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	# BERT

	## Overview

	BERT モデルは、Jacob Devlin、Ming-Wei Chang、Kenton Lee、Kristina Toutanova によって [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://huggingface.co/papers/1810.04805) で提案されました。それは
	マスクされた言語モデリング目標と次の文の組み合わせを使用して事前トレーニングされた双方向トランスフォーマー
	Toronto Book Corpus と Wikipedia からなる大規模なコーパスでの予測。

	論文の要約は次のとおりです。

	*BERT と呼ばれる新しい言語表現モデルを導入します。これは Bidirectional Encoder Representations の略です
	トランスフォーマーより。最近の言語表現モデルとは異なり、BERT は深い双方向性を事前にトレーニングするように設計されています。
	すべてのレイヤーの左と右の両方のコンテキストを共同で条件付けすることにより、ラベルのないテキストから表現します。結果として、
	事前トレーニングされた BERT モデルは、出力層を 1 つ追加するだけで微調整して、最先端のモデルを作成できます。
	実質的なタスク固有のものを必要とせず、質問応答や言語推論などの幅広いタスクに対応
	アーキテクチャの変更。*

	*BERT は概念的にはシンプルですが、経験的に強力です。 11 の自然な要素に関する新しい最先端の結果が得られます。
	言語処理タスク（GLUE スコアを 80.5% に押し上げる（7.7% ポイントの絶対改善）、MultiNLI を含む）
	精度は 86.7% (絶対値 4.6% 向上)、SQuAD v1.1 質問応答テスト F1 は 93.2 (絶対値 1.5 ポイント)
	改善) および SQuAD v2.0 テスト F1 から 83.1 (5.1 ポイントの絶対改善)。*

	## Usage tips

	- BERT は絶対位置埋め込みを備えたモデルであるため、通常は入力を右側にパディングすることをお勧めします。
	左。
	- BERT は、マスク言語モデリング (MLM) および次の文予測 (NSP) の目標を使用してトレーニングされました。それは
	マスクされたトークンの予測や NLU では一般に効率的ですが、テキスト生成には最適ではありません。
	- ランダムマスキングを使用して入力を破壊します。より正確には、事前トレーニング中に、トークンの指定された割合 (通常は 15%) が次によってマスクされます。

	* 確率0.8の特別なマスクトークン
	* 確率 0.1 でマスクされたトークンとは異なるランダムなトークン
	* 確率 0.1 の同じトークン

	- モデルは元の文を予測する必要がありますが、2 番目の目的があります。入力は 2 つの文 A と B (間に分離トークンあり) です。確率 50% では、文はコーパス内で連続していますが、残りの 50% では関連性がありません。モデルは、文が連続しているかどうかを予測する必要があります。

	このモデルは [thomwolf](https://huggingface.co/thomwolf) によって提供されました。元のコードは [こちら](https://github.com/google-research/bert) にあります。

	## Resources

	BERT を始めるのに役立つ公式 Hugging Face およびコミュニティ (🌎 で示される) リソースのリスト。ここに含めるリソースの送信に興味がある場合は、お気軽にプルリクエストを開いてください。審査させていただきます。リソースは、既存のリソースを複製するのではなく、何か新しいものを示すことが理想的です。

	- に関するブログ投稿 [別の言語での BERT テキスト分類](https://www.philschmid.de/bert-text-classification-in-a-different-language)。
	- [マルチラベルテキスト分類のための BERT (およびその友人) の微調整](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/BERT/Fine_tuning_BERT_(and_friends)_for_multi_label_text_classification.ipynb) のノートブック.
	- 方法に関するノートブック [PyTorch を使用したマルチラベル分類のための BERT の微調整](https://colab.research.google.com/github/abhmishra91/transformers-tutorials/blob/master/transformers_multi_label_classification.ipynb)。
	- 方法に関するノートブック [要約のために BERT を使用して EncoderDecoder モデルをウォームスタートする](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb)。
	- [BertForSequenceClassification](/docs/transformers/main/ja/model_doc/bert#transformers.BertForSequenceClassification) は、この [サンプルスクリプト](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification) および [ノートブック](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb)。
	- `TFBertForSequenceClassification` は、この [サンプルスクリプト](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification) および [ノートブック](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb)。
	- `FlaxBertForSequenceClassification` は、この [サンプルスクリプト](https://github.com/huggingface/transformers/tree/main/examples/flax/text-classification) および [ノートブック](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_flax.ipynb)。
	- [テキスト分類タスクガイド(英語版)](../../en/tasks/sequence_classification)

	- [Hugging Face Transformers with Keras: Fine-tune a non-English BERT for Named Entity Recognition](https://www.philschmid.de/huggingface-transformers-keras-tf) の使用方法に関するブログ投稿。
	- 各単語の最初の単語部分のみを使用した [固有表現認識のための BERT の微調整](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/Custom_Named_Entity_Recognition_with_BERT_only_first_wordpiece.ipynb) のノートブックトークン化中の単語ラベル内。単語のラベルをすべての単語部分に伝播するには、代わりにノートブックのこの [バージョン](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/BERT/Custom_Named_Entity_Recognition_with_BERT.ipynb) を参照してください。
	- [BertForTokenClassification](/docs/transformers/main/ja/model_doc/bert#transformers.BertForTokenClassification) は、この [サンプルスクリプト](https://github.com/huggingface/transformers/tree/main/examples/pytorch/token-classification) および [ノートブック](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb)。
	- `TFBertForTokenClassification` は、この [サンプルスクリプト](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/token-classification) および [ノートブック](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb)。
	- `FlaxBertForTokenClassification` は、この [サンプルスクリプト](https://github.com/huggingface/transformers/tree/main/examples/flax/token-classification) によってサポートされています。
	- [トークン分類](https://huggingface.co/course/chapter7/2?fw=pt) 🤗 ハグフェイスコースの章。
	- [トークン分類タスクガイド](../tasks/token_classification)

