
 
BERT
	
Overview
	
The BERT model was proposed in BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It’s a
bidirectional transformer pretrained using a combination of masked language modeling objective and next sentence
prediction on a large corpus comprising the Toronto Book Corpus and Wikipedia.
The abstract from the paper is the following:
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations
from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional
representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result,
the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models
for a wide range of tasks, such as question answering and language inference, without substantial task-specific
architecture modifications.
BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural
language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI
accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute
improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).
Tips:
BERT is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than
the left.
BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. It is
efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation.
Corrupts the inputs by using random masking, more precisely, during pretraining, a given percentage of tokens (usually 15%) is masked by:
a special mask token with probability 0.8
a random token different from the one masked with probability 0.1
the same token with probability 0.1
The model must predict the original sentence, but has a second objective: inputs are two sentences A and B (with a separation token in between). With probability 50%, the sentences are consecutive in the corpus, in the remaining 50% they are not related. The model has to predict if the sentences are consecutive or not.
This model was contributed by thomwolf. The original code can be found here.
Resources
	
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BERT. If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
Text Classification
A blog post on BERT Text Classification in a different language.
A notebook for Finetuning BERT (and friends) for multi-label text classification.
A notebook on how to Finetune BERT for multi-label classification using PyTorch. 🌎
A notebook on how to warm-start an EncoderDecoder model with BERT for summarization.
BertForSequenceClassification is supported by this example script and notebook.
TFBertForSequenceClassification is supported by this example script and notebook.
FlaxBertForSequenceClassification is supported by this example script and notebook.
Text classification task guide
Token Classification
A blog post on how to use Hugging Face Transformers with Keras: Fine-tune a non-English BERT for Named Entity Recognition.
A notebook for Finetuning BERT for named-entity recognition using only the first wordpiece of each word in the word label during tokenization. To propagate the label of the word to all wordpieces, see this version of the notebook instead.
BertForTokenClassification is supported by this example script and notebook.
TFBertForTokenClassification is supported by this example script and notebook.
FlaxBertForTokenClassification is supported by this example script.
Token classification chapter of the 🤗 Hugging Face Course.
Token classification task guide
Fill-Mask
BertForMaskedLM is supported by this example script and notebook.
TFBertForMaskedLM is supported by this example script and notebook.
FlaxBertForMaskedLM is supported by this example script and notebook.
Masked language modeling chapter of the 🤗 Hugging Face Course.
Masked language modeling task guide
Question Answering
BertForQuestionAnswering is supported by this example script and notebook.
TFBertForQuestionAnswering is supported by this example script and notebook.
FlaxBertForQuestionAnswering is supported by this example script.
Question answering chapter of the 🤗 Hugging Face Course.
Question answering task guide
Multiple choice
BertForMultipleChoice is supported by this example script and notebook.
TFBertForMultipleChoice is supported by this example script and notebook.
Multiple choice task guide
⚡️ Inference
A blog post on how to Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia.
A blog post on how to Accelerate BERT inference with DeepSpeed-Inference on GPUs.
⚙️ Pretraining
A blog post on Pre-Training BERT with Hugging Face Transformers and Habana Gaudi.
🚀 Deploy
A blog post on how to Convert Transformers to ONNX with Hugging Face Optimum.
A blog post on how to Setup Deep Learning environment for Hugging Face Transformers with Habana Gaudi on AWS.
A blog post on Autoscaling BERT with Hugging Face Transformers, Amazon SageMaker and Terraform module.
A blog post on Serverless BERT with HuggingFace, AWS Lambda, and Docker.
A blog post on Hugging Face Transformers BERT fine-tuning using Amazon SageMaker and Training Compiler.
A blog post on Task-specific knowledge distillation for BERT using Transformers & Amazon SageMaker.
BertConfig
	
class transformers.BertConfig
<
source
>
(
vocab_size = 30522
hidden_size = 768
num_hidden_layers = 12
num_attention_heads = 12
intermediate_size = 3072
hidden_act = 'gelu'
hidden_dropout_prob = 0.1
attention_probs_dropout_prob = 0.1
max_position_embeddings = 512
type_vocab_size = 2
initializer_range = 0.02
layer_norm_eps = 1e-12
pad_token_id = 0
position_embedding_type = 'absolute'
use_cache = True
classifier_dropout = None
**kwargs
)
Parameters 
vocab_size (int, optional, defaults to 30522) —
Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
inputs_ids passed when calling BertModel or TFBertModel.
hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (int, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder.
num_attention_heads (int, optional, defaults to 12) —
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (int, optional, defaults to 3072) —
Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder.
hidden_act (str or Callable, optional, defaults to "gelu") —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu",
"relu", "silu" and "gelu_new" are supported.
hidden_dropout_prob (float, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (float, 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. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (int, optional, defaults to 2) —
The vocabulary size of the token_type_ids passed when calling BertModel or TFBertModel.
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.
position_embedding_type (str, optional, defaults to "absolute") —
Type of position embedding. Choose one of "absolute", "relative_key", "relative_key_query". For
positional embeddings use "absolute". For more information on "relative_key", please refer to
Self-Attention with Relative Position Representations (Shaw et al.).
For more information on "relative_key_query", please refer to Method 4 in Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.).
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.
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.
classifier_dropout (float, optional) —
The dropout ratio for the classification head.
This is the configuration class to store the configuration of a BertModel or a TFBertModel. 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 BERT
bert-base-uncased architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Examples:
	Copied
 from transformers import BertConfig, BertModel
 # Initializing a BERT bert-base-uncased style configuration
 configuration = BertConfig()
 # Initializing a model (with random weights) from the bert-base-uncased style configuration
 model = BertModel(configuration)
 # Accessing the model configuration
 configuration = model.config
BertTokenizer
	
class transformers.BertTokenizer
<
source
>
(
vocab_file
do_lower_case = True
do_basic_tokenize = True
never_split = None
unk_token = '[UNK]'
sep_token = '[SEP]'
pad_token = '[PAD]'
cls_token = '[CLS]'
mask_token = '[MASK]'
tokenize_chinese_chars = True
strip_accents = None
**kwargs
)
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).
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).
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_tokens
<
source
>
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters 
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.
Returns
List[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]
get_special_tokens_mask
<
source
>
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
already_has_special_tokens: bool = False
)
→
List[int]
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.
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.
create_token_type_ids_from_sequences
<
source
>
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters 
token_ids_0 (List[int]) —
List of IDs.
token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.
Returns
List[int]
List of token type IDs according to the given sequence(s).
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
pair mask has the following format:
	Copied
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |
If token_ids_1 is None, this method only returns the first portion of the mask (0s).
save_vocabulary
<
source
>
(
save_directory: str
filename_prefix: typing.Optional[str] = None
)
BertTokenizerFast
	
class transformers.BertTokenizerFast
<
source
>
(
vocab_file = None
tokenizer_file = None
do_lower_case = True
unk_token = '[UNK]'
sep_token = '[SEP]'
pad_token = '[PAD]'
cls_token = '[CLS]'
mask_token = '[MASK]'
tokenize_chinese_chars = True
strip_accents = None
**kwargs
)
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.
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.
clean_text (bool, optional, defaults to True) —
Whether or not to clean the text before tokenization by removing any control characters and replacing all
whitespaces by the classic one.
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).
wordpieces_prefix (str, optional, defaults to "##") —
The prefix for subwords.
Construct a “fast” BERT tokenizer (backed by HuggingFace’s tokenizers library). Based on WordPiece.
This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
build_inputs_with_special_tokens
<
source
>
(
token_ids_0
token_ids_1 = None
)
→
List[int]
Parameters 
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.
Returns
List[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]
create_token_type_ids_from_sequences
<
source
>
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters 
token_ids_0 (List[int]) —
List of IDs.
token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.
Returns
List[int]
List of token type IDs according to the given sequence(s).
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
pair mask has the following format:
	Copied
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |
If token_ids_1 is None, this method only returns the first portion of the mask (0s).
TFBertTokenizer
	
class transformers.TFBertTokenizer
<
source
>
(
*args
**kwargs
)
Parameters 
vocab_list (list) —
List containing the vocabulary.
do_lower_case (bool, optional, defaults to True) —
Whether or not to lowercase the input when tokenizing.
cls_token_id (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.
sep_token_id (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_id (str, optional, defaults to "[PAD]") —
The token used for padding, for example when batching sequences of different lengths.
padding (str, defaults to "longest") —
The type of padding to use. Can be either "longest", to pad only up to the longest sample in the batch,
or `“max_length”, to pad all inputs to the maximum length supported by the tokenizer.
truncation (bool, optional, defaults to True) —
Whether to truncate the sequence to the maximum length.
max_length (int, optional, defaults to 512) —
The maximum length of the sequence, used for padding (if padding is “max_length”) and/or truncation (if
truncation is True).
pad_to_multiple_of (int, optional, defaults to None) —
If set, the sequence will be padded to a multiple of this value.
return_token_type_ids (bool, optional, defaults to True) —
Whether to return token_type_ids.
return_attention_mask (bool, optional, defaults to True) —
Whether to return the attention_mask.
use_fast_bert_tokenizer (bool, optional, defaults to True) —
If True, will use the FastBertTokenizer class from Tensorflow Text. If False, will use the BertTokenizer
class instead. BertTokenizer supports some additional options, but is slower and cannot be exported to
TFLite.
This is an in-graph tokenizer for BERT. It should be initialized similarly to other tokenizers, using the
from_pretrained() method. It can also be initialized with the from_tokenizer() method, which imports settings
from an existing standard tokenizer object.
In-graph tokenizers, unlike other Hugging Face tokenizers, are actually Keras layers and are designed to be run
when the model is called, rather than during preprocessing. As a result, they have somewhat more limited options
than standard tokenizer classes. They are most useful when you want to create an end-to-end model that goes
straight from tf.string inputs to outputs.
from_pretrained
<
source
>
(
pretrained_model_name_or_path: typing.Union[str, os.PathLike]
*init_inputs
**kwargs
)
Parameters 
pretrained_model_name_or_path (str or os.PathLike) —
The name or path to the pre-trained tokenizer.
Instantiate a TFBertTokenizer from a pre-trained tokenizer.
Examples:
	Copied
from transformers import TFBertTokenizer
tf_tokenizer = TFBertTokenizer.from_pretrained("bert-base-uncased")
from_tokenizer
<
source
>
(
tokenizer: PreTrainedTokenizerBase
**kwargs
)
Parameters 
tokenizer (PreTrainedTokenizerBase) —
The tokenizer to use to initialize the TFBertTokenizer.
Initialize a TFBertTokenizer from an existing Tokenizer.
Examples:
	Copied
from transformers import AutoTokenizer, TFBertTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
tf_tokenizer = TFBertTokenizer.from_tokenizer(tokenizer)
Bert specific outputs
	
class transformers.models.bert.modeling_bert.BertForPreTrainingOutput
<
source
>
(
loss: typing.Optional[torch.FloatTensor] = None
prediction_logits: FloatTensor = None
seq_relationship_logits: FloatTensor = None
hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None
attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None
)
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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
Output type of BertForPreTraining.
class transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput
<
source
>
(
loss: tf.Tensor | None = None
prediction_logits: tf.Tensor = None
seq_relationship_logits: tf.Tensor = None
hidden_states: Optional[Union[Tuple[tf.Tensor], tf.Tensor]] = None
attentions: Optional[Union[Tuple[tf.Tensor], tf.Tensor]] = None
)
Parameters 
prediction_logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
seq_relationship_logits (tf.Tensor of shape (batch_size, 2)) —
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
Output type of TFBertForPreTraining.
class transformers.models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput
<
source
>
(
prediction_logits: Array = None
seq_relationship_logits: Array = None
hidden_states: typing.Optional[typing.Tuple[jax.Array]] = None
attentions: typing.Optional[typing.Tuple[jax.Array]] = None
)
Parameters 
prediction_logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
seq_relationship_logits (jnp.ndarray of shape (batch_size, 2)) —
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
Output type of BertForPreTraining.
replace
<
source
>
(
**updates
)
“Returns a new object replacing the specified fields with new values.
BertModel
	
class transformers.BertModel
<
source
>
(
config
add_pooling_layer = True
)
Parameters 
config (BertConfig) — 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.
The bare Bert Model transformer outputting raw hidden-states without any specific head on top.
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.
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.
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
encoder_hidden_states: typing.Optional[torch.Tensor] = None
encoder_attention_mask: typing.Optional[torch.Tensor] = None
past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, 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.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
encoder_hidden_states  (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) —
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).
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).
Returns
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor)
A 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) and inputs.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (torch.FloatTensor of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, 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 (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if
config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values
input) to speed up sequential decoding.
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.
Example:
	Copied
 from transformers import AutoTokenizer, BertModel
 import torch
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = BertModel.from_pretrained("bert-base-uncased")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
BertForPreTraining
	
class transformers.BertForPreTraining
<
source
>
(
config
)
Parameters 
config (BertConfig) — 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.
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. 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.
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
next_sentence_label: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.models.bert.modeling_bert.BertForPreTrainingOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, 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.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
labels (torch.LongTensor of shape (batch_size, sequence_length), optional):
Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked),
the loss is only computed for the tokens with labels in [0, ..., config.vocab_size]
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.
kwargs (Dict[str, any], optional, defaults to {}):
Used to hide legacy arguments that have been deprecated.
Returns
transformers.models.bert.modeling_bert.BertForPreTrainingOutput or tuple(torch.FloatTensor)
A 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) and inputs.
loss (optional, returned when labels is provided, torch.FloatTensor of shape (1,)) — Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.
prediction_logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The 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.
Example:
	Copied
 from transformers import AutoTokenizer, BertForPreTraining
 import torch
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = BertForPreTraining.from_pretrained("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
BertLMHeadModel
	
class transformers.BertLMHeadModel
<
source
>
(
config
)
Parameters 
config (BertConfig) — 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.
Bert Model with a language modeling head on top for CLM fine-tuning.
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.
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
encoder_hidden_states: typing.Optional[torch.Tensor] = None
encoder_attention_mask: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
past_key_values: typing.Optional[typing.List[torch.Tensor]] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, 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.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
encoder_hidden_states  (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:
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 (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) —
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).
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).
Returns
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
A 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) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, 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 (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of torch.FloatTensor tuples of length config.n_layers, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if config.is_decoder = True.
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
The 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.
Example:
	Copied
 import torch
 from transformers import AutoTokenizer, BertLMHeadModel
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = BertLMHeadModel.from_pretrained("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
BertForMaskedLM
	
class transformers.BertForMaskedLM
<
source
>
(
config
)
Parameters 
config (BertConfig) — 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.
Bert Model with a language modeling head on top.
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.
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
encoder_hidden_states: typing.Optional[torch.Tensor] = None
encoder_attention_mask: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, 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.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), the
loss is only computed for the tokens with labels in [0, ..., config.vocab_size]
Returns
transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor)
A 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) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Masked language modeling (MLM) loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, 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.
The 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.
Example:
	Copied
 from transformers import AutoTokenizer, BertForMaskedLM
 import torch
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = BertForMaskedLM.from_pretrained("bert-base-uncased")
 inputs = tokenizer("The capital of France is [MASK].", return_tensors="pt")
 with torch.no_grad():
...     logits = model(**inputs).logits
 # retrieve index of [MASK]
 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)
'paris'
 labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
 # mask labels of non-[MASK] tokens
 labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
 outputs = model(**inputs, labels=labels)
 round(outputs.loss.item(), 2)
0.88
BertForNextSentencePrediction
	
class transformers.BertForNextSentencePrediction
<
source
>
(
config
)
Parameters 
config (BertConfig) — 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.
Bert Model with a next sentence prediction (classification) head on top.
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.
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
**kwargs
)
→
transformers.modeling_outputs.NextSentencePredictorOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, 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.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
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.
Returns
transformers.modeling_outputs.NextSentencePredictorOutput or tuple(torch.FloatTensor)
A 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) and inputs.
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.
The 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.
Example:
	Copied
 from transformers import AutoTokenizer, BertForNextSentencePrediction
 import torch
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = BertForNextSentencePrediction.from_pretrained("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] < logits[0, 1]  # next sentence was random
BertForSequenceClassification
	
class transformers.BertForSequenceClassification
<
source
>
(
config
)
Parameters 
config (BertConfig) — 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.
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. 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.
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, 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.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
labels (torch.LongTensor of shape (batch_size,), optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If
config.num_labels > 1 a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)
A 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) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (torch.FloatTensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, 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.
The 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.
Example of single-label classification:
	Copied
 import torch
 from transformers import AutoTokenizer, BertForSequenceClassification
 tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-yelp-polarity")
 model = BertForSequenceClassification.from_pretrained("textattack/bert-base-uncased-yelp-polarity")
 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]
'LABEL_1'
 # 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("textattack/bert-base-uncased-yelp-polarity", num_labels=num_labels)
 labels = torch.tensor([1])
 loss = model(**inputs, labels=labels).loss
 round(loss.item(), 2)
0.01
Example of multi-label classification:
	Copied
 import torch
 from transformers import AutoTokenizer, BertForSequenceClassification
 tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-yelp-polarity")
 model = BertForSequenceClassification.from_pretrained("textattack/bert-base-uncased-yelp-polarity", 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(
...     "textattack/bert-base-uncased-yelp-polarity", 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
BertForMultipleChoice
	
class transformers.BertForMultipleChoice
<
source
>
(
config
)
Parameters 
config (BertConfig) — 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.
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. 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.
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.MultipleChoiceModelOutput or tuple(torch.FloatTensor)
Parameters 
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.FloatTensor of shape (batch_size, num_choices, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
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?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (torch.FloatTensor of shape (batch_size, num_choices, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
labels (torch.LongTensor of shape (batch_size,), optional) —
Labels for computing the multiple choice classification loss. Indices should be in [0, ..., num_choices-1] where num_choices is the size of the second dimension of the input tensors. (See
input_ids above)
Returns
transformers.modeling_outputs.MultipleChoiceModelOutput or tuple(torch.FloatTensor)
A 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) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification loss.
logits (torch.FloatTensor of shape (batch_size, num_choices)) — num_choices is the second dimension of the input tensors. (see input_ids above).
Classification scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, 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.
The 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.
Example:
	Copied
 from transformers import AutoTokenizer, BertForMultipleChoice
 import torch
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = BertForMultipleChoice.from_pretrained("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
BertForTokenClassification
	
class transformers.BertForTokenClassification
<
source
>
(
config
)
Parameters 
config (BertConfig) — 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.
Bert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
This model inherits from PreTrainedModel. 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.
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, 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.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for computing the token classification loss. Indices should be in [0, ..., config.num_labels - 1].
Returns
transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)
A 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) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided)  — Classification loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, 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.
The 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.
Example:
	Copied
 from transformers import AutoTokenizer, BertForTokenClassification
 import torch
 tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
 model = BertForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
 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
['O', 'I-ORG', 'I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'I-LOC', 'O', 'I-LOC', 'I-LOC'] 
 labels = predicted_token_class_ids
 loss = model(**inputs, labels=labels).loss
 round(loss.item(), 2)
0.01
BertForQuestionAnswering
	
class transformers.BertForQuestionAnswering
<
source
>
(
config
)
Parameters 
config (BertConfig) — 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.
Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute span start logits and span end logits).
This model inherits from PreTrainedModel. 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.
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
start_positions: typing.Optional[torch.Tensor] = None
end_positions: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, 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.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
start_positions (torch.LongTensor of shape (batch_size,), optional) —
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (torch.LongTensor of shape (batch_size,), optional) —
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.
Returns
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
A 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) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).
end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-end scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, 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.
The 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.
Example:
	Copied
 from transformers import AutoTokenizer, BertForQuestionAnswering
 import torch
 tokenizer = AutoTokenizer.from_pretrained("deepset/bert-base-cased-squad2")
 model = BertForQuestionAnswering.from_pretrained("deepset/bert-base-cased-squad2")
 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)
'a nice puppet'
 # 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)
7.41
TFBertModel
	
class transformers.TFBertModel
<
source
>
(
*args
**kwargs
)
Parameters 
config (BertConfig) — 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.
The bare Bert Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
a single Tensor with input_ids only and nothing else: model(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
input_ids: TFModelInputType | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
encoder_hidden_states: np.ndarray | tf.Tensor | None = None
encoder_attention_mask: np.ndarray | tf.Tensor | None = None
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None
use_cache: Optional[bool] = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions or tuple(tf.Tensor)
Parameters 
input_ids (np.ndarray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
attention_mask (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (np.ndarray or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (np.ndarray or tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (bool, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
encoder_hidden_states  (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (tf.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 (Tuple[Tuple[tf.Tensor]] of length config.n_layers) —
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).
use_cache (bool, optional, defaults to True) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values). Set to False during training, True during generation
Returns
transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (BertConfig) and inputs.
last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (tf.Tensor of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.
This output is usually not a good summary of the semantic content of the input, you’re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.
past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
The TFBertModel 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.
Example:
	Copied
 from transformers import AutoTokenizer, TFBertModel
 import tensorflow as tf
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = TFBertModel.from_pretrained("bert-base-uncased")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
 outputs = model(inputs)
 last_hidden_states = outputs.last_hidden_state
TFBertForPreTraining
	
class transformers.TFBertForPreTraining
<
source
>
(
*args
**kwargs
)
Parameters 
config (BertConfig) — 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.
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 TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
a single Tensor with input_ids only and nothing else: model(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
input_ids: TFModelInputType | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
labels: np.ndarray | tf.Tensor | None = None
next_sentence_label: np.ndarray | tf.Tensor | None = None
training: Optional[bool] = False
)
→
transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput or tuple(tf.Tensor)
Parameters 
input_ids (np.ndarray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
attention_mask (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (np.ndarray or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (np.ndarray or tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (bool, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
labels (tf.Tensor of shape (batch_size, sequence_length), optional) —
Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), the
loss is only computed for the tokens with labels in [0, ..., config.vocab_size]
next_sentence_label (tf.Tensor 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.
kwargs (Dict[str, any], optional, defaults to {}) —
Used to hide legacy arguments that have been deprecated.
Returns
transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput or tuple(tf.Tensor)
A transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (BertConfig) and inputs.
prediction_logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
seq_relationship_logits (tf.Tensor of shape (batch_size, 2)) — Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The TFBertForPreTraining 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.
Examples:
	Copied
 import tensorflow as tf
 from transformers import AutoTokenizer, TFBertForPreTraining
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = TFBertForPreTraining.from_pretrained("bert-base-uncased")
 input_ids = tokenizer("Hello, my dog is cute", add_special_tokens=True, return_tensors="tf")
 # Batch size 1
 outputs = model(input_ids)
 prediction_logits, seq_relationship_logits = outputs[:2]
TFBertModelLMHeadModel
	
class transformers.TFBertLMHeadModel
<
source
>
(
*args
**kwargs
)
call
<
source
>
(
input_ids: TFModelInputType | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
encoder_hidden_states: np.ndarray | tf.Tensor | None = None
encoder_attention_mask: np.ndarray | tf.Tensor | None = None
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None
use_cache: Optional[bool] = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
labels: np.ndarray | tf.Tensor | None = None
training: Optional[bool] = False
**kwargs
)
→
transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions or tuple(tf.Tensor)
Returns
transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (BertConfig) and inputs.
loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) — Language modeling loss (for next-token prediction).
logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
encoder_hidden_states  (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (tf.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 (Tuple[Tuple[tf.Tensor]] of length config.n_layers)
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).
use_cache (bool, optional, defaults to True):
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values). Set to False during training, True during generation
labels (tf.Tensor or np.ndarray of shape (batch_size, sequence_length), optional):
Labels for computing the cross entropy classification loss. Indices should be in [0, ..., config.vocab_size - 1].
Example:
	Copied
 from transformers import AutoTokenizer, TFBertLMHeadModel
 import tensorflow as tf
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = TFBertLMHeadModel.from_pretrained("bert-base-uncased")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
 outputs = model(inputs)
 logits = outputs.logits
TFBertForMaskedLM
	
class transformers.TFBertForMaskedLM
<
source
>
(
*args
**kwargs
)
Parameters 
config (BertConfig) — 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.
Bert Model with a language modeling head on top.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
a single Tensor with input_ids only and nothing else: model(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
input_ids: TFModelInputType | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
labels: np.ndarray | tf.Tensor | None = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFMaskedLMOutput or tuple(tf.Tensor)
Parameters 
input_ids (np.ndarray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
attention_mask (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (np.ndarray or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (np.ndarray or tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (bool, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
labels (tf.Tensor or np.ndarray 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]
Returns
transformers.modeling_tf_outputs.TFMaskedLMOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFMaskedLMOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (BertConfig) and inputs.
loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) — Masked language modeling (MLM) loss.
logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The TFBertForMaskedLM 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.
Example:
	Copied
 from transformers import AutoTokenizer, TFBertForMaskedLM
 import tensorflow as tf
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = TFBertForMaskedLM.from_pretrained("bert-base-uncased")
 inputs = tokenizer("The capital of France is [MASK].", return_tensors="tf")
 logits = model(**inputs).logits
 # retrieve index of [MASK]
 mask_token_index = tf.where((inputs.input_ids == tokenizer.mask_token_id)[0])
 selected_logits = tf.gather_nd(logits[0], indices=mask_token_index)
 predicted_token_id = tf.math.argmax(selected_logits, axis=-1)
 tokenizer.decode(predicted_token_id)
'paris'
	Copied
 labels = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
 # mask labels of non-[MASK] tokens
 labels = tf.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
 outputs = model(**inputs, labels=labels)
 round(float(outputs.loss), 2)
0.88
TFBertForNextSentencePrediction
	
class transformers.TFBertForNextSentencePrediction
<
source
>
(
*args
**kwargs
)
Parameters 
config (BertConfig) — 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.
Bert Model with a next sentence prediction (classification) head on top.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
a single Tensor with input_ids only and nothing else: model(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
input_ids: TFModelInputType | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
next_sentence_label: np.ndarray | tf.Tensor | None = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFNextSentencePredictorOutput or tuple(tf.Tensor)
Parameters 
input_ids (np.ndarray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
attention_mask (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (np.ndarray or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (np.ndarray or tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (bool, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
Returns
transformers.modeling_tf_outputs.TFNextSentencePredictorOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFNextSentencePredictorOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (BertConfig) and inputs.
loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when next_sentence_label is provided) — Next sentence prediction loss.
logits (tf.Tensor of shape (batch_size, 2)) — Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The TFBertForNextSentencePrediction 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.
Examples:
	Copied
 import tensorflow as tf
 from transformers import AutoTokenizer, TFBertForNextSentencePrediction
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = TFBertForNextSentencePrediction.from_pretrained("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="tf")
 logits = model(encoding["input_ids"], token_type_ids=encoding["token_type_ids"])[0]
 assert logits[0][0] < logits[0][1]  # the next sentence was random
TFBertForSequenceClassification
	
class transformers.TFBertForSequenceClassification
<
source
>
(
*args
**kwargs
)
Parameters 
config (BertConfig) — 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.
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 TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
a single Tensor with input_ids only and nothing else: model(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
input_ids: TFModelInputType | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
labels: np.ndarray | tf.Tensor | None = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)
Parameters 
input_ids (np.ndarray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
attention_mask (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (np.ndarray or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (np.ndarray or tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (bool, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
labels (tf.Tensor or np.ndarray 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).
Returns
transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFSequenceClassifierOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (BertConfig) and inputs.
loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (tf.Tensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The TFBertForSequenceClassification 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.
Example:
	Copied
 from transformers import AutoTokenizer, TFBertForSequenceClassification
 import tensorflow as tf
 tokenizer = AutoTokenizer.from_pretrained("ydshieh/bert-base-uncased-yelp-polarity")
 model = TFBertForSequenceClassification.from_pretrained("ydshieh/bert-base-uncased-yelp-polarity")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
 logits = model(**inputs).logits
 predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
 model.config.id2label[predicted_class_id]
'LABEL_1'
	Copied
 # 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 = TFBertForSequenceClassification.from_pretrained("ydshieh/bert-base-uncased-yelp-polarity", num_labels=num_labels)
 labels = tf.constant(1)
 loss = model(**inputs, labels=labels).loss
 round(float(loss), 2)
0.01
TFBertForMultipleChoice
	
class transformers.TFBertForMultipleChoice
<
source
>
(
*args
**kwargs
)
Parameters 
config (BertConfig) — 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.
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 TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
a single Tensor with input_ids only and nothing else: model(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
input_ids: TFModelInputType | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
labels: np.ndarray | tf.Tensor | None = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or tuple(tf.Tensor)
Parameters 
input_ids (np.ndarray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, num_choices, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
attention_mask (np.ndarray or tf.Tensor of shape (batch_size, num_choices, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (np.ndarray or tf.Tensor of shape (batch_size, num_choices, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (np.ndarray or tf.Tensor of shape (batch_size, num_choices, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (np.ndarray or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (np.ndarray or tf.Tensor of shape (batch_size, num_choices, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (bool, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
labels (tf.Tensor or np.ndarray of shape (batch_size,), optional) —
Labels for computing the multiple choice classification loss. Indices should be in [0, ..., num_choices]
where num_choices is the size of the second dimension of the input tensors. (See input_ids above)
Returns
transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (BertConfig) and inputs.
loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) — Classification loss.
logits (tf.Tensor of shape (batch_size, num_choices)) — num_choices is the second dimension of the input tensors. (see input_ids above).
Classification scores (before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The TFBertForMultipleChoice 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.
Example:
	Copied
 from transformers import AutoTokenizer, TFBertForMultipleChoice
 import tensorflow as tf
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = TFBertForMultipleChoice.from_pretrained("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."
 encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="tf", padding=True)
 inputs = {k: tf.expand_dims(v, 0) for k, v in encoding.items()}
 outputs = model(inputs)  # batch size is 1
 # the linear classifier still needs to be trained
 logits = outputs.logits
TFBertForTokenClassification
	
class transformers.TFBertForTokenClassification
<
source
>
(
*args
**kwargs
)
Parameters 
config (BertConfig) — 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.
Bert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
a single Tensor with input_ids only and nothing else: model(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
input_ids: TFModelInputType | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
labels: np.ndarray | tf.Tensor | None = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFTokenClassifierOutput or tuple(tf.Tensor)
Parameters 
input_ids (np.ndarray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
attention_mask (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (np.ndarray or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (np.ndarray or tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (bool, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
labels (tf.Tensor or np.ndarray of shape (batch_size, sequence_length), optional) —
Labels for computing the token classification loss. Indices should be in [0, ..., config.num_labels - 1].
Returns
transformers.modeling_tf_outputs.TFTokenClassifierOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFTokenClassifierOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (BertConfig) and inputs.
loss (tf.Tensor of shape (n,), optional, where n is the number of unmasked labels, returned when labels is provided)  — Classification loss.
logits (tf.Tensor of shape (batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The TFBertForTokenClassification 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.
Example:
	Copied
 from transformers import AutoTokenizer, TFBertForTokenClassification
 import tensorflow as tf
 tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
 model = TFBertForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
 inputs = tokenizer(
...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="tf"
... )
 logits = model(**inputs).logits
 predicted_token_class_ids = tf.math.argmax(logits, axis=-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] for t in predicted_token_class_ids[0].numpy().tolist()]
 predicted_tokens_classes
['O', 'I-ORG', 'I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'I-LOC', 'O', 'I-LOC', 'I-LOC'] 
	Copied
 labels = predicted_token_class_ids
 loss = tf.math.reduce_mean(model(**inputs, labels=labels).loss)
 round(float(loss), 2)
0.01
TFBertForQuestionAnswering
	
class transformers.TFBertForQuestionAnswering
<
source
>
(
*args
**kwargs
)
Parameters 
config (BertConfig) — 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.
Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layer on top of the hidden-states output to compute span start logits and span end logits).
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
a single Tensor with input_ids only and nothing else: model(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
input_ids: TFModelInputType | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
start_positions: np.ndarray | tf.Tensor | None = None
end_positions: np.ndarray | tf.Tensor | None = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor)
Parameters 
input_ids (np.ndarray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
attention_mask (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
head_mask (np.ndarray or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (np.ndarray or tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (bool, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
start_positions (tf.Tensor or np.ndarray of shape (batch_size,), optional) —
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (tf.Tensor or np.ndarray 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.
Returns
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (BertConfig) and inputs.
loss (tf.Tensor of shape (batch_size, ), optional, returned when start_positions and end_positions are provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (tf.Tensor of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).
end_logits (tf.Tensor of shape (batch_size, sequence_length)) — Span-end scores (before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The TFBertForQuestionAnswering 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.
Example:
	Copied
 from transformers import AutoTokenizer, TFBertForQuestionAnswering
 import tensorflow as tf
 tokenizer = AutoTokenizer.from_pretrained("ydshieh/bert-base-cased-squad2")
 model = TFBertForQuestionAnswering.from_pretrained("ydshieh/bert-base-cased-squad2")
 question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
 inputs = tokenizer(question, text, return_tensors="tf")
 outputs = model(**inputs)
 answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
 answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])
 predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
 tokenizer.decode(predict_answer_tokens)
'a nice puppet'
	Copied
 # target is "nice puppet"
 target_start_index = tf.constant([14])
 target_end_index = tf.constant([15])
 outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
 loss = tf.math.reduce_mean(outputs.loss)
 round(float(loss), 2)
7.41
FlaxBertModel
	
class transformers.FlaxBertModel
<
source
>
(
config: BertConfig
input_shape: typing.Tuple = (1, 1)
seed: int = 0
dtype: dtype = <class 'jax.numpy.float32'>
_do_init: bool = True
gradient_checkpointing: bool = False
**kwargs
)
Parameters 
config (BertConfig) — 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.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
The bare Bert Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a Flax Linen flax.linen.Module
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.
Finally, this model supports inherent JAX features such as:
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
__call__
<
source
>
(
input_ids
attention_mask = None
token_type_ids = None
position_ids = None
head_mask = None
encoder_hidden_states = None
encoder_attention_mask = None
params: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
past_key_values: dict = None
)
→
transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters 
input_ids (numpy.ndarray of shape (batch_size, 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 (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
head_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
1 indicates the head is not masked,
0 indicates the head is masked.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (BertConfig) and inputs.
last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (jnp.ndarray of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The FlaxBertPreTrainedModel 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.
Example:
	Copied
 from transformers import AutoTokenizer, FlaxBertModel
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = FlaxBertModel.from_pretrained("bert-base-uncased")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
FlaxBertForPreTraining
	
class transformers.FlaxBertForPreTraining
<
source
>
(
config: BertConfig
input_shape: typing.Tuple = (1, 1)
seed: int = 0
dtype: dtype = <class 'jax.numpy.float32'>
_do_init: bool = True
gradient_checkpointing: bool = False
**kwargs
)
Parameters 
config (BertConfig) — 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.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
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 FlaxPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a Flax Linen flax.linen.Module
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.
Finally, this model supports inherent JAX features such as:
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
__call__
<
source
>
(
input_ids
attention_mask = None
token_type_ids = None
position_ids = None
head_mask = None
encoder_hidden_states = None
encoder_attention_mask = None
params: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
past_key_values: dict = None
)
→
transformers.models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (numpy.ndarray of shape (batch_size, 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 (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
head_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
1 indicates the head is not masked,
0 indicates the head is masked.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput or tuple(torch.FloatTensor)
A transformers.models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput 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.
prediction_logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
seq_relationship_logits (jnp.ndarray of shape (batch_size, 2)) — Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The FlaxBertPreTrainedModel 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.
Example:
	Copied
 from transformers import AutoTokenizer, FlaxBertForPreTraining
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = FlaxBertForPreTraining.from_pretrained("bert-base-uncased")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
 outputs = model(**inputs)
 prediction_logits = outputs.prediction_logits
 seq_relationship_logits = outputs.seq_relationship_logits
FlaxBertForCausalLM
	
class transformers.FlaxBertForCausalLM
<
source
>
(
config: BertConfig
input_shape: typing.Tuple = (1, 1)
seed: int = 0
dtype: dtype = <class 'jax.numpy.float32'>
_do_init: bool = True
gradient_checkpointing: bool = False
**kwargs
)
Parameters 
config (BertConfig) — 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.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
Bert Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for
autoregressive tasks.
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a Flax Linen flax.linen.Module
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.
Finally, this model supports inherent JAX features such as:
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
__call__
<
source
>
(
input_ids
attention_mask = None
token_type_ids = None
position_ids = None
head_mask = None
encoder_hidden_states = None
encoder_attention_mask = None
params: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
past_key_values: dict = None
)
→
transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
Parameters 
input_ids (numpy.ndarray of shape (batch_size, 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 (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
head_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
1 indicates the head is not masked,
0 indicates the head is masked.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (BertConfig) and inputs.
logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.
past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of jnp.ndarray tuples of length config.n_layers, with each tuple containing the cached key, value
states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting.
Only relevant if config.is_decoder = True.
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
The FlaxBertPreTrainedModel 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.
Example:
	Copied
 from transformers import AutoTokenizer, FlaxBertForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = FlaxBertForCausalLM.from_pretrained("bert-base-uncased")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
 outputs = model(**inputs)
 # retrieve logts for next token
 next_token_logits = outputs.logits[:, -1]
FlaxBertForMaskedLM
	
class transformers.FlaxBertForMaskedLM
<
source
>
(
config: BertConfig
input_shape: typing.Tuple = (1, 1)
seed: int = 0
dtype: dtype = <class 'jax.numpy.float32'>
_do_init: bool = True
gradient_checkpointing: bool = False
**kwargs
)
Parameters 
config (BertConfig) — 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.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
Bert Model with a language modeling head on top.
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a Flax Linen flax.linen.Module
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.
Finally, this model supports inherent JAX features such as:
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
__call__
<
source
>
(
input_ids
attention_mask = None
token_type_ids = None
position_ids = None
head_mask = None
encoder_hidden_states = None
encoder_attention_mask = None
params: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
past_key_values: dict = None
)
→
transformers.modeling_flax_outputs.FlaxMaskedLMOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (numpy.ndarray of shape (batch_size, 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 (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
head_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
1 indicates the head is not masked,
0 indicates the head is masked.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxMaskedLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxMaskedLMOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (BertConfig) and inputs.
logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The FlaxBertPreTrainedModel 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.
Example:
	Copied
 from transformers import AutoTokenizer, FlaxBertForMaskedLM
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = FlaxBertForMaskedLM.from_pretrained("bert-base-uncased")
 inputs = tokenizer("The capital of France is [MASK].", return_tensors="jax")
 outputs = model(**inputs)
 logits = outputs.logits
FlaxBertForNextSentencePrediction
	
class transformers.FlaxBertForNextSentencePrediction
<
source
>
(
config: BertConfig
input_shape: typing.Tuple = (1, 1)
seed: int = 0
dtype: dtype = <class 'jax.numpy.float32'>
_do_init: bool = True
gradient_checkpointing: bool = False
**kwargs
)
Parameters 
config (BertConfig) — 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.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
Bert Model with a next sentence prediction (classification) head on top.
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a Flax Linen flax.linen.Module
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.
Finally, this model supports inherent JAX features such as:
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
__call__
<
source
>
(
input_ids
attention_mask = None
token_type_ids = None
position_ids = None
head_mask = None
encoder_hidden_states = None
encoder_attention_mask = None
params: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
past_key_values: dict = None
)
→
transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (numpy.ndarray of shape (batch_size, 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 (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
head_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
1 indicates the head is not masked,
0 indicates the head is masked.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput 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.
logits (jnp.ndarray of shape (batch_size, 2)) — Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The FlaxBertPreTrainedModel 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.
Example:
	Copied
 from transformers import AutoTokenizer, FlaxBertForNextSentencePrediction
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = FlaxBertForNextSentencePrediction.from_pretrained("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="jax")
 outputs = model(**encoding)
 logits = outputs.logits
 assert logits[0, 0] < logits[0, 1]  # next sentence was random
FlaxBertForSequenceClassification
	
class transformers.FlaxBertForSequenceClassification
<
source
>
(
config: BertConfig
input_shape: typing.Tuple = (1, 1)
seed: int = 0
dtype: dtype = <class 'jax.numpy.float32'>
_do_init: bool = True
gradient_checkpointing: bool = False
**kwargs
)
Parameters 
config (BertConfig) — 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.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
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 FlaxPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a Flax Linen flax.linen.Module
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.
Finally, this model supports inherent JAX features such as:
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
__call__
<
source
>
(
input_ids
attention_mask = None
token_type_ids = None
position_ids = None
head_mask = None
encoder_hidden_states = None
encoder_attention_mask = None
params: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
past_key_values: dict = None
)
→
transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (numpy.ndarray of shape (batch_size, 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 (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
head_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
1 indicates the head is not masked,
0 indicates the head is masked.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (BertConfig) and inputs.
logits (jnp.ndarray of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The FlaxBertPreTrainedModel 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.
Example:
	Copied
 from transformers import AutoTokenizer, FlaxBertForSequenceClassification
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = FlaxBertForSequenceClassification.from_pretrained("bert-base-uncased")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
 outputs = model(**inputs)
 logits = outputs.logits
FlaxBertForMultipleChoice
	
class transformers.FlaxBertForMultipleChoice
<
source
>
(
config: BertConfig
input_shape: typing.Tuple = (1, 1)
seed: int = 0
dtype: dtype = <class 'jax.numpy.float32'>
_do_init: bool = True
gradient_checkpointing: bool = False
**kwargs
)
Parameters 
config (BertConfig) — 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.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
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 FlaxPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a Flax Linen flax.linen.Module
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.
Finally, this model supports inherent JAX features such as:
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
__call__
<
source
>
(
input_ids
attention_mask = None
token_type_ids = None
position_ids = None
head_mask = None
encoder_hidden_states = None
encoder_attention_mask = None
params: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
past_key_values: dict = None
)
→
transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (numpy.ndarray of shape (batch_size, num_choices, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (numpy.ndarray of shape (batch_size, num_choices, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (numpy.ndarray of shape (batch_size, num_choices, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (numpy.ndarray of shape (batch_size, num_choices, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
head_mask (numpy.ndarray of shape (batch_size, num_choices, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
1 indicates the head is not masked,
0 indicates the head is masked.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (BertConfig) and inputs.
logits (jnp.ndarray of shape (batch_size, num_choices)) — num_choices is the second dimension of the input tensors. (see input_ids above).
Classification scores (before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The FlaxBertPreTrainedModel 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.
Example:
	Copied
 from transformers import AutoTokenizer, FlaxBertForMultipleChoice
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = FlaxBertForMultipleChoice.from_pretrained("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."
 encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="jax", padding=True)
 outputs = model(**{k: v[None, :] for k, v in encoding.items()})
 logits = outputs.logits
FlaxBertForTokenClassification
	
class transformers.FlaxBertForTokenClassification
<
source
>
(
config: BertConfig
input_shape: typing.Tuple = (1, 1)
seed: int = 0
dtype: dtype = <class 'jax.numpy.float32'>
_do_init: bool = True
gradient_checkpointing: bool = False
**kwargs
)
Parameters 
config (BertConfig) — 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.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
Bert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a Flax Linen flax.linen.Module
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.
Finally, this model supports inherent JAX features such as:
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
__call__
<
source
>
(
input_ids
attention_mask = None
token_type_ids = None
position_ids = None
head_mask = None
encoder_hidden_states = None
encoder_attention_mask = None
params: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
past_key_values: dict = None
)
→
transformers.modeling_flax_outputs.FlaxTokenClassifierOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (numpy.ndarray of shape (batch_size, 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 (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
head_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
1 indicates the head is not masked,
0 indicates the head is masked.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxTokenClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxTokenClassifierOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (BertConfig) and inputs.
logits (jnp.ndarray of shape (batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The FlaxBertPreTrainedModel 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.
Example:
	Copied
 from transformers import AutoTokenizer, FlaxBertForTokenClassification
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = FlaxBertForTokenClassification.from_pretrained("bert-base-uncased")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
 outputs = model(**inputs)
 logits = outputs.logits
FlaxBertForQuestionAnswering
	
class transformers.FlaxBertForQuestionAnswering
<
source
>
(
config: BertConfig
input_shape: typing.Tuple = (1, 1)
seed: int = 0
dtype: dtype = <class 'jax.numpy.float32'>
_do_init: bool = True
gradient_checkpointing: bool = False
**kwargs
)
Parameters 
config (BertConfig) — 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.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute span start logits and span end logits).
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a Flax Linen flax.linen.Module
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.
Finally, this model supports inherent JAX features such as:
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
__call__
<
source
>
(
input_ids
attention_mask = None
token_type_ids = None
position_ids = None
head_mask = None
encoder_hidden_states = None
encoder_attention_mask = None
params: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
past_key_values: dict = None
)
→
transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (numpy.ndarray of shape (batch_size, 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 (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
token_type_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
head_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
1 indicates the head is not masked,
0 indicates the head is masked.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (BertConfig) and inputs.
start_logits (jnp.ndarray of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).
end_logits (jnp.ndarray of shape (batch_size, sequence_length)) — Span-end scores (before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The FlaxBertPreTrainedModel 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.
Example:
	Copied
 from transformers import AutoTokenizer, FlaxBertForQuestionAnswering
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = FlaxBertForQuestionAnswering.from_pretrained("bert-base-uncased")
 question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
 inputs = tokenizer(question, text, return_tensors="jax")
 outputs = model(**inputs)
 start_scores = outputs.start_logits
 end_scores = outputs.end_logits
←BARTpho
BertGeneration→
BERT
Overview
Resources
BertConfig
BertTokenizer
BertTokenizerFast
TFBertTokenizer
Bert specific outputs
BertModel
BertForPreTraining
BertLMHeadModel
BertForMaskedLM
BertForNextSentencePrediction
BertForSequenceClassification
BertForMultipleChoice
BertForTokenClassification
BertForQuestionAnswering
TFBertModel
TFBertForPreTraining
TFBertModelLMHeadModel
TFBertForMaskedLM
TFBertForNextSentencePrediction
TFBertForSequenceClassification
TFBertForMultipleChoice
TFBertForTokenClassification
TFBertForQuestionAnswering
FlaxBertModel
FlaxBertForPreTraining
FlaxBertForCausalLM
FlaxBertForMaskedLM
FlaxBertForNextSentencePrediction
FlaxBertForSequenceClassification
FlaxBertForMultipleChoice
FlaxBertForTokenClassification
FlaxBertForQuestionAnswering
