This model was released on 2018-10-11 and added to Hugging Face Transformers on 2020-11-16.
BERT
BERT is a bidirectional transformer pretrained on unlabeled text to predict masked tokens in a sentence and to predict whether one sentence follows another. The main idea is that by randomly masking some tokens, the model can train on text to the left and right, giving it a more thorough understanding. BERT is also very versatile because its learned language representations can be adapted for other NLP tasks by fine-tuning an additional layer or head.
You can find all the original BERT checkpoints under theBERT collection.
Click on the BERT models in the right sidebar for more examples of how to apply BERT to different language tasks.
The example below demonstrates how to predict the[MASK] token withPipeline,AutoModel, and from the command line.
import torchfrom transformersimport pipelinepipeline = pipeline( task="fill-mask", model="google-bert/bert-base-uncased", dtype=torch.float16, device=0)pipeline("Plants create [MASK] through a process known as photosynthesis.")
Notes
- Inputs should be padded on the right because BERT uses absolute position embeddings.
BertConfig
classtransformers.BertConfig
<source>(vocab_size = 30522hidden_size = 768num_hidden_layers = 12num_attention_heads = 12intermediate_size = 3072hidden_act = 'gelu'hidden_dropout_prob = 0.1attention_probs_dropout_prob = 0.1max_position_embeddings = 512type_vocab_size = 2initializer_range = 0.02layer_norm_eps = 1e-12pad_token_id = 0use_cache = Trueclassifier_dropout = None**kwargs)
Parameters
- vocab_size (
int,optional, defaults to 30522) —Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by theinputs_idspassed when callingBertModel orTFBertModel. - hidden_size (
int,optional, defaults to 768) —Dimensionality of the encoder layers and the pooler layer. - num_hidden_layers (
int,optional, defaults to 12) —Number of hidden layers in the Transformer encoder. - num_attention_heads (
int,optional, defaults to 12) —Number of attention heads for each attention layer in the Transformer encoder. - intermediate_size (
int,optional, defaults to 3072) —Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder. - hidden_act (
strorCallable,optional, defaults to"gelu") —The non-linear activation function (function or string) in the encoder and pooler. If string,"gelu","relu","silu"and"gelu_new"are supported. - hidden_dropout_prob (
float,optional, defaults to 0.1) —The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. - attention_probs_dropout_prob (
float,optional, defaults to 0.1) —The dropout ratio for the attention probabilities. - max_position_embeddings (
int,optional, defaults to 512) —The maximum sequence length that this model might ever be used with. Typically set this to something largejust in case (e.g., 512 or 1024 or 2048). - type_vocab_size (
int,optional, defaults to 2) —The vocabulary size of thetoken_type_idspassed when callingBertModel orTFBertModel. - initializer_range (
float,optional, defaults to 0.02) —The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - layer_norm_eps (
float,optional, defaults to 1e-12) —The epsilon used by the layer normalization layers. - is_decoder (
bool,optional, defaults toFalse) —Whether the model is used as a decoder or not. IfFalse, the model is used as an encoder. - use_cache (
bool,optional, defaults toTrue) —Whether or not the model should return the last key/values attentions (not used by all models). Onlyrelevant ifconfig.is_decoder=True. - classifier_dropout (
float,optional) —The dropout ratio for the classification head.
This is the configuration class to store the configuration of aBertModel or aTFBertModel. It is used toinstantiate a BERT model according to the specified arguments, defining the model architecture. Instantiating aconfiguration with the defaults will yield a similar configuration to that of the BERTgoogle-bert/bert-base-uncased architecture.
Configuration objects inherit fromPreTrainedConfig and can be used to control the model outputs. Read thedocumentation fromPreTrainedConfig for more information.
