---
local: bart
sections:
- local: overview
  sections:
  - local: examples
    title: Examples
  title: Overview
- local: implementation-notes
  title: Implementation Notes
- local: mask-filling
  title: Mask Filling
- local: transformers.BartConfig
  title: BartConfig
- local: transformers.BartTokenizer
  title: BartTokenizer
- local: transformers.BartTokenizerFast
  title: BartTokenizerFast
- local: transformers.BartModel
  title: BartModel
- local: transformers.BartForConditionalGeneration
  title: BartForConditionalGeneration
- local: transformers.BartForSequenceClassification
  title: BartForSequenceClassification
- local: transformers.BartForQuestionAnswering
  title: BartForQuestionAnswering
- local: transformers.BartForCausalLM
  title: BartForCausalLM
- local: transformers.TFBartModel
  title: TFBartModel
- local: transformers.TFBartForConditionalGeneration
  title: TFBartForConditionalGeneration
- local: transformers.FlaxBartModel
  title: FlaxBartModel
- local: transformers.FlaxBartForConditionalGeneration
  title: FlaxBartForConditionalGeneration
- local: transformers.FlaxBartForSequenceClassification
  title: FlaxBartForSequenceClassification
- local: transformers.FlaxBartForQuestionAnswering
  title: FlaxBartForQuestionAnswering
title: BART
---
<script>
import Tip from "./Tip.svelte";
import Youtube from "./Youtube.svelte";
import Docstring from "./Docstring.svelte";
import CodeBlock from "./CodeBlock.svelte";
import CodeBlockFw from "./CodeBlockFw.svelte";
import DocNotebookDropdown from "./DocNotebookDropdown.svelte";
import IconCopyLink from "./IconCopyLink.svelte";
export let fw: "pt" | "tf"
</script>
<!--Copyright 2020 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->

<h1 id="bart">BART</h1>

**DISCLAIMER:** If you see something strange, file a [Github Issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title) and assign
@patrickvonplaten

<h2 id="overview">Overview</h2>

The Bart model was proposed in [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation,
Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan
Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019.

According to the abstract,

- Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a
  left-to-right decoder (like GPT).
- The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme,
  where spans of text are replaced with a single mask token.
- BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It
  matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new
  state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains
  of up to 6 ROUGE.

This model was contributed by [sshleifer](https://huggingface.co/sshleifer). The Authors' code can be found [here](https://github.com/pytorch/fairseq/tree/master/examples/bart).


<h3 id="examples">Examples</h3>

- Examples and scripts for fine-tuning BART and other models for sequence to sequence tasks can be found in
  [examples/pytorch/summarization/](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization/README.md).
- An example of how to train [BartForConditionalGeneration](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartForConditionalGeneration) with a Hugging Face `datasets`
  object can be found in this [forum discussion](https://discuss.huggingface.co/t/train-bart-for-conditional-generation-e-g-summarization/1904).
- [Distilled checkpoints](https://huggingface.co/models?search=distilbart) are described in this [paper](https://arxiv.org/abs/2010.13002).


<h2 id="implementation-notes">Implementation Notes</h2>

- Bart doesn't use `token_type_ids` for sequence classification. Use [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer) or
  [encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) to get the proper splitting.
- The forward pass of [BartModel](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartModel) will create the `decoder_input_ids` if they are not passed.
  This is different than some other modeling APIs. A typical use case of this feature is mask filling.
- Model predictions are intended to be identical to the original implementation when
  `force_bos_token_to_be_generated=True`. This only works, however, if the string you pass to
  `fairseq.encode` starts with a space.
- [generate()](/docs/transformers/pr_15592/en/main_classes/model#transformers.generation_utils.GenerationMixin.generate) should be used for conditional generation tasks like
  summarization, see the example in that docstrings.
- Models that load the *facebook/bart-large-cnn* weights will not have a `mask_token_id`, or be able to perform
  mask-filling tasks.

<h2 id="mask-filling">Mask Filling</h2>

The `facebook/bart-base` and `facebook/bart-large` checkpoints can be used to fill multi-token masks.

```python
from transformers import BartForConditionalGeneration, BartTokenizer

model = BartForConditionalGeneration.from_pretrained("facebook/bart-large", forced_bos_token_id=0)
tok = BartTokenizer.from_pretrained("facebook/bart-large")
example_english_phrase = "UN Chief Says There Is No &amp;lt;mask> in Syria"
batch = tok(example_english_phrase, return_tensors="pt")
generated_ids = model.generate(batch["input_ids"])
assert tok.batch_decode(generated_ids, skip_special_tokens=True) == [
    "UN Chief Says There Is No Plan to Stop Chemical Weapons in Syria"
]
```

<h2 id="transformers.BartConfig">BartConfig</h2>

<div class="docstring">

<docstring><name>class transformers.BartConfig</name><anchor>transformers.BartConfig</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/configuration_bart.py#L36</source><parameters>[{"name": "vocab_size", "val": " = 50265"}, {"name": "max_position_embeddings", "val": " = 1024"}, {"name": "encoder_layers", "val": " = 12"}, {"name": "encoder_ffn_dim", "val": " = 4096"}, {"name": "encoder_attention_heads", "val": " = 16"}, {"name": "decoder_layers", "val": " = 12"}, {"name": "decoder_ffn_dim", "val": " = 4096"}, {"name": "decoder_attention_heads", "val": " = 16"}, {"name": "encoder_layerdrop", "val": " = 0.0"}, {"name": "decoder_layerdrop", "val": " = 0.0"}, {"name": "activation_function", "val": " = 'gelu'"}, {"name": "d_model", "val": " = 1024"}, {"name": "dropout", "val": " = 0.1"}, {"name": "attention_dropout", "val": " = 0.0"}, {"name": "activation_dropout", "val": " = 0.0"}, {"name": "init_std", "val": " = 0.02"}, {"name": "classifier_dropout", "val": " = 0.0"}, {"name": "scale_embedding", "val": " = False"}, {"name": "use_cache", "val": " = True"}, {"name": "num_labels", "val": " = 3"}, {"name": "pad_token_id", "val": " = 1"}, {"name": "bos_token_id", "val": " = 0"}, {"name": "eos_token_id", "val": " = 2"}, {"name": "is_encoder_decoder", "val": " = True"}, {"name": "decoder_start_token_id", "val": " = 2"}, {"name": "forced_eos_token_id", "val": " = 2"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **vocab_size** (`int`, *optional*, defaults to 50265) --
  Vocabulary size of the BART model. Defines the number of different tokens that can be represented by the
  `inputs_ids` passed when calling [BartModel](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartModel) or [TFBartModel](/docs/transformers/pr_15592/en/model_doc/bart#transformers.TFBartModel).
- **d_model** (`int`, *optional*, defaults to 1024) --
  Dimensionality of the layers and the pooler layer.
- **encoder_layers** (`int`, *optional*, defaults to 12) --
  Number of encoder layers.
- **decoder_layers** (`int`, *optional*, defaults to 12) --
  Number of decoder layers.
- **encoder_attention_heads** (`int`, *optional*, defaults to 16) --
  Number of attention heads for each attention layer in the Transformer encoder.
- **decoder_attention_heads** (`int`, *optional*, defaults to 16) --
  Number of attention heads for each attention layer in the Transformer decoder.
- **decoder_ffn_dim** (`int`, *optional*, defaults to 4096) --
  Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
- **encoder_ffn_dim** (`int`, *optional*, defaults to 4096) --
  Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
- **activation_function** (`str` or `function`, *optional*, defaults to `"gelu"`) --
  The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
  `"relu"`, `"silu"` and `"gelu_new"` are supported.
- **dropout** (`float`, *optional*, defaults to 0.1) --
  The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
- **attention_dropout** (`float`, *optional*, defaults to 0.0) --
  The dropout ratio for the attention probabilities.
- **activation_dropout** (`float`, *optional*, defaults to 0.0) --
  The dropout ratio for activations inside the fully connected layer.
- **classifier_dropout** (`float`, *optional*, defaults to 0.0) --
  The dropout ratio for classifier.
- **max_position_embeddings** (`int`, *optional*, defaults to 1024) --
  The maximum sequence length that this model might ever be used with. Typically set this to something large
  just in case (e.g., 512 or 1024 or 2048).
- **init_std** (`float`, *optional*, defaults to 0.02) --
  The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
  encoder_layerdrop -- (`float`, *optional*, defaults to 0.0):
  The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
  for more details.
  decoder_layerdrop -- (`float`, *optional*, defaults to 0.0):
  The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
  for more details.
- **scale_embedding** (`bool`, *optional*, defaults to `False`) --
  Scale embeddings by diving by sqrt(d_model).
- **use_cache** (`bool`, *optional*, defaults to `True`) --
  Whether or not the model should return the last key/values attentions (not used by all models).
  num_labels -- (`int`, *optional*, defaults to 3):
  The number of labels to use in [BartForSequenceClassification](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartForSequenceClassification).
- **forced_eos_token_id** (`int`, *optional*, defaults to 2) --
  The id of the token to force as the last generated token when `max_length` is reached. Usually set to
  `eos_token_id`.</paramsdesc><paramgroups>0</paramgroups></docstring>

