---
local: albert
sections:
- local: overview
  title: Overview
- local: transformers.AlbertConfig
  title: AlbertConfig
- local: transformers.AlbertTokenizer
  title: AlbertTokenizer
- local: transformers.AlbertTokenizerFast
  title: AlbertTokenizerFast
- local: transformers.models.albert.modeling_albert.AlbertForPreTrainingOutput
  title: Albert specific outputs
- local: transformers.AlbertModel
  title: AlbertModel
- local: transformers.AlbertForPreTraining
  title: AlbertForPreTraining
- local: transformers.AlbertForMaskedLM
  title: AlbertForMaskedLM
- local: transformers.AlbertForSequenceClassification
  title: AlbertForSequenceClassification
- local: transformers.AlbertForMultipleChoice
  title: AlbertForMultipleChoice
- local: transformers.AlbertForTokenClassification
  title: AlbertForTokenClassification
- local: transformers.AlbertForQuestionAnswering
  title: AlbertForQuestionAnswering
- local: transformers.TFAlbertModel
  title: TFAlbertModel
- local: transformers.TFAlbertForPreTraining
  title: TFAlbertForPreTraining
- local: transformers.TFAlbertForMaskedLM
  title: TFAlbertForMaskedLM
- local: transformers.TFAlbertForSequenceClassification
  title: TFAlbertForSequenceClassification
- local: transformers.TFAlbertForMultipleChoice
  title: TFAlbertForMultipleChoice
- local: transformers.TFAlbertForTokenClassification
  title: TFAlbertForTokenClassification
- local: transformers.TFAlbertForQuestionAnswering
  title: TFAlbertForQuestionAnswering
- local: transformers.FlaxAlbertModel
  title: FlaxAlbertModel
- local: transformers.FlaxAlbertForPreTraining
  title: FlaxAlbertForPreTraining
- local: transformers.FlaxAlbertForMaskedLM
  title: FlaxAlbertForMaskedLM
- local: transformers.FlaxAlbertForSequenceClassification
  title: FlaxAlbertForSequenceClassification
- local: transformers.FlaxAlbertForMultipleChoice
  title: FlaxAlbertForMultipleChoice
- local: transformers.FlaxAlbertForTokenClassification
  title: FlaxAlbertForTokenClassification
- local: transformers.FlaxAlbertForQuestionAnswering
  title: FlaxAlbertForQuestionAnswering
title: ALBERT
---
<script>
import Tip from "./Tip.svelte";
import Youtube from "./Youtube.svelte";
import Docstring from "./Docstring.svelte";
import CodeBlock from "./CodeBlock.svelte";
import CodeBlockFw from "./CodeBlockFw.svelte";
import DocNotebookDropdown from "./DocNotebookDropdown.svelte";
import IconCopyLink from "./IconCopyLink.svelte";
export let fw: "pt" | "tf"
</script>
<!--Copyright 2020 The HuggingFace Team. All rights reserved.

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

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

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

<h1 id="albert">ALBERT</h1>

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

The ALBERT model was proposed in [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942) by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma,
Radu Soricut. It presents two parameter-reduction techniques to lower memory consumption and increase the training
speed of BERT:

- Splitting the embedding matrix into two smaller matrices.
- Using repeating layers split among groups.

The abstract from the paper is the following:

*Increasing model size when pretraining natural language representations often results in improved performance on
downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations,
longer training times, and unexpected model degradation. To address these problems, we present two parameter-reduction
techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows
that our proposed methods lead to models that scale much better compared to the original BERT. We also use a
self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks
with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and
SQuAD benchmarks while having fewer parameters compared to BERT-large.*

Tips:

- ALBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
  than the left.
- ALBERT uses repeating layers which results in a small memory footprint, however the computational cost remains
  similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same
  number of (repeating) layers.

This model was contributed by [lysandre](https://huggingface.co/lysandre). This model jax version was contributed by
[kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/google-research/ALBERT).

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

<div class="docstring">

<docstring><name>class transformers.AlbertConfig</name><anchor>transformers.AlbertConfig</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/configuration_albert.py#L36</source><parameters>[{"name": "vocab_size", "val": " = 30000"}, {"name": "embedding_size", "val": " = 128"}, {"name": "hidden_size", "val": " = 4096"}, {"name": "num_hidden_layers", "val": " = 12"}, {"name": "num_hidden_groups", "val": " = 1"}, {"name": "num_attention_heads", "val": " = 64"}, {"name": "intermediate_size", "val": " = 16384"}, {"name": "inner_group_num", "val": " = 1"}, {"name": "hidden_act", "val": " = 'gelu_new'"}, {"name": "hidden_dropout_prob", "val": " = 0"}, {"name": "attention_probs_dropout_prob", "val": " = 0"}, {"name": "max_position_embeddings", "val": " = 512"}, {"name": "type_vocab_size", "val": " = 2"}, {"name": "initializer_range", "val": " = 0.02"}, {"name": "layer_norm_eps", "val": " = 1e-12"}, {"name": "classifier_dropout_prob", "val": " = 0.1"}, {"name": "position_embedding_type", "val": " = 'absolute'"}, {"name": "pad_token_id", "val": " = 0"}, {"name": "bos_token_id", "val": " = 2"}, {"name": "eos_token_id", "val": " = 3"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **vocab_size** (`int`, *optional*, defaults to 30000) --
  Vocabulary size of the ALBERT model. Defines the number of different tokens that can be represented by the
  `inputs_ids` passed when calling [AlbertModel](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertModel) or [TFAlbertModel](/docs/transformers/pr_15592/en/model_doc/albert#transformers.TFAlbertModel).
- **embedding_size** (`int`, *optional*, defaults to 128) --
  Dimensionality of vocabulary embeddings.
- **hidden_size** (`int`, *optional*, defaults to 4096) --
  Dimensionality of the encoder layers and the pooler layer.
- **num_hidden_layers** (`int`, *optional*, defaults to 12) --
  Number of hidden layers in the Transformer encoder.
- **num_hidden_groups** (`int`, *optional*, defaults to 1) --
  Number of groups for the hidden layers, parameters in the same group are shared.
- **num_attention_heads** (`int`, *optional*, defaults to 64) --
  Number of attention heads for each attention layer in the Transformer encoder.
- **intermediate_size** (`int`, *optional*, defaults to 16384) --
  The dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
- **inner_group_num** (`int`, *optional*, defaults to 1) --
  The number of inner repetition of attention and ffn.
- **hidden_act** (`str` or `Callable`, *optional*, defaults to `"gelu_new"`) --
  The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
  `"relu"`, `"silu"` and `"gelu_new"` are supported.
- **hidden_dropout_prob** (`float`, *optional*, defaults to 0) --
  The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
- **attention_probs_dropout_prob** (`float`, *optional*, defaults to 0) --
  The dropout ratio for the attention probabilities.
- **max_position_embeddings** (`int`, *optional*, defaults to 512) --
  The maximum sequence length that this model might ever be used with. Typically set this to something large
  (e.g., 512 or 1024 or 2048).
- **type_vocab_size** (`int`, *optional*, defaults to 2) --
  The vocabulary size of the `token_type_ids` passed when calling [AlbertModel](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertModel) or [TFAlbertModel](/docs/transformers/pr_15592/en/model_doc/albert#transformers.TFAlbertModel).
- **initializer_range** (`float`, *optional*, defaults to 0.02) --
  The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- **layer_norm_eps** (`float`, *optional*, defaults to 1e-12) --
  The epsilon used by the layer normalization layers.
- **classifier_dropout_prob** (`float`, *optional*, defaults to 0.1) --
  The dropout ratio for attached classifiers.
- **position_embedding_type** (`str`, *optional*, defaults to `"absolute"`) --
  Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
  positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
  [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
  For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
  with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).</paramsdesc><paramgroups>0</paramgroups></docstring>

