---
local: t5
sections:
- local: overview
  title: Overview
- local: training
  title: Training
- local: inference
  title: Inference
- local: performance
  title: Performance
- local: example-scripts
  title: Example scripts
- local: transformers.T5Config
  title: T5Config
- local: transformers.T5Tokenizer
  title: T5Tokenizer
- local: transformers.T5TokenizerFast
  title: T5TokenizerFast
- local: transformers.T5Model
  title: T5Model
- local: transformers.T5ForConditionalGeneration
  title: T5ForConditionalGeneration
- local: transformers.T5EncoderModel
  title: T5EncoderModel
- local: transformers.TFT5Model
  title: TFT5Model
- local: transformers.TFT5ForConditionalGeneration
  title: TFT5ForConditionalGeneration
- local: transformers.TFT5EncoderModel
  title: TFT5EncoderModel
- local: transformers.FlaxT5Model
  title: FlaxT5Model
- local: transformers.FlaxT5ForConditionalGeneration
  title: FlaxT5ForConditionalGeneration
title: T5
---
<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>
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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

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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="t5">T5</h1>

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

The T5 model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang,
Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.

The abstract from the paper is the following:

*Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream
task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning
has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of
transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a
text-to-text format. Our systematic study compares pretraining objectives, architectures, unlabeled datasets, transfer
approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration
with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering
summarization, question answering, text classification, and more. To facilitate future work on transfer learning for
NLP, we release our dataset, pre-trained models, and code.*

Tips:

- T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which
  each task is converted into a text-to-text format. T5 works well on a variety of tasks out-of-the-box by prepending a
  different prefix to the input corresponding to each task, e.g., for translation: *translate English to German: ...*,
  for summarization: *summarize: ...*.

- T5 uses relative scalar embeddings. Encoder input padding can be done on the left and on the right.

- See the [training](#training), [inference](#inference) and [scripts](#scripts) sections below for all details regarding usage.

T5 comes in different sizes:

- [t5-small](https://huggingface.co/t5-small)

- [t5-base](https://huggingface.co/t5-base)

- [t5-large](https://huggingface.co/t5-large)

- [t5-3b](https://huggingface.co/t5-3b)

- [t5-11b](https://huggingface.co/t5-11b).

Based on the original T5 model, Google has released some follow-up works:

- **T5v1.1**: T5v1.1 is an improved version of T5 with some architectural tweaks, and is pre-trained on C4 only without
  mixing in the supervised tasks. Refer to the documentation of T5v1.1 which can be found [here](t5v1.1).

- **mT5**: mT5 is a multilingual T5 model. It is pre-trained on the mC4 corpus, which includes 101 languages. Refer to
  the documentation of mT5 which can be found [here](mt5).

- **byT5**: byT5 is a T5 model pre-trained on byte sequences rather than SentencePiece subword token sequences. Refer
  to the documentation of byT5 which can be found [here](byt5).

All checkpoints can be found on the [hub](https://huggingface.co/models?search=t5).

This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://github.com/google-research/text-to-text-transfer-transformer).

<a id='training'></a>

<h2 id="training">Training</h2>

T5 is an encoder-decoder model and converts all NLP problems into a text-to-text format. It is trained using teacher
forcing. This means that for training, we always need an input sequence and a corresponding target sequence. The input
sequence is fed to the model using `input_ids`. The target sequence is shifted to the right, i.e., prepended by a
start-sequence token and fed to the decoder using the `decoder_input_ids`. In teacher-forcing style, the target
sequence is then appended by the EOS token and corresponds to the `labels`. The PAD token is hereby used as the
start-sequence token. T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.

One can use [T5ForConditionalGeneration](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5ForConditionalGeneration) (or the Tensorflow/Flax variant), which includes the
language modeling head on top of the decoder.

- Unsupervised denoising training

  In this setup, spans of the input sequence are masked by so-called sentinel tokens (*a.k.a* unique mask tokens) and
  the output sequence is formed as a concatenation of the same sentinel tokens and the *real* masked tokens. Each
  sentinel token represents a unique mask token for this sentence and should start with `&amp;lt;extra_id_0>`,
  `&amp;lt;extra_id_1>`, ... up to `&amp;lt;extra_id_99>`. As a default, 100 sentinel tokens are available in
  [T5Tokenizer](/docs/transformers/pr_15592/en/model_doc/mt5#transformers.T5Tokenizer).

  For instance, the sentence "The cute dog walks in the park" with the masks put on "cute dog" and "the" should be
  processed as follows:

  ```python
  from transformers import T5Tokenizer, T5ForConditionalGeneration

  tokenizer = T5Tokenizer.from_pretrained("t5-small")
  model = T5ForConditionalGeneration.from_pretrained("t5-small")

  input_ids = tokenizer("The &amp;lt;extra_id_0> walks in &amp;lt;extra_id_1> park", return_tensors="pt").input_ids
  labels = tokenizer("&amp;lt;extra_id_0> cute dog &amp;lt;extra_id_1> the &amp;lt;extra_id_2>", return_tensors="pt").input_ids
  # the forward function automatically creates the correct decoder_input_ids
  loss = model(input_ids=input_ids, labels=labels).loss
  ```

  If you're interested in pre-training T5 on a new corpus, check out the [run_t5_mlm_flax.py](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling) script in the Examples
  directory.