This is the configuration class to store the configuration of a [BartModel](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartModel). It is used to instantiate a BART
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the BART
[facebook/bart-large](https://huggingface.co/facebook/bart-large) architecture.

Configuration objects inherit from [PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the
documentation from [PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig) for more information.




Example:

```python
>>> from transformers import BartModel, BartConfig

>>> # Initializing a BART facebook/bart-large style configuration
>>> configuration = BartConfig()

>>> # Initializing a model from the facebook/bart-large style configuration
>>> model = BartModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

</div>

<h2 id="transformers.BartTokenizer">BartTokenizer</h2>

<div class="docstring">

<docstring><name>class transformers.BartTokenizer</name><anchor>transformers.BartTokenizer</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/tokenization_bart.py#L101</source><parameters>[{"name": "vocab_file", "val": ""}, {"name": "merges_file", "val": ""}, {"name": "errors", "val": " = 'replace'"}, {"name": "bos_token", "val": " = '<s>'"}, {"name": "eos_token", "val": " = '</s>'"}, {"name": "sep_token", "val": " = '</s>'"}, {"name": "cls_token", "val": " = '<s>'"}, {"name": "unk_token", "val": " = '<unk>'"}, {"name": "pad_token", "val": " = '<pad>'"}, {"name": "mask_token", "val": " = '<mask>'"}, {"name": "add_prefix_space", "val": " = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **vocab_file** (`str`) --
  Path to the vocabulary file.
- **merges_file** (`str`) --
  Path to the merges file.
- **errors** (`str`, *optional*, defaults to `"replace"`) --
  Paradigm to follow when decoding bytes to UTF-8. See
  [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
- **bos_token** (`str`, *optional*, defaults to `"<s>"`) --
  The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

  <Tip>

  When building a sequence using special tokens, this is not the token that is used for the beginning of
  sequence. The token used is the `cls_token`.

  </Tip>

- **eos_token** (`str`, *optional*, defaults to `"</s>"`) --
  The end of sequence token.

  <Tip>

  When building a sequence using special tokens, this is not the token that is used for the end of sequence.
  The token used is the `sep_token`.

  </Tip>

- **sep_token** (`str`, *optional*, defaults to `"&amp;lt;/s>"`) --
  The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
  sequence classification or for a text and a question for question answering. It is also used as the last
  token of a sequence built with special tokens.
- **cls_token** (`str`, *optional*, defaults to `"&amp;lt;s>"`) --
  The classifier token which is used when doing sequence classification (classification of the whole sequence
  instead of per-token classification). It is the first token of the sequence when built with special tokens.
- **unk_token** (`str`, *optional*, defaults to `"&amp;lt;unk>"`) --
  The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
  token instead.
- **pad_token** (`str`, *optional*, defaults to `"&amp;lt;pad>"`) --
  The token used for padding, for example when batching sequences of different lengths.
- **mask_token** (`str`, *optional*, defaults to `"&amp;lt;mask>"`) --
  The token used for masking values. This is the token used when training this model with masked language
  modeling. This is the token which the model will try to predict.
- **add_prefix_space** (`bool`, *optional*, defaults to `False`) --
  Whether or not to add an initial space to the input. This allows to treat the leading word just as any
  other word. (BART tokenizer detect beginning of words by the preceding space).</paramsdesc><paramgroups>0</paramgroups></docstring>

Constructs a BART tokenizer, which is smilar to the ROBERTa tokenizer, using byte-level Byte-Pair-Encoding.

This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:

```
>>> from transformers import BartTokenizer
>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
>>> tokenizer("Hello world")['input_ids']
[0, 31414, 232, 2]
>>> tokenizer(" Hello world")['input_ids']
[0, 20920, 232, 2]
```

You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.

<Tip>

When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one).

</Tip>

This tokenizer inherits from [PreTrainedTokenizer](/docs/transformers/pr_15592/en/main_classes/tokenizer#transformers.PreTrainedTokenizer) which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.





<div class="docstring">
<docstring><name>build\_inputs\_with\_special\_tokens</name><anchor>transformers.BartTokenizer.build_inputs_with_special_tokens</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/tokenization_bart.py#L338</source><parameters>[{"name": "token_ids_0", "val": ": typing.List[int]"}, {"name": "token_ids_1", "val": ": typing.Optional[typing.List[int]] = None"}]</parameters><paramsdesc>- **token_ids_0** (`List[int]`) --
  List of IDs to which the special tokens will be added.
- **token_ids_1** (`List[int]`, *optional*) --
  Optional second list of IDs for sequence pairs.</paramsdesc><paramgroups>0</paramgroups><rettype>`List[int]`</rettype><retdesc>List of [input IDs](../glossary#input-ids) with the appropriate special tokens.</retdesc></docstring>

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BART sequence has the following format:

- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s>&amp;lt;/s> B &amp;lt;/s>`








</div>
<div class="docstring">
<docstring><name>convert\_tokens\_to\_string</name><anchor>transformers.BartTokenizer.convert_tokens_to_string</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/tokenization_bart.py#L303</source><parameters>[{"name": "tokens", "val": ""}]</parameters></docstring>
Converts a sequence of tokens (string) in a single string.

</div>
<div class="docstring">
<docstring><name>create\_token\_type\_ids\_from\_sequences</name><anchor>transformers.BartTokenizer.create_token_type_ids_from_sequences</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/tokenization_bart.py#L390</source><parameters>[{"name": "token_ids_0", "val": ": typing.List[int]"}, {"name": "token_ids_1", "val": ": typing.Optional[typing.List[int]] = None"}]</parameters><paramsdesc>- **token_ids_0** (`List[int]`) --
  List of IDs.
- **token_ids_1** (`List[int]`, *optional*) --
  Optional second list of IDs for sequence pairs.</paramsdesc><paramgroups>0</paramgroups><rettype>`List[int]`</rettype><retdesc>List of zeros.</retdesc></docstring>

Create a mask from the two sequences passed to be used in a sequence-pair classification task. BART does not
make use of token type ids, therefore a list of zeros is returned.








</div>
<div class="docstring">
<docstring><name>get\_special\_tokens\_mask</name><anchor>transformers.BartTokenizer.get_special_tokens_mask</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/tokenization_bart.py#L363</source><parameters>[{"name": "token_ids_0", "val": ": typing.List[int]"}, {"name": "token_ids_1", "val": ": typing.Optional[typing.List[int]] = None"}, {"name": "already_has_special_tokens", "val": ": bool = False"}]</parameters><paramsdesc>- **token_ids_0** (`List[int]`) --
  List of IDs.
- **token_ids_1** (`List[int]`, *optional*) --
  Optional second list of IDs for sequence pairs.
- **already_has_special_tokens** (`bool`, *optional*, defaults to `False`) --
  Whether or not the token list is already formatted with special tokens for the model.</paramsdesc><paramgroups>0</paramgroups><rettype>`List[int]`</rettype><retdesc>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</retdesc></docstring>

Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.








</div></div>

<h2 id="transformers.BartTokenizerFast">BartTokenizerFast</h2>

<div class="docstring">

<docstring><name>class transformers.BartTokenizerFast</name><anchor>transformers.BartTokenizerFast</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/tokenization_bart_fast.py#L70</source><parameters>[{"name": "vocab_file", "val": " = None"}, {"name": "merges_file", "val": " = None"}, {"name": "tokenizer_file", "val": " = None"}, {"name": "errors", "val": " = 'replace'"}, {"name": "bos_token", "val": " = '<s>'"}, {"name": "eos_token", "val": " = '</s>'"}, {"name": "sep_token", "val": " = '</s>'"}, {"name": "cls_token", "val": " = '<s>'"}, {"name": "unk_token", "val": " = '<unk>'"}, {"name": "pad_token", "val": " = '<pad>'"}, {"name": "mask_token", "val": " = '<mask>'"}, {"name": "add_prefix_space", "val": " = False"}, {"name": "trim_offsets", "val": " = True"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **vocab_file** (`str`) --
  Path to the vocabulary file.
- **merges_file** (`str`) --
  Path to the merges file.
- **errors** (`str`, *optional*, defaults to `"replace"`) --
  Paradigm to follow when decoding bytes to UTF-8. See
  [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
- **bos_token** (`str`, *optional*, defaults to `"<s>"`) --
  The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

  <Tip>

  When building a sequence using special tokens, this is not the token that is used for the beginning of
  sequence. The token used is the `cls_token`.

  </Tip>

- **eos_token** (`str`, *optional*, defaults to `"</s>"`) --
  The end of sequence token.

  <Tip>

  When building a sequence using special tokens, this is not the token that is used for the end of sequence.
  The token used is the `sep_token`.

  </Tip>

- **sep_token** (`str`, *optional*, defaults to `"&amp;lt;/s>"`) --
  The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
  sequence classification or for a text and a question for question answering. It is also used as the last
  token of a sequence built with special tokens.
- **cls_token** (`str`, *optional*, defaults to `"&amp;lt;s>"`) --
  The classifier token which is used when doing sequence classification (classification of the whole sequence
  instead of per-token classification). It is the first token of the sequence when built with special tokens.
- **unk_token** (`str`, *optional*, defaults to `"&amp;lt;unk>"`) --
  The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
  token instead.
- **pad_token** (`str`, *optional*, defaults to `"&amp;lt;pad>"`) --
  The token used for padding, for example when batching sequences of different lengths.
- **mask_token** (`str`, *optional*, defaults to `"&amp;lt;mask>"`) --
  The token used for masking values. This is the token used when training this model with masked language
  modeling. This is the token which the model will try to predict.
- **add_prefix_space** (`bool`, *optional*, defaults to `False`) --
  Whether or not to add an initial space to the input. This allows to treat the leading word just as any
  other word. (BART tokenizer detect beginning of words by the preceding space).
- **trim_offsets** (`bool`, *optional*, defaults to `True`) --
  Whether the post processing step should trim offsets to avoid including whitespaces.</paramsdesc><paramgroups>0</paramgroups></docstring>

Construct a "fast" BART tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer,
using byte-level Byte-Pair-Encoding.

This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:

```
>>> from transformers import BartTokenizerFast
>>> tokenizer = BartTokenizerFast.from_pretrained("facebook/bart-base")
>>> tokenizer("Hello world")['input_ids']
[0, 31414, 232, 2]
>>> tokenizer(" Hello world")['input_ids']
[0, 20920, 232, 2]
```

You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.

<Tip>

When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.

</Tip>

This tokenizer inherits from [PreTrainedTokenizerFast](/docs/transformers/pr_15592/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast) which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.





<div class="docstring">
<docstring><name>create\_token\_type\_ids\_from\_sequences</name><anchor>transformers.BartTokenizerFast.create_token_type_ids_from_sequences</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/tokenization_bart_fast.py#L282</source><parameters>[{"name": "token_ids_0", "val": ": typing.List[int]"}, {"name": "token_ids_1", "val": ": typing.Optional[typing.List[int]] = None"}]</parameters><paramsdesc>- **token_ids_0** (`List[int]`) --
  List of IDs.
- **token_ids_1** (`List[int]`, *optional*) --
  Optional second list of IDs for sequence pairs.</paramsdesc><paramgroups>0</paramgroups><rettype>`List[int]`</rettype><retdesc>List of zeros.</retdesc></docstring>

Create a mask from the two sequences passed to be used in a sequence-pair classification task. BART does not
make use of token type ids, therefore a list of zeros is returned.








</div></div>

<h2 id="transformers.BartModel">BartModel</h2>

<div class="docstring">

<docstring><name>class transformers.BartModel</name><anchor>transformers.BartModel</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_bart.py#L1123</source><parameters>[{"name": "config", "val": ": BartConfig"}]</parameters><paramsdesc>- **config** ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) --
  Model configuration class with all the parameters of the model. Initializing with a config file does not
  load the weights associated with the model, only the configuration. Check out the
  [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
The bare BART Model outputting raw hidden-states without any specific head on top.
This model inherits from [PreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.BartModel.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_bart.py#L1150</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "decoder_input_ids", "val": " = None"}, {"name": "decoder_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "decoder_head_mask", "val": " = None"}, {"name": "cross_attn_head_mask", "val": " = None"}, {"name": "encoder_outputs", "val": " = None"}, {"name": "past_key_values", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "decoder_inputs_embeds", "val": " = None"}, {"name": "use_cache", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_input_ids** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)

  Bart uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
  is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).

  For translation and summarization training, `decoder_input_ids` should be provided. If no
  `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
  for denoising pre-training following the paper.
- **decoder_attention_mask** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
  be used by default.

  If you want to change padding behavior, you should read `modeling_bart._prepare_decoder_inputs` and
  modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information
  on the default strategy.
- **head_mask** (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **decoder_head_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **cross_attn_head_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
  1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **encoder_outputs** (`tuple(tuple(torch.FloatTensor)`, *optional*) --
  Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
  `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
  hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) --
  Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

  If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
  don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
  ``decoder_input_ids``` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of
  shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids`
  you can choose to directly pass an embedded representation. This is useful if you want more control over
  how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup
  matrix.
- **decoder_inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
  representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
  input (see `past_key_values`). This is useful if you want more control over how to convert
  `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.

  If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
  of `inputs_embeds`.
- **use_cache** (`bool`, *optional*) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`).
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.Seq2SeqModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.Seq2SeqModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) and inputs.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the decoder of the model.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
- **decoder_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **cross_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.
- **encoder_last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Sequence of hidden-states at the output of the last layer of the encoder of the model.
- **encoder_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
- **encoder_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.</retdesc></docstring>
The [BartModel](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartModel) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> from transformers import BartTokenizer, BartModel
>>> import torch

>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")
>>> model = BartModel.from_pretrained("facebook/bart-large")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state
```