This is the configuration class to store the configuration of a [AlbertModel](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertModel) or a [TFAlbertModel](/docs/transformers/pr_15592/en/model_doc/albert#transformers.TFAlbertModel). It is used
to instantiate an ALBERT 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 ALBERT
[xxlarge](https://huggingface.co/albert-xxlarge-v2) 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.



Examples:

```python
>>> from transformers import AlbertConfig, AlbertModel

>>> # Initializing an ALBERT-xxlarge style configuration
>>> albert_xxlarge_configuration = AlbertConfig()

>>> # Initializing an ALBERT-base style configuration
>>> albert_base_configuration = AlbertConfig(
...     hidden_size=768,
...     num_attention_heads=12,
...     intermediate_size=3072,
... )

>>> # Initializing a model from the ALBERT-base style configuration
>>> model = AlbertModel(albert_xxlarge_configuration)

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

</div>

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

<div class="docstring">

<docstring><name>class transformers.AlbertTokenizer</name><anchor>transformers.AlbertTokenizer</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/tokenization_albert.py#L59</source><parameters>[{"name": "vocab_file", "val": ""}, {"name": "do_lower_case", "val": " = True"}, {"name": "remove_space", "val": " = True"}, {"name": "keep_accents", "val": " = False"}, {"name": "bos_token", "val": " = '[CLS]'"}, {"name": "eos_token", "val": " = '[SEP]'"}, {"name": "unk_token", "val": " = '<unk>'"}, {"name": "sep_token", "val": " = '[SEP]'"}, {"name": "pad_token", "val": " = '<pad>'"}, {"name": "cls_token", "val": " = '[CLS]'"}, {"name": "mask_token", "val": " = '[MASK]'"}, {"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.
- **do_lower_case** (`bool`, *optional*, defaults to `True`) --
  Whether or not to lowercase the input when tokenizing.
- **remove_space** (`bool`, *optional*, defaults to `True`) --
  Whether or not to strip the text when tokenizing (removing excess spaces before and after the string).
- **keep_accents** (`bool`, *optional*, defaults to `False`) --
  Whether or not to keep accents when tokenizing.
- **bos_token** (`str`, *optional*, defaults to `"[CLS]"`) --
  The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

  <Tip>

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

  </Tip>

- **eos_token** (`str`, *optional*, defaults to `"[SEP]"`) --
  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.
- **sep_token** (`str`, *optional*, defaults to `"[SEP]"`) --
  The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
  sequence classification or for a text and a question for question answering. It is also used as the last
  token of a sequence built with special tokens.
- **pad_token** (`str`, *optional*, defaults to `"&amp;lt;pad>"`) --
  The token used for padding, for example when batching sequences of different lengths.
- **cls_token** (`str`, *optional*, defaults to `"[CLS]"`) --
  The classifier token which is used when doing sequence classification (classification of the whole sequence
  instead of per-token classification). It is the first token of the sequence when built with special tokens.
- **mask_token** (`str`, *optional*, defaults to `"[MASK]"`) --
  The token used for masking values. This is the token used when training this model with masked language
  modeling. This is the token which the model will try to predict.
- **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 an ALBERT 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.AlbertTokenizer.build_inputs_with_special_tokens</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/tokenization_albert.py#L255</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. An ALBERT sequence has the following format:

- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`








</div>
<div class="docstring">
<docstring><name>get\_special\_tokens\_mask</name><anchor>transformers.AlbertTokenizer.get_special_tokens_mask</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/tokenization_albert.py#L280</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.AlbertTokenizer.create_token_type_ids_from_sequences</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/tokenization_albert.py#L308</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 [token type IDs](../glossary#token-type-ids) according to the given sequence(s).</retdesc></docstring>

Create a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
sequence pair mask has the following format:

```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |
```

If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).








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

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

<div class="docstring">

<docstring><name>class transformers.AlbertTokenizerFast</name><anchor>transformers.AlbertTokenizerFast</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/tokenization_albert_fast.py#L73</source><parameters>[{"name": "vocab_file", "val": " = None"}, {"name": "tokenizer_file", "val": " = None"}, {"name": "do_lower_case", "val": " = True"}, {"name": "remove_space", "val": " = True"}, {"name": "keep_accents", "val": " = False"}, {"name": "bos_token", "val": " = '[CLS]'"}, {"name": "eos_token", "val": " = '[SEP]'"}, {"name": "unk_token", "val": " = '<unk>'"}, {"name": "sep_token", "val": " = '[SEP]'"}, {"name": "pad_token", "val": " = '<pad>'"}, {"name": "cls_token", "val": " = '[CLS]'"}, {"name": "mask_token", "val": " = '[MASK]'"}, {"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.
- **do_lower_case** (`bool`, *optional*, defaults to `True`) --
  Whether or not to lowercase the input when tokenizing.
- **remove_space** (`bool`, *optional*, defaults to `True`) --
  Whether or not to strip the text when tokenizing (removing excess spaces before and after the string).
- **keep_accents** (`bool`, *optional*, defaults to `False`) --
  Whether or not to keep accents when tokenizing.
- **bos_token** (`str`, *optional*, defaults to `"[CLS]"`) --
  The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

  <Tip>

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

  </Tip>

- **eos_token** (`str`, *optional*, defaults to `"[SEP]"`) --
  The end of sequence token. .. note:: 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`.
- **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.
- **sep_token** (`str`, *optional*, defaults to `"[SEP]"`) --
  The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
  sequence classification or for a text and a question for question answering. It is also used as the last
  token of a sequence built with special tokens.
- **pad_token** (`str`, *optional*, defaults to `"&amp;lt;pad>"`) --
  The token used for padding, for example when batching sequences of different lengths.
- **cls_token** (`str`, *optional*, defaults to `"[CLS]"`) --
  The classifier token which is used when doing sequence classification (classification of the whole sequence
  instead of per-token classification). It is the first token of the sequence when built with special tokens.
- **mask_token** (`str`, *optional*, defaults to `"[MASK]"`) --
  The token used for masking values. This is the token used when training this model with masked language
  modeling. This is the token which the model will try to predict.</paramsdesc><paramgroups>0</paramgroups></docstring>

Construct a "fast" ALBERT 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.AlbertTokenizerFast.build_inputs_with_special_tokens</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/tokenization_albert_fast.py#L171</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. An ALBERT sequence has the following format:

- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`








</div>
<div class="docstring">
<docstring><name>create\_token\_type\_ids\_from\_sequences</name><anchor>transformers.AlbertTokenizerFast.create_token_type_ids_from_sequences</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/tokenization_albert_fast.py#L196</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 [token type IDs](../glossary#token-type-ids) according to the given sequence(s).</retdesc></docstring>

Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
sequence pair mask has the following format:

```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |
```

if token_ids_1 is None, only returns the first portion of the mask (0s).








</div></div>

<h2 id="transformers.models.albert.modeling_albert.AlbertForPreTrainingOutput">Albert specific outputs</h2>

<div class="docstring">

<docstring><name>class transformers.models.albert.modeling\_albert.AlbertForPreTrainingOutput</name><anchor>transformers.models.albert.modeling_albert.AlbertForPreTrainingOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_albert.py#L518</source><parameters>[{"name": "loss", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "prediction_logits", "val": ": FloatTensor = None"}, {"name": "sop_logits", "val": ": FloatTensor = None"}, {"name": "hidden_states", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}]</parameters><paramsdesc>- **loss** (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`) --
  Total loss as the sum of the masked language modeling loss and the next sequence prediction
  (classification) loss.
- **prediction_logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --
  Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **sop_logits** (`torch.FloatTensor` of shape `(batch_size, 2)`) --
  Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
  before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) --
  Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

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

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</paramsdesc><paramgroups>0</paramgroups></docstring>

Output type of [AlbertForPreTraining](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertForPreTraining).




</div>

<div class="docstring">

<docstring><name>class transformers.models.albert.modeling\_tf\_albert.TFAlbertForPreTrainingOutput</name><anchor>transformers.models.albert.modeling_tf_albert.TFAlbertForPreTrainingOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_tf_albert.py#L649</source><parameters>[{"name": "loss", "val": ": Tensor = None"}, {"name": "prediction_logits", "val": ": Tensor = None"}, {"name": "sop_logits", "val": ": Tensor = None"}, {"name": "hidden_states", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}, {"name": "attentions", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}]</parameters><paramsdesc>- **prediction_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).
- **sop_logits** (`tf.Tensor` of shape `(batch_size, 2)`) --
  Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
  before SoftMax).
- **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 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.</paramsdesc><paramgroups>0</paramgroups></docstring>

Output type of [TFAlbertForPreTraining](/docs/transformers/pr_15592/en/model_doc/albert#transformers.TFAlbertForPreTraining).




</div>

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

<div class="docstring">

<docstring><name>class transformers.AlbertModel</name><anchor>transformers.AlbertModel</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_albert.py#L621</source><parameters>[{"name": "config", "val": ""}, {"name": "add_pooling_layer", "val": " = True"}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- 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 ALBERT Model transformer outputting raw hidden-states without any specific head on top.

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

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





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.AlbertModel.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_albert.py#L665</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "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.

  Indices can be obtained using [AlbertTokenizer](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertTokenizer). 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)
- **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)
- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **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.BaseModelOutputWithPooling](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.BaseModelOutputWithPooling](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) 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.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **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 [AlbertModel](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertModel) 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 AlbertTokenizer, AlbertModel
>>> import torch

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = AlbertModel.from_pretrained("albert-base-v2")

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

>>> last_hidden_states = outputs.last_hidden_state
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.AlbertForPreTraining</name><anchor>transformers.AlbertForPreTraining</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_albert.py#L751</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- 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>

Albert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
`sentence order prediction (classification)` head.


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.AlbertForPreTraining.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_albert.py#L771</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "labels", "val": " = None"}, {"name": "sentence_order_label", "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.

  Indices can be obtained using [AlbertTokenizer](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertTokenizer). 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)
- **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)
- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

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

- **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
  config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
  loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
- **sentence_order_label** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
  (see `input_ids` docstring) Indices should be in `[0, 1]`. `0` indicates original order (sequence A, then
  sequence B), `1` indicates switched order (sequence B, then sequence A).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.models.albert.modeling_albert.AlbertForPreTrainingOutput](/docs/transformers/pr_15592/en/model_doc/albert#transformers.models.albert.modeling_albert.AlbertForPreTrainingOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.models.albert.modeling_albert.AlbertForPreTrainingOutput](/docs/transformers/pr_15592/en/model_doc/albert#transformers.models.albert.modeling_albert.AlbertForPreTrainingOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **loss** (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`) -- Total loss as the sum of the masked language modeling loss and the next sequence prediction
  (classification) loss.
- **prediction_logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **sop_logits** (`torch.FloatTensor` of shape `(batch_size, 2)`) -- Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
  before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

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

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [AlbertForPreTraining](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertForPreTraining) 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 AlbertTokenizer, AlbertForPreTraining
>>> import torch

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = AlbertForPreTraining.from_pretrained("albert-base-v2")

>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(
...     0
>>> )  # Batch size 1
>>> outputs = model(input_ids)

>>> prediction_logits = outputs.prediction_logits
>>> sop_logits = outputs.sop_logits
```