- Supervised training

  In this setup, the input sequence and output sequence are a standard sequence-to-sequence input-output mapping.
  Suppose that we want to fine-tune the model for translation for example, and we have a training example: the input
  sequence "The house is wonderful." and output sequence "Das Haus ist wunderbar.", then they should be prepared for
  the model as follows:

  ```python
  from transformers import T5Tokenizer, T5ForConditionalGeneration

  tokenizer = T5Tokenizer.from_pretrained("t5-small")
  model = T5ForConditionalGeneration.from_pretrained("t5-small")

  input_ids = tokenizer("translate English to German: The house is wonderful.", return_tensors="pt").input_ids
  labels = tokenizer("Das Haus ist wunderbar.", return_tensors="pt").input_ids
  # the forward function automatically creates the correct decoder_input_ids
  loss = model(input_ids=input_ids, labels=labels).loss
  ```

  As you can see, only 2 inputs are required for the model in order to compute a loss: `input_ids` (which are the
  `input_ids` of the encoded input sequence) and `labels` (which are the `input_ids` of the encoded
  target sequence). The model will automatically create the `decoder_input_ids` based on the `labels`, by
  shifting them one position to the right and prepending the `config.decoder_start_token_id`, which for T5 is
  equal to 0 (i.e. the id of the pad token). Also note the task prefix: we prepend the input sequence with 'translate
  English to German: ' before encoding it. This will help in improving the performance, as this task prefix was used
  during T5's pre-training.

  However, the example above only shows a single training example. In practice, one trains deep learning models in
  batches. This entails that we must pad/truncate examples to the same length. For encoder-decoder models, one
  typically defines a `max_source_length` and `max_target_length`, which determine the maximum length of the
  input and output sequences respectively (otherwise they are truncated). These should be carefully set depending on
  the task.

  In addition, we must make sure that padding token id's of the `labels` are not taken into account by the loss
  function. In PyTorch and Tensorflow, this can be done by replacing them with -100, which is the `ignore_index`
  of the `CrossEntropyLoss`. In Flax, one can use the `decoder_attention_mask` to ignore padded tokens from
  the loss (see the [Flax summarization script](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization) for details). We also pass
  `attention_mask` as additional input to the model, which makes sure that padding tokens of the inputs are
  ignored. The code example below illustrates all of this.

  ```python
  from transformers import T5Tokenizer, T5ForConditionalGeneration
  import torch

  tokenizer = T5Tokenizer.from_pretrained("t5-small")
  model = T5ForConditionalGeneration.from_pretrained("t5-small")

  # the following 2 hyperparameters are task-specific
  max_source_length = 512
  max_target_length = 128

  # Suppose we have the following 2 training examples:
  input_sequence_1 = "Welcome to NYC"
  output_sequence_1 = "Bienvenue à NYC"

  input_sequence_2 = "HuggingFace is a company"
  output_sequence_2 = "HuggingFace est une entreprise"

  # encode the inputs
  task_prefix = "translate English to French: "
  input_sequences = [input_sequence_1, input_sequence_2]
  encoding = tokenizer(
      [task_prefix + sequence for sequence in input_sequences],
      padding="longest",
      max_length=max_source_length,
      truncation=True,
      return_tensors="pt",
  )
  input_ids, attention_mask = encoding.input_ids, encoding.attention_mask

  # encode the targets
  target_encoding = tokenizer(
      [output_sequence_1, output_sequence_2], padding="longest", max_length=max_target_length, truncation=True
  )
  labels = target_encoding.input_ids

  # replace padding token id's of the labels by -100
  labels = torch.tensor(labels)
  labels[labels == tokenizer.pad_token_id] = -100

  # forward pass
  loss = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels).loss
  ```

Additional training tips:

- T5 models need a slightly higher learning rate than the default one set in the `Trainer` when using the AdamW
  optimizer. Typically, 1e-4 and 3e-4 work well for most problems (classification, summarization, translation, question
  answering, question generation). Note that T5 was pre-trained using the AdaFactor optimizer.

- According to [this forum post](https://discuss.huggingface.co/t/t5-finetuning-tips/684), task prefixes matter when
  (1) doing multi-task training (2) your task is similar or related to one of the supervised tasks used in T5's
  pre-training mixture (see Appendix D of the [paper](https://arxiv.org/pdf/1910.10683.pdf) for the task prefixes
  used).

- If training on TPU, it is recommended to pad all examples of the dataset to the same length or make use of
  *pad_to_multiple_of* to have a small number of predefined bucket sizes to fit all examples in. Dynamically padding
  batches to the longest example is not recommended on TPU as it triggers a recompilation for every batch shape that is
  encountered during training thus significantly slowing down the training. only padding up to the longest example in a
  batch) leads to very slow training on TPU.

<a id='inference'></a>

<h2 id="inference">Inference</h2>

At inference time, it is recommended to use [generate()](/docs/transformers/pr_15592/en/main_classes/model#transformers.generation_utils.GenerationMixin.generate). This
method takes care of encoding the input and feeding the encoded hidden states via cross-attention layers to the decoder
and auto-regressively generates the decoder output. Check out [this blog post](https://huggingface.co/blog/how-to-generate) to know all the details about generating text with Transformers.
There's also [this blog post](https://huggingface.co/blog/encoder-decoder#encoder-decoder) which explains how
generation works in general in encoder-decoder models.

```python
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("t5-small")
model = T5ForConditionalGeneration.from_pretrained("t5-small")

input_ids = tokenizer("translate English to German: The house is wonderful.", return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# Das Haus ist wunderbar.
```

Note that T5 uses the `pad_token_id` as the `decoder_start_token_id`, so when doing generation without using
[generate()](/docs/transformers/pr_15592/en/main_classes/model#transformers.generation_utils.GenerationMixin.generate), make sure you start it with the `pad_token_id`.

The example above only shows a single example. You can also do batched inference, like so:

```python
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("t5-small")
model = T5ForConditionalGeneration.from_pretrained("t5-small")

# when generating, we will use the logits of right-most token to predict the next token
# so the padding should be on the left
tokenizer.padding_side = "left"
tokenizer.pad_token = tokenizer.eos_token  # to avoid an error

task_prefix = "translate English to German: "
sentences = ["The house is wonderful.", "I like to work in NYC."]  # use different length sentences to test batching
inputs = tokenizer([task_prefix + sentence for sentence in sentences], return_tensors="pt", padding=True)

output_sequences = model.generate(
    input_ids=inputs["input_ids"],
    attention_mask=inputs["attention_mask"],
    do_sample=False,  # disable sampling to test if batching affects output
)

print(tokenizer.batch_decode(output_sequences, skip_special_tokens=True))

# ['Das Haus ist wunderbar.', 'Ich arbeite gerne in NYC.']
```

<a id='scripts'></a>

<h2 id="performance">Performance</h2>

If you'd like a faster training and inference performance, install [apex](https://github.com/NVIDIA/apex#quick-start) and then the model will automatically use `apex.normalization.FusedRMSNorm` instead of `T5LayerNorm`. The former uses an optimized fused kernel which is several times faster than the latter.


<h2 id="example-scripts">Example scripts</h2>

T5 is supported by several example scripts, both for pre-training and fine-tuning.

- pre-training: the [run_t5_mlm_flax.py](https://github.com/huggingface/transformers/blob/master/examples/flax/language-modeling/run_t5_mlm_flax.py)
  script allows you to further pre-train T5 or pre-train T5 from scratch on your own data. The [t5_tokenizer_model.py](https://github.com/huggingface/transformers/blob/master/examples/flax/language-modeling/t5_tokenizer_model.py)
  script allows you to further train a T5 tokenizer or train a T5 Tokenizer from scratch on your own data. Note that
  Flax (a neural network library on top of JAX) is particularly useful to train on TPU hardware.

- fine-tuning: T5 is supported by the official summarization scripts ([PyTorch](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization), [Tensorflow](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization), and [Flax](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization)) and translation scripts
  ([PyTorch](https://github.com/huggingface/transformers/tree/master/examples/pytorch/translation) and [Tensorflow](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/translation)). These scripts allow
  you to easily fine-tune T5 on custom data for summarization/translation.

<h2 id="transformers.T5Config">T5Config</h2>

<div class="docstring">

<docstring><name>class transformers.T5Config</name><anchor>transformers.T5Config</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/configuration_t5.py#L34</source><parameters>[{"name": "vocab_size", "val": " = 32128"}, {"name": "d_model", "val": " = 512"}, {"name": "d_kv", "val": " = 64"}, {"name": "d_ff", "val": " = 2048"}, {"name": "num_layers", "val": " = 6"}, {"name": "num_decoder_layers", "val": " = None"}, {"name": "num_heads", "val": " = 8"}, {"name": "relative_attention_num_buckets", "val": " = 32"}, {"name": "dropout_rate", "val": " = 0.1"}, {"name": "layer_norm_epsilon", "val": " = 1e-06"}, {"name": "initializer_factor", "val": " = 1.0"}, {"name": "feed_forward_proj", "val": " = 'relu'"}, {"name": "is_encoder_decoder", "val": " = True"}, {"name": "use_cache", "val": " = True"}, {"name": "pad_token_id", "val": " = 0"}, {"name": "eos_token_id", "val": " = 1"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **vocab_size** (`int`, *optional*, defaults to 32128) --
  Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the
  `inputs_ids` passed when calling [T5Model](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Model) or [TFT5Model](/docs/transformers/pr_15592/en/model_doc/t5#transformers.TFT5Model).
- **d_model** (`int`, *optional*, defaults to 512) --
  Size of the encoder layers and the pooler layer.
- **d_kv** (`int`, *optional*, defaults to 64) --
  Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model //
  num_heads`.
- **d_ff** (`int`, *optional*, defaults to 2048) --
  Size of the intermediate feed forward layer in each `T5Block`.
- **num_layers** (`int`, *optional*, defaults to 6) --
  Number of hidden layers in the Transformer encoder.
- **num_decoder_layers** (`int`, *optional*) --
  Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set.
- **num_heads** (`int`, *optional*, defaults to 8) --
  Number of attention heads for each attention layer in the Transformer encoder.
- **relative_attention_num_buckets** (`int`, *optional*, defaults to 32) --
  The number of buckets to use for each attention layer.
- **dropout_rate** (`float`, *optional*, defaults to 0.1) --
  The ratio for all dropout layers.
- **layer_norm_eps** (`float`, *optional*, defaults to 1e-6) --
  The epsilon used by the layer normalization layers.
- **initializer_factor** (`float`, *optional*, defaults to 1) --
  A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
  testing).
- **feed_forward_proj** (`string`, *optional*, defaults to `"relu"`) --
  Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. T5v1.1 uses the
  `"gated-gelu"` feed forward projection. Original T5 uses `"relu"`.
- **use_cache** (`bool`, *optional*, defaults to `True`) --
  Whether or not the model should return the last key/values attentions (not used by all models).</paramsdesc><paramgroups>0</paramgroups></docstring>

This is the configuration class to store the configuration of a [T5Model](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Model) or a [TFT5Model](/docs/transformers/pr_15592/en/model_doc/t5#transformers.TFT5Model). It is used to
instantiate a T5 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 T5
[t5-small](https://huggingface.co/t5-small) 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.




</div>

<h2 id="transformers.T5Tokenizer">T5Tokenizer</h2>

<div class="docstring">

<docstring><name>class transformers.T5Tokenizer</name><anchor>transformers.T5Tokenizer</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/tokenization_t5.py#L53</source><parameters>[{"name": "vocab_file", "val": ""}, {"name": "eos_token", "val": " = '</s>'"}, {"name": "unk_token", "val": " = '<unk>'"}, {"name": "pad_token", "val": " = '<pad>'"}, {"name": "extra_ids", "val": " = 100"}, {"name": "additional_special_tokens", "val": " = None"}, {"name": "sp_model_kwargs", "val": ": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **vocab_file** (`str`) --
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
- **eos_token** (`str`, *optional*, defaults to `"&amp;lt;/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>

- **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.
- **extra_ids** (`int`, *optional*, defaults to 100) --
  Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
  accessible as "&amp;lt;extra_id_&amp;lcub;%d}>" where "&amp;lcub;%d}" is a number between 0 and extra_ids-1. Extra tokens are
  indexed from the end of the vocabulary up to beginning ("&amp;lt;extra_id_0>" is the last token in the vocabulary
  like in T5 preprocessing see
  [here](https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117)).
- **additional_special_tokens** (`List[str]`, *optional*) --
  Additional special tokens used by the tokenizer.
- **sp_model_kwargs** (`dict`, *optional*) --
  Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
  SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
  to set:

  - `enable_sampling`: Enable subword regularization.
  - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.

    - `nbest_size = &amp;lcub;0,1}`: No sampling is performed.
    - `nbest_size > 1`: samples from the nbest_size results.
    - `nbest_size &amp;lt; 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
      using forward-filtering-and-backward-sampling algorithm.

  - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
    BPE-dropout.
- **sp_model** (`SentencePieceProcessor`) --
  The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).</paramsdesc><paramgroups>0</paramgroups></docstring>

Construct a T5 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).

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.T5Tokenizer.build_inputs_with_special_tokens</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/tokenization_t5.py#L223</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 sequence has the following format:

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








</div>
<div class="docstring">
<docstring><name>get\_special\_tokens\_mask</name><anchor>transformers.T5Tokenizer.get_special_tokens_mask</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/tokenization_t5.py#L163</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 class="docstring">
<docstring><name>create\_token\_type\_ids\_from\_sequences</name><anchor>transformers.T5Tokenizer.create_token_type_ids_from_sequences</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/tokenization_t5.py#L201</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. T5 does not make
use of token type ids, therefore a list of zeros is returned.








</div>
<div class="docstring"></div></div>

<h2 id="transformers.T5TokenizerFast">T5TokenizerFast</h2>

<div class="docstring">

<docstring><name>class transformers.T5TokenizerFast</name><anchor>transformers.T5TokenizerFast</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/tokenization_t5_fast.py#L63</source><parameters>[{"name": "vocab_file", "val": " = None"}, {"name": "tokenizer_file", "val": " = None"}, {"name": "eos_token", "val": " = '</s>'"}, {"name": "unk_token", "val": " = '<unk>'"}, {"name": "pad_token", "val": " = '<pad>'"}, {"name": "extra_ids", "val": " = 100"}, {"name": "additional_special_tokens", "val": " = None"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **vocab_file** (`str`) --
  [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
  contains the vocabulary necessary to instantiate a tokenizer.
- **eos_token** (`str`, *optional*, defaults to `"&amp;lt;/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>

- **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.
- **extra_ids** (`int`, *optional*, defaults to 100) --
  Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
  accessible as "&amp;lt;extra_id_&amp;lcub;%d}>" where "&amp;lcub;%d}" is a number between 0 and extra_ids-1. Extra tokens are
  indexed from the end of the vocabulary up to beginning ("&amp;lt;extra_id_0>" is the last token in the vocabulary
  like in T5 preprocessing see
  [here](https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117)).
- **additional_special_tokens** (`List[str]`, *optional*) --
  Additional special tokens used by the tokenizer.</paramsdesc><paramgroups>0</paramgroups></docstring>

Construct a "fast" T5 tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models).

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>build\_inputs\_with\_special\_tokens</name><anchor>transformers.T5TokenizerFast.build_inputs_with_special_tokens</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/tokenization_t5_fast.py#L166</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 sequence has the following format:

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








</div>
<div class="docstring">
<docstring><name>create\_token\_type\_ids\_from\_sequences</name><anchor>transformers.T5TokenizerFast.create_token_type_ids_from_sequences</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/tokenization_t5_fast.py#L192</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. T5 does not make
use of token type ids, therefore a list of zeros is returned.








</div></div>

<h2 id="transformers.T5Model">T5Model</h2>

<div class="docstring">

<docstring><name>class transformers.T5Model</name><anchor>transformers.T5Model</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_t5.py#L1264</source><parameters>[{"name": "config", "val": ": T5Config"}]</parameters><paramsdesc>- **config** ([T5Config](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Config)) -- 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 T5 Model transformer outputting raw hidden-states without any specific head on top.

The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan
Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a
text-to-text denoising generative setting.

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.T5Model.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_t5.py#L1340</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. T5 is a model with relative position embeddings so you
  should be able to pad the inputs on both the right and the left.

  Indices can be obtained using [T5Tokenizer](/docs/transformers/pr_15592/en/model_doc/mt5#transformers.T5Tokenizer). 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 detail.

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

  To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
- **attention_mask** (`torch.FloatTensor` 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 [T5Tokenizer](/docs/transformers/pr_15592/en/model_doc/mt5#transformers.T5Tokenizer). 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)

  T5 uses the `pad_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`).

  To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5
  Training](./t5#training).
- **decoder_attention_mask** (`torch.BoolTensor` 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.
- **head_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-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.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-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 `(num_heads,)` or `(num_layers, num_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)` 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))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`) --
  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)`.
- **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 ([T5Config](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Config)) 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 [T5Model](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Model) 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 T5Tokenizer, T5Model

>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
>>> model = T5Model.from_pretrained("t5-small")

>>> input_ids = tokenizer(
...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
>>> ).input_ids  # Batch size 1
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1

>>> # forward pass
>>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
>>> last_hidden_states = outputs.last_hidden_state
```

</div>
<div class="docstring">
<docstring><name>parallelize</name><anchor>transformers.T5Model.parallelize</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_t5.py#L1296</source><parameters>[{"name": "device_map", "val": " = None"}]</parameters><paramsdesc>- **device_map** (`Dict[int, list]`, optional, defaults to None) --
  A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always
  automatically mapped to the first device (for esoteric reasons). That means that the first device should
  have fewer attention modules mapped to it than other devices. For reference, the t5 models have the
  following number of attention modules:

  - t5-small: 6
  - t5-base: 12
  - t5-large: 24
  - t5-3b: 24
  - t5-11b: 24</paramsdesc><paramgroups>0</paramgroups></docstring>

This is an experimental feature and is a subject to change at a moment's notice.

Uses a device map to distribute attention modules of the model across several devices. If no device map is given,
it will evenly distribute blocks across all devices.



Example:

```python
# Here is an example of a device map on a machine with 4 GPUs using t5-3b, which has a total of 24 attention modules:
model = T5ForConditionalGeneration.from_pretrained("t5-3b")
device_map = &amp;lcub;
    0: [0, 1, 2],
    1: [3, 4, 5, 6, 7, 8, 9],
    2: [10, 11, 12, 13, 14, 15, 16],
    3: [17, 18, 19, 20, 21, 22, 23],
}
model.parallelize(device_map)
```


</div>
<div class="docstring">
<docstring><name>deparallelize</name><anchor>transformers.T5Model.deparallelize</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_t5.py#L1308</source><parameters>[]</parameters></docstring>

Moves the model to cpu from a model parallel state.

Example:

```python
# On a 4 GPU machine with t5-3b:
model = T5ForConditionalGeneration.from_pretrained("t5-3b")
device_map = &amp;lcub;
    0: [0, 1, 2],
    1: [3, 4, 5, 6, 7, 8, 9],
    2: [10, 11, 12, 13, 14, 15, 16],
    3: [17, 18, 19, 20, 21, 22, 23],
}
model.parallelize(device_map)  # Splits the model across several devices
model.deparallelize()  # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache()
```


</div></div>

<h2 id="transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</h2>

<div class="docstring">

<docstring><name>class transformers.T5ForConditionalGeneration</name><anchor>transformers.T5ForConditionalGeneration</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_t5.py#L1453</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([T5Config](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Config)) -- 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>
T5 Model with a `language modeling` head on top.

The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan
Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a
text-to-text denoising generative setting.

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.T5ForConditionalGeneration.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_t5.py#L1534</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. T5 is a model with relative position embeddings so you
  should be able to pad the inputs on both the right and the left.

  Indices can be obtained using [T5Tokenizer](/docs/transformers/pr_15592/en/model_doc/mt5#transformers.T5Tokenizer). 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 detail.

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

  To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
- **attention_mask** (`torch.FloatTensor` 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 [T5Tokenizer](/docs/transformers/pr_15592/en/model_doc/mt5#transformers.T5Tokenizer). 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)

  T5 uses the `pad_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`).

  To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5
  Training](./t5#training).
- **decoder_attention_mask** (`torch.BoolTensor` 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.
- **head_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-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.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-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 `(num_heads,)` or `(num_layers, num_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)` 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))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`) --
  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)`.
- **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 `[-100, 0, ...,
  config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
  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 ([T5Config](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Config)) 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 [T5ForConditionalGeneration](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5ForConditionalGeneration) 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>







Examples:

```python
>>> from transformers import T5Tokenizer, T5ForConditionalGeneration

>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
>>> model = T5ForConditionalGeneration.from_pretrained("t5-small")

>>> # training
>>> input_ids = tokenizer("The &amp;lt;extra_id_0> walks in &amp;lt;extra_id_1> park", return_tensors="pt").input_ids
>>> labels = tokenizer("&amp;lt;extra_id_0> cute dog &amp;lt;extra_id_1> the &amp;lt;extra_id_2>", return_tensors="pt").input_ids
>>> outputs = model(input_ids=input_ids, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits

>>> # inference
>>> input_ids = tokenizer(
...     "summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
>>> ).input_ids  # Batch size 1
>>> outputs = model.generate(input_ids)
>>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
>>> # studies have shown that owning a dog is good for you.
```

</div>
<div class="docstring">
<docstring><name>parallelize</name><anchor>transformers.T5ForConditionalGeneration.parallelize</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_t5.py#L1490</source><parameters>[{"name": "device_map", "val": " = None"}]</parameters><paramsdesc>- **device_map** (`Dict[int, list]`, optional, defaults to None) --
  A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always
  automatically mapped to the first device (for esoteric reasons). That means that the first device should
  have fewer attention modules mapped to it than other devices. For reference, the t5 models have the
  following number of attention modules:

  - t5-small: 6
  - t5-base: 12
  - t5-large: 24
  - t5-3b: 24
  - t5-11b: 24</paramsdesc><paramgroups>0</paramgroups></docstring>

This is an experimental feature and is a subject to change at a moment's notice.

Uses a device map to distribute attention modules of the model across several devices. If no device map is given,
it will evenly distribute blocks across all devices.



Example:

```python
# Here is an example of a device map on a machine with 4 GPUs using t5-3b, which has a total of 24 attention modules:
model = T5ForConditionalGeneration.from_pretrained("t5-3b")
device_map = &amp;lcub;
    0: [0, 1, 2],
    1: [3, 4, 5, 6, 7, 8, 9],
    2: [10, 11, 12, 13, 14, 15, 16],
    3: [17, 18, 19, 20, 21, 22, 23],
}
model.parallelize(device_map)
```


</div>
<div class="docstring">
<docstring><name>deparallelize</name><anchor>transformers.T5ForConditionalGeneration.deparallelize</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_t5.py#L1503</source><parameters>[]</parameters></docstring>

Moves the model to cpu from a model parallel state.

Example:

```python
# On a 4 GPU machine with t5-3b:
model = T5ForConditionalGeneration.from_pretrained("t5-3b")
device_map = &amp;lcub;
    0: [0, 1, 2],
    1: [3, 4, 5, 6, 7, 8, 9],
    2: [10, 11, 12, 13, 14, 15, 16],
    3: [17, 18, 19, 20, 21, 22, 23],
}
model.parallelize(device_map)  # Splits the model across several devices
model.deparallelize()  # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache()
```


</div></div>

<h2 id="transformers.T5EncoderModel">T5EncoderModel</h2>

<div class="docstring">

<docstring><name>class transformers.T5EncoderModel</name><anchor>transformers.T5EncoderModel</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_t5.py#L1747</source><parameters>[{"name": "config", "val": ": T5Config"}]</parameters><paramsdesc>- **config** ([T5Config](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Config)) -- 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 T5 Model transformer outputting encoder's raw hidden-states without any specific head on top.

The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan
Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a
text-to-text denoising generative setting.

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.T5EncoderModel.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_t5.py#L1805</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "inputs_embeds", "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. T5 is a model with relative position embeddings so you
  should be able to pad the inputs on both the right and the left.

  Indices can be obtained using [T5Tokenizer](/docs/transformers/pr_15592/en/model_doc/mt5#transformers.T5Tokenizer). 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 detail.

  To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
- **attention_mask** (`torch.FloatTensor` 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)
- **head_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

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

- **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.
- **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.BaseModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.BaseModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput) 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 ([T5Config](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Config)) 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 model.
- **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.</retdesc></docstring>
The [T5EncoderModel](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5EncoderModel) 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 T5Tokenizer, T5EncoderModel

>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
>>> model = T5EncoderModel.from_pretrained("t5-small")
>>> input_ids = tokenizer(
...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
>>> ).input_ids  # Batch size 1
>>> outputs = model(input_ids=input_ids)
>>> last_hidden_states = outputs.last_hidden_state
```

</div>
<div class="docstring">
<docstring><name>parallelize</name><anchor>transformers.T5EncoderModel.parallelize</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_t5.py#L1768</source><parameters>[{"name": "device_map", "val": " = None"}]</parameters><paramsdesc>- **device_map** (`Dict[int, list]`, optional, defaults to None) --
  A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always
  automatically mapped to the first device (for esoteric reasons). That means that the first device should
  have fewer attention modules mapped to it than other devices. For reference, the t5 models have the
  following number of attention modules:

  - t5-small: 6
  - t5-base: 12
  - t5-large: 24
  - t5-3b: 24
  - t5-11b: 24</paramsdesc><paramgroups>0</paramgroups></docstring>

This is an experimental feature and is a subject to change at a moment's notice.

Uses a device map to distribute attention modules of the model across several devices. If no device map is given,
it will evenly distribute blocks across all devices.



Example:

```python
# Here is an example of a device map on a machine with 4 GPUs using t5-3b, which has a total of 24 attention modules:
model = T5ForConditionalGeneration.from_pretrained("t5-3b")
device_map = &amp;lcub;
    0: [0, 1, 2],
    1: [3, 4, 5, 6, 7, 8, 9],
    2: [10, 11, 12, 13, 14, 15, 16],
    3: [17, 18, 19, 20, 21, 22, 23],
}
model.parallelize(device_map)
```


</div>
<div class="docstring">
<docstring><name>deparallelize</name><anchor>transformers.T5EncoderModel.deparallelize</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_t5.py#L1779</source><parameters>[]</parameters></docstring>

Moves the model to cpu from a model parallel state.

Example:

```python
# On a 4 GPU machine with t5-3b:
model = T5ForConditionalGeneration.from_pretrained("t5-3b")
device_map = &amp;lcub;
    0: [0, 1, 2],
    1: [3, 4, 5, 6, 7, 8, 9],
    2: [10, 11, 12, 13, 14, 15, 16],
    3: [17, 18, 19, 20, 21, 22, 23],
}
model.parallelize(device_map)  # Splits the model across several devices
model.deparallelize()  # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache()
```


</div></div>

<h2 id="transformers.TFT5Model">TFT5Model</h2>

<div class="docstring">

<docstring><name>class transformers.TFT5Model</name><anchor>transformers.TFT5Model</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_tf_t5.py#L1131</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([T5Config](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Config)) -- 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 T5 Model transformer outputting raw hidden-stateswithout any specific head on top.

The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan
Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a
text-to-text denoising generative setting.

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(inputs_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.TFT5Model.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_tf_t5.py#L1157</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": "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"}, {"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. T5 is a model with relative position embeddings so you
  should be able to pad the inputs on the right or the left.

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

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

  To know more on how to prepare `inputs` for pretraining take a look at [T5 Training](./t5#training).
- **decoder_input_ids** (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Provide for sequence to sequence training. T5 uses the `pad_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`).

  To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5
  Training](./t5#training).
- **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_attention_mask** (`tf.Tensor` 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.
  head_mask -- (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
  Mask to nullify selected heads of the self-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 `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-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(tf.FloatTensor)`, *optional*) --
Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` 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(tf.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`) --
    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)`.
- **inputs_embeds** (`tf.Tensor` 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** (`tf.Tensor` 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*, 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`).
- **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 ([T5Config](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Config)) 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 [TFT5Model](/docs/transformers/pr_15592/en/model_doc/t5#transformers.TFT5Model) 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>







Examples:

```python
>>> from transformers import T5Tokenizer, TFT5Model

>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
>>> model = TFT5Model.from_pretrained("t5-small")

>>> input_ids = tokenizer(
...     "Studies have been shown that owning a dog is good for you", return_tensors="tf"
>>> ).input_ids  # Batch size 1
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="tf").input_ids  # Batch size 1

>>> # forward pass
>>> outputs = model(input_ids, decoder_input_ids=decoder_input_ids)
>>> last_hidden_states = outputs.last_hidden_state
```

</div></div>

<h2 id="transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</h2>

<div class="docstring">

<docstring><name>class transformers.TFT5ForConditionalGeneration</name><anchor>transformers.TFT5ForConditionalGeneration</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_tf_t5.py#L1300</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([T5Config](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Config)) -- 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>
T5 Model with a `language modeling` head on top.

The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan
Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a
text-to-text denoising generative setting.

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(inputs_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.TFT5ForConditionalGeneration.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_tf_t5.py#L1355</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": "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"}, {"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. T5 is a model with relative position embeddings so you
  should be able to pad the inputs on the right or the left.

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

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

  To know more on how to prepare `inputs` for pretraining take a look at [T5 Training](./t5#training).
- **decoder_input_ids** (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Provide for sequence to sequence training. T5 uses the `pad_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`).

  To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5
  Training](./t5#training).
- **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_attention_mask** (`tf.Tensor` 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.
  head_mask -- (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
  Mask to nullify selected heads of the self-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 `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-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(tf.FloatTensor)`, *optional*) --
Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` 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(tf.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`) --
    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)`.
- **inputs_embeds** (`tf.Tensor` 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** (`tf.Tensor` 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*, 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`).
- **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 cross entropy classification loss. Indices should be in `[0, ...,
  config.vocab_size - 1]`.</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 ([T5Config](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Config)) 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 [TFT5ForConditionalGeneration](/docs/transformers/pr_15592/en/model_doc/t5#transformers.TFT5ForConditionalGeneration) 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>







Examples:

```python
>>> from transformers import T5Tokenizer, TFT5ForConditionalGeneration

>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
>>> model = TFT5ForConditionalGeneration.from_pretrained("t5-small")

>>> # training
>>> inputs = tokenizer("The &amp;lt;extra_id_0> walks in &amp;lt;extra_id_1> park", return_tensors="tf").input_ids
>>> labels = tokenizer("&amp;lt;extra_id_0> cute dog &amp;lt;extra_id_1> the &amp;lt;extra_id_2>", return_tensors="tf").input_ids
>>> outputs = model(inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits

>>> # inference
>>> inputs = tokenizer(
...     "summarize: studies have shown that owning a dog is good for you", return_tensors="tf"
>>> ).input_ids  # Batch size 1
>>> outputs = model.generate(inputs)
>>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
>>> # studies have shown that owning a dog is good for you
```

</div></div>

<h2 id="transformers.TFT5EncoderModel">TFT5EncoderModel</h2>

<div class="docstring">

<docstring><name>class transformers.TFT5EncoderModel</name><anchor>transformers.TFT5EncoderModel</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_tf_t5.py#L1612</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([T5Config](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Config)) -- 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 T5 Model transformer outputting encoder's raw hidden-stateswithout any specific head on top.

The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan
Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a
text-to-text denoising generative setting.

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(inputs_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.TFT5EncoderModel.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_tf_t5.py#L1630</source><parameters>[{"name": "input_ids", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "inputs_embeds", "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>- **inputs** (`tf.Tensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
  should be able to pad the inputs on the right or the left.

  Indices can be obtained using [T5Tokenizer](/docs/transformers/pr_15592/en/model_doc/mt5#transformers.T5Tokenizer). See [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) and
  [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) for details.

  To know more on how to prepare `inputs` for pre-training take a look at [T5 Training](./t5#training).
- **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)
- **inputs_embeds** (`tf.Tensor` 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.
  head_mask -- (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **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.
- **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.TFBaseModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.modeling_tf_outputs.TFBaseModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutput) 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 ([T5Config](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Config)) 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 model.
- **hidden_states** (`tuple(tf.FloatTensor)`, *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 model at the output of each layer plus the initial embedding outputs.
- **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 after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [TFT5EncoderModel](/docs/transformers/pr_15592/en/model_doc/t5#transformers.TFT5EncoderModel) 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>







Examples:

```python
>>> from transformers import T5Tokenizer, TFT5EncoderModel

>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
>>> model = TFT5EncoderModel.from_pretrained("t5-small")

>>> input_ids = tokenizer(
...     "Studies have been shown that owning a dog is good for you", return_tensors="tf"
>>> ).input_ids  # Batch size 1
>>> outputs = model(input_ids)
```

</div></div>

<h2 id="transformers.FlaxT5Model">FlaxT5Model</h2>

<div class="docstring">

<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxT5PreTrainedModel.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_flax_t5.py#L952</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": ": ndarray = None"}, {"name": "decoder_attention_mask", "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. T5 is a model with relative position embeddings so you
  should be able to pad the inputs on both the right and the left.

  Indices can be obtained using [T5Tokenizer](/docs/transformers/pr_15592/en/model_doc/mt5#transformers.T5Tokenizer). 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 detail.

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

  To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
- **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 [T5Tokenizer](/docs/transformers/pr_15592/en/model_doc/mt5#transformers.T5Tokenizer). 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)

  T5 uses the `pad_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`).

  To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5
  Training](./t5#training).
- **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.
- **encoder_outputs** (`tuple(tuple(jnp.ndarray)`, *optional*) --
  Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
  `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` 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(jnp.ndarray))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`) --
  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)`.


- **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 ([T5Config](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Config)) 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 `FlaxT5PreTrainedModel`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 T5Tokenizer, FlaxT5Model

>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
>>> model = FlaxT5Model.from_pretrained("t5-small")

>>> input_ids = tokenizer(
...     "Studies have been shown that owning a dog is good for you", return_tensors="np"
>>> ).input_ids
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="np").input_ids

>>> # forward pass
>>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
>>> last_hidden_states = outputs.last_hidden_state
```


</div>
<div class="docstring">
<docstring><name>encode</name><anchor>transformers.FlaxT5PreTrainedModel.encode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_flax_t5.py#L1037</source><parameters>[{"name": "input_ids", "val": ": ndarray"}, {"name": "attention_mask", "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. T5 is a model with relative position embeddings so you
  should be able to pad the inputs on both the right and the left.

  Indices can be obtained using [T5Tokenizer](/docs/transformers/pr_15592/en/model_doc/mt5#transformers.T5Tokenizer). 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 detail.

  To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
- **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)
- **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.t5.configuration_t5.T5Config'>`) 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 T5Tokenizer, FlaxT5ForConditionalGeneration

>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
>>> model = FlaxT5ForConditionalGeneration.from_pretrained("t5-small")

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

</div>
<div class="docstring">
<docstring><name>decode</name><anchor>transformers.FlaxT5PreTrainedModel.decode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_flax_t5.py#L1095</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": "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 [T5Tokenizer](/docs/transformers/pr_15592/en/model_doc/mt5#transformers.T5Tokenizer). 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 training, `decoder_input_ids` should be provided.
- **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.
- **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.t5.configuration_t5.T5Config'>`) 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
>>> from transformers import T5Tokenizer, FlaxT5ForConditionalGeneration
>>> import jax.numpy as jnp

>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
>>> model = FlaxT5ForConditionalGeneration.from_pretrained("t5-small")

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, return_tensors="np")
>>> 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.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</h2>

<div class="docstring">

<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxT5PreTrainedModel.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_flax_t5.py#L952</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": ": ndarray = None"}, {"name": "decoder_attention_mask", "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. T5 is a model with relative position embeddings so you
  should be able to pad the inputs on both the right and the left.

  Indices can be obtained using [T5Tokenizer](/docs/transformers/pr_15592/en/model_doc/mt5#transformers.T5Tokenizer). 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 detail.

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

  To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
- **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 [T5Tokenizer](/docs/transformers/pr_15592/en/model_doc/mt5#transformers.T5Tokenizer). 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)

  T5 uses the `pad_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`).

  To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5
  Training](./t5#training).
- **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.
- **encoder_outputs** (`tuple(tuple(jnp.ndarray)`, *optional*) --
  Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
  `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` 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(jnp.ndarray))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`) --
  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)`.


- **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 ([T5Config](/docs/transformers/pr_15592/en/model_doc/t5#transformers.T5Config)) 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 `FlaxT5PreTrainedModel`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 T5Tokenizer, FlaxT5ForConditionalGeneration

>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
>>> model = FlaxT5ForConditionalGeneration.from_pretrained("t5-small")

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

>>> # Generate Summary
>>> summary_ids = model.generate(inputs["input_ids"]).sequences
>>> print(tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False))
```


</div>
<div class="docstring">
<docstring><name>encode</name><anchor>transformers.FlaxT5PreTrainedModel.encode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_flax_t5.py#L1037</source><parameters>[{"name": "input_ids", "val": ": ndarray"}, {"name": "attention_mask", "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. T5 is a model with relative position embeddings so you
  should be able to pad the inputs on both the right and the left.

  Indices can be obtained using [T5Tokenizer](/docs/transformers/pr_15592/en/model_doc/mt5#transformers.T5Tokenizer). 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 detail.

  To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
- **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)
- **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.t5.configuration_t5.T5Config'>`) 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 T5Tokenizer, FlaxT5ForConditionalGeneration

>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
>>> model = FlaxT5ForConditionalGeneration.from_pretrained("t5-small")

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

</div>
<div class="docstring">
<docstring><name>decode</name><anchor>transformers.FlaxT5ForConditionalGeneration.decode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/t5/modeling_flax_t5.py#L1463</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": "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 [T5Tokenizer](/docs/transformers/pr_15592/en/model_doc/mt5#transformers.T5Tokenizer). 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 training, `decoder_input_ids` should be provided.
- **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.
- **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.t5.configuration_t5.T5Config'>`) 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
>>> from transformers import T5Tokenizer, FlaxT5ForConditionalGeneration
>>> import jax.numpy as jnp

>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
>>> model = FlaxT5ForConditionalGeneration.from_pretrained("t5-small")

>>> text = "summarize: My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, return_tensors="np")
>>> 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>