</div></div>

<h2 id="transformers.BartForConditionalGeneration">BartForConditionalGeneration</h2>

<div class="docstring">

<docstring><name>class transformers.BartForConditionalGeneration</name><anchor>transformers.BartForConditionalGeneration</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_bart.py#L1249</source><parameters>[{"name": "config", "val": ": BartConfig"}]</parameters><paramsdesc>- **config** ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) --
  Model configuration class with all the parameters of the model. Initializing with a config file does not
  load the weights associated with the model, only the configuration. Check out the
  [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
The BART Model with a language modeling head. Can be used for summarization.
This model inherits from [PreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.BartForConditionalGeneration.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_bart.py#L1288</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "decoder_input_ids", "val": " = None"}, {"name": "decoder_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "decoder_head_mask", "val": " = None"}, {"name": "cross_attn_head_mask", "val": " = None"}, {"name": "encoder_outputs", "val": " = None"}, {"name": "past_key_values", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "decoder_inputs_embeds", "val": " = None"}, {"name": "labels", "val": " = None"}, {"name": "use_cache", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_input_ids** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)

  Bart uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
  is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).

  For translation and summarization training, `decoder_input_ids` should be provided. If no
  `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
  for denoising pre-training following the paper.
- **decoder_attention_mask** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
  be used by default.

  If you want to change padding behavior, you should read `modeling_bart._prepare_decoder_inputs` and
  modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information
  on the default strategy.
- **head_mask** (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **decoder_head_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **cross_attn_head_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
  1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **encoder_outputs** (`tuple(tuple(torch.FloatTensor)`, *optional*) --
  Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
  `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
  hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) --
  Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

  If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
  don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
  ``decoder_input_ids``` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of
  shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids`
  you can choose to directly pass an embedded representation. This is useful if you want more control over
  how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup
  matrix.
- **decoder_inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
  representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
  input (see `past_key_values`). This is useful if you want more control over how to convert
  `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.

  If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
  of `inputs_embeds`.
- **use_cache** (`bool`, *optional*) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`).
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.

- **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
  config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
  (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.Seq2SeqLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.Seq2SeqLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Language modeling loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
- **decoder_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **cross_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.
- **encoder_last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Sequence of hidden-states at the output of the last layer of the encoder of the model.
- **encoder_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
- **encoder_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.</retdesc></docstring>
The [BartForConditionalGeneration](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartForConditionalGeneration) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Summarization example:

```python
>>> from transformers import BartTokenizer, BartForConditionalGeneration

>>> model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")

>>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="pt")

>>> # Generate Summary
>>> summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=5)
>>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
```

Mask filling example:

```python
>>> from transformers import BartTokenizer, BartForConditionalGeneration

>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")
>>> TXT = "My friends are &amp;lt;mask> but they eat too many carbs."

>>> model = BartForConditionalGeneration.from_pretrained("facebook/bart-large")
>>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"]
>>> logits = model(input_ids).logits

>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
>>> probs = logits[0, masked_index].softmax(dim=0)
>>> values, predictions = probs.topk(5)

>>> tokenizer.decode(predictions).split()
```


</div></div>

<h2 id="transformers.BartForSequenceClassification">BartForSequenceClassification</h2>

<div class="docstring">

<docstring><name>class transformers.BartForSequenceClassification</name><anchor>transformers.BartForSequenceClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_bart.py#L1418</source><parameters>[{"name": "config", "val": ": BartConfig"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) --
  Model configuration class with all the parameters of the model. Initializing with a config file does not
  load the weights associated with the model, only the configuration. Check out the
  [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

Bart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
tasks.

This model inherits from [PreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.BartForSequenceClassification.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_bart.py#L1431</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "decoder_input_ids", "val": " = None"}, {"name": "decoder_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "decoder_head_mask", "val": " = None"}, {"name": "cross_attn_head_mask", "val": " = None"}, {"name": "encoder_outputs", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "decoder_inputs_embeds", "val": " = None"}, {"name": "labels", "val": " = None"}, {"name": "use_cache", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_input_ids** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)

  Bart uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
  is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).

  For translation and summarization training, `decoder_input_ids` should be provided. If no
  `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
  for denoising pre-training following the paper.
- **decoder_attention_mask** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
  be used by default.

  If you want to change padding behavior, you should read `modeling_bart._prepare_decoder_inputs` and
  modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information
  on the default strategy.
- **head_mask** (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **decoder_head_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **cross_attn_head_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
  1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **encoder_outputs** (`tuple(tuple(torch.FloatTensor)`, *optional*) --
  Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
  `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
  hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) --
  Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

  If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
  don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
  ``decoder_input_ids``` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of
  shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids`
  you can choose to directly pass an embedded representation. This is useful if you want more control over
  how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup
  matrix.
- **decoder_inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
  representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
  input (see `past_key_values`). This is useful if you want more control over how to convert
  `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.

  If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
  of `inputs_embeds`.
- **use_cache** (`bool`, *optional*) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`).
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.

- **labels** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
  config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `label` is provided) -- Classification (or regression if config.num_labels==1) loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) -- Classification (or regression if config.num_labels==1) scores (before SoftMax).
- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
- **decoder_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **cross_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.
- **encoder_last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Sequence of hidden-states at the output of the last layer of the encoder of the model.
- **encoder_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
- **encoder_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.</retdesc></docstring>
The [BartForSequenceClassification](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartForSequenceClassification) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example of single-label classification:

```python
>>> from transformers import BartTokenizer, BartForSequenceClassification
>>> import torch

>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")
>>> model = BartForSequenceClassification.from_pretrained("facebook/bart-large")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
```

Example of multi-label classification:

```python
>>> from transformers import BartTokenizer, BartForSequenceClassification
>>> import torch

>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")
>>> model = BartForSequenceClassification.from_pretrained("facebook/bart-large", problem_type="multi_label_classification")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> labels = torch.tensor([[1, 1]], dtype=torch.float)  # need dtype=float for BCEWithLogitsLoss
>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
```


</div></div>

<h2 id="transformers.BartForQuestionAnswering">BartForQuestionAnswering</h2>

<div class="docstring">

<docstring><name>class transformers.BartForQuestionAnswering</name><anchor>transformers.BartForQuestionAnswering</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_bart.py#L1543</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) --
  Model configuration class with all the parameters of the model. Initializing with a config file does not
  load the weights associated with the model, only the configuration. Check out the
  [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

BART Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layer on top of the hidden-states output to compute `span start logits` and `span end logits`).