</div></div>

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

<div class="docstring">

<docstring><name>class transformers.AlbertForMaskedLM</name><anchor>transformers.AlbertForMaskedLM</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_albert.py#L898</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- 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>
Albert Model with a `language modeling` head on top.

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

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





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.AlbertForMaskedLM.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_albert.py#L920</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "labels", "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.

  Indices can be obtained using [AlbertTokenizer](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertTokenizer). 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)
- **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)
- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

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

- **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
  config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
  loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.MaskedLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.MaskedLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Masked language modeling (MLM) 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).
- **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 [AlbertForMaskedLM](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertForMaskedLM) 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 AlbertTokenizer, AlbertForMaskedLM
>>> import torch

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = AlbertForMaskedLM.from_pretrained("albert-base-v2")

>>> inputs = tokenizer("The capital of France is [MASK].", return_tensors="pt")
>>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]

>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.AlbertForSequenceClassification</name><anchor>transformers.AlbertForSequenceClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_albert.py#L987</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- 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>

Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.


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

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





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.AlbertForSequenceClassification.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_albert.py#L1000</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "labels", "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.

  Indices can be obtained using [AlbertTokenizer](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertTokenizer). 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)
- **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)
- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

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

- **labels** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
  config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
  `config.num_labels > 1` a classification loss is computed (Cross-Entropy).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.SequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.SequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Classification (or regression if config.num_labels==1) loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) -- Classification (or regression if config.num_labels==1) scores (before SoftMax).
- **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 [AlbertForSequenceClassification](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertForSequenceClassification) forward method, overrides the `__call__` special method.

<Tip>

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

</Tip>







Example of single-label classification:

```python
>>> from transformers import AlbertTokenizer, AlbertForSequenceClassification
>>> import torch

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = AlbertForSequenceClassification.from_pretrained("albert-base-v2")

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

Example of multi-label classification:

```python
>>> from transformers import AlbertTokenizer, AlbertForSequenceClassification
>>> import torch

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = AlbertForSequenceClassification.from_pretrained("albert-base-v2", problem_type="multi_label_classification")

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


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.AlbertForMultipleChoice</name><anchor>transformers.AlbertForMultipleChoice</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_albert.py#L1277</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- 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>

Albert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.


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

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





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.AlbertForMultipleChoice.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_albert.py#L1288</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "labels", "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, num_choices, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [AlbertTokenizer](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertTokenizer). 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)
- **attention_mask** (`torch.FloatTensor` of shape `(batch_size, num_choices, 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)
- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **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, num_choices, 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.

- **labels** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
  num_choices-1]` where *num_choices* is the size of the second dimension of the input tensors. (see
  *input_ids* above)</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.MultipleChoiceModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.MultipleChoiceModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided) -- Classification loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, num_choices)`) -- *num_choices* is the second dimension of the input tensors. (see *input_ids* above).

  Classification scores (before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

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

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [AlbertForMultipleChoice](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertForMultipleChoice) 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 AlbertTokenizer, AlbertForMultipleChoice
>>> import torch

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = AlbertForMultipleChoice.from_pretrained("albert-base-v2")

>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."
>>> labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1

>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
>>> outputs = model(**&amp;lcub;k: v.unsqueeze(0) for k, v in encoding.items()}, labels=labels)  # batch size is 1

>>> # the linear classifier still needs to be trained
>>> loss = outputs.loss
>>> logits = outputs.logits
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.AlbertForTokenClassification</name><anchor>transformers.AlbertForTokenClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_albert.py#L1087</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- 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>

Albert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.


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

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





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.AlbertForTokenClassification.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_albert.py#L1107</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "labels", "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.

  Indices can be obtained using [AlbertTokenizer](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertTokenizer). 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)
- **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)
- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

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

- **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.TokenClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.TokenClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided)  -- Classification loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`) -- Classification scores (before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

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

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [AlbertForTokenClassification](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertForTokenClassification) 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 AlbertTokenizer, AlbertForTokenClassification
>>> import torch

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = AlbertForTokenClassification.from_pretrained("albert-base-v2")

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

>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.AlbertForQuestionAnswering</name><anchor>transformers.AlbertForQuestionAnswering</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_albert.py#L1174</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- 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>

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


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

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





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.AlbertForQuestionAnswering.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_albert.py#L1188</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "start_positions", "val": " = None"}, {"name": "end_positions", "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.