	- [BertForMaskedLM](/docs/transformers/main/ja/model_doc/bert#transformers.BertForMaskedLM) は、この [サンプルスクリプト](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling#robertabertdistilbert-and-masked-language-modeling) でサポートされており、 [ノートブック](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)。
	- `TFBertForMaskedLM` は、この [サンプルスクリプト](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/lang-modeling#run_mlmpy) および [ノートブック](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb)。
	- `FlaxBertForMaskedLM` は、この [サンプルスクリプト](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling#masked-language-modeling) および [ノートブック]( https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/masked_language_modeling_flax.ipynb)。
	- [マスクされた言語モデリング](https://huggingface.co/course/chapter7/3?fw=pt) 🤗 顔ハグコースの章。
	- [マスクされた言語モデリングタスクガイド](../tasks/masked_lang_modeling)

	- [BertForQuestionAnswering](/docs/transformers/main/ja/model_doc/bert#transformers.BertForQuestionAnswering) は、この [サンプルスクリプト](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering) および [ノートブック](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb)。
	- `TFBertForQuestionAnswering` は、この [サンプルスクリプト](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering) および [ノートブック](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb)。
	- `FlaxBertForQuestionAnswering` は、この [サンプルスクリプト](https://github.com/huggingface/transformers/tree/main/examples/flax/question-answering) でサポートされています。
	- [質問回答](https://huggingface.co/course/chapter7/7?fw=pt) 🤗 ハグフェイスコースの章。
	- [質問回答タスクガイド](../tasks/question_answering)

	複数の選択肢
	- [BertForMultipleChoice](/docs/transformers/main/ja/model_doc/bert#transformers.BertForMultipleChoice) は、この [サンプルスクリプト](https://github.com/huggingface/transformers/tree/main/examples/pytorch/multiple-choice) および [ノートブック](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb)。
	- `TFBertForMultipleChoice` は、この [サンプルスクリプト](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/multiple-choice) および [ノートブック](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb)。
	- [多肢選択タスクガイド](../tasks/multiple_choice)

	⚡️ 推論
	- 方法に関するブログ投稿 [Hugging Face Transformers と AWS Inferentia を使用して BERT 推論を高速化する](https://huggingface.co/blog/bert-inferentia-sagemaker)。
	- 方法に関するブログ投稿 [GPU 上の DeepSpeed-Inference を使用して BERT 推論を高速化する](https://www.philschmid.de/bert-deepspeed-inference)。

	⚙️ 事前トレーニング
	- [Hugging Face Transformers と Habana Gaudi を使用した BERT の事前トレーニングに関するブログ投稿](https://www.philschmid.de/pre-training-bert-habana)。

	🚀 デプロイ
	- 方法に関するブログ投稿 [ハグフェイス最適化でトランスフォーマーを ONNX に変換する](https://www.philschmid.de/convert-transformers-to-onnx)。
	- 方法に関するブログ投稿 [AWS 上の Habana Gaudi を使用したハグ顔トランスフォーマーのための深層学習環境のセットアップ](https://www.philschmid.de/getting-started-habana-gaudi#conclusion)。
	- に関するブログ投稿 [Hugging Face Transformers、Amazon SageMaker、および Terraform モジュールを使用した自動スケーリング BERT](https://www.philschmid.de/terraform-huggingface-amazon-sagemaker-advanced)。
	- に関するブログ投稿 [HuggingFace、AWS Lambda、Docker を使用したサーバーレス BERT](https://www.philschmid.de/serverless-bert-with-huggingface-aws-lambda-docker)。
	- に関するブログ投稿 [Amazon SageMaker と Training Compiler を使用した Hugging Face Transformers BERT 微調整](https://www.philschmid.de/huggingface-amazon-sagemaker-training-compiler)。
	- に関するブログ投稿 [Transformers と Amazon SageMaker を使用した BERT のタスク固有の知識の蒸留](https://www.philschmid.de/knowledge-distillation-bert-transformers)

	## BertConfig[[transformers.BertConfig]]

	#### transformers.BertConfig[[transformers.BertConfig]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/configuration_bert.py#L25)

	This is the configuration class to store the configuration of a BertModel. It is used to instantiate a Bert
	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 [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased)

