BERTΒΆ
OverviewΒΆ
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. Itβs 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 itβs 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.
This model was contributed by thomwolf. The original code can be found here.
BertConfigΒΆ
-
class
transformers.
BertConfig
(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, gradient_checkpointing=False, position_embedding_type='absolute', use_cache=True, **kwargs)[source]ΒΆ This is the configuration class to store the configuration of a
BertModel
or aTFBertModel
. 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 fromPretrainedConfig
for more information.- Parameters
vocab_size (
int
, optional, defaults to 30522) β Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by theinputs_ids
passed when callingBertModel
orTFBertModel
.hidden_size (
int
, optional, defaults to 768) β Dimensionality of the encoder layers and the pooler layer.num_hidden_layers (
int
, optional, defaults to 12) β Number of hidden layers in the Transformer encoder.num_attention_heads (
int
, optional, defaults to 12) β Number of attention heads for each attention layer in the Transformer encoder.intermediate_size (
int
, optional, defaults to 3072) β Dimensionality of the βintermediateβ (often named feed-forward) layer in the Transformer encoder.hidden_act (
str
orCallable
, optional, defaults to"gelu"
) β 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) β The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.attention_probs_dropout_prob (
float
, optional, defaults to 0.1) β The dropout ratio for the attention probabilities.max_position_embeddings (
int
, optional, defaults to 512) β 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) β The vocabulary size of thetoken_type_ids
passed when callingBertModel
orTFBertModel
.initializer_range (
float
, optional, defaults to 0.02) β The standard deviation of the truncated_normal_initializer for initializing all weight matrices.layer_norm_eps (
float
, optional, defaults to 1e-12) β The epsilon used by the layer normalization layers.gradient_checkpointing (
bool
, optional, defaults toFalse
) β If True, use gradient checkpointing to save memory at the expense of slower backward pass.position_embedding_type (
str
, optional, defaults to"absolute"
) β 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.).use_cache (
bool
, optional, defaults toTrue
) β Whether or not the model should return the last key/values attentions (not used by all models). Only relevant ifconfig.is_decoder=True
.
Examples:
>>> from transformers import BertModel, BertConfig >>> # Initializing a BERT bert-base-uncased style configuration >>> configuration = BertConfig() >>> # Initializing a model from the bert-base-uncased style configuration >>> model = BertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config
BertTokenizerΒΆ
-
class
transformers.
BertTokenizer
(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)[source]ΒΆ 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.- Parameters
vocab_file (
str
) β File containing the vocabulary.do_lower_case (
bool
, optional, defaults toTrue
) β Whether or not to lowercase the input when tokenizing.do_basic_tokenize (
bool
, optional, defaults toTrue
) β Whether or not to do basic tokenization before WordPiece.never_split (
Iterable
, optional) β Collection of tokens which will never be split during tokenization. Only has an effect whendo_basic_tokenize=True
unk_token (
str
, optional, defaults to"[UNK]"
) β 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]"
) β 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]"
) β The token used for padding, for example when batching sequences of different lengths.cls_token (
str
, optional, defaults to"[CLS]"
) β 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]"
) β 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 toTrue
) βWhether or not to tokenize Chinese characters.
This should likely be deactivated for Japanese (see this issue).
strip_accents β (
bool
, optional): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value forlowercase
(as in the original BERT).
-
build_inputs_with_special_tokens
(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int][source]ΒΆ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A BERT sequence has the following format:
single sequence:
[CLS] X [SEP]
pair of sequences:
[CLS] A [SEP] B [SEP]
- Parameters
token_ids_0 (
List[int]
) β List of IDs to which the special tokens will be added.token_ids_1 (
List[int]
, optional) β Optional second list of IDs for sequence pairs.
- Returns
List of input IDs with the appropriate special tokens.
- Return type
List[int]
-
create_token_type_ids_from_sequences
(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int][source]ΒΆ 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:
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
isNone
, this method only returns the first portion of the mask (0s).- Parameters
token_ids_0 (
List[int]
) β List of IDs.token_ids_1 (
List[int]
, optional) β Optional second list of IDs for sequence pairs.
- Returns
List of token type IDs according to the given sequence(s).
- Return type
List[int]
-
get_special_tokens_mask
(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False) → List[int][source]ΒΆ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer
prepare_for_model
method.- Parameters
token_ids_0 (
List[int]
) β List of IDs.token_ids_1 (
List[int]
, optional) β Optional second list of IDs for sequence pairs.already_has_special_tokens (
bool
, optional, defaults toFalse
) β Whether or not the token list is already formatted with special tokens for the model.
- Returns
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
- Return type
List[int]
-
save_vocabulary
(save_directory: str, filename_prefix: Optional[str] = None) → Tuple[str][source]ΒΆ Save only the vocabulary of the tokenizer (vocabulary + added tokens).
This method wonβt save the configuration and special token mappings of the tokenizer. Use
_save_pretrained()
to save the whole state of the tokenizer.- Parameters
save_directory (
str
) β The directory in which to save the vocabulary.filename_prefix (
str
, optional) β An optional prefix to add to the named of the saved files.
- Returns
Paths to the files saved.
- Return type
Tuple(str)
BertTokenizerFastΒΆ
-
class
transformers.
BertTokenizerFast
(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)[source]ΒΆ Construct a βfastβ BERT tokenizer (backed by HuggingFaceβs 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.- Parameters
vocab_file (
str
) β File containing the vocabulary.do_lower_case (
bool
, optional, defaults toTrue
) β Whether or not to lowercase the input when tokenizing.unk_token (
str
, optional, defaults to"[UNK]"
) β 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]"
) β 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]"
) β The token used for padding, for example when batching sequences of different lengths.cls_token (
str
, optional, defaults to"[CLS]"
) β 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]"
) β 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 toTrue
) β 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 toTrue
) β Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this issue).strip_accents β (
bool
, optional): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value forlowercase
(as in the original BERT).wordpieces_prefix β (
str
, optional, defaults to"##"
): The prefix for subwords.
-
build_inputs_with_special_tokens
(token_ids_0, token_ids_1=None)[source]ΒΆ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A BERT sequence has the following format:
single sequence:
[CLS] X [SEP]
pair of sequences:
[CLS] A [SEP] B [SEP]
- Parameters
token_ids_0 (
List[int]
) β List of IDs to which the special tokens will be added.token_ids_1 (
List[int]
, optional) β Optional second list of IDs for sequence pairs.
- Returns
List of input IDs with the appropriate special tokens.
- Return type
List[int]
-
create_token_type_ids_from_sequences
(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int][source]ΒΆ 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:
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
isNone
, this method only returns the first portion of the mask (0s).- Parameters
token_ids_0 (
List[int]
) β List of IDs.token_ids_1 (
List[int]
, optional) β Optional second list of IDs for sequence pairs.
- Returns
List of token type IDs according to the given sequence(s).
- Return type
List[int]
-
save_vocabulary
(save_directory: str, filename_prefix: Optional[str] = None) → Tuple[str][source]ΒΆ Save only the vocabulary of the tokenizer (vocabulary + added tokens).
This method wonβt save the configuration and special token mappings of the tokenizer. Use
_save_pretrained()
to save the whole state of the tokenizer.- Parameters
save_directory (
str
) β The directory in which to save the vocabulary.filename_prefix (
str
, optional) β An optional prefix to add to the named of the saved files.
- Returns
Paths to the files saved.
- Return type
Tuple(str)
-
slow_tokenizer_class
ΒΆ alias of
transformers.models.bert.tokenization_bert.BertTokenizer
Bert specific outputsΒΆ
-
class
transformers.models.bert.modeling_bert.
BertForPreTrainingOutput
(loss: Optional[torch.FloatTensor] = None, prediction_logits: torch.FloatTensor = None, seq_relationship_logits: torch.FloatTensor = None, hidden_states: Optional[Tuple[torch.FloatTensor]] = None, attentions: Optional[Tuple[torch.FloatTensor]] = None)[source]ΒΆ Output type of
BertForPreTraining
.- Parameters
loss (optional, returned when
labels
is provided,torch.FloatTensor
of shape(1,)
) β 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)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).seq_relationship_logits (
torch.FloatTensor
of shape(batch_size, 2)
) β Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) βTuple 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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) βTuple 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.
-
class
transformers.models.bert.modeling_tf_bert.
TFBertForPreTrainingOutput
(loss: Optional[tensorflow.python.framework.ops.Tensor] = None, prediction_logits: tensorflow.python.framework.ops.Tensor = None, seq_relationship_logits: tensorflow.python.framework.ops.Tensor = None, hidden_states: Optional[Union[Tuple[tensorflow.python.framework.ops.Tensor], tensorflow.python.framework.ops.Tensor]] = None, attentions: Optional[Union[Tuple[tensorflow.python.framework.ops.Tensor], tensorflow.python.framework.ops.Tensor]] = None)[source]ΒΆ Output type of
TFBertForPreTraining
.- Parameters
prediction_logits (
tf.Tensor
of shape(batch_size, sequence_length, config.vocab_size)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).seq_relationship_logits (
tf.Tensor
of shape(batch_size, 2)
) β Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) βTuple 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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) βTuple 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.
BertModelΒΆ
-
class
transformers.
BertModel
(config, add_pooling_layer=True)[source]ΒΆ 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.
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
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 toTrue
. To be used in a Seq2Seq model, the model needs to initialized with bothis_decoder
argument andadd_cross_attention
set toTrue
; anencoder_hidden_states
is then expected as an input to the forward pass.-
forward
(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]ΒΆ The
BertModel
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
torch.FloatTensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) β Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.encoder_hidden_states (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
, optional) β 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) βMask 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 lengthconfig.n_layers
with each tuple having 4 tensors of shape(batch_size, num_heads, sequence_length - 1, embed_size_per_head)
) βContains 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 lastdecoder_input_ids
(those that donβt have their past key value states given to this model) of shape(batch_size, 1)
instead of alldecoder_input_ids
of shape(batch_size, sequence_length)
.use_cache (
bool
, optional) β If set toTrue
,past_key_values
key value states are returned and can be used to speed up decoding (seepast_key_values
).
- Returns
A
BaseModelOutputWithPoolingAndCrossAttentions
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.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)
) β Sequence of hidden-states at the output of the last layer of the model.pooler_output (
torch.FloatTensor
of shape(batch_size, hidden_size)
) β 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(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.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 whenoutput_attentions=True
andconfig.add_cross_attention=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the decoderβs 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 whenuse_cache=True
is passed or whenconfig.use_cache=True
) β Tuple oftuple(torch.FloatTensor)
of lengthconfig.n_layers
, with each tuple having 2 tensors of shape(batch_size, num_heads, sequence_length, embed_size_per_head)
) and optionally ifconfig.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 (seepast_key_values
input) to speed up sequential decoding.
- Return type
BaseModelOutputWithPoolingAndCrossAttentions
ortuple(torch.FloatTensor)
Example:
>>> from transformers import BertTokenizer, BertModel >>> import torch >>> tokenizer = BertTokenizer.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
BertForPreTrainingΒΆ
-
class
transformers.
BertForPreTraining
(config)[source]ΒΆ 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.
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
forward
(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, next_sentence_label=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]ΒΆ The
BertForPreTraining
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
torch.FloatTensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) β Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.labels (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) β Labels for computing the masked language modeling loss. Indices should be in[-100, 0, ..., config.vocab_size]
(seeinput_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) βLabels 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 {}) β Used to hide legacy arguments that have been deprecated.
- Returns
A
BertForPreTrainingOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.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,)
) β 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)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).seq_relationship_logits (
torch.FloatTensor
of shape(batch_size, 2)
) β Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Example:
>>> from transformers import BertTokenizer, BertForPreTraining >>> import torch >>> tokenizer = BertTokenizer.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
- Return type
BertForPreTrainingOutput
ortuple(torch.FloatTensor)
BertLMHeadModelΒΆ
-
class
transformers.
BertLMHeadModel
(config)[source]ΒΆ 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.
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
forward
(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, labels=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]ΒΆ The
BertLMHeadModel
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
torch.FloatTensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) β Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.encoder_hidden_states (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
, optional) β 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) βMask 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) β Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in[-100, 0, ..., config.vocab_size]
(seeinput_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 lengthconfig.n_layers
with each tuple having 4 tensors of shape(batch_size, num_heads, sequence_length - 1, embed_size_per_head)
) βContains 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 lastdecoder_input_ids
(those that donβt have their past key value states given to this model) of shape(batch_size, 1)
instead of alldecoder_input_ids
of shape(batch_size, sequence_length)
.use_cache (
bool
, optional) β If set toTrue
,past_key_values
key value states are returned and can be used to speed up decoding (seepast_key_values
).
- Returns
A
CausalLMOutputWithCrossAttentions
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabels
is provided) β Language modeling loss (for next-token prediction).logits (
torch.FloatTensor
of shape(batch_size, sequence_length, config.vocab_size)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.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 whenuse_cache=True
is passed or whenconfig.use_cache=True
) β Tuple oftorch.FloatTensor
tuples of lengthconfig.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 ifconfig.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.
Example:
>>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig >>> import torch >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') >>> config = BertConfig.from_pretrained("bert-base-cased") >>> config.is_decoder = True >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.logits
- Return type
CausalLMOutputWithCrossAttentions
ortuple(torch.FloatTensor)
BertForMaskedLMΒΆ
-
class
transformers.
BertForMaskedLM
(config)[source]ΒΆ 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.
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
forward
(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]ΒΆ The
BertForMaskedLM
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
torch.FloatTensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) β Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.labels (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) β Labels for computing the masked language modeling loss. Indices should be in[-100, 0, ..., config.vocab_size]
(seeinput_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
A
MaskedLMOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabels
is provided) β Masked language modeling (MLM) loss.logits (
torch.FloatTensor
of shape(batch_size, sequence_length, config.vocab_size)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.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.
- Return type
MaskedLMOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import BertTokenizer, BertForMaskedLM >>> import torch >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') >>> model = BertForMaskedLM.from_pretrained('bert-base-uncased') >>> inputs = tokenizer("The capital of France is [MASK].", return_tensors="pt") >>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"] >>> outputs = model(**inputs, labels=labels) >>> loss = outputs.loss >>> logits = outputs.logits
BertForNextSentencePredictionΒΆ
-
class
transformers.
BertForNextSentencePrediction
(config)[source]ΒΆ 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.
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
forward
(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None, **kwargs)[source]ΒΆ The
BertForNextSentencePrediction
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
torch.FloatTensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) β Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.labels (
torch.LongTensor
of shape(batch_size,)
, optional) βLabels 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
A
NextSentencePredictorOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.loss (
torch.FloatTensor
of shape(1,)
, optional, returned whennext_sentence_label
is provided) β Next sequence prediction (classification) loss.logits (
torch.FloatTensor
of shape(batch_size, 2)
) β Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Example:
>>> from transformers import BertTokenizer, BertForNextSentencePrediction >>> import torch >>> tokenizer = BertTokenizer.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
- Return type
NextSentencePredictorOutput
ortuple(torch.FloatTensor)
BertForSequenceClassificationΒΆ
-
class
transformers.
BertForSequenceClassification
(config)[source]ΒΆ 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.
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
forward
(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]ΒΆ The
BertForSequenceClassification
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
torch.FloatTensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) β Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.labels (
torch.LongTensor
of shape(batch_size,)
, optional) β Labels for computing the sequence classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]
. Ifconfig.num_labels == 1
a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1
a classification loss is computed (Cross-Entropy).
- Returns
A
SequenceClassifierOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabels
is provided) β Classification (or regression if config.num_labels==1) loss.logits (
torch.FloatTensor
of shape(batch_size, config.num_labels)
) β Classification (or regression if config.num_labels==1) scores (before SoftMax).hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.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.
- Return type
SequenceClassifierOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import BertTokenizer, BertForSequenceClassification >>> import torch >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') >>> model = BertForSequenceClassification.from_pretrained('bert-base-uncased') >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> labels = torch.tensor([1]).unsqueeze(0) # Batch size 1 >>> outputs = model(**inputs, labels=labels) >>> loss = outputs.loss >>> logits = outputs.logits
BertForMultipleChoiceΒΆ
-
class
transformers.
BertForMultipleChoice
(config)[source]ΒΆ 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.
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
forward
(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]ΒΆ The
BertForMultipleChoice
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
torch.LongTensor
of shape(batch_size, num_choices, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
torch.FloatTensor
of shape(batch_size, num_choices, sequence_length)
, optional) βMask 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.
token_type_ids (
torch.LongTensor
of shape(batch_size, num_choices, sequence_length)
, optional) βSegment 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.
position_ids (
torch.LongTensor
of shape(batch_size, num_choices, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
torch.FloatTensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) β Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.labels (
torch.LongTensor
of shape(batch_size,)
, optional) β Labels for computing the multiple choice classification loss. Indices should be in[0, ..., num_choices-1]
wherenum_choices
is the size of the second dimension of the input tensors. (Seeinput_ids
above)
- Returns
A
MultipleChoiceModelOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.loss (
torch.FloatTensor
of shape (1,), optional, returned whenlabels
is provided) β Classification loss.logits (
torch.FloatTensor
of shape(batch_size, num_choices)
) β 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 whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.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.
- Return type
MultipleChoiceModelOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import BertTokenizer, BertForMultipleChoice >>> import torch >>> tokenizer = BertTokenizer.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
BertForTokenClassificationΒΆ
-
class
transformers.
BertForTokenClassification
(config)[source]ΒΆ 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.
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
forward
(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]ΒΆ The
BertForTokenClassification
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
torch.FloatTensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) β Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.labels (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) β Labels for computing the token classification loss. Indices should be in[0, ..., config.num_labels - 1]
.
- Returns
A
TokenClassifierOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabels
is provided) β Classification loss.logits (
torch.FloatTensor
of shape(batch_size, sequence_length, config.num_labels)
) β Classification scores (before SoftMax).hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.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.
- Return type
TokenClassifierOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import BertTokenizer, BertForTokenClassification >>> import torch >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') >>> model = BertForTokenClassification.from_pretrained('bert-base-uncased') >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> labels = torch.tensor([1] * inputs["input_ids"].size(1)).unsqueeze(0) # Batch size 1 >>> outputs = model(**inputs, labels=labels) >>> loss = outputs.loss >>> logits = outputs.logits
BertForQuestionAnsweringΒΆ
-
class
transformers.
BertForQuestionAnswering
(config)[source]ΒΆ 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.
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
forward
(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]ΒΆ The
BertForQuestionAnswering
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
torch.FloatTensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) β Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.start_positions (
torch.LongTensor
of shape(batch_size,)
, optional) β 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) β 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
A
QuestionAnsweringModelOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabels
is provided) β 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)
) β Span-start scores (before SoftMax).end_logits (
torch.FloatTensor
of shape(batch_size, sequence_length)
) β Span-end scores (before SoftMax).hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.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.
- Return type
QuestionAnsweringModelOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import BertTokenizer, BertForQuestionAnswering >>> import torch >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') >>> model = BertForQuestionAnswering.from_pretrained('bert-base-uncased') >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" >>> inputs = tokenizer(question, text, return_tensors='pt') >>> start_positions = torch.tensor([1]) >>> end_positions = torch.tensor([3]) >>> outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions) >>> loss = outputs.loss >>> start_scores = outputs.start_logits >>> end_scores = outputs.end_logits
TFBertModelΒΆ
-
class
transformers.
TFBertModel
(*args, **kwargs)[source]ΒΆ 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.
Note
TF 2.0 models accepts two formats as inputs:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using
tf.keras.Model.fit()
method which currently requires having all the tensors in the first argument of the model call function:model(inputs)
.If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
a single Tensor with
input_ids
only and nothing else:model(inputs_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])
ormodel([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})
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
call
(input_ids: Optional[Union[List[tensorflow.python.framework.ops.Tensor], List[numpy.ndarray], Dict[str, tensorflow.python.framework.ops.Tensor], Dict[str, numpy.ndarray], numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, attention_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, token_type_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, position_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, head_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, inputs_embeds: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, **kwargs) → Union[transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling, Tuple[tensorflow.python.framework.ops.Tensor]][source]ΒΆ The
TFBertModel
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
np.ndarray
,tf.Tensor
,List[tf.Tensor]
Dict[str, tf.Tensor]
orDict[str, np.ndarray]
and each example must have the shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.__call__()
andtransformers.PreTrainedTokenizer.encode()
for details.attention_mask (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
np.ndarray
ortf.Tensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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
ortf.Tensor
of shape(batch_size, sequence_length, hidden_size)
, optional) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
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) β Whether or not to return the hidden states of all layers. Seehidden_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) β Whether or not to return aModelOutput
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 toFalse
) β Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).
- Returns
A
TFBaseModelOutputWithPooling
or a tuple oftf.Tensor
(ifreturn_dict=False
is passed or whenconfig.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)
) β Sequence of hidden-states at the output of the last layer of the model.pooler_output (
tf.Tensor
of shape(batch_size, hidden_size)
) β 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, youβre often better with averaging or pooling the sequence of hidden-states for the whole input sequence.
hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftf.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftf.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.
- Return type
TFBaseModelOutputWithPooling
ortuple(tf.Tensor)
Example:
>>> from transformers import BertTokenizer, TFBertModel >>> import tensorflow as tf >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') >>> model = TFBertModel.from_pretrained('bert-base-cased') >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf") >>> outputs = model(inputs) >>> last_hidden_states = outputs.last_hidden_state
TFBertForPreTrainingΒΆ
-
class
transformers.
TFBertForPreTraining
(*args, **kwargs)[source]ΒΆ - 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.
Note
TF 2.0 models accepts two formats as inputs:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using
tf.keras.Model.fit()
method which currently requires having all the tensors in the first argument of the model call function:model(inputs)
.If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
a single Tensor with
input_ids
only and nothing else:model(inputs_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])
ormodel([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})
- Args:
- 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.
- config (
-
call
(input_ids: Optional[Union[List[tensorflow.python.framework.ops.Tensor], List[numpy.ndarray], Dict[str, tensorflow.python.framework.ops.Tensor], Dict[str, numpy.ndarray], numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, attention_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, token_type_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, position_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, head_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, inputs_embeds: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, next_sentence_label: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, training: Optional[bool] = False, **kwargs) → Union[transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput, Tuple[tensorflow.python.framework.ops.Tensor]][source]ΒΆ The
TFBertForPreTraining
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
np.ndarray
,tf.Tensor
,List[tf.Tensor]
Dict[str, tf.Tensor]
orDict[str, np.ndarray]
and each example must have the shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.__call__()
andtransformers.PreTrainedTokenizer.encode()
for details.attention_mask (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
np.ndarray
ortf.Tensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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
ortf.Tensor
of shape(batch_size, sequence_length, hidden_size)
, optional) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
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) β Whether or not to return the hidden states of all layers. Seehidden_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) β Whether or not to return aModelOutput
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 toFalse
) β Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).labels (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) β Labels for computing the masked language modeling loss. Indices should be in[-100, 0, ..., config.vocab_size]
(seeinput_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) βLabels 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 {}) β Used to hide legacy arguments that have been deprecated.
- Returns
A
TFBertForPreTrainingOutput
or a tuple oftf.Tensor
(ifreturn_dict=False
is passed or whenconfig.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)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).seq_relationship_logits (
tf.Tensor
of shape(batch_size, 2)
) β Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftf.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftf.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.
Examples:
>>> import tensorflow as tf >>> from transformers import BertTokenizer, TFBertForPreTraining >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') >>> model = TFBertForPreTraining.from_pretrained('bert-base-uncased') >>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1 >>> outputs = model(input_ids) >>> prediction_scores, seq_relationship_scores = outputs[:2]
- Return type
TFBertForPreTrainingOutput
ortuple(tf.Tensor)
TFBertModelLMHeadModelΒΆ
-
class
transformers.
TFBertLMHeadModel
(*args, **kwargs)[source]ΒΆ -
call
(input_ids: Optional[Union[List[tensorflow.python.framework.ops.Tensor], List[numpy.ndarray], Dict[str, tensorflow.python.framework.ops.Tensor], Dict[str, numpy.ndarray], numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, attention_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, token_type_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, position_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, head_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, inputs_embeds: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, training: Optional[bool] = False, **kwargs) → Union[transformers.modeling_tf_outputs.TFCausalLMOutput, Tuple[tensorflow.python.framework.ops.Tensor]][source]ΒΆ - labels (
tf.Tensor
ornp.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]
.
- Returns
A
TFCausalLMOutput
or a tuple oftf.Tensor
(ifreturn_dict=False
is passed or whenconfig.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 whenlabels
is provided) β Language modeling loss (for next-token prediction).logits (
tf.Tensor
of shape(batch_size, sequence_length, config.vocab_size)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftf.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftf.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.
- Return type
TFCausalLMOutput
ortuple(tf.Tensor)
Example:
>>> from transformers import BertTokenizer, TFBertLMHeadModel >>> import tensorflow as tf >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') >>> model = TFBertLMHeadModel.from_pretrained('bert-base-cased') >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf") >>> outputs = model(inputs) >>> logits = outputs.logits
- labels (
-
TFBertForMaskedLMΒΆ
-
class
transformers.
TFBertForMaskedLM
(*args, **kwargs)[source]ΒΆ 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.
Note
TF 2.0 models accepts two formats as inputs:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using
tf.keras.Model.fit()
method which currently requires having all the tensors in the first argument of the model call function:model(inputs)
.If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
a single Tensor with
input_ids
only and nothing else:model(inputs_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])
ormodel([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})
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
call
(input_ids: Optional[Union[List[tensorflow.python.framework.ops.Tensor], List[numpy.ndarray], Dict[str, tensorflow.python.framework.ops.Tensor], Dict[str, numpy.ndarray], numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, attention_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, token_type_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, position_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, head_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, inputs_embeds: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, training: Optional[bool] = False, **kwargs) → Union[transformers.modeling_tf_outputs.TFMaskedLMOutput, Tuple[tensorflow.python.framework.ops.Tensor]][source]ΒΆ The
TFBertForMaskedLM
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
np.ndarray
,tf.Tensor
,List[tf.Tensor]
Dict[str, tf.Tensor]
orDict[str, np.ndarray]
and each example must have the shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.__call__()
andtransformers.PreTrainedTokenizer.encode()
for details.attention_mask (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
np.ndarray
ortf.Tensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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
ortf.Tensor
of shape(batch_size, sequence_length, hidden_size)
, optional) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
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) β Whether or not to return the hidden states of all layers. Seehidden_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) β Whether or not to return aModelOutput
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 toFalse
) β 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
ornp.ndarray
of shape(batch_size, sequence_length)
, optional) β Labels for computing the masked language modeling loss. Indices should be in[-100, 0, ..., config.vocab_size]
(seeinput_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
A
TFMaskedLMOutput
or a tuple oftf.Tensor
(ifreturn_dict=False
is passed or whenconfig.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 whenlabels
is provided) β Masked language modeling (MLM) loss.logits (
tf.Tensor
of shape(batch_size, sequence_length, config.vocab_size)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftf.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftf.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.
- Return type
TFMaskedLMOutput
ortuple(tf.Tensor)
Example:
>>> from transformers import BertTokenizer, TFBertForMaskedLM >>> import tensorflow as tf >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') >>> model = TFBertForMaskedLM.from_pretrained('bert-base-cased') >>> inputs = tokenizer("The capital of France is [MASK].", return_tensors="tf") >>> inputs["labels"] = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"] >>> outputs = model(inputs) >>> loss = outputs.loss >>> logits = outputs.logits
TFBertForNextSentencePredictionΒΆ
-
class
transformers.
TFBertForNextSentencePrediction
(*args, **kwargs)[source]ΒΆ 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.
Note
TF 2.0 models accepts two formats as inputs:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using
tf.keras.Model.fit()
method which currently requires having all the tensors in the first argument of the model call function:model(inputs)
.If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
a single Tensor with
input_ids
only and nothing else:model(inputs_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])
ormodel([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})
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
call
(input_ids: Optional[Union[List[tensorflow.python.framework.ops.Tensor], List[numpy.ndarray], Dict[str, tensorflow.python.framework.ops.Tensor], Dict[str, numpy.ndarray], numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, attention_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, token_type_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, position_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, head_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, inputs_embeds: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, next_sentence_label: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, training: Optional[bool] = False, **kwargs) → Union[transformers.modeling_tf_outputs.TFNextSentencePredictorOutput, Tuple[tensorflow.python.framework.ops.Tensor]][source]ΒΆ The
TFBertForNextSentencePrediction
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
np.ndarray
,tf.Tensor
,List[tf.Tensor]
Dict[str, tf.Tensor]
orDict[str, np.ndarray]
and each example must have the shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.__call__()
andtransformers.PreTrainedTokenizer.encode()
for details.attention_mask (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
np.ndarray
ortf.Tensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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
ortf.Tensor
of shape(batch_size, sequence_length, hidden_size)
, optional) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
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) β Whether or not to return the hidden states of all layers. Seehidden_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) β Whether or not to return aModelOutput
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 toFalse
) β Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).
- Returns
A
TFNextSentencePredictorOutput
or a tuple oftf.Tensor
(ifreturn_dict=False
is passed or whenconfig.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 whennext_sentence_label
is provided) β Next sentence prediction loss.logits (
tf.Tensor
of shape(batch_size, 2)
) β Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftf.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftf.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.
Examples:
>>> import tensorflow as tf >>> from transformers import BertTokenizer, TFBertForNextSentencePrediction >>> tokenizer = BertTokenizer.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
- Return type
TFNextSentencePredictorOutput
ortuple(tf.Tensor)
TFBertForSequenceClassificationΒΆ
-
class
transformers.
TFBertForSequenceClassification
(*args, **kwargs)[source]ΒΆ 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.
Note
TF 2.0 models accepts two formats as inputs:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using
tf.keras.Model.fit()
method which currently requires having all the tensors in the first argument of the model call function:model(inputs)
.If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
a single Tensor with
input_ids
only and nothing else:model(inputs_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])
ormodel([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})
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
call
(input_ids: Optional[Union[List[tensorflow.python.framework.ops.Tensor], List[numpy.ndarray], Dict[str, tensorflow.python.framework.ops.Tensor], Dict[str, numpy.ndarray], numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, attention_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, token_type_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, position_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, head_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, inputs_embeds: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, training: Optional[bool] = False, **kwargs) → Union[transformers.modeling_tf_outputs.TFSequenceClassifierOutput, Tuple[tensorflow.python.framework.ops.Tensor]][source]ΒΆ The
TFBertForSequenceClassification
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
np.ndarray
,tf.Tensor
,List[tf.Tensor]
Dict[str, tf.Tensor]
orDict[str, np.ndarray]
and each example must have the shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.__call__()
andtransformers.PreTrainedTokenizer.encode()
for details.attention_mask (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
np.ndarray
ortf.Tensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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
ortf.Tensor
of shape(batch_size, sequence_length, hidden_size)
, optional) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
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) β Whether or not to return the hidden states of all layers. Seehidden_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) β Whether or not to return aModelOutput
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 toFalse
) β 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
ornp.ndarray
of shape(batch_size,)
, optional) β Labels for computing the sequence classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]
. Ifconfig.num_labels == 1
a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1
a classification loss is computed (Cross-Entropy).
- Returns
A
TFSequenceClassifierOutput
or a tuple oftf.Tensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.loss (
tf.Tensor
of shape(batch_size, )
, optional, returned whenlabels
is provided) β Classification (or regression if config.num_labels==1) loss.logits (
tf.Tensor
of shape(batch_size, config.num_labels)
) β Classification (or regression if config.num_labels==1) scores (before SoftMax).hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftf.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftf.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.
- Return type
TFSequenceClassifierOutput
ortuple(tf.Tensor)
Example:
>>> from transformers import BertTokenizer, TFBertForSequenceClassification >>> import tensorflow as tf >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') >>> model = TFBertForSequenceClassification.from_pretrained('bert-base-cased') >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf") >>> inputs["labels"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1 >>> outputs = model(inputs) >>> loss = outputs.loss >>> logits = outputs.logits
TFBertForMultipleChoiceΒΆ
-
class
transformers.
TFBertForMultipleChoice
(*args, **kwargs)[source]ΒΆ 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.
Note
TF 2.0 models accepts two formats as inputs:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using
tf.keras.Model.fit()
method which currently requires having all the tensors in the first argument of the model call function:model(inputs)
.If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
a single Tensor with
input_ids
only and nothing else:model(inputs_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])
ormodel([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})
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
call
(input_ids: Optional[Union[List[tensorflow.python.framework.ops.Tensor], List[numpy.ndarray], Dict[str, tensorflow.python.framework.ops.Tensor], Dict[str, numpy.ndarray], numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, attention_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, token_type_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, position_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, head_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, inputs_embeds: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, training: Optional[bool] = False, **kwargs) → Union[transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput, Tuple[tensorflow.python.framework.ops.Tensor]][source]ΒΆ The
TFBertForMultipleChoice
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
np.ndarray
,tf.Tensor
,List[tf.Tensor]
Dict[str, tf.Tensor]
orDict[str, np.ndarray]
and each example must have the shape(batch_size, num_choices, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.__call__()
andtransformers.PreTrainedTokenizer.encode()
for details.attention_mask (
np.ndarray
ortf.Tensor
of shape(batch_size, num_choices, sequence_length)
, optional) βMask 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.
token_type_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, num_choices, sequence_length)
, optional) βSegment 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.
position_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, num_choices, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
np.ndarray
ortf.Tensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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
ortf.Tensor
of shape(batch_size, num_choices, sequence_length, hidden_size)
, optional) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
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) β Whether or not to return the hidden states of all layers. Seehidden_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) β Whether or not to return aModelOutput
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 toFalse
) β 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
ornp.ndarray
of shape(batch_size,)
, optional) β Labels for computing the multiple choice classification loss. Indices should be in[0, ..., num_choices]
wherenum_choices
is the size of the second dimension of the input tensors. (Seeinput_ids
above)
- Returns
A
TFMultipleChoiceModelOutput
or a tuple oftf.Tensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.loss (
tf.Tensor
of shape (batch_size, ), optional, returned whenlabels
is provided) β Classification loss.logits (
tf.Tensor
of shape(batch_size, num_choices)
) β 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 whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftf.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftf.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.
- Return type
TFMultipleChoiceModelOutput
ortuple(tf.Tensor)
Example:
>>> from transformers import BertTokenizer, TFBertForMultipleChoice >>> import tensorflow as tf >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') >>> model = TFBertForMultipleChoice.from_pretrained('bert-base-cased') >>> 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
TFBertForTokenClassificationΒΆ
-
class
transformers.
TFBertForTokenClassification
(*args, **kwargs)[source]ΒΆ 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.
Note
TF 2.0 models accepts two formats as inputs:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using
tf.keras.Model.fit()
method which currently requires having all the tensors in the first argument of the model call function:model(inputs)
.If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
a single Tensor with
input_ids
only and nothing else:model(inputs_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])
ormodel([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})
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
call
(input_ids: Optional[Union[List[tensorflow.python.framework.ops.Tensor], List[numpy.ndarray], Dict[str, tensorflow.python.framework.ops.Tensor], Dict[str, numpy.ndarray], numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, attention_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, token_type_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, position_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, head_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, inputs_embeds: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, training: Optional[bool] = False, **kwargs) → Union[transformers.modeling_tf_outputs.TFTokenClassifierOutput, Tuple[tensorflow.python.framework.ops.Tensor]][source]ΒΆ The
TFBertForTokenClassification
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
np.ndarray
,tf.Tensor
,List[tf.Tensor]
Dict[str, tf.Tensor]
orDict[str, np.ndarray]
and each example must have the shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.__call__()
andtransformers.PreTrainedTokenizer.encode()
for details.attention_mask (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
np.ndarray
ortf.Tensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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
ortf.Tensor
of shape(batch_size, sequence_length, hidden_size)
, optional) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
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) β Whether or not to return the hidden states of all layers. Seehidden_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) β Whether or not to return aModelOutput
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 toFalse
) β 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
ornp.ndarray
of shape(batch_size, sequence_length)
, optional) β Labels for computing the token classification loss. Indices should be in[0, ..., config.num_labels - 1]
.
- Returns
A
TFTokenClassifierOutput
or a tuple oftf.Tensor
(ifreturn_dict=False
is passed or whenconfig.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 whenlabels
is provided) β Classification loss.logits (
tf.Tensor
of shape(batch_size, sequence_length, config.num_labels)
) β Classification scores (before SoftMax).hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftf.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftf.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.
- Return type
TFTokenClassifierOutput
ortuple(tf.Tensor)
Example:
>>> from transformers import BertTokenizer, TFBertForTokenClassification >>> import tensorflow as tf >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') >>> model = TFBertForTokenClassification.from_pretrained('bert-base-cased') >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf") >>> input_ids = inputs["input_ids"] >>> inputs["labels"] = tf.reshape(tf.constant([1] * tf.size(input_ids).numpy()), (-1, tf.size(input_ids))) # Batch size 1 >>> outputs = model(inputs) >>> loss = outputs.loss >>> logits = outputs.logits
TFBertForQuestionAnsweringΒΆ
-
class
transformers.
TFBertForQuestionAnswering
(*args, **kwargs)[source]ΒΆ 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.
Note
TF 2.0 models accepts two formats as inputs:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using
tf.keras.Model.fit()
method which currently requires having all the tensors in the first argument of the model call function:model(inputs)
.If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
a single Tensor with
input_ids
only and nothing else:model(inputs_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])
ormodel([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})
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
call
(input_ids: Optional[Union[List[tensorflow.python.framework.ops.Tensor], List[numpy.ndarray], Dict[str, tensorflow.python.framework.ops.Tensor], Dict[str, numpy.ndarray], numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, attention_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, token_type_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, position_ids: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, head_mask: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, inputs_embeds: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, start_positions: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, end_positions: Optional[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]] = None, training: Optional[bool] = False, **kwargs) → Union[transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput, Tuple[tensorflow.python.framework.ops.Tensor]][source]ΒΆ The
TFBertForQuestionAnswering
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
np.ndarray
,tf.Tensor
,List[tf.Tensor]
Dict[str, tf.Tensor]
orDict[str, np.ndarray]
and each example must have the shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.__call__()
andtransformers.PreTrainedTokenizer.encode()
for details.attention_mask (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
np.ndarray
ortf.Tensor
of shape(batch_size, sequence_length)
, optional) βIndices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
.head_mask (
np.ndarray
ortf.Tensor
of shape(num_heads,)
or(num_layers, num_heads)
, optional) βMask 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
ortf.Tensor
of shape(batch_size, sequence_length, hidden_size)
, optional) β Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the modelβs internal embedding lookup matrix.output_attentions (
bool
, optional) β Whether or not to return the attentions tensors of all attention layers. Seeattentions
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) β Whether or not to return the hidden states of all layers. Seehidden_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) β Whether or not to return aModelOutput
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 toFalse
) β 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
ornp.ndarray
of shape(batch_size,)
, optional) β 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
ornp.ndarray
of shape(batch_size,)
, optional) β 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
A
TFQuestionAnsweringModelOutput
or a tuple oftf.Tensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.loss (
tf.Tensor
of shape(batch_size, )
, optional, returned whenstart_positions
andend_positions
are provided) β 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)
) β Span-start scores (before SoftMax).end_logits (
tf.Tensor
of shape(batch_size, sequence_length)
) β Span-end scores (before SoftMax).hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftf.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftf.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.
- Return type
TFQuestionAnsweringModelOutput
ortuple(tf.Tensor)
Example:
>>> from transformers import BertTokenizer, TFBertForQuestionAnswering >>> import tensorflow as tf >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') >>> model = TFBertForQuestionAnswering.from_pretrained('bert-base-cased') >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" >>> input_dict = tokenizer(question, text, return_tensors='tf') >>> outputs = model(input_dict) >>> start_logits = outputs.start_logits >>> end_logits = outputs.end_logits >>> all_tokens = tokenizer.convert_ids_to_tokens(input_dict["input_ids"].numpy()[0]) >>> answer = ' '.join(all_tokens[tf.math.argmax(start_logits, 1)[0] : tf.math.argmax(end_logits, 1)[0]+1])
FlaxBertModelΒΆ
-
class
transformers.
FlaxBertModel
(config: transformers.models.bert.configuration_bert.BertConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: numpy.dtype = <class 'jax._src.numpy.lax_numpy.float32'>, **kwargs)[source]ΒΆ 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:
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
__call__
(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None)ΒΆ The
FlaxBertPreTrainedModel
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]
.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.
- Returns
A
FlaxBaseModelOutputWithPooling
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.last_hidden_state (
jax_xla.DeviceArray
of shape(batch_size, sequence_length, hidden_size)
) β Sequence of hidden-states at the output of the last layer of the model.pooler_output (
jax_xla.DeviceArray
of shape(batch_size, hidden_size)
) β 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(jax_xla.DeviceArray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjax_xla.DeviceArray
(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(jax_xla.DeviceArray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjax_xla.DeviceArray
(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.
- Return type
FlaxBaseModelOutputWithPooling
ortuple(torch.FloatTensor)
Example:
>>> from transformers import BertTokenizer, FlaxBertModel >>> tokenizer = BertTokenizer.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
FlaxBertForPreTrainingΒΆ
-
class
transformers.
FlaxBertForPreTraining
(config: transformers.models.bert.configuration_bert.BertConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: numpy.dtype = <class 'jax._src.numpy.lax_numpy.float32'>, **kwargs)[source]ΒΆ 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:
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
__call__
(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None)ΒΆ The
FlaxBertPreTrainedModel
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]
.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.
- Returns
A
FlaxBertForPreTrainingOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.prediction_logits (
jax_xla.DeviceArray
of shape(batch_size, sequence_length, config.vocab_size)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).seq_relationship_logits (
jax_xla.DeviceArray
of shape(batch_size, 2)
) β Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).hidden_states (
tuple(jax_xla.DeviceArray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjax_xla.DeviceArray
(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(jax_xla.DeviceArray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjax_xla.DeviceArray
(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.
- Return type
FlaxBertForPreTrainingOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import BertTokenizer, FlaxBertForPreTraining >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') >>> model = FlaxBertForPreTraining.from_pretrained('bert-base-uncased') >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax") >>> outputs = model(**inputs) >>> prediction_logits = outputs.prediction_logits >>> seq_relationship_logits = outputs.seq_relationship_logits
FlaxBertForMaskedLMΒΆ
-
class
transformers.
FlaxBertForMaskedLM
(config: transformers.models.bert.configuration_bert.BertConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: numpy.dtype = <class 'jax._src.numpy.lax_numpy.float32'>, **kwargs)[source]ΒΆ 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:
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
__call__
(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None)ΒΆ The
FlaxBertPreTrainedModel
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]
.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.
- Returns
A
FlaxMaskedLMOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.logits (
jax_xla.DeviceArray
of shape(batch_size, sequence_length, config.vocab_size)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).hidden_states (
tuple(jax_xla.DeviceArray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjax_xla.DeviceArray
(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(jax_xla.DeviceArray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjax_xla.DeviceArray
(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.
- Return type
FlaxMaskedLMOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import BertTokenizer, FlaxBertForMaskedLM >>> tokenizer = BertTokenizer.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
FlaxBertForNextSentencePredictionΒΆ
-
class
transformers.
FlaxBertForNextSentencePrediction
(config: transformers.models.bert.configuration_bert.BertConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: numpy.dtype = <class 'jax._src.numpy.lax_numpy.float32'>, **kwargs)[source]ΒΆ 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:
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
__call__
(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None)ΒΆ The
FlaxBertPreTrainedModel
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]
.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.
- Returns
A
FlaxNextSentencePredictorOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.logits (
jax_xla.DeviceArray
of shape(batch_size, 2)
) β Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).hidden_states (
tuple(jax_xla.DeviceArray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjax_xla.DeviceArray
(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(jax_xla.DeviceArray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjax_xla.DeviceArray
(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.
- Return type
FlaxNextSentencePredictorOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import BertTokenizer, FlaxBertForNextSentencePrediction >>> tokenizer = BertTokenizer.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
FlaxBertForSequenceClassificationΒΆ
-
class
transformers.
FlaxBertForSequenceClassification
(config: transformers.models.bert.configuration_bert.BertConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: numpy.dtype = <class 'jax._src.numpy.lax_numpy.float32'>, **kwargs)[source]ΒΆ 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:
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
__call__
(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None)ΒΆ The
FlaxBertPreTrainedModel
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]
.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.
- Returns
A
FlaxSequenceClassifierOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.logits (
jax_xla.DeviceArray
of shape(batch_size, config.num_labels)
) β Classification (or regression if config.num_labels==1) scores (before SoftMax).hidden_states (
tuple(jax_xla.DeviceArray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjax_xla.DeviceArray
(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(jax_xla.DeviceArray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjax_xla.DeviceArray
(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.
- Return type
FlaxSequenceClassifierOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import BertTokenizer, FlaxBertForSequenceClassification >>> tokenizer = BertTokenizer.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, labels=labels) >>> logits = outputs.logits
FlaxBertForMultipleChoiceΒΆ
-
class
transformers.
FlaxBertForMultipleChoice
(config: transformers.models.bert.configuration_bert.BertConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: numpy.dtype = <class 'jax._src.numpy.lax_numpy.float32'>, **kwargs)[source]ΒΆ 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:
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
__call__
(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None)ΒΆ The
FlaxBertPreTrainedModel
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
numpy.ndarray
of shape(batch_size, num_choices, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
numpy.ndarray
of shape(batch_size, num_choices, sequence_length)
, optional) βMask 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.
token_type_ids (
numpy.ndarray
of shape(batch_size, num_choices, sequence_length)
, optional) βSegment 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.
position_ids (
numpy.ndarray
of shape(batch_size, num_choices, sequence_length)
, optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]
.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.
- Returns
A
FlaxMultipleChoiceModelOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.logits (
jax_xla.DeviceArray
of shape(batch_size, num_choices)
) β num_choices is the second dimension of the input tensors. (see input_ids above).Classification scores (before SoftMax).
hidden_states (
tuple(jax_xla.DeviceArray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjax_xla.DeviceArray
(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(jax_xla.DeviceArray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjax_xla.DeviceArray
(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.
- Return type
FlaxMultipleChoiceModelOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import BertTokenizer, FlaxBertForMultipleChoice >>> tokenizer = BertTokenizer.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
FlaxBertForTokenClassificationΒΆ
-
class
transformers.
FlaxBertForTokenClassification
(config: transformers.models.bert.configuration_bert.BertConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: numpy.dtype = <class 'jax._src.numpy.lax_numpy.float32'>, **kwargs)[source]ΒΆ 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:
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
__call__
(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None)ΒΆ The
FlaxBertPreTrainedModel
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]
.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.
- Returns
A
FlaxTokenClassifierOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.logits (
jax_xla.DeviceArray
of shape(batch_size, sequence_length, config.num_labels)
) β Classification scores (before SoftMax).hidden_states (
tuple(jax_xla.DeviceArray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjax_xla.DeviceArray
(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(jax_xla.DeviceArray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjax_xla.DeviceArray
(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.
- Return type
FlaxTokenClassifierOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import BertTokenizer, FlaxBertForTokenClassification >>> tokenizer = BertTokenizer.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
FlaxBertForQuestionAnsweringΒΆ
-
class
transformers.
FlaxBertForQuestionAnswering
(config: transformers.models.bert.configuration_bert.BertConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: numpy.dtype = <class 'jax._src.numpy.lax_numpy.float32'>, **kwargs)[source]ΒΆ 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:
- Parameters
config (
BertConfig
) β 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 thefrom_pretrained()
method to load the model weights.
-
__call__
(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None)ΒΆ The
FlaxBertPreTrainedModel
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
) βIndices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.attention_mask (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) βMask 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.
token_type_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) βSegment 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.
position_ids (
numpy.ndarray
of shape(batch_size, sequence_length)
, optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]
.return_dict (
bool
, optional) β Whether or not to return aModelOutput
instead of a plain tuple.
- Returns
A
FlaxQuestionAnsweringModelOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (BertConfig
) and inputs.start_logits (
jax_xla.DeviceArray
of shape(batch_size, sequence_length)
) β Span-start scores (before SoftMax).end_logits (
jax_xla.DeviceArray
of shape(batch_size, sequence_length)
) β Span-end scores (before SoftMax).hidden_states (
tuple(jax_xla.DeviceArray)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple ofjax_xla.DeviceArray
(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(jax_xla.DeviceArray)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple ofjax_xla.DeviceArray
(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.
- Return type
FlaxQuestionAnsweringModelOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import BertTokenizer, FlaxBertForQuestionAnswering >>> tokenizer = BertTokenizer.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