This model inherits from [PreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.BartForQuestionAnswering.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_bart.py#L1555</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "decoder_input_ids", "val": " = None"}, {"name": "decoder_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "decoder_head_mask", "val": " = None"}, {"name": "cross_attn_head_mask", "val": " = None"}, {"name": "encoder_outputs", "val": " = None"}, {"name": "start_positions", "val": " = None"}, {"name": "end_positions", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "decoder_inputs_embeds", "val": " = None"}, {"name": "use_cache", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_input_ids** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)

  Bart uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
  is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).

  For translation and summarization training, `decoder_input_ids` should be provided. If no
  `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
  for denoising pre-training following the paper.
- **decoder_attention_mask** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
  be used by default.

  If you want to change padding behavior, you should read `modeling_bart._prepare_decoder_inputs` and
  modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information
  on the default strategy.
- **head_mask** (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **decoder_head_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **cross_attn_head_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
  1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **encoder_outputs** (`tuple(tuple(torch.FloatTensor)`, *optional*) --
  Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
  `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
  hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) --
  Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

  If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
  don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
  ``decoder_input_ids``` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of
  shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids`
  you can choose to directly pass an embedded representation. This is useful if you want more control over
  how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup
  matrix.
- **decoder_inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
  representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
  input (see `past_key_values`). This is useful if you want more control over how to convert
  `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.

  If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
  of `inputs_embeds`.
- **use_cache** (`bool`, *optional*) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`).
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.

- **start_positions** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for position (index) of the start of the labelled span for computing the token classification loss.
  Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
  are not taken into account for computing the loss.
- **end_positions** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for position (index) of the end of the labelled span for computing the token classification loss.
  Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
  are not taken into account for computing the loss.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
- **start_logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) -- Span-start scores (before SoftMax).
- **end_logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) -- Span-end scores (before SoftMax).
- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
- **decoder_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **cross_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.
- **encoder_last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Sequence of hidden-states at the output of the last layer of the encoder of the model.
- **encoder_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
- **encoder_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.</retdesc></docstring>
The [BartForQuestionAnswering](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartForQuestionAnswering) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> from transformers import BartTokenizer, BartForQuestionAnswering
>>> import torch

>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")
>>> model = BartForQuestionAnswering.from_pretrained("facebook/bart-large")

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> start_positions = torch.tensor([1])
>>> end_positions = torch.tensor([3])

>>> outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
>>> loss = outputs.loss
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logits
```


</div></div>

<h2 id="transformers.BartForCausalLM">BartForCausalLM</h2>

<div class="docstring">

<div class="docstring">
<docstring><name>forward</name><anchor>transformers.BartForCausalLM.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_bart.py#L1702</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "encoder_hidden_states", "val": " = None"}, {"name": "encoder_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "cross_attn_head_mask", "val": " = None"}, {"name": "past_key_values", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "labels", "val": " = None"}, {"name": "use_cache", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
  provide it.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **encoder_hidden_states**  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
  if the model is configured as a decoder.
- **encoder_attention_mask** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
  in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- **head_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **cross_attn_head_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) --
  Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
  shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
  shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
  tensors are only required when the model is used as a decoder in a Sequence to Sequence model.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
  cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

  If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
  that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
  all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
- **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
  config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
  (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
- **use_cache** (`bool`, *optional*) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
  (see `past_key_values`).

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under
  returned tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
  for more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.CausalLMOutputWithCrossAttentions](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.CausalLMOutputWithCrossAttentions](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Language modeling loss (for next-token prediction).
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.
- **cross_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Cross attentions weights after the attention softmax, used to compute the weighted average in the
  cross-attention heads.
- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- Tuple of `torch.FloatTensor` tuples of length `config.n_layers`, with each tuple containing the cached key,
  value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
  setting. Only relevant if `config.is_decoder = True`.

  Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
  `past_key_values` input) to speed up sequential decoding.</retdesc></docstring>







Example:

```python
>>> from transformers import BartTokenizer, BartForCausalLM

>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")
>>> model = BartForCausalLM.from_pretrained("facebook/bart-large", add_cross_attention=False)
>>> assert model.config.is_decoder, f"&amp;lcub;model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> logits = outputs.logits
```

</div></div>

<h2 id="transformers.TFBartModel">TFBartModel</h2>

<div class="docstring">

<docstring><name>class transformers.TFBartModel</name><anchor>transformers.TFBartModel</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_tf_bart.py#L1187</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
The bare BART Model outputting raw hidden-states without any specific head on top.
This model inherits from [TFPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.TFPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.

<Tip>

TF 2.0 models accepts two formats as inputs:

- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using `tf.keras.Model.fit` method which currently requires having all the
tensors in the first argument of the model call function: `model(inputs)`.

If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
first positional argument :

- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model(&amp;lcub;"input_ids": input_ids, "token_type_ids": token_type_ids})`

</Tip>





<div class="docstring">
<docstring><name>call</name><anchor>transformers.TFBartModel.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_tf_bart.py#L1202</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "decoder_input_ids", "val": " = None"}, {"name": "decoder_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "decoder_head_mask", "val": " = None"}, {"name": "cross_attn_head_mask", "val": " = None"}, {"name": "encoder_outputs", "val": ": typing.Union[typing.Tuple, transformers.modeling_tf_outputs.TFBaseModelOutput, NoneType] = None"}, {"name": "past_key_values", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "decoder_inputs_embeds", "val": " = None"}, {"name": "use_cache", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}, {"name": "training", "val": " = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **input_ids** (`tf.Tensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [BertTokenizer](/docs/transformers/pr_15592/en/model_doc/bert#transformers.BertTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_input_ids** (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)

  Bart uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
  is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).

  For translation and summarization training, `decoder_input_ids` should be provided. If no
  `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
  for denoising pre-training following the paper.
- **decoder_attention_mask** (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
- **head_mask** (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **decoder_head_mask** (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **cross_attn_head_mask** (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **encoder_outputs** (`tf.FloatTensor`, *optional*) --
  hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
  of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
- **past_key_values** (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) --
  contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
  If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
  don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
  `decoder_input_ids` of shape `(batch_size, sequence_length)`.
- **use_cache** (`bool`, *optional*, defaults to `True`) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`). Set to `False` during training, `True` during generation
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
  config will be used instead.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
  used instead.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple. This argument can be used
  in eager mode, in graph mode the value will always be set to True.
- **training** (`bool`, *optional*, defaults to `False`) --
  Whether or not to use the model in training mode (some modules like dropout modules have different
  behaviors between training and evaluation).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_tf_outputs.TFSeq2SeqModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.modeling_tf_outputs.TFSeq2SeqModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput) or a tuple of `tf.Tensor` (if
`return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the
configuration ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) and inputs.

- **last_hidden_state** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the decoder of the model.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
  sequence_length, embed_size_per_head)`).

  Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
  used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
- **decoder_attentions** (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **cross_attentions** (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.
- **encoder_last_hidden_state** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Sequence of hidden-states at the output of the last layer of the encoder of the model.
- **encoder_hidden_states** (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
- **encoder_attentions** (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.</retdesc></docstring>
The [TFBartModel](/docs/transformers/pr_15592/en/model_doc/bart#transformers.TFBartModel) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> from transformers import BartTokenizer, TFBartModel
>>> import tensorflow as tf

>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")
>>> model = TFBartModel.from_pretrained("facebook/bart-large")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)

>>> last_hidden_states = outputs.last_hidden_state
```


</div></div>

<h2 id="transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</h2>

<div class="docstring">

<docstring><name>class transformers.TFBartForConditionalGeneration</name><anchor>transformers.TFBartForConditionalGeneration</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_tf_bart.py#L1296</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
The BART Model with a language modeling head. Can be used for summarization.
This model inherits from [TFPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.TFPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.

