ``loss``, a ``logits`` an optional ``hidden_states`` and
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an optional ``attentions`` attribute. Here we have the ``loss`` since we passed along ``labels``, but we don't have
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``hidden_states`` and ``attentions`` because we didn't pass ``output_hidden_states=True`` or
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``output_attentions=True``.
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You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you
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will get ``None``. Here for instance ``outputs.loss`` is the loss computed by the model, and ``outputs.attentions`` is
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``None``.
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When considering our ``outputs`` object as tuple, it only considers the attributes that don't have ``None`` values.
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Here for instance, it has two elements, ``loss`` then ``logits``, so
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.. code-block::
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outputs[:2]
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will return the tuple ``(outputs.loss, outputs.logits)`` for instance.
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When considering our ``outputs`` object as dictionary, it only considers the attributes that don't have ``None``
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values. Here for instance, it has two keys that are ``loss`` and ``logits``.
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We document here the generic model outputs that are used by more than one model type. Specific output types are
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documented on their corresponding model page.
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``ModelOutput``
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~~~~~~~~~~~~~~~
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.. autoclass:: transformers.file_utils.ModelOutput
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:members:
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``BaseModelOutput``
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~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.BaseModelOutput
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:members:
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``BaseModelOutputWithPooling``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPooling
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:members:
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``BaseModelOutputWithPast``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPast
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:members:
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``Seq2SeqModelOutput``
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~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.Seq2SeqModelOutput
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:members:
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``CausalLMOutput``
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~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.CausalLMOutput
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:members:
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``CausalLMOutputWithPast``
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.CausalLMOutputWithPast
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:members:
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``MaskedLMOutput``
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~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.MaskedLMOutput
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:members:
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``Seq2SeqLMOutput``
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~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.Seq2SeqLMOutput
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:members:
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``NextSentencePredictorOutput``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.NextSentencePredictorOutput
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:members:
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``SequenceClassifierOutput``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.SequenceClassifierOutput
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:members:
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``Seq2SeqSequenceClassifierOutput``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput
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:members:
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``MultipleChoiceModelOutput``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.MultipleChoiceModelOutput
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:members:
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``TokenClassifierOutput``
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~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.TokenClassifierOutput
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:members:
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``QuestionAnsweringModelOutput``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.QuestionAnsweringModelOutput
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:members:
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``Seq2SeqQuestionAnsweringModelOutput``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput
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:members:
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