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

	Examples:

	```python
	>>> from transformers import BertConfig, BertModel

	>>> # Initializing a BERT google-bert/bert-base-uncased style configuration
	>>> configuration = BertConfig()

	>>> # Initializing a model (with random weights) from the google-bert/bert-base-uncased style configuration
	>>> model = BertModel(configuration)

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

	Parameters:

	vocab_size (`int`, optional, defaults to `30522`) : Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids`.

	hidden_size (`int`, optional, defaults to `768`) : Dimension of the hidden representations.

	num_hidden_layers (`int`, optional, defaults to `12`) : Number of hidden layers in the Transformer decoder.

	num_attention_heads (`int`, optional, defaults to `12`) : Number of attention heads for each attention layer in the Transformer decoder.

	intermediate_size (`int`, optional, defaults to `3072`) : Dimension of the MLP representations.

	hidden_act (`str`, optional, defaults to `gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc.

	hidden_dropout_prob (`Union[float, int]`, optional, defaults to `0.1`) : The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

	attention_probs_dropout_prob (`Union[float, int]`, optional, defaults to `0.1`) : 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.

	type_vocab_size (`int`, optional, defaults to `2`) : The vocabulary size of the `token_type_ids`.

	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.

	pad_token_id (`int`, optional, defaults to `0`) : Token id used for padding in the vocabulary.

	use_cache (`bool`, optional, defaults to `True`) : Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True` or when the model is a decoder-only generative model.

	classifier_dropout (`Union[float, int]`, optional) : The dropout ratio for classifier.

	is_decoder (`bool`, optional, defaults to `False`) : Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.

	add_cross_attention (`bool`, optional, defaults to `False`) : Whether cross-attention layers should be added to the model.

	bos_token_id (`int`, optional) : Token id used for beginning-of-stream in the vocabulary.

	eos_token_id (`Union[int, list[int]]`, optional) : Token id used for end-of-stream in the vocabulary.

	tie_word_embeddings (`bool`, optional, defaults to `True`) : Whether to tie weight embeddings according to model's `tied_weights_keys` mapping.

	## BertTokenizer[[transformers.BertTokenizer]]

	#### transformers.BertTokenizer[[transformers.BertTokenizer]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/tokenization_bert.py#L41)

	Construct a BERT tokenizer (backed by HuggingFace's tokenizers library). Based on WordPiece.

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

	get_special_tokens_masktransformers.BertTokenizer.get_special_tokens_maskhttps://github.com/huggingface/transformers/blob/main/src/transformers/tokenization_utils_base.py#L1318[{"name": "token_ids_0", "val": ": list[int]"}, {"name": "token_ids_1", "val": ": list[int] \| None = None"}, {"name": "already_has_special_tokens", "val": ": bool = False"}]- token_ids_0 -- List of IDs for the (possibly already formatted) sequence.
	- token_ids_1 -- Unused when `already_has_special_tokens=True`. Must be None in that case.
	- already_has_special_tokens -- Whether the sequence is already formatted with special tokens.0A list of integers in the range [0, 1]1 for a special token, 0 for a sequence token.

	Retrieve sequence ids from a token list that has no special tokens added.

	For fast tokenizers, data collators call this with `already_has_special_tokens=True` to build a mask over an
	already-formatted sequence. In that case, we compute the mask by checking membership in `all_special_ids`.

	Parameters:

	vocab (`str` or `dict[str, int]`, optional) : Custom vocabulary dictionary. If not provided, vocabulary is loaded from `vocab_file`.

	do_lower_case (`bool`, optional, defaults to `True`) : Whether or not to lowercase the input when tokenizing.

	unk_token (`str`, optional, defaults to `"[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 `"[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.

	tokenize_chinese_chars (`bool`, optional, defaults to `True`) : Whether or not to tokenize Chinese characters.

	strip_accents (`bool`, optional) : Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for `lowercase` (as in the original BERT).

	Returns:

	`A list of integers in the range [0, 1]`

	1 for a special token, 0 for a sequence token.
	#### save_vocabulary[[transformers.BertTokenizer.save_vocabulary]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/tokenization_utils_tokenizers.py#L509)

	## BertTokenizerLegacy[[transformers.BertTokenizerLegacy]]

	#### transformers.BertTokenizerLegacy[[transformers.BertTokenizerLegacy]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/tokenization_bert_legacy.py#L49)

	Construct a BERT tokenizer. Based on WordPiece.

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

	build_inputs_with_special_tokenstransformers.BertTokenizerLegacy.build_inputs_with_special_tokenshttps://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/tokenization_bert_legacy.py#L184[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": list[int] \| None = None"}]- 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.0`List[int]`List of [input IDs](../glossary#input-ids) with the appropriate special tokens.

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

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

	Parameters:

	vocab_file (`str`) : File containing the vocabulary.

	do_lower_case (`bool`, optional, defaults to `True`) : Whether or not to lowercase the input when tokenizing.

	do_basic_tokenize (`bool`, optional, defaults to `True`) : Whether or not to do basic tokenization before WordPiece.

	never_split (`Iterable`, optional) : Collection of tokens which will never be split during tokenization. Only has an effect when `do_basic_tokenize=True`

	unk_token (`str`, optional, defaults to `"[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 `"[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.

	tokenize_chinese_chars (`bool`, optional, defaults to `True`) : Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this [issue](https://github.com/huggingface/transformers/issues/328)).

	strip_accents (`bool`, optional) : Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for `lowercase` (as in the original BERT).

	clean_up_tokenization_spaces (`bool`, optional, defaults to `True`) : Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra spaces.

	Returns:

	``List[int]``

	List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
	#### convert_tokens_to_string[[transformers.BertTokenizerLegacy.convert_tokens_to_string]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/tokenization_bert_legacy.py#L179)

	Converts a sequence of tokens (string) in a single string.
	#### get_special_tokens_mask[[transformers.BertTokenizerLegacy.get_special_tokens_mask]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/tokenization_bert_legacy.py#L209)

	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.

	Parameters:

	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.

	Returns:

	``List[int]``

	A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.

	## BertTokenizerFast[[transformers.BertTokenizer]]

	#### transformers.BertTokenizer[[transformers.BertTokenizer]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/tokenization_bert.py#L41)

	Construct a BERT tokenizer (backed by HuggingFace's tokenizers library). Based on WordPiece.

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

	Parameters:

	vocab (`str` or `dict[str, int]`, optional) : Custom vocabulary dictionary. If not provided, vocabulary is loaded from `vocab_file`.

	do_lower_case (`bool`, optional, defaults to `True`) : Whether or not to lowercase the input when tokenizing.

	unk_token (`str`, optional, defaults to `"[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 `"[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.

	tokenize_chinese_chars (`bool`, optional, defaults to `True`) : Whether or not to tokenize Chinese characters.

	strip_accents (`bool`, optional) : Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for `lowercase` (as in the original BERT).

	## Bert specific outputs[[transformers.models.bert.modeling_bert.BertForPreTrainingOutput]]

