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https://github.com/huggingface/transformers.git
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88ef8893cd
* add past_key_values * add use_cache option * make mask before cutting ids * adjust position_ids according to past_key_values * flatten past_key_values * fix positional embeds * fix _reorder_cache * set use_cache to false when not decoder, fix attention mask init * add test for caching * add past_key_values for Roberta * fix position embeds * add caching test for roberta * add doc * make style * doc, fix attention mask, test * small fixes * adress patrick's comments * input_ids shouldn't start with pad token * use_cache only when decoder * make consistent with bert * make copies consistent * add use_cache to encoder * add past_key_values to tapas attention * apply suggestions from code review * make coppies consistent * add attn mask in tests * remove copied from longformer * apply suggestions from code review * fix bart test * nit * simplify model outputs * fix doc * fix output ordering
302 lines
11 KiB
ReStructuredText
302 lines
11 KiB
ReStructuredText
..
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Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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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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BaseModelOutputWithCrossAttentions
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithCrossAttentions
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:members:
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BaseModelOutputWithPoolingAndCrossAttentions
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions
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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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BaseModelOutputWithPastAndCrossAttentions
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions
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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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CausalLMOutputWithCrossAttentions
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_outputs.CausalLMOutputWithCrossAttentions
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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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TFBaseModelOutput
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutput
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:members:
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TFBaseModelOutputWithPooling
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling
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:members:
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TFBaseModelOutputWithPast
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutputWithPast
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:members:
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TFSeq2SeqModelOutput
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqModelOutput
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:members:
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TFCausalLMOutput
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFCausalLMOutput
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:members:
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TFCausalLMOutputWithPast
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFCausalLMOutputWithPast
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:members:
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TFMaskedLMOutput
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFMaskedLMOutput
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:members:
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TFSeq2SeqLMOutput
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqLMOutput
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:members:
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TFNextSentencePredictorOutput
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFNextSentencePredictorOutput
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:members:
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TFSequenceClassifierOutput
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFSequenceClassifierOutput
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:members:
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TFSeq2SeqSequenceClassifierOutput
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput
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:members:
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TFMultipleChoiceModelOutput
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput
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:members:
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TFTokenClassifierOutput
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFTokenClassifierOutput
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:members:
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TFQuestionAnsweringModelOutput
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput
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:members:
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TFSeq2SeqQuestionAnsweringModelOutput
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput
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:members:
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