<Tip>

TF 2.0 models accepts two formats as inputs:

- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using `tf.keras.Model.fit` method which currently requires having all the
tensors in the first argument of the model call function: `model(inputs)`.

If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
first positional argument :

- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model(&amp;lcub;"input_ids": input_ids, "token_type_ids": token_type_ids})`

</Tip>





<div class="docstring">
<docstring><name>call</name><anchor>transformers.TFBartForConditionalGeneration.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_tf_bart.py#L1331</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "decoder_input_ids", "val": " = None"}, {"name": "decoder_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "decoder_head_mask", "val": " = None"}, {"name": "cross_attn_head_mask", "val": " = None"}, {"name": "encoder_outputs", "val": ": typing.Optional[transformers.modeling_tf_outputs.TFBaseModelOutput] = None"}, {"name": "past_key_values", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "decoder_inputs_embeds", "val": " = None"}, {"name": "use_cache", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}, {"name": "labels", "val": " = None"}, {"name": "training", "val": " = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **input_ids** (`tf.Tensor` of shape `(&amp;lcub;0})`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [BertTokenizer](/docs/transformers/pr_15592/en/model_doc/bert#transformers.BertTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`tf.Tensor` of shape `(&amp;lcub;0})`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_input_ids** (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)

  Bart uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
  is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).

  For translation and summarization training, `decoder_input_ids` should be provided. If no
  `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
  for denoising pre-training following the paper.
- **decoder_attention_mask** (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
- **head_mask** (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **decoder_head_mask** (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **cross_attn_head_mask** (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **encoder_outputs** (`tf.FloatTensor`, *optional*) --
  hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
  of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
- **past_key_values** (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) --
  contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
  If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
  don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
  `decoder_input_ids` of shape `(batch_size, sequence_length)`.
- **use_cache** (`bool`, *optional*, defaults to `True`) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`). Set to `False` during training, `True` during generation
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
  config will be used instead.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
  used instead.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple. This argument can be used
  in eager mode, in graph mode the value will always be set to True.
- **training** (`bool`, *optional*, defaults to `False`) --
  Whether or not to use the model in training mode (some modules like dropout modules have different
  behaviors between training and evaluation).

- **labels** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
  config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
  (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_tf_outputs.TFSeq2SeqLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.modeling_tf_outputs.TFSeq2SeqLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput) or a tuple of `tf.Tensor` (if
`return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the
configuration ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) and inputs.

- **loss** (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided) -- Language modeling loss.
- **logits** (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **past_key_values** (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
  sequence_length, embed_size_per_head)`).

  Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
  used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
- **decoder_attentions** (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **cross_attentions** (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.
- **encoder_last_hidden_state** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Sequence of hidden-states at the output of the last layer of the encoder of the model.
- **encoder_hidden_states** (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
- **encoder_attentions** (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.</retdesc></docstring>
The [TFBartForConditionalGeneration](/docs/transformers/pr_15592/en/model_doc/bart#transformers.TFBartForConditionalGeneration) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Summarization example:

```python
>>> from transformers import BartTokenizer, TFBartForConditionalGeneration

>>> model = TFBartForConditionalGeneration.from_pretrained("facebook/bart-large")
>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")

>>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="tf")

>>> # Generate Summary
>>> summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=5)
>>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
```

Mask filling example:

```python
>>> from transformers import BartTokenizer, TFBartForConditionalGeneration

>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")
>>> TXT = "My friends are &amp;lt;mask> but they eat too many carbs."

>>> model = TFBartForConditionalGeneration.from_pretrained("facebook/bart-large")
>>> input_ids = tokenizer([TXT], return_tensors="tf")["input_ids"]
>>> logits = model(input_ids).logits
>>> probs = tf.nn.softmax(logits[0])
>>> # probs[5] is associated with the mask token
```


</div></div>

<h2 id="transformers.FlaxBartModel">FlaxBartModel</h2>

<div class="docstring">

<docstring><name>class transformers.FlaxBartModel</name><anchor>transformers.FlaxBartModel</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1244</source><parameters>[{"name": "config", "val": ": BartConfig"}, {"name": "input_shape", "val": ": typing.Tuple[int] = (1, 1)"}, {"name": "seed", "val": ": int = 0"}, {"name": "dtype", "val": ": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
- **dtype** (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`) --
  The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
  `jax.numpy.bfloat16` (on TPUs).

  This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
  specified all the computation will be performed with the given `dtype`.

  **Note that this only specifies the dtype of the computation and does not influence the dtype of model
  parameters.**

  If you wish to change the dtype of the model parameters, see [to_fp16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16) and
  [to_bf16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16).</paramsdesc><paramgroups>0</paramgroups></docstring>
The bare Bart Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from [FlaxPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxBartPreTrainedModel.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1179</source><parameters>[{"name": "input_ids", "val": ": ndarray"}, {"name": "attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_input_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "train", "val": ": bool = False"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}]</parameters><paramsdesc>- **input_ids** (`jnp.ndarray` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_input_ids** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)

  For translation and summarization training, `decoder_input_ids` should be provided. If no
  `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
  for denoising pre-training following the paper.
- **decoder_attention_mask** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
  be used by default.

  If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
  paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.
- **decoder_position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
  range `[0, config.max_position_embeddings - 1]`.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) and inputs.

- **last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the decoder of the model.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
- **decoder_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **cross_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.
- **encoder_last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Sequence of hidden-states at the output of the last layer of the encoder of the model.
- **encoder_hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
- **encoder_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.</retdesc></docstring>
The `FlaxBartPreTrainedModel`forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> from transformers import BartTokenizer, FlaxBartModel

>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
>>> model = FlaxBartModel.from_pretrained("facebook/bart-base")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state
```


</div>
<div class="docstring">
<docstring><name>encode</name><anchor>transformers.FlaxBartPreTrainedModel.encode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1002</source><parameters>[{"name": "input_ids", "val": ": ndarray"}, {"name": "attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "train", "val": ": bool = False"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}]</parameters><paramsdesc>- **input_ids** (`jnp.ndarray` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_flax_outputs.FlaxBaseModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxBaseModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (`&amp;lt;class 'transformers.models.bart.configuration_bart.BartConfig'>`) and inputs.

- **last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>







Example:

```python
>>> from transformers import BartTokenizer, FlaxBartForConditionalGeneration

>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)
```

</div>
<div class="docstring">
<docstring><name>decode</name><anchor>transformers.FlaxBartPreTrainedModel.decode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1065</source><parameters>[{"name": "decoder_input_ids", "val": ""}, {"name": "encoder_outputs", "val": ""}, {"name": "encoder_attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "past_key_values", "val": ": dict = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "train", "val": ": bool = False"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}]</parameters><paramsdesc>- **decoder_input_ids** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)

  For translation and summarization training, `decoder_input_ids` should be provided. If no
  `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
  for denoising pre-training following the paper.
- **encoder_outputs** (`tuple(tuple(jnp.ndarray)`) --
  Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
  `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
  hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- **encoder_attention_mask** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_attention_mask** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
  be used by default.

  If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
  paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
- **decoder_position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
  range `[0, config.max_position_embeddings - 1]`.
- **past_key_values** (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`) --
  Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
  auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (`&amp;lt;class 'transformers.models.bart.configuration_bart.BartConfig'>`) and inputs.

- **last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
  `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
  encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
  `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
  input) to speed up sequential decoding.
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.
- **cross_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.</retdesc></docstring>







Example:

```python
>>> import jax.numpy as jnp
>>> from transformers import BartTokenizer, FlaxBartForConditionalGeneration

>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)

>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id

>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state
```

</div></div>

<h2 id="transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</h2>

<div class="docstring">

<docstring><name>class transformers.FlaxBartForConditionalGeneration</name><anchor>transformers.FlaxBartForConditionalGeneration</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1330</source><parameters>[{"name": "config", "val": ": BartConfig"}, {"name": "input_shape", "val": ": typing.Tuple[int] = (1, 1)"}, {"name": "seed", "val": ": int = 0"}, {"name": "dtype", "val": ": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
- **dtype** (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`) --
  The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
  `jax.numpy.bfloat16` (on TPUs).

  This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
  specified all the computation will be performed with the given `dtype`.

  **Note that this only specifies the dtype of the computation and does not influence the dtype of model
  parameters.**

  If you wish to change the dtype of the model parameters, see [to_fp16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16) and
  [to_bf16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16).</paramsdesc><paramgroups>0</paramgroups></docstring>
The BART Model with a language modeling head. Can be used for summarization.
This model inherits from [FlaxPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxBartPreTrainedModel.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1179</source><parameters>[{"name": "input_ids", "val": ": ndarray"}, {"name": "attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_input_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "train", "val": ": bool = False"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}]</parameters><paramsdesc>- **input_ids** (`jnp.ndarray` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_input_ids** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)

  For translation and summarization training, `decoder_input_ids` should be provided. If no
  `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
  for denoising pre-training following the paper.
- **decoder_attention_mask** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
  be used by default.

  If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
  paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.
- **decoder_position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
  range `[0, config.max_position_embeddings - 1]`.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) and inputs.

- **logits** (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **past_key_values** (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
- **decoder_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **cross_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.
- **encoder_last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Sequence of hidden-states at the output of the last layer of the encoder of the model.
- **encoder_hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
- **encoder_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.</retdesc></docstring>
The `FlaxBartPreTrainedModel`forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Summarization example:

```python
>>> from transformers import BartTokenizer, FlaxBartForConditionalGeneration

>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")

>>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="np")

>>> # Generate Summary
>>> summary_ids = model.generate(inputs["input_ids"]).sequences
>>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
```

Mask filling example:

```python
>>> import jax
>>> from transformers import BartTokenizer, FlaxBartForConditionalGeneration

>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large")
>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")

>>> TXT = "My friends are &amp;lt;mask> but they eat too many carbs."
>>> input_ids = tokenizer([TXT], return_tensors="jax")["input_ids"]

>>> logits = model(input_ids).logits
>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero()[0].item()
>>> probs = jax.nn.softmax(logits[0, masked_index], axis=0)
>>> values, predictions = jax.lax.top_k(probs, k=1)

>>> tokenizer.decode(predictions).split()
```


</div>
<div class="docstring">
<docstring><name>encode</name><anchor>transformers.FlaxBartPreTrainedModel.encode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1002</source><parameters>[{"name": "input_ids", "val": ": ndarray"}, {"name": "attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "train", "val": ": bool = False"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}]</parameters><paramsdesc>- **input_ids** (`jnp.ndarray` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_flax_outputs.FlaxBaseModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxBaseModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (`&amp;lt;class 'transformers.models.bart.configuration_bart.BartConfig'>`) and inputs.

- **last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>







Example:

```python
>>> from transformers import BartTokenizer, FlaxBartForConditionalGeneration

>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)
```

</div>
<div class="docstring">
<docstring><name>decode</name><anchor>transformers.FlaxBartForConditionalGeneration.decode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1334</source><parameters>[{"name": "decoder_input_ids", "val": ""}, {"name": "encoder_outputs", "val": ""}, {"name": "encoder_attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "past_key_values", "val": ": dict = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "train", "val": ": bool = False"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}]</parameters><paramsdesc>- **decoder_input_ids** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)

  For translation and summarization training, `decoder_input_ids` should be provided. If no
  `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
  for denoising pre-training following the paper.
- **encoder_outputs** (`tuple(tuple(jnp.ndarray)`) --
  Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
  `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
  hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- **encoder_attention_mask** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_attention_mask** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
  be used by default.

  If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
  paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
- **decoder_position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
  range `[0, config.max_position_embeddings - 1]`.
- **past_key_values** (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`) --
  Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
  auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (`&amp;lt;class 'transformers.models.bart.configuration_bart.BartConfig'>`) and inputs.

- **logits** (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.
- **cross_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Cross attentions weights after the attention softmax, used to compute the weighted average in the
  cross-attention heads.
- **past_key_values** (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- Tuple of `jnp.ndarray` tuples of length `config.n_layers`, with each tuple containing the cached key, value
  states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting.
  Only relevant if `config.is_decoder = True`.

  Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
  `past_key_values` input) to speed up sequential decoding.</retdesc></docstring>







Example:

```python
>>> import jax.numpy as jnp
>>> from transformers import BartTokenizer, FlaxBartForConditionalGeneration

>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)

>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id

>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits
```

</div></div>

<h2 id="transformers.FlaxBartForSequenceClassification">FlaxBartForSequenceClassification</h2>

<div class="docstring">

<docstring><name>class transformers.FlaxBartForSequenceClassification</name><anchor>transformers.FlaxBartForSequenceClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1643</source><parameters>[{"name": "config", "val": ": BartConfig"}, {"name": "input_shape", "val": ": typing.Tuple[int] = (1, 1)"}, {"name": "seed", "val": ": int = 0"}, {"name": "dtype", "val": ": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
- **dtype** (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`) --
  The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
  `jax.numpy.bfloat16` (on TPUs).

  This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
  specified all the computation will be performed with the given `dtype`.

  **Note that this only specifies the dtype of the computation and does not influence the dtype of model
  parameters.**

  If you wish to change the dtype of the model parameters, see [to_fp16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16) and
  [to_bf16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16).</paramsdesc><paramgroups>0</paramgroups></docstring>

Bart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
tasks.

This model inherits from [FlaxPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxBartPreTrainedModel.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1179</source><parameters>[{"name": "input_ids", "val": ": ndarray"}, {"name": "attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_input_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "train", "val": ": bool = False"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}]</parameters><paramsdesc>- **input_ids** (`jnp.ndarray` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_input_ids** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)

  For translation and summarization training, `decoder_input_ids` should be provided. If no
  `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
  for denoising pre-training following the paper.
- **decoder_attention_mask** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
  be used by default.

  If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
  paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.
- **decoder_position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
  range `[0, config.max_position_embeddings - 1]`.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) and inputs.

- **logits** (`jnp.ndarray` of shape `(batch_size, config.num_labels)`) -- Classification (or regression if config.num_labels==1) scores (before SoftMax).
- **past_key_values** (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
- **decoder_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **cross_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.
- **encoder_last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Sequence of hidden-states at the output of the last layer of the encoder of the model.
- **encoder_hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
- **encoder_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.</retdesc></docstring>
The `FlaxBartPreTrainedModel`forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> from transformers import BartTokenizer, FlaxBartForSequenceClassification

>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
>>> model = FlaxBartForSequenceClassification.from_pretrained("facebook/bart-base")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")

>>> outputs = model(**inputs)
>>> logits = outputs.logits
```


</div>
<div class="docstring">
<docstring><name>encode</name><anchor>transformers.FlaxBartPreTrainedModel.encode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1002</source><parameters>[{"name": "input_ids", "val": ": ndarray"}, {"name": "attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "train", "val": ": bool = False"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}]</parameters><paramsdesc>- **input_ids** (`jnp.ndarray` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_flax_outputs.FlaxBaseModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxBaseModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (`&amp;lt;class 'transformers.models.bart.configuration_bart.BartConfig'>`) and inputs.

- **last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>







Example:

```python
>>> from transformers import BartTokenizer, FlaxBartForConditionalGeneration

>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)
```

</div>
<div class="docstring">
<docstring><name>decode</name><anchor>transformers.FlaxBartPreTrainedModel.decode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1065</source><parameters>[{"name": "decoder_input_ids", "val": ""}, {"name": "encoder_outputs", "val": ""}, {"name": "encoder_attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "past_key_values", "val": ": dict = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "train", "val": ": bool = False"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}]</parameters><paramsdesc>- **decoder_input_ids** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)

  For translation and summarization training, `decoder_input_ids` should be provided. If no
  `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
  for denoising pre-training following the paper.
- **encoder_outputs** (`tuple(tuple(jnp.ndarray)`) --
  Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
  `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
  hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- **encoder_attention_mask** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_attention_mask** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
  be used by default.

  If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
  paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
- **decoder_position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
  range `[0, config.max_position_embeddings - 1]`.
- **past_key_values** (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`) --
  Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
  auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (`&amp;lt;class 'transformers.models.bart.configuration_bart.BartConfig'>`) and inputs.

- **last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
  `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
  encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
  `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
  input) to speed up sequential decoding.
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.
- **cross_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.</retdesc></docstring>







Example:

```python
>>> import jax.numpy as jnp
>>> from transformers import BartTokenizer, FlaxBartForConditionalGeneration

>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)

>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id

>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state
```

</div></div>

<h2 id="transformers.FlaxBartForQuestionAnswering">FlaxBartForQuestionAnswering</h2>

<div class="docstring">

<docstring><name>class transformers.FlaxBartForQuestionAnswering</name><anchor>transformers.FlaxBartForQuestionAnswering</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1730</source><parameters>[{"name": "config", "val": ": BartConfig"}, {"name": "input_shape", "val": ": typing.Tuple[int] = (1, 1)"}, {"name": "seed", "val": ": int = 0"}, {"name": "dtype", "val": ": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
- **dtype** (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`) --
  The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
  `jax.numpy.bfloat16` (on TPUs).

  This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
  specified all the computation will be performed with the given `dtype`.

  **Note that this only specifies the dtype of the computation and does not influence the dtype of model
  parameters.**

  If you wish to change the dtype of the model parameters, see [to_fp16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16) and
  [to_bf16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16).</paramsdesc><paramgroups>0</paramgroups></docstring>

BART Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layer on top of the hidden-states output to compute `span start logits` and `span end logits`).

This model inherits from [FlaxPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxBartPreTrainedModel.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1179</source><parameters>[{"name": "input_ids", "val": ": ndarray"}, {"name": "attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_input_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "train", "val": ": bool = False"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}]</parameters><paramsdesc>- **input_ids** (`jnp.ndarray` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_input_ids** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)

  For translation and summarization training, `decoder_input_ids` should be provided. If no
  `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
  for denoising pre-training following the paper.
- **decoder_attention_mask** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
  be used by default.

  If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
  paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.
- **decoder_position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
  range `[0, config.max_position_embeddings - 1]`.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([BartConfig](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartConfig)) and inputs.

- **start_logits** (`jnp.ndarray` of shape `(batch_size, sequence_length)`) -- Span-start scores (before SoftMax).
- **end_logits** (`jnp.ndarray` of shape `(batch_size, sequence_length)`) -- Span-end scores (before SoftMax).
- **past_key_values** (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
- **decoder_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **cross_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.
- **encoder_last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Sequence of hidden-states at the output of the last layer of the encoder of the model.
- **encoder_hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
- **encoder_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.</retdesc></docstring>
The `FlaxBartPreTrainedModel`forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> from transformers import BartTokenizer, FlaxBartForQuestionAnswering

>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
>>> model = FlaxBartForQuestionAnswering.from_pretrained("facebook/bart-base")

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="jax")

>>> outputs = model(**inputs)
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logits
```


</div>
<div class="docstring">
<docstring><name>encode</name><anchor>transformers.FlaxBartPreTrainedModel.encode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1002</source><parameters>[{"name": "input_ids", "val": ": ndarray"}, {"name": "attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "train", "val": ": bool = False"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}]</parameters><paramsdesc>- **input_ids** (`jnp.ndarray` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_flax_outputs.FlaxBaseModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxBaseModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (`&amp;lt;class 'transformers.models.bart.configuration_bart.BartConfig'>`) and inputs.

- **last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>







Example:

```python
>>> from transformers import BartTokenizer, FlaxBartForConditionalGeneration

>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)
```

</div>
<div class="docstring">
<docstring><name>decode</name><anchor>transformers.FlaxBartPreTrainedModel.decode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/modeling_flax_bart.py#L1065</source><parameters>[{"name": "decoder_input_ids", "val": ""}, {"name": "encoder_outputs", "val": ""}, {"name": "encoder_attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_attention_mask", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "decoder_position_ids", "val": ": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"}, {"name": "past_key_values", "val": ": dict = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "train", "val": ": bool = False"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}]</parameters><paramsdesc>- **decoder_input_ids** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)

  For translation and summarization training, `decoder_input_ids` should be provided. If no
  `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
  for denoising pre-training following the paper.
- **encoder_outputs** (`tuple(tuple(jnp.ndarray)`) --
  Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
  `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
  hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- **encoder_attention_mask** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_attention_mask** (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
  be used by default.

  If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
  paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
- **decoder_position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
  range `[0, config.max_position_embeddings - 1]`.
- **past_key_values** (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`) --
  Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
  auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (`&amp;lt;class 'transformers.models.bart.configuration_bart.BartConfig'>`) and inputs.

- **last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
  `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
  encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
  `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
  input) to speed up sequential decoding.
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.
- **cross_attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.</retdesc></docstring>







Example:

```python
>>> import jax.numpy as jnp
>>> from transformers import BartTokenizer, FlaxBartForConditionalGeneration

>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)

>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id

>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state
```

</div></div>