Examples:
>>>from transformersimport BertConfig, BertModel>>># Initializing a BERT google-bert/bert-base-uncased style configuration>>>configuration = BertConfig()>>># Initializing a model (with random weights) from the google-bert/bert-base-uncased style configuration>>>model = BertModel(configuration)>>># Accessing the model configuration>>>configuration = model.config
BertTokenizer
classtransformers.BertTokenizer
<source>(vocab_filedo_lower_case = Truedo_basic_tokenize = Truenever_split = Noneunk_token = '[UNK]'sep_token = '[SEP]'pad_token = '[PAD]'cls_token = '[CLS]'mask_token = '[MASK]'tokenize_chinese_chars = Truestrip_accents = Noneclean_up_tokenization_spaces = True**kwargs)
Parameters
- vocab_file (
str) —File containing the vocabulary. - do_lower_case (
bool,optional, defaults toTrue) —Whether or not to lowercase the input when tokenizing. - do_basic_tokenize (
bool,optional, defaults toTrue) —Whether or not to do basic tokenization before WordPiece. - never_split (
Iterable,optional) —Collection of tokens which will never be split during tokenization. Only has an effect whendo_basic_tokenize=True - unk_token (
str,optional, defaults to"[UNK]") —The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be thistoken instead. - sep_token (
str,optional, defaults to"[SEP]") —The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences forsequence classification or for a text and a question for question answering. It is also used as the lasttoken of a sequence built with special tokens. - pad_token (
str,optional, defaults to"[PAD]") —The token used for padding, for example when batching sequences of different lengths. - cls_token (
str,optional, defaults to"[CLS]") —The classifier token which is used when doing sequence classification (classification of the whole sequenceinstead of per-token classification). It is the first token of the sequence when built with special tokens. - mask_token (
str,optional, defaults to"[MASK]") —The token used for masking values. This is the token used when training this model with masked languagemodeling. This is the token which the model will try to predict. - tokenize_chinese_chars (
bool,optional, defaults toTrue) —Whether or not to tokenize Chinese characters.This should likely be deactivated for Japanese (see thisissue).
- strip_accents (
bool,optional) —Whether or not to strip all accents. If this option is not specified, then it will be determined by thevalue forlowercase(as in the original BERT). - clean_up_tokenization_spaces (
bool,optional, defaults toTrue) —Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts likeextra spaces.
Construct a BERT tokenizer. Based on WordPiece.
This tokenizer inherits fromPreTrainedTokenizer which contains most of the main methods. Users should refer tothis superclass for more information regarding those methods.
build_inputs_with_special_tokens
<source>(token_ids_0: listtoken_ids_1: typing.Optional[list[int]] = None)→List[int]
Parameters
- token_ids_0 (
List[int]) —List of IDs to which the special tokens will be added. - token_ids_1 (
List[int],optional) —Optional second list of IDs for sequence pairs.
Returns
List[int]
List ofinput IDs with the appropriate special tokens.
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating andadding special tokens. A BERT sequence has the following format:
- single sequence:
[CLS] X [SEP] - pair of sequences:
[CLS] A [SEP] B [SEP]
get_special_tokens_mask
<source>(token_ids_0: listtoken_ids_1: typing.Optional[list[int]] = Nonealready_has_special_tokens: bool = False)→List[int]
Parameters
- token_ids_0 (
List[int]) —List of IDs. - token_ids_1 (
List[int],optional) —Optional second list of IDs for sequence pairs. - already_has_special_tokens (
bool,optional, defaults toFalse) —Whether or not the token list is already formatted with special tokens for the model.
Returns
List[int]
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
Retrieve sequence ids from a token list that has no special tokens added. This method is called when addingspecial tokens using the tokenizerprepare_for_model method.
create_token_type_ids_from_sequences
<source>(token_ids_0: list[int]token_ids_1: Optional[list[int]] = None)→list[int]
Create the token type IDs corresponding to the sequences passed.What are token typeIDs?
Should be overridden in a subclass if the model has a special way of building those.