  Indices can be obtained using [AlbertTokenizer](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertTokenizer). 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)
- **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)
- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

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

- **start_positions** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for position (index) of the start of the labelled span for computing the token classification loss.
  Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
  are not taken into account for computing the loss.
- **end_positions** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for position (index) of the end of the labelled span for computing the token classification loss.
  Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
  are not taken into account for computing the loss.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.QuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.QuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
- **start_logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) -- Span-start scores (before SoftMax).
- **end_logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) -- Span-end scores (before SoftMax).
- **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 [AlbertForQuestionAnswering](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertForQuestionAnswering) 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 AlbertTokenizer, AlbertForQuestionAnswering
>>> import torch

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = AlbertForQuestionAnswering.from_pretrained("albert-base-v2")

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

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


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.TFAlbertModel</name><anchor>transformers.TFAlbertModel</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_tf_albert.py#L776</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- 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 Albert Model transformer outputting raw hidden-states without any specific head on top.

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

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

<Tip>

TF 2.0 models accepts two formats as inputs:

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

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

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

- a single Tensor with `input_ids` only and nothing else: `model(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.TFAlbertModel.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_tf_albert.py#L782</source><parameters>[{"name": "input_ids", "val": ": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"}, {"name": "attention_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "token_type_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "position_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "head_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "inputs_embeds", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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": "training", "val": ": typing.Optional[bool] = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **input_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [AlbertTokenizer](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertTokenizer). 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)
- **attention_mask** (`Numpy array` or `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)
- **token_type_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`Numpy array` or `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**.

- **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.
- **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.TFBaseModelOutputWithPooling](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) 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.
- **pooler_output** (`tf.Tensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) further processed by a
  Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
  prediction (classification) objective during pretraining.

  This output is usually *not* a good summary of the semantic content of the input, you're often better with
  averaging or pooling the sequence of hidden-states for the whole input sequence.
- **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 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 [TFAlbertModel](/docs/transformers/pr_15592/en/model_doc/albert#transformers.TFAlbertModel) 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 AlbertTokenizer, TFAlbertModel
>>> import tensorflow as tf

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = TFAlbertModel.from_pretrained("albert-base-v2")

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

>>> last_hidden_states = outputs.last_hidden_state
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.TFAlbertForPreTraining</name><anchor>transformers.TFAlbertForPreTraining</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_tf_albert.py#L852</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- 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>

Albert Model with two heads on top for pretraining: a `masked language modeling` head and a `sentence order
prediction` (classification) head.


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.TFAlbertForPreTraining.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_tf_albert.py#L868</source><parameters>[{"name": "input_ids", "val": ": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"}, {"name": "attention_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "token_type_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "position_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "head_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "inputs_embeds", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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": "labels", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "sentence_order_label", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "training", "val": ": typing.Optional[bool] = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **input_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [AlbertTokenizer](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertTokenizer). 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)
- **attention_mask** (`Numpy array` or `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)
- **token_type_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`Numpy array` or `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**.

- **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.
- **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.models.albert.modeling_tf_albert.TFAlbertForPreTrainingOutput](/docs/transformers/pr_15592/en/model_doc/albert#transformers.models.albert.modeling_tf_albert.TFAlbertForPreTrainingOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.models.albert.modeling_tf_albert.TFAlbertForPreTrainingOutput](/docs/transformers/pr_15592/en/model_doc/albert#transformers.models.albert.modeling_tf_albert.TFAlbertForPreTrainingOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **prediction_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).
- **sop_logits** (`tf.Tensor` of shape `(batch_size, 2)`) -- Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
  before SoftMax).
- **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 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 [TFAlbertForPreTraining](/docs/transformers/pr_15592/en/model_doc/albert#transformers.TFAlbertForPreTraining) 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
>>> import tensorflow as tf
>>> from transformers import AlbertTokenizer, TFAlbertForPreTraining

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = TFAlbertForPreTraining.from_pretrained("albert-base-v2")

>>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[
...     None, :
>>> ]  # Batch size 1
>>> outputs = model(input_ids)

>>> prediction_logits = outputs.prediction_logits
>>> sop_logits = outputs.sop_logits
```

</div></div>

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

<div class="docstring">

<docstring><name>class transformers.TFAlbertForMaskedLM</name><anchor>transformers.TFAlbertForMaskedLM</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_tf_albert.py#L989</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- 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>
Albert Model with a `language modeling` head on top.

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

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

<Tip>

TF 2.0 models accepts two formats as inputs:

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

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

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

- a single Tensor with `input_ids` only and nothing else: `model(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.TFAlbertForMaskedLM.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_tf_albert.py#L1002</source><parameters>[{"name": "input_ids", "val": ": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"}, {"name": "attention_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "token_type_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "position_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "head_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "inputs_embeds", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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": "labels", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "training", "val": ": typing.Optional[bool] = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **input_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [AlbertTokenizer](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertTokenizer). 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)
- **attention_mask** (`Numpy array` or `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)
- **token_type_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`Numpy array` or `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**.

- **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.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
  config will be used instead.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
  used instead.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple. This argument can be used
  in eager mode, in graph mode the value will always be set to True.
- **training** (`bool`, *optional*, defaults to `False`) --
  Whether or not to use the model in training mode (some modules like dropout modules have different
  behaviors between training and evaluation).

- **labels** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
  config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
  loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_tf_outputs.TFMaskedLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.modeling_tf_outputs.TFMaskedLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **loss** (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided) -- Masked language modeling (MLM) 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).
- **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 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 [TFAlbertForMaskedLM](/docs/transformers/pr_15592/en/model_doc/albert#transformers.TFAlbertForMaskedLM) 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 AlbertTokenizer, TFAlbertForMaskedLM
>>> import tensorflow as tf

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = TFAlbertForMaskedLM.from_pretrained("albert-base-v2")

>>> inputs = tokenizer("The capital of France is [MASK].", return_tensors="tf")
>>> inputs["labels"] = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]

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


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.TFAlbertForSequenceClassification</name><anchor>transformers.TFAlbertForSequenceClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_tf_albert.py#L1093</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- 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>

Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.


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.TFAlbertForSequenceClassification.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_tf_albert.py#L1109</source><parameters>[{"name": "input_ids", "val": ": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"}, {"name": "attention_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "token_type_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "position_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "head_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "inputs_embeds", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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": "labels", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "training", "val": ": typing.Optional[bool] = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **input_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [AlbertTokenizer](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertTokenizer). 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)
- **attention_mask** (`Numpy array` or `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)
- **token_type_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`Numpy array` or `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**.

- **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.
- **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,)`, *optional*) --
  Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
  config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
  `config.num_labels > 1` a classification loss is computed (Cross-Entropy).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_tf_outputs.TFSequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.modeling_tf_outputs.TFSequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **loss** (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `labels` is provided) -- Classification (or regression if config.num_labels==1) loss.
- **logits** (`tf.Tensor` of shape `(batch_size, config.num_labels)`) -- Classification (or regression if config.num_labels==1) scores (before SoftMax).
- **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 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 [TFAlbertForSequenceClassification](/docs/transformers/pr_15592/en/model_doc/albert#transformers.TFAlbertForSequenceClassification) 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 AlbertTokenizer, TFAlbertForSequenceClassification
>>> import tensorflow as tf

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = TFAlbertForSequenceClassification.from_pretrained("albert-base-v2")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> inputs["labels"] = tf.reshape(tf.constant(1), (-1, 1))  # Batch size 1

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


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.TFAlbertForMultipleChoice</name><anchor>transformers.TFAlbertForMultipleChoice</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_tf_albert.py#L1422</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- 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>

Albert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.


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.TFAlbertForMultipleChoice.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_tf_albert.py#L1446</source><parameters>[{"name": "input_ids", "val": ": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"}, {"name": "attention_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "token_type_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "position_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "head_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "inputs_embeds", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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": "labels", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "training", "val": ": typing.Optional[bool] = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **input_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, num_choices, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [AlbertTokenizer](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertTokenizer). 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)
- **attention_mask** (`Numpy array` or `tf.Tensor` of shape `(batch_size, num_choices, 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)
- **token_type_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`Numpy array` or `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**.

- **inputs_embeds** (`tf.Tensor` of shape `(batch_size, num_choices, 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. 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,)`, *optional*) --
  Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
  where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above)</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **loss** (`tf.Tensor` of shape *(batch_size, )*, *optional*, returned when `labels` is provided) -- Classification loss.
- **logits** (`tf.Tensor` of shape `(batch_size, num_choices)`) -- *num_choices* is the second dimension of the input tensors. (see *input_ids* above).

  Classification scores (before SoftMax).
- **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 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 [TFAlbertForMultipleChoice](/docs/transformers/pr_15592/en/model_doc/albert#transformers.TFAlbertForMultipleChoice) 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 AlbertTokenizer, TFAlbertForMultipleChoice
>>> import tensorflow as tf

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = TFAlbertForMultipleChoice.from_pretrained("albert-base-v2")

>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."

>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="tf", padding=True)
>>> inputs = &amp;lcub;k: tf.expand_dims(v, 0) for k, v in encoding.items()}
>>> outputs = model(inputs)  # batch size is 1

>>> # the linear classifier still needs to be trained
>>> logits = outputs.logits
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.TFAlbertForTokenClassification</name><anchor>transformers.TFAlbertForTokenClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_tf_albert.py#L1197</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- 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>

Albert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.


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.TFAlbertForTokenClassification.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_tf_albert.py#L1218</source><parameters>[{"name": "input_ids", "val": ": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"}, {"name": "attention_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "token_type_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "position_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "head_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "inputs_embeds", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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": "labels", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "training", "val": ": typing.Optional[bool] = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **input_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [AlbertTokenizer](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertTokenizer). 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)
- **attention_mask** (`Numpy array` or `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)
- **token_type_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`Numpy array` or `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**.

- **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.
- **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 token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_tf_outputs.TFTokenClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFTokenClassifierOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.modeling_tf_outputs.TFTokenClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFTokenClassifierOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **loss** (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of unmasked labels, returned when `labels` is provided)  -- Classification loss.
- **logits** (`tf.Tensor` of shape `(batch_size, sequence_length, config.num_labels)`) -- Classification scores (before SoftMax).
- **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 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 [TFAlbertForTokenClassification](/docs/transformers/pr_15592/en/model_doc/albert#transformers.TFAlbertForTokenClassification) 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 AlbertTokenizer, TFAlbertForTokenClassification
>>> import tensorflow as tf

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = TFAlbertForTokenClassification.from_pretrained("albert-base-v2")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> input_ids = inputs["input_ids"]
>>> inputs["labels"] = tf.reshape(
...     tf.constant([1] * tf.size(input_ids).numpy()), (-1, tf.size(input_ids))
>>> )  # Batch size 1

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


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.TFAlbertForQuestionAnswering</name><anchor>transformers.TFAlbertForQuestionAnswering</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_tf_albert.py#L1304</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- 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>

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


This model inherits from [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.TFAlbertForQuestionAnswering.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_tf_albert.py#L1318</source><parameters>[{"name": "input_ids", "val": ": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"}, {"name": "attention_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "token_type_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "position_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "head_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "inputs_embeds", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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": "start_positions", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "end_positions", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "training", "val": ": typing.Optional[bool] = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **input_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [AlbertTokenizer](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertTokenizer). 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)
- **attention_mask** (`Numpy array` or `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)
- **token_type_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`Numpy array` or `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**.

- **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.
- **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).

- **start_positions** (`tf.Tensor` of shape `(batch_size,)`, *optional*) --
  Labels for position (index) of the start of the labelled span for computing the token classification loss.
  Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
  are not taken into account for computing the loss.
- **end_positions** (`tf.Tensor` of shape `(batch_size,)`, *optional*) --
  Labels for position (index) of the end of the labelled span for computing the token classification loss.
  Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
  are not taken into account for computing the loss.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **loss** (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `start_positions` and `end_positions` are provided) -- Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
- **start_logits** (`tf.Tensor` of shape `(batch_size, sequence_length)`) -- Span-start scores (before SoftMax).
- **end_logits** (`tf.Tensor` of shape `(batch_size, sequence_length)`) -- Span-end scores (before SoftMax).
- **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 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 [TFAlbertForQuestionAnswering](/docs/transformers/pr_15592/en/model_doc/albert#transformers.TFAlbertForQuestionAnswering) 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 AlbertTokenizer, TFAlbertForQuestionAnswering
>>> import tensorflow as tf

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = TFAlbertForQuestionAnswering.from_pretrained("albert-base-v2")

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> input_dict = tokenizer(question, text, return_tensors="tf")
>>> outputs = model(input_dict)
>>> start_logits = outputs.start_logits
>>> end_logits = outputs.end_logits

>>> all_tokens = tokenizer.convert_ids_to_tokens(input_dict["input_ids"].numpy()[0])
>>> answer = " ".join(all_tokens[tf.math.argmax(start_logits, 1)[0] : tf.math.argmax(end_logits, 1)[0] + 1])
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.FlaxAlbertModel</name><anchor>transformers.FlaxAlbertModel</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_flax_albert.py#L668</source><parameters>[{"name": "config", "val": ": AlbertConfig"}, {"name": "input_shape", "val": ": typing.Tuple = (1, 1)"}, {"name": "seed", "val": ": int = 0"}, {"name": "dtype", "val": ": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
- **dtype** (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`) --
  The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
  `jax.numpy.bfloat16` (on TPUs).

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

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

  If you wish to change the dtype of the model parameters, see [to_fp16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16) and
  [to_bf16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16).</paramsdesc><paramgroups>0</paramgroups></docstring>
The bare Albert Model transformer outputting raw hidden-states without any specific head on top.

This model inherits from [FlaxPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

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

Finally, this model supports inherent JAX features such as:

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





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxAlbertPreTrainedModel.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_flax_albert.py#L545</source><parameters>[{"name": "input_ids", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}, {"name": "train", "val": ": bool = False"}, {"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"}]</parameters><paramsdesc>- **input_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

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

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`numpy.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)
- **token_type_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.
- **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.FlaxBaseModelOutputWithPooling](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) 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.
- **pooler_output** (`jnp.ndarray` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) further processed by a
  Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
  prediction (classification) objective during pretraining.
- **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>
The `FlaxAlbertPreTrainedModel`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 AlbertTokenizer, FlaxAlbertModel

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = FlaxAlbertModel.from_pretrained("albert-base-v2")

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

>>> last_hidden_states = outputs.last_hidden_state
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.FlaxAlbertForPreTraining</name><anchor>transformers.FlaxAlbertForPreTraining</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_flax_albert.py#L739</source><parameters>[{"name": "config", "val": ": AlbertConfig"}, {"name": "input_shape", "val": ": typing.Tuple = (1, 1)"}, {"name": "seed", "val": ": int = 0"}, {"name": "dtype", "val": ": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
- **dtype** (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`) --
  The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
  `jax.numpy.bfloat16` (on TPUs).

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

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

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

Albert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
`sentence order prediction (classification)` head.


This model inherits from [FlaxPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

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

Finally, this model supports inherent JAX features such as:

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





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxAlbertPreTrainedModel.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_flax_albert.py#L545</source><parameters>[{"name": "input_ids", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}, {"name": "train", "val": ": bool = False"}, {"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"}]</parameters><paramsdesc>- **input_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

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

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`numpy.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)
- **token_type_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.
- **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.models.albert.modeling_flax_albert.FlaxAlbertForPreTrainingOutput`or `tuple(torch.FloatTensor)`</rettype><retdesc>A `transformers.models.albert.modeling_flax_albert.FlaxAlbertForPreTrainingOutput`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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **prediction_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).
- **sop_logits** (`jnp.ndarray` of shape `(batch_size, 2)`) -- Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
  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.</retdesc></docstring>
The `FlaxAlbertPreTrainedModel`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 AlbertTokenizer, FlaxAlbertForPreTraining

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = FlaxAlbertForPreTraining.from_pretrained("albert-base-v2")

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

>>> prediction_logits = outputs.prediction_logits
>>> seq_relationship_logits = outputs.sop_logits
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.FlaxAlbertForMaskedLM</name><anchor>transformers.FlaxAlbertForMaskedLM</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_flax_albert.py#L822</source><parameters>[{"name": "config", "val": ": AlbertConfig"}, {"name": "input_shape", "val": ": typing.Tuple = (1, 1)"}, {"name": "seed", "val": ": int = 0"}, {"name": "dtype", "val": ": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
- **dtype** (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`) --
  The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
  `jax.numpy.bfloat16` (on TPUs).

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

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

  If you wish to change the dtype of the model parameters, see [to_fp16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16) and
  [to_bf16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16).</paramsdesc><paramgroups>0</paramgroups></docstring>
Albert Model with a `language modeling` head on top.

This model inherits from [FlaxPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

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

Finally, this model supports inherent JAX features such as:

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





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxAlbertPreTrainedModel.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_flax_albert.py#L545</source><parameters>[{"name": "input_ids", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}, {"name": "train", "val": ": bool = False"}, {"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"}]</parameters><paramsdesc>- **input_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

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

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`numpy.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)
- **token_type_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.
- **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.FlaxMaskedLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxMaskedLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) 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.</retdesc></docstring>
The `FlaxAlbertPreTrainedModel`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 AlbertTokenizer, FlaxAlbertForMaskedLM

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = FlaxAlbertForMaskedLM.from_pretrained("albert-base-v2")

>>> inputs = tokenizer("The capital of France is [MASK].", return_tensors="jax")

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


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.FlaxAlbertForSequenceClassification</name><anchor>transformers.FlaxAlbertForSequenceClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_flax_albert.py#L892</source><parameters>[{"name": "config", "val": ": AlbertConfig"}, {"name": "input_shape", "val": ": typing.Tuple = (1, 1)"}, {"name": "seed", "val": ": int = 0"}, {"name": "dtype", "val": ": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
- **dtype** (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`) --
  The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
  `jax.numpy.bfloat16` (on TPUs).

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

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

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

Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.


This model inherits from [FlaxPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

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

Finally, this model supports inherent JAX features such as:

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





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxAlbertPreTrainedModel.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_flax_albert.py#L545</source><parameters>[{"name": "input_ids", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}, {"name": "train", "val": ": bool = False"}, {"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"}]</parameters><paramsdesc>- **input_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

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

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`numpy.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)
- **token_type_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.
- **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.FlaxSequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **logits** (`jnp.ndarray` of shape `(batch_size, config.num_labels)`) -- Classification (or regression if config.num_labels==1) scores (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.</retdesc></docstring>
The `FlaxAlbertPreTrainedModel`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 AlbertTokenizer, FlaxAlbertForSequenceClassification

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = FlaxAlbertForSequenceClassification.from_pretrained("albert-base-v2")

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

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


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.FlaxAlbertForMultipleChoice</name><anchor>transformers.FlaxAlbertForMultipleChoice</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_flax_albert.py#L966</source><parameters>[{"name": "config", "val": ": AlbertConfig"}, {"name": "input_shape", "val": ": typing.Tuple = (1, 1)"}, {"name": "seed", "val": ": int = 0"}, {"name": "dtype", "val": ": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
- **dtype** (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`) --
  The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
  `jax.numpy.bfloat16` (on TPUs).

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

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

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

Albert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.


This model inherits from [FlaxPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

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

Finally, this model supports inherent JAX features such as:

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





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxAlbertPreTrainedModel.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_flax_albert.py#L545</source><parameters>[{"name": "input_ids", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}, {"name": "train", "val": ": bool = False"}, {"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"}]</parameters><paramsdesc>- **input_ids** (`numpy.ndarray` of shape `(batch_size, num_choices, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

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

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`numpy.ndarray` of shape `(batch_size, num_choices, 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)
- **token_type_ids** (`numpy.ndarray` of shape `(batch_size, num_choices, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`numpy.ndarray` of shape `(batch_size, num_choices, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.
- **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.FlaxMultipleChoiceModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **logits** (`jnp.ndarray` of shape `(batch_size, num_choices)`) -- *num_choices* is the second dimension of the input tensors. (see *input_ids* above).

  Classification scores (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.</retdesc></docstring>
The `FlaxAlbertPreTrainedModel`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 AlbertTokenizer, FlaxAlbertForMultipleChoice

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = FlaxAlbertForMultipleChoice.from_pretrained("albert-base-v2")

>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."

>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="jax", padding=True)
>>> outputs = model(**&amp;lcub;k: v[None, :] for k, v in encoding.items()})

>>> logits = outputs.logits
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.FlaxAlbertForTokenClassification</name><anchor>transformers.FlaxAlbertForTokenClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_flax_albert.py#L1040</source><parameters>[{"name": "config", "val": ": AlbertConfig"}, {"name": "input_shape", "val": ": typing.Tuple = (1, 1)"}, {"name": "seed", "val": ": int = 0"}, {"name": "dtype", "val": ": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
- **dtype** (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`) --
  The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
  `jax.numpy.bfloat16` (on TPUs).

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

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

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

Albert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.


This model inherits from [FlaxPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

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

Finally, this model supports inherent JAX features such as:

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





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxAlbertPreTrainedModel.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_flax_albert.py#L545</source><parameters>[{"name": "input_ids", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}, {"name": "train", "val": ": bool = False"}, {"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"}]</parameters><paramsdesc>- **input_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

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

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`numpy.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)
- **token_type_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.
- **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.FlaxTokenClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxTokenClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **logits** (`jnp.ndarray` of shape `(batch_size, sequence_length, config.num_labels)`) -- Classification scores (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.</retdesc></docstring>
The `FlaxAlbertPreTrainedModel`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 AlbertTokenizer, FlaxAlbertForTokenClassification

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = FlaxAlbertForTokenClassification.from_pretrained("albert-base-v2")

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

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


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.FlaxAlbertForQuestionAnswering</name><anchor>transformers.FlaxAlbertForQuestionAnswering</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_flax_albert.py#L1109</source><parameters>[{"name": "config", "val": ": AlbertConfig"}, {"name": "input_shape", "val": ": typing.Tuple = (1, 1)"}, {"name": "seed", "val": ": int = 0"}, {"name": "dtype", "val": ": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
- **dtype** (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`) --
  The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
  `jax.numpy.bfloat16` (on TPUs).

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

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

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

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


This model inherits from [FlaxPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

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

Finally, this model supports inherent JAX features such as:

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





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxAlbertPreTrainedModel.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/albert/modeling_flax_albert.py#L545</source><parameters>[{"name": "input_ids", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}, {"name": "train", "val": ": bool = False"}, {"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"}]</parameters><paramsdesc>- **input_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

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

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`numpy.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)
- **token_type_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.
- **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.FlaxQuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput) 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 ([AlbertConfig](/docs/transformers/pr_15592/en/model_doc/albert#transformers.AlbertConfig)) and inputs.

- **start_logits** (`jnp.ndarray` of shape `(batch_size, sequence_length)`) -- Span-start scores (before SoftMax).
- **end_logits** (`jnp.ndarray` of shape `(batch_size, sequence_length)`) -- Span-end scores (before SoftMax).
- **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>
The `FlaxAlbertPreTrainedModel`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 AlbertTokenizer, FlaxAlbertForQuestionAnswering

>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = FlaxAlbertForQuestionAnswering.from_pretrained("albert-base-v2")

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

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


</div></div>
