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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 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 によって提供されました。元のコードはこちらにあります。

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。
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。
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。
BertForSequenceClassification は、このサンプルスクリプトおよびノートブック。
TFBertForSequenceClassification は、このサンプルスクリプトおよびノートブック。
FlaxBertForSequenceClassification は、このサンプルスクリプトおよびノートブック。
テキスト分類タスクガイド(英語版)
Hugging Face Transformers with Keras: Fine-tune a non-English BERT for Named Entity Recognition 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を参照してください。
BertForTokenClassification は、このサンプルスクリプトおよびノートブック。
TFBertForTokenClassification は、このサンプルスクリプトおよびノートブック。
FlaxBertForTokenClassification は、このサンプルスクリプトによってサポートされています。
トークン分類 🤗 ハグフェイスコースの章。
トークン分類タスクガイド
BertForMaskedLM は、このサンプルスクリプトでサポートされており、ノートブック。
TFBertForMaskedLM は、このサンプルスクリプトおよびノートブック。
FlaxBertForMaskedLM は、このサンプルスクリプトおよびノートブック。
マスクされた言語モデリング 🤗 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
BertForQuestionAnswering は、このサンプルスクリプトおよびノートブック。
TFBertForQuestionAnswering は、このサンプルスクリプトおよびノートブック。
FlaxBertForQuestionAnswering は、このサンプルスクリプトでサポートされています。
質問回答 🤗 ハグフェイスコースの章。
質問回答タスクガイド

複数の選択肢

BertForMultipleChoice は、このサンプルスクリプトおよびノートブック。
TFBertForMultipleChoice は、このサンプルスクリプトおよびノートブック。
多肢選択タスクガイド

⚡️ 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。
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。

⚙️ 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。

🚀 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。
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。
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。
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。
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。
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

BertConfig[[transformers.BertConfig]]

transformers.BertConfig[[transformers.BertConfig]]

Source

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

Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.

Examples:

>>> 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

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

This tokenizer inherits from 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

BertTokenizerLegacy[[transformers.BertTokenizerLegacy]]

transformers.BertTokenizerLegacy[[transformers.BertTokenizerLegacy]]

Source

Construct a BERT tokenizer. Based on WordPiece.

This tokenizer inherits from PreTrainedTokenizer 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.0List[int]List of 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.

pad_token (str, optional, defaults to "[PAD]") : The token used for padding, for example when batching sequences of different lengths.

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).

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 with the appropriate special tokens.

convert_tokens_to_string[[transformers.BertTokenizerLegacy.convert_tokens_to_string]]

Source

Converts a sequence of tokens (string) in a single string.

get_special_tokens_mask[[transformers.BertTokenizerLegacy.get_special_tokens_mask]]

Source

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

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

This tokenizer inherits from 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.

pad_token (str, optional, defaults to "[PAD]") : The token used for padding, for example when batching sequences of different lengths.

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

Output type of 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.

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).

BertModel[[transformers.BertModel]]

transformers.BertModel[[transformers.BertModel]]

Source

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 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. 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 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

What are 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?
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?
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?
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. 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).0BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor)A 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) and inputs. The 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.

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) : 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() 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 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) and inputs.

BertForPreTraining[[transformers.BertForPreTraining]]

transformers.BertForPreTraining[[transformers.BertForPreTraining]]

Source

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 is also a PyTorch 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

What are 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?
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?
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?
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.0BertForPreTrainingOutput or tuple(torch.FloatTensor)A 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) and inputs. The BertForPreTraining forward method, overrides the __call__ special method.

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:

>>> 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) : 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() 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 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) and inputs.

BertLMHeadModel[[transformers.BertLMHeadModel]]

transformers.BertLMHeadModel[[transformers.BertLMHeadModel]]

Source

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

This model is also a PyTorch 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

What are 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?
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?
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?
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. 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).0CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)A 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) and inputs. The BertLMHeadModel forward method, overrides the __call__ special method.

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.

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:

>>> 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) : 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() 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 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) and inputs.

BertForMaskedLM[[transformers.BertForMaskedLM]]

transformers.BertForMaskedLM[[transformers.BertForMaskedLM]]

Source

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

This model is also a PyTorch 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

What are 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?
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?
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?
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]0MaskedLMOutput or tuple(torch.FloatTensor)A 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) and inputs. The BertForMaskedLM forward method, overrides the __call__ special method.

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:

>>> 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) : 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() 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 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) and inputs.

BertForNextSentencePrediction[[transformers.BertForNextSentencePrediction]]

transformers.BertForNextSentencePrediction[[transformers.BertForNextSentencePrediction]]

Source

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

This model is also a PyTorch 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

What are 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?
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?
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?
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.0NextSentencePredictorOutput or tuple(torch.FloatTensor)A 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) and inputs. The BertForNextSentencePrediction forward method, overrides the __call__ special method.

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:

>>> 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:

>>> 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) : 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() 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 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) and inputs.

BertForMultipleChoice[[transformers.BertForMultipleChoice]]

transformers.BertForMultipleChoice[[transformers.BertForMultipleChoice]]

Source

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 is also a PyTorch 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

What are 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?
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?
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?
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)0MultipleChoiceModelOutput or tuple(torch.FloatTensor)A 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) and inputs. The BertForMultipleChoice forward method, overrides the __call__ special method.

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:

>>> 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) : 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() 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 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) and inputs.

BertForTokenClassification[[transformers.BertForTokenClassification]]

transformers.BertForTokenClassification[[transformers.BertForTokenClassification]]

Source

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 is also a PyTorch 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

What are 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?
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?
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?
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].0TokenClassifierOutput or tuple(torch.FloatTensor)A 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) and inputs. The BertForTokenClassification forward method, overrides the __call__ special method.

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:

>>> 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) : 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() 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 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) and inputs.

BertForQuestionAnswering[[transformers.BertForQuestionAnswering]]

transformers.BertForQuestionAnswering[[transformers.BertForQuestionAnswering]]

Source

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 is also a PyTorch 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

What are 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?
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?
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?
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.0QuestionAnsweringModelOutput or tuple(torch.FloatTensor)A 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) and inputs. The BertForQuestionAnswering forward method, overrides the __call__ special method.

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:

>>> 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) : 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() 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 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) and inputs.

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