	#### transformers.models.bert.modeling_bert.BertForPreTrainingOutput[[transformers.models.bert.modeling_bert.BertForPreTrainingOutput]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L567)

	Output type of [BertForPreTraining](/docs/transformers/main/ja/model_doc/bert#transformers.BertForPreTraining).

	Parameters:

	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).

	seq_relationship_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, if the model has an embedding layer, + 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 optional 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.

	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).

	seq_relationship_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, if the model has an embedding layer, + 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 optional 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.

	## BertModel[[transformers.BertModel]]

	#### transformers.BertModel[[transformers.BertModel]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L598)

	The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
	cross-attention is added between the self-attention layers, following the architecture described in [Attention is
	all you need](https://huggingface.co/papers/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
	Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.

	To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
	to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
	`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.

	This model inherits from [PreTrainedModel](/docs/transformers/main/ja/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.

	forwardtransformers.BertModel.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L624[{"name": "input_ids", "val": ": torch.Tensor \| None = None"}, {"name": "attention_mask", "val": ": torch.Tensor \| None = None"}, {"name": "token_type_ids", "val": ": torch.Tensor \| None = None"}, {"name": "position_ids", "val": ": torch.Tensor \| None = None"}, {"name": "inputs_embeds", "val": ": torch.Tensor \| None = None"}, {"name": "encoder_hidden_states", "val": ": torch.Tensor \| None = None"}, {"name": "encoder_attention_mask", "val": ": torch.Tensor \| None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache \| None = None"}, {"name": "use_cache", "val": ": bool \| None = None"}, {"name": "kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) --
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

	Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
	[PreTrainedTokenizer.__call__()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

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

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

	[What are attention masks?](../glossary#attention-mask)
	- token_type_ids (`torch.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 (`torch.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.n_positions - 1]`.

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

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.
	- past_key_values (`~cache_utils.Cache`, optional) --
	Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
	blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
	returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

	Only `Cache` instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
	If no `past_key_values` are passed, `DynamicCache` will be initialized by default.

	The model will output the same cache format that is fed as input.

	If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't
	have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids`
	of shape `(batch_size, sequence_length)`.
	- use_cache (`bool`, optional) --
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
	`past_key_values`).0[BaseModelOutputWithPoolingAndCrossAttentions](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPoolingAndCrossAttentions](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions) 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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.
	The [BertModel](/docs/transformers/main/ja/model_doc/bert#transformers.BertModel) forward method, overrides the `__call__` special method.

	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.

	- 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, if the model has an embedding layer, +
	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 optional initial embedding outputs.
	- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
	sequence_length)`.

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

	Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
	weighted average in the cross-attention heads.
	- past_key_values (`Cache`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a `Cache` instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

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

	Parameters:

	config ([BertModel](/docs/transformers/main/ja/model_doc/bert#transformers.BertModel)) : 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/main/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

	add_pooling_layer (`bool`, optional, defaults to `True`) : Whether to add a pooling layer

	Returns:

	`[BaseModelOutputWithPoolingAndCrossAttentions](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions) or `tuple(torch.FloatTensor)``

	A [BaseModelOutputWithPoolingAndCrossAttentions](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions) 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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.

	## BertForPreTraining[[transformers.BertForPreTraining]]

	#### transformers.BertForPreTraining[[transformers.BertForPreTraining]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L731)

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

	This model inherits from [PreTrainedModel](/docs/transformers/main/ja/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.

	forwardtransformers.BertForPreTraining.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L753[{"name": "input_ids", "val": ": torch.Tensor \| None = None"}, {"name": "attention_mask", "val": ": torch.Tensor \| None = None"}, {"name": "token_type_ids", "val": ": torch.Tensor \| None = None"}, {"name": "position_ids", "val": ": torch.Tensor \| None = None"}, {"name": "inputs_embeds", "val": ": torch.Tensor \| None = None"}, {"name": "labels", "val": ": torch.Tensor \| None = None"}, {"name": "next_sentence_label", "val": ": torch.Tensor \| None = None"}, {"name": "kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) --
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

	Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
	[PreTrainedTokenizer.__call__()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

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

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

	[What are attention masks?](../glossary#attention-mask)
	- token_type_ids (`torch.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 (`torch.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.n_positions - 1]`.

	[What are position IDs?](../glossary#position-ids)
	- inputs_embeds (`torch.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.
	- 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]`
	- next_sentence_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 sequence B is a continuation of sequence A,
	- 1 indicates sequence B is a random sequence.0[BertForPreTrainingOutput](/docs/transformers/main/ja/model_doc/bert#transformers.models.bert.modeling_bert.BertForPreTrainingOutput) or `tuple(torch.FloatTensor)`A [BertForPreTrainingOutput](/docs/transformers/main/ja/model_doc/bert#transformers.models.bert.modeling_bert.BertForPreTrainingOutput) 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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.
	The [BertForPreTraining](/docs/transformers/main/ja/model_doc/bert#transformers.BertForPreTraining) forward method, overrides the `__call__` special method.

	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.

	- 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).
	- seq_relationship_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, if the model has an embedding layer, +
	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 optional 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.

	Example:

	```python
	>>> from transformers import AutoTokenizer, BertForPreTraining
	>>> import torch

	>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
	>>> model = BertForPreTraining.from_pretrained("google-bert/bert-base-uncased")

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

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

	Parameters:

	config ([BertForPreTraining](/docs/transformers/main/ja/model_doc/bert#transformers.BertForPreTraining)) : 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/main/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

	Returns:

	`[BertForPreTrainingOutput](/docs/transformers/main/ja/model_doc/bert#transformers.models.bert.modeling_bert.BertForPreTrainingOutput) or `tuple(torch.FloatTensor)``

	A [BertForPreTrainingOutput](/docs/transformers/main/ja/model_doc/bert#transformers.models.bert.modeling_bert.BertForPreTrainingOutput) 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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.

	## BertLMHeadModel[[transformers.BertLMHeadModel]]

	#### transformers.BertLMHeadModel[[transformers.BertLMHeadModel]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L828)

	Bert Model with a `language modeling` head on top for CLM fine-tuning.

	This model inherits from [PreTrainedModel](/docs/transformers/main/ja/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.

	forwardtransformers.BertLMHeadModel.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L853[{"name": "input_ids", "val": ": torch.Tensor \| None = None"}, {"name": "attention_mask", "val": ": torch.Tensor \| None = None"}, {"name": "token_type_ids", "val": ": torch.Tensor \| None = None"}, {"name": "position_ids", "val": ": torch.Tensor \| None = None"}, {"name": "inputs_embeds", "val": ": torch.Tensor \| None = None"}, {"name": "encoder_hidden_states", "val": ": torch.Tensor \| None = None"}, {"name": "encoder_attention_mask", "val": ": torch.Tensor \| None = None"}, {"name": "labels", "val": ": torch.Tensor \| None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache \| None = None"}, {"name": "use_cache", "val": ": bool \| None = None"}, {"name": "logits_to_keep", "val": ": int \| torch.Tensor = 0"}, {"name": "kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) --
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

	Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
	[PreTrainedTokenizer.__call__()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

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

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

	[What are attention masks?](../glossary#attention-mask)
	- token_type_ids (`torch.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 (`torch.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.n_positions - 1]`.

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

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.
	- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) --
	Labels for computing the left-to-right language modeling loss (next word prediction). 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 n `[0, ..., config.vocab_size]`
	- past_key_values (`~cache_utils.Cache`, optional) --
	Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
	blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
	returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

	Only `Cache` instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
	If no `past_key_values` are passed, `DynamicCache` will be initialized by default.

	The model will output the same cache format that is fed as input.

	If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't
	have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids`
	of shape `(batch_size, sequence_length)`.
	- use_cache (`bool`, optional) --
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
	`past_key_values`).
	- logits_to_keep (`Union[int, torch.Tensor]`, optional, defaults to `0`) --
	If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
	`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
	token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
	If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
	This is useful when using packed tensor format (single dimension for batch and sequence length).0[CausalLMOutputWithCrossAttentions](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`A [CausalLMOutputWithCrossAttentions](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or a tuple of
	`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
	elements depending on the configuration ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.
	The [BertLMHeadModel](/docs/transformers/main/ja/model_doc/bert#transformers.BertLMHeadModel) forward method, overrides the `__call__` special method.

	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.

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

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

	Cross attentions weights after the attention softmax, used to compute the weighted average in the
	cross-attention heads.
	- past_key_values (`Cache`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a `Cache` instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

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

	Example:

	```python
	>>> import torch
	>>> from transformers import AutoTokenizer, BertLMHeadModel

	>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
	>>> model = BertLMHeadModel.from_pretrained("google-bert/bert-base-uncased")

	>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
	>>> outputs = model(**inputs, labels=inputs["input_ids"])
	>>> loss = outputs.loss
	>>> logits = outputs.logits
	```

	Parameters:

	config ([BertLMHeadModel](/docs/transformers/main/ja/model_doc/bert#transformers.BertLMHeadModel)) : 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/main/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

	Returns:

	`[CausalLMOutputWithCrossAttentions](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or `tuple(torch.FloatTensor)``

	A [CausalLMOutputWithCrossAttentions](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or a tuple of
	`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
	elements depending on the configuration ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.

	## BertForMaskedLM[[transformers.BertForMaskedLM]]

	#### transformers.BertForMaskedLM[[transformers.BertForMaskedLM]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L913)

	The Bert Model with a `language modeling` head on top."

	This model inherits from [PreTrainedModel](/docs/transformers/main/ja/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.

	forwardtransformers.BertForMaskedLM.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L941[{"name": "input_ids", "val": ": torch.Tensor \| None = None"}, {"name": "attention_mask", "val": ": torch.Tensor \| None = None"}, {"name": "token_type_ids", "val": ": torch.Tensor \| None = None"}, {"name": "position_ids", "val": ": torch.Tensor \| None = None"}, {"name": "inputs_embeds", "val": ": torch.Tensor \| None = None"}, {"name": "encoder_hidden_states", "val": ": torch.Tensor \| None = None"}, {"name": "encoder_attention_mask", "val": ": torch.Tensor \| None = None"}, {"name": "labels", "val": ": torch.Tensor \| None = None"}, {"name": "kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) --
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

	Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
	[PreTrainedTokenizer.__call__()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

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

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

	[What are attention masks?](../glossary#attention-mask)
	- token_type_ids (`torch.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 (`torch.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.n_positions - 1]`.

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

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.
	- 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]`0[MaskedLMOutput](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.MaskedLMOutput) or `tuple(torch.FloatTensor)`A [MaskedLMOutput](/docs/transformers/main/ja/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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.
	The [BertForMaskedLM](/docs/transformers/main/ja/model_doc/bert#transformers.BertForMaskedLM) forward method, overrides the `__call__` special method.

	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.

	- 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, if the model has an embedding layer, +
	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 optional 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.

	Example:

	```python
	>>> from transformers import AutoTokenizer, BertForMaskedLM
	>>> import torch

	>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
	>>> model = BertForMaskedLM.from_pretrained("google-bert/bert-base-uncased")

	>>> inputs = tokenizer("The capital of France is .", return_tensors="pt")

	>>> with torch.no_grad():
	... logits = model(**inputs).logits

	>>> # retrieve index of
	>>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]

	>>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
	>>> tokenizer.decode(predicted_token_id)
	...

	>>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
	>>> # mask labels of non- tokens
	>>> labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)

	>>> outputs = model(**inputs, labels=labels)
	>>> round(outputs.loss.item(), 2)
	...
	```

	Parameters:

	config ([BertForMaskedLM](/docs/transformers/main/ja/model_doc/bert#transformers.BertForMaskedLM)) : 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/main/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

	Returns:

	`[MaskedLMOutput](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.MaskedLMOutput) or `tuple(torch.FloatTensor)``

	A [MaskedLMOutput](/docs/transformers/main/ja/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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.

	## BertForNextSentencePrediction[[transformers.BertForNextSentencePrediction]]

	#### transformers.BertForNextSentencePrediction[[transformers.BertForNextSentencePrediction]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L994)

	Bert Model with a `next sentence prediction (classification)` head on top.

	This model inherits from [PreTrainedModel](/docs/transformers/main/ja/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.

	forwardtransformers.BertForNextSentencePrediction.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L1004[{"name": "input_ids", "val": ": torch.Tensor \| None = None"}, {"name": "attention_mask", "val": ": torch.Tensor \| None = None"}, {"name": "token_type_ids", "val": ": torch.Tensor \| None = None"}, {"name": "position_ids", "val": ": torch.Tensor \| None = None"}, {"name": "inputs_embeds", "val": ": torch.Tensor \| None = None"}, {"name": "labels", "val": ": torch.Tensor \| None = None"}, {"name": "kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) --
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

	Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
	[PreTrainedTokenizer.__call__()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

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

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

	[What are attention masks?](../glossary#attention-mask)
	- token_type_ids (`torch.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 (`torch.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.n_positions - 1]`.

	[What are position IDs?](../glossary#position-ids)
	- inputs_embeds (`torch.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.
	- labels (`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 sequence B is a continuation of sequence A,
	- 1 indicates sequence B is a random sequence.0[NextSentencePredictorOutput](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.NextSentencePredictorOutput) or `tuple(torch.FloatTensor)`A [NextSentencePredictorOutput](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.NextSentencePredictorOutput) 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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.
	The [BertForNextSentencePrediction](/docs/transformers/main/ja/model_doc/bert#transformers.BertForNextSentencePrediction) forward method, overrides the `__call__` special method.

	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.

	- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `next_sentence_label` is provided) -- Next sequence prediction (classification) loss.
	- 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, if the model has an embedding layer, +
	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 optional 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.

	Example:

	```python
	>>> from transformers import AutoTokenizer, BertForNextSentencePrediction
	>>> import torch

	>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
	>>> model = BertForNextSentencePrediction.from_pretrained("google-bert/bert-base-uncased")

	>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
	>>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
	>>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")

	>>> outputs = model(**encoding, labels=torch.LongTensor([1]))
	>>> logits = outputs.logits
	>>> assert logits[0, 0]

	Parameters:

	config ([BertForNextSentencePrediction](/docs/transformers/main/ja/model_doc/bert#transformers.BertForNextSentencePrediction)) : 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/main/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

	Returns:

	`[NextSentencePredictorOutput](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.NextSentencePredictorOutput) or `tuple(torch.FloatTensor)``

	A [NextSentencePredictorOutput](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.NextSentencePredictorOutput) 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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.</div>

	## BertForSequenceClassification[[transformers.BertForSequenceClassification]]

	#### transformers.BertForSequenceClassification[[transformers.BertForSequenceClassification]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L1076)

	Bert 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/main/ja/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.

	forwardtransformers.BertForSequenceClassification.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L1092[{"name": "input_ids", "val": ": torch.Tensor \| None = None"}, {"name": "attention_mask", "val": ": torch.Tensor \| None = None"}, {"name": "token_type_ids", "val": ": torch.Tensor \| None = None"}, {"name": "position_ids", "val": ": torch.Tensor \| None = None"}, {"name": "inputs_embeds", "val": ": torch.Tensor \| None = None"}, {"name": "labels", "val": ": torch.Tensor \| None = None"}, {"name": "kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) --
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

	Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
	[PreTrainedTokenizer.__call__()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

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

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

	[What are attention masks?](../glossary#attention-mask)
	- token_type_ids (`torch.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 (`torch.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.n_positions - 1]`.

	[What are position IDs?](../glossary#position-ids)
	- inputs_embeds (`torch.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.
	- 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).0[SequenceClassifierOutput](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or `tuple(torch.FloatTensor)`A [SequenceClassifierOutput](/docs/transformers/main/ja/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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.
	The [BertForSequenceClassification](/docs/transformers/main/ja/model_doc/bert#transformers.BertForSequenceClassification) forward method, overrides the `__call__` special method.

	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.

	- 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, if the model has an embedding layer, +
	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 optional 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.

	Example of single-label classification:

	```python
	>>> import torch
	>>> from transformers import AutoTokenizer, BertForSequenceClassification

	>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
	>>> model = BertForSequenceClassification.from_pretrained("google-bert/bert-base-uncased")

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

	>>> with torch.no_grad():
	... logits = model(**inputs).logits

	>>> predicted_class_id = logits.argmax().item()
	>>> model.config.id2label[predicted_class_id]
	...

	>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
	>>> num_labels = len(model.config.id2label)
	>>> model = BertForSequenceClassification.from_pretrained("google-bert/bert-base-uncased", num_labels=num_labels)

	>>> labels = torch.tensor([1])
	>>> loss = model(**inputs, labels=labels).loss
	>>> round(loss.item(), 2)
	...
	```

	Example of multi-label classification:

	```python
	>>> import torch
	>>> from transformers import AutoTokenizer, BertForSequenceClassification

	>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
	>>> model = BertForSequenceClassification.from_pretrained("google-bert/bert-base-uncased", problem_type="multi_label_classification")

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

	>>> with torch.no_grad():
	... logits = model(**inputs).logits

	>>> predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]

	>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
	>>> num_labels = len(model.config.id2label)
	>>> model = BertForSequenceClassification.from_pretrained(
	... "google-bert/bert-base-uncased", num_labels=num_labels, problem_type="multi_label_classification"
	... )

	>>> labels = torch.sum(
	... torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
	... ).to(torch.float)
	>>> loss = model(**inputs, labels=labels).loss
	```

	Parameters:

	config ([BertForSequenceClassification](/docs/transformers/main/ja/model_doc/bert#transformers.BertForSequenceClassification)) : 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/main/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

	Returns:

	`[SequenceClassifierOutput](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or `tuple(torch.FloatTensor)``

	A [SequenceClassifierOutput](/docs/transformers/main/ja/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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.</div>

	## BertForMultipleChoice[[transformers.BertForMultipleChoice]]

	#### transformers.BertForMultipleChoice[[transformers.BertForMultipleChoice]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L1157)

	The Bert 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/main/ja/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.

	forwardtransformers.BertForMultipleChoice.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L1171[{"name": "input_ids", "val": ": torch.Tensor \| None = None"}, {"name": "attention_mask", "val": ": torch.Tensor \| None = None"}, {"name": "token_type_ids", "val": ": torch.Tensor \| None = None"}, {"name": "position_ids", "val": ": torch.Tensor \| None = None"}, {"name": "inputs_embeds", "val": ": torch.Tensor \| None = None"}, {"name": "labels", "val": ": torch.Tensor \| None = None"}, {"name": "kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- 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 [AutoTokenizer](/docs/transformers/main/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
	[PreTrainedTokenizer.__call__()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

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

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

	[What are attention masks?](../glossary#attention-mask)
	- 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)
	- 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.
	- 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)0[MultipleChoiceModelOutput](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput) or `tuple(torch.FloatTensor)`A [MultipleChoiceModelOutput](/docs/transformers/main/ja/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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.
	The [BertForMultipleChoice](/docs/transformers/main/ja/model_doc/bert#transformers.BertForMultipleChoice) forward method, overrides the `__call__` special method.

	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.

	- 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, if the model has an embedding layer, +
	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 optional 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.

	Example:

	```python
	>>> from transformers import AutoTokenizer, BertForMultipleChoice
	>>> import torch

	>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
	>>> model = BertForMultipleChoice.from_pretrained("google-bert/bert-base-uncased")

	>>> 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(**{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
	```

	Parameters:

	config ([BertForMultipleChoice](/docs/transformers/main/ja/model_doc/bert#transformers.BertForMultipleChoice)) : 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/main/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

	Returns:

	`[MultipleChoiceModelOutput](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput) or `tuple(torch.FloatTensor)``

	A [MultipleChoiceModelOutput](/docs/transformers/main/ja/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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.</div>

	## BertForTokenClassification[[transformers.BertForTokenClassification]]

	#### transformers.BertForTokenClassification[[transformers.BertForTokenClassification]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L1255)

	The Bert transformer 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/main/ja/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.

	forwardtransformers.BertForTokenClassification.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L1270[{"name": "input_ids", "val": ": torch.Tensor \| None = None"}, {"name": "attention_mask", "val": ": torch.Tensor \| None = None"}, {"name": "token_type_ids", "val": ": torch.Tensor \| None = None"}, {"name": "position_ids", "val": ": torch.Tensor \| None = None"}, {"name": "inputs_embeds", "val": ": torch.Tensor \| None = None"}, {"name": "labels", "val": ": torch.Tensor \| None = None"}, {"name": "kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) --
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

	Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
	[PreTrainedTokenizer.__call__()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

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

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

	[What are attention masks?](../glossary#attention-mask)
	- token_type_ids (`torch.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 (`torch.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.n_positions - 1]`.

	[What are position IDs?](../glossary#position-ids)
	- inputs_embeds (`torch.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.
	- 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]`.0[TokenClassifierOutput](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or `tuple(torch.FloatTensor)`A [TokenClassifierOutput](/docs/transformers/main/ja/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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.
	The [BertForTokenClassification](/docs/transformers/main/ja/model_doc/bert#transformers.BertForTokenClassification) forward method, overrides the `__call__` special method.

	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.

	- 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, if the model has an embedding layer, +
	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 optional 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.

	Example:

	```python
	>>> from transformers import AutoTokenizer, BertForTokenClassification
	>>> import torch

	>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
	>>> model = BertForTokenClassification.from_pretrained("google-bert/bert-base-uncased")

	>>> inputs = tokenizer(
	... "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
	... )

	>>> with torch.no_grad():
	... logits = model(**inputs).logits

	>>> predicted_token_class_ids = logits.argmax(-1)

	>>> # Note that tokens are classified rather then input words which means that
	>>> # there might be more predicted token classes than words.
	>>> # Multiple token classes might account for the same word
	>>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
	>>> predicted_tokens_classes
	...

	>>> labels = predicted_token_class_ids
	>>> loss = model(**inputs, labels=labels).loss
	>>> round(loss.item(), 2)
	...
	```

	Parameters:

	config ([BertForTokenClassification](/docs/transformers/main/ja/model_doc/bert#transformers.BertForTokenClassification)) : 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/main/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

	Returns:

	`[TokenClassifierOutput](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or `tuple(torch.FloatTensor)``

	A [TokenClassifierOutput](/docs/transformers/main/ja/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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.</div>

	## BertForQuestionAnswering[[transformers.BertForQuestionAnswering]]

	#### transformers.BertForQuestionAnswering[[transformers.BertForQuestionAnswering]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L1315)

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

	This model inherits from [PreTrainedModel](/docs/transformers/main/ja/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.

	forwardtransformers.BertForQuestionAnswering.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py#L1326[{"name": "input_ids", "val": ": torch.Tensor \| None = None"}, {"name": "attention_mask", "val": ": torch.Tensor \| None = None"}, {"name": "token_type_ids", "val": ": torch.Tensor \| None = None"}, {"name": "position_ids", "val": ": torch.Tensor \| None = None"}, {"name": "inputs_embeds", "val": ": torch.Tensor \| None = None"}, {"name": "start_positions", "val": ": torch.Tensor \| None = None"}, {"name": "end_positions", "val": ": torch.Tensor \| None = None"}, {"name": "kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) --
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

	Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
	[PreTrainedTokenizer.__call__()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

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

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

	[What are attention masks?](../glossary#attention-mask)
	- token_type_ids (`torch.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 (`torch.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.n_positions - 1]`.

	[What are position IDs?](../glossary#position-ids)
	- inputs_embeds (`torch.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.
	- start_positions (`torch.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 (`torch.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.0[QuestionAnsweringModelOutput](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput) or `tuple(torch.FloatTensor)`A [QuestionAnsweringModelOutput](/docs/transformers/main/ja/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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.
	The [BertForQuestionAnswering](/docs/transformers/main/ja/model_doc/bert#transformers.BertForQuestionAnswering) forward method, overrides the `__call__` special method.

	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.

	- 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, if the model has an embedding layer, +
	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 optional 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.

	Example:

	```python
	>>> from transformers import AutoTokenizer, BertForQuestionAnswering
	>>> import torch

	>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
	>>> model = BertForQuestionAnswering.from_pretrained("google-bert/bert-base-uncased")

	>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

	>>> inputs = tokenizer(question, text, return_tensors="pt")
	>>> with torch.no_grad():
	... outputs = model(**inputs)

	>>> answer_start_index = outputs.start_logits.argmax()
	>>> answer_end_index = outputs.end_logits.argmax()

	>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
	>>> tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)
	...

	>>> # target is "nice puppet"
	>>> target_start_index = torch.tensor([14])
	>>> target_end_index = torch.tensor([15])

	>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
	>>> loss = outputs.loss
	>>> round(loss.item(), 2)
	...
	```

	Parameters:

	config ([BertForQuestionAnswering](/docs/transformers/main/ja/model_doc/bert#transformers.BertForQuestionAnswering)) : 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/main/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

	Returns:

	`[QuestionAnsweringModelOutput](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput) or `tuple(torch.FloatTensor)``

	A [QuestionAnsweringModelOutput](/docs/transformers/main/ja/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 ([BertConfig](/docs/transformers/main/ja/model_doc/bert#transformers.BertConfig)) and inputs.

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