save_vocabulary
<source>(save_directory: strfilename_prefix: typing.Optional[str] = None)
BertTokenizerFast
classtransformers.BertTokenizerFast
<source>(vocab_file = Nonetokenizer_file = Nonedo_lower_case = Trueunk_token = '[UNK]'sep_token = '[SEP]'pad_token = '[PAD]'cls_token = '[CLS]'mask_token = '[MASK]'tokenize_chinese_chars = Truestrip_accents = None**kwargs)
Parameters
- vocab_file (
str) —File containing the vocabulary. - do_lower_case (
bool,optional, defaults toTrue) —Whether or not to lowercase the input when tokenizing. - unk_token (
str,optional, defaults to"[UNK]") —The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be thistoken instead. - sep_token (
str,optional, defaults to"[SEP]") —The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences forsequence classification or for a text and a question for question answering. It is also used as the lasttoken of a sequence built with special tokens. - pad_token (
str,optional, defaults to"[PAD]") —The token used for padding, for example when batching sequences of different lengths. - cls_token (
str,optional, defaults to"[CLS]") —The classifier token which is used when doing sequence classification (classification of the whole sequenceinstead of per-token classification). It is the first token of the sequence when built with special tokens. - mask_token (
str,optional, defaults to"[MASK]") —The token used for masking values. This is the token used when training this model with masked languagemodeling. This is the token which the model will try to predict. - clean_text (
bool,optional, defaults toTrue) —Whether or not to clean the text before tokenization by removing any control characters and replacing allwhitespaces by the classic one. - tokenize_chinese_chars (
bool,optional, defaults toTrue) —Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (seethisissue). - strip_accents (
bool,optional) —Whether or not to strip all accents. If this option is not specified, then it will be determined by thevalue forlowercase(as in the original BERT). - wordpieces_prefix (
str,optional, defaults to"##") —The prefix for subwords.
Construct a “fast” BERT tokenizer (backed by HuggingFace’stokenizers library). Based on WordPiece.
This tokenizer inherits fromPreTrainedTokenizerFast which contains most of the main methods. Users shouldrefer to this superclass for more information regarding those methods.
build_inputs_with_special_tokens
<source>(token_ids_0token_ids_1 = None)→List[int]
Parameters
- token_ids_0 (
List[int]) —List of IDs to which the special tokens will be added. - token_ids_1 (
List[int],optional) —Optional second list of IDs for sequence pairs.
Returns
List[int]
List ofinput IDs with the appropriate special tokens.
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating andadding special tokens. A BERT sequence has the following format:
- single sequence:
[CLS] X [SEP] - pair of sequences:
[CLS] A [SEP] B [SEP]
BertModel
classtransformers.BertModel
<source>(configadd_pooling_layer = True)
Parameters
- config (BertModel) —Model configuration class with all the parameters of the model. Initializing with a config file does notload the weights associated with the model, only the configuration. Check out thefrom_pretrained() method to load the model weights.
- add_pooling_layer (
bool,optional, defaults toTrue) —Whether to add a pooling layer
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer ofcross-attention is added between the self-attention layers, following the architecture described inAttention isall 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 theis_decoder argument of the configuration settoTrue. 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.
This model inherits fromPreTrainedModel. Check the superclass documentation for the generic methods thelibrary implements for all its model (such as downloading or saving, resizing the input embeddings, pruning headsetc.)
This model is also a PyTorchtorch.nn.Module subclass.Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usageand behavior.
forward
<source>(input_ids: typing.Optional[torch.Tensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Nonetoken_type_ids: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.Tensor] = Noneinputs_embeds: typing.Optional[torch.Tensor] = Noneencoder_hidden_states: typing.Optional[torch.Tensor] = Noneencoder_attention_mask: typing.Optional[torch.Tensor] = Nonepast_key_values: typing.Optional[transformers.cache_utils.Cache] = Noneuse_cache: typing.Optional[bool] = Nonecache_position: typing.Optional[torch.Tensor] = None**kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs])→transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions ortuple(torch.FloatTensor)
Parameters
- input_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.Indices can be obtained usingAutoTokenizer. SeePreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
- attention_mask (
torch.Tensorof shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that arenot masked,
- 0 for tokens that aremasked.
- token_type_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to asentence A token,
- 1 corresponds to asentence B token.
- position_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.n_positions - 1]. - inputs_embeds (
torch.Tensorof shape(batch_size, sequence_length, hidden_size),optional) —Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. Thisis useful if you want more control over how to convertinput_idsindices into associated vectors than themodel’s internal embedding lookup matrix. - encoder_hidden_states (
torch.Tensorof shape(batch_size, sequence_length, hidden_size),optional) —Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attentionif the model is configured as a decoder. - encoder_attention_mask (
torch.Tensorof shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used inthe cross-attention if the model is configured as a decoder. Mask values selected in[0, 1]:- 1 for tokens that arenot masked,
- 0 for tokens that aremasked.
- past_key_values (
~cache_utils.Cache,optional) —Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attentionblocks) that can be used to speed up sequential decoding. This typically consists in thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=Trueorconfig.use_cache=True.OnlyCache instance is allowed as input, see ourkv cache guide.If no
past_key_valuesare passed,DynamicCache will be initialized by default.The model will output the same cache format that is fed as input.
If
past_key_valuesare used, the user is expected to input only unprocessedinput_ids(those that don’thave their past key value states given to this model) of shape(batch_size, unprocessed_length)instead of allinput_idsof shape(batch_size, sequence_length). - use_cache (
bool,optional) —If set toTrue,past_key_valueskey value states are returned and can be used to speed up decoding (seepast_key_values). - cache_position (
torch.Tensorof shape(sequence_length),optional) —Indices depicting the position of the input sequence tokens in the sequence. Contrarily toposition_ids,this tensor is not affected by padding. It is used to update the cache in the correct position and to inferthe complete sequence length.
Returns
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions ortuple(torch.FloatTensor)
Atransformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (BertConfig) and inputs.
last_hidden_state (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.pooler_output (
torch.FloatTensorof shape(batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) after further processingthrough the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returnsthe classification token after processing through a linear layer and a tanh activation function. The linearlayer weights are trained from the next sentence prediction (classification) objective during pretraining.hidden_states (
tuple(torch.FloatTensor),optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings, if the model has an embedding layer, +one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (
tuple(torch.FloatTensor),optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — 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-attentionheads.
cross_attentions (
tuple(torch.FloatTensor),optional, returned whenoutput_attentions=Trueandconfig.add_cross_attention=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.FloatTensor(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length).Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute theweighted average in the cross-attention heads.
past_key_values (
Cache,optional, returned whenuse_cache=Trueis passed or whenconfig.use_cache=True) — It is aCache instance. For more details, see ourkv cache guide.Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
config.is_encoder_decoder=Truein the cross-attention blocks) that can be used (seepast_key_valuesinput) to speed up sequential decoding.
TheBertModel forward method, overrides the__call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps whilethe latter silently ignores them.
BertForPreTraining
classtransformers.BertForPreTraining
<source>(config)
Parameters
- config (BertForPreTraining) —Model configuration class with all the parameters of the model. Initializing with a config file does notload the weights associated with the model, only the configuration. Check out thefrom_pretrained() method to load the model weights.
Bert Model with two heads on top as done during the pretraining: amasked language modeling head and anext sentence prediction (classification) head.
This model inherits fromPreTrainedModel. Check the superclass documentation for the generic methods thelibrary implements for all its model (such as downloading or saving, resizing the input embeddings, pruning headsetc.)
This model is also a PyTorchtorch.nn.Module subclass.Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usageand behavior.
forward
<source>(input_ids: typing.Optional[torch.Tensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Nonetoken_type_ids: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.Tensor] = Noneinputs_embeds: typing.Optional[torch.Tensor] = Nonelabels: typing.Optional[torch.Tensor] = Nonenext_sentence_label: typing.Optional[torch.Tensor] = None**kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs])→transformers.models.bert.modeling_bert.BertForPreTrainingOutput ortuple(torch.FloatTensor)
Parameters
- input_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.Indices can be obtained usingAutoTokenizer. SeePreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
- attention_mask (
torch.Tensorof shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that arenot masked,
- 0 for tokens that aremasked.
- token_type_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to asentence A token,
- 1 corresponds to asentence B token.
- position_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.n_positions - 1]. - inputs_embeds (
torch.Tensorof shape(batch_size, sequence_length, hidden_size),optional) —Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. Thisis useful if you want more control over how to convertinput_idsindices into associated vectors than themodel’s internal embedding lookup matrix. - labels (
torch.LongTensorof shape(batch_size, sequence_length),optional) —Labels for computing the masked language modeling loss. Indices should be in[-100, 0, ..., config.vocab_size](seeinput_idsdocstring) Tokens with indices set to-100are ignored (masked),the loss is only computed for the tokens with labels in[0, ..., config.vocab_size] - next_sentence_label (
torch.LongTensorof shape(batch_size,),optional) —Labels for computing the next sequence prediction (classification) loss. Input should be a sequencepair (seeinput_idsdocstring) Indices should be in[0, 1]:- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
Returns
transformers.models.bert.modeling_bert.BertForPreTrainingOutput ortuple(torch.FloatTensor)
Atransformers.models.bert.modeling_bert.BertForPreTrainingOutput or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (BertConfig) and inputs.
loss (
*optional*, returned whenlabelsis provided,torch.FloatTensorof shape(1,)) — Total loss as the sum of the masked language modeling loss and the next sequence prediction(classification) loss.prediction_logits (
torch.FloatTensorof shape(batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).seq_relationship_logits (
torch.FloatTensorof shape(batch_size, 2)) — Prediction scores of the next sequence prediction (classification) head (scores of True/False continuationbefore SoftMax).hidden_states (
tuple[torch.FloatTensor],optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings, if the model has an embedding layer, +one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (
tuple[torch.FloatTensor],optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — 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-attentionheads.
TheBertForPreTraining forward method, overrides the__call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps whilethe latter silently ignores them.
Example:
>>>from transformersimport AutoTokenizer, BertForPreTraining>>>import torch>>>tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")>>>model = BertForPreTraining.from_pretrained("google-bert/bert-base-uncased")>>>inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")>>>outputs = model(**inputs)>>>prediction_logits = outputs.prediction_logits>>>seq_relationship_logits = outputs.seq_relationship_logits
BertLMHeadModel
classtransformers.BertLMHeadModel
<source>(config)
Parameters
- config (BertLMHeadModel) —Model configuration class with all the parameters of the model. Initializing with a config file does notload the weights associated with the model, only the configuration. Check out thefrom_pretrained() method to load the model weights.
Bert Model with alanguage modeling head on top for CLM fine-tuning.
This model inherits fromPreTrainedModel. Check the superclass documentation for the generic methods thelibrary implements for all its model (such as downloading or saving, resizing the input embeddings, pruning headsetc.)
This model is also a PyTorchtorch.nn.Module subclass.Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usageand behavior.
forward
<source>(input_ids: typing.Optional[torch.Tensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Nonetoken_type_ids: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.Tensor] = Noneinputs_embeds: typing.Optional[torch.Tensor] = Noneencoder_hidden_states: typing.Optional[torch.Tensor] = Noneencoder_attention_mask: typing.Optional[torch.Tensor] = Nonelabels: typing.Optional[torch.Tensor] = Nonepast_key_values: typing.Optional[transformers.cache_utils.Cache] = Noneuse_cache: typing.Optional[bool] = Nonecache_position: typing.Optional[torch.Tensor] = Nonelogits_to_keep: typing.Union[int, torch.Tensor] = 0**kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs])→transformers.modeling_outputs.CausalLMOutputWithCrossAttentions ortuple(torch.FloatTensor)
Parameters
- input_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.Indices can be obtained usingAutoTokenizer. SeePreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
- attention_mask (
torch.Tensorof shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that arenot masked,
- 0 for tokens that aremasked.
- token_type_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to asentence A token,
- 1 corresponds to asentence B token.
- position_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.n_positions - 1]. - inputs_embeds (
torch.Tensorof shape(batch_size, sequence_length, hidden_size),optional) —Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. Thisis useful if you want more control over how to convertinput_idsindices into associated vectors than themodel’s internal embedding lookup matrix. - encoder_hidden_states (
torch.Tensorof shape(batch_size, sequence_length, hidden_size),optional) —Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attentionif the model is configured as a decoder. - encoder_attention_mask (
torch.Tensorof shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used inthe cross-attention if the model is configured as a decoder. Mask values selected in[0, 1]:- 1 for tokens that arenot masked,
- 0 for tokens that aremasked.
- labels (
torch.LongTensorof shape(batch_size, sequence_length),optional) —Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in[-100, 0, ..., config.vocab_size](seeinput_idsdocstring) Tokens with indices set to-100areignored (masked), the loss is only computed for the tokens with labels n[0, ..., config.vocab_size] - past_key_values (
~cache_utils.Cache,optional) —Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attentionblocks) that can be used to speed up sequential decoding. This typically consists in thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=Trueorconfig.use_cache=True.OnlyCache instance is allowed as input, see ourkv cache guide.If no
past_key_valuesare passed,DynamicCache will be initialized by default.The model will output the same cache format that is fed as input.
If
past_key_valuesare used, the user is expected to input only unprocessedinput_ids(those that don’thave their past key value states given to this model) of shape(batch_size, unprocessed_length)instead of allinput_idsof shape(batch_size, sequence_length). - use_cache (
bool,optional) —If set toTrue,past_key_valueskey value states are returned and can be used to speed up decoding (seepast_key_values). - cache_position (
torch.Tensorof shape(sequence_length),optional) —Indices depicting the position of the input sequence tokens in the sequence. Contrarily toposition_ids,this tensor is not affected by padding. It is used to update the cache in the correct position and to inferthe complete sequence length. - logits_to_keep (
Union[int, torch.Tensor], defaults to0) —If anint, compute logits for the lastlogits_to_keeptokens. If0, calculate logits for allinput_ids(special case). Only last token logits are needed for generation, and calculating them only for thattoken can save memory, which becomes pretty significant for long sequences or large vocabulary size.If atorch.Tensor, must be 1D corresponding to the indices to keep in the sequence length dimension.This is useful when using packed tensor format (single dimension for batch and sequence length).
Returns
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions ortuple(torch.FloatTensor)
Atransformers.modeling_outputs.CausalLMOutputWithCrossAttentions or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (BertConfig) and inputs.
loss (
torch.FloatTensorof shape(1,),optional, returned whenlabelsis provided) — Language modeling loss (for next-token prediction).logits (
torch.FloatTensorof shape(batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).hidden_states (
tuple(torch.FloatTensor),optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings, if the model has an embedding layer, +one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (
tuple(torch.FloatTensor),optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — 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-attentionheads.
cross_attentions (
tuple(torch.FloatTensor),optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — 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 thecross-attention heads.
past_key_values (
Cache,optional, returned whenuse_cache=Trueis passed or whenconfig.use_cache=True) — It is aCache instance. For more details, see ourkv cache guide.Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_valuesinput) to speed up sequential decoding.
TheBertLMHeadModel forward method, overrides the__call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps whilethe latter silently ignores them.
Example:
>>>import torch>>>from transformersimport AutoTokenizer, BertLMHeadModel>>>tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")>>>model = BertLMHeadModel.from_pretrained("google-bert/bert-base-uncased")>>>inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")>>>outputs = model(**inputs, labels=inputs["input_ids"])>>>loss = outputs.loss>>>logits = outputs.logits
BertForMaskedLM
classtransformers.BertForMaskedLM
<source>(config)
Parameters
- config (BertForMaskedLM) —Model configuration class with all the parameters of the model. Initializing with a config file does notload the weights associated with the model, only the configuration. Check out thefrom_pretrained() method to load the model weights.
The Bert Model with alanguage modeling head on top.”
This model inherits fromPreTrainedModel. Check the superclass documentation for the generic methods thelibrary implements for all its model (such as downloading or saving, resizing the input embeddings, pruning headsetc.)
This model is also a PyTorchtorch.nn.Module subclass.Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usageand behavior.
forward
<source>(input_ids: typing.Optional[torch.Tensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Nonetoken_type_ids: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.Tensor] = Noneinputs_embeds: typing.Optional[torch.Tensor] = Noneencoder_hidden_states: typing.Optional[torch.Tensor] = Noneencoder_attention_mask: typing.Optional[torch.Tensor] = Nonelabels: typing.Optional[torch.Tensor] = None**kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs])→transformers.modeling_outputs.MaskedLMOutput ortuple(torch.FloatTensor)
Parameters
- input_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.Indices can be obtained usingAutoTokenizer. SeePreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
- attention_mask (
torch.Tensorof shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that arenot masked,
- 0 for tokens that aremasked.
- token_type_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to asentence A token,
- 1 corresponds to asentence B token.
- position_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.n_positions - 1]. - inputs_embeds (
torch.Tensorof shape(batch_size, sequence_length, hidden_size),optional) —Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. Thisis useful if you want more control over how to convertinput_idsindices into associated vectors than themodel’s internal embedding lookup matrix. - encoder_hidden_states (
torch.Tensorof shape(batch_size, sequence_length, hidden_size),optional) —Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attentionif the model is configured as a decoder. - encoder_attention_mask (
torch.Tensorof shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used inthe cross-attention if the model is configured as a decoder. Mask values selected in[0, 1]:- 1 for tokens that arenot masked,
- 0 for tokens that aremasked.
- labels (
torch.LongTensorof shape(batch_size, sequence_length),optional) —Labels for computing the masked language modeling loss. Indices should be in[-100, 0, ..., config.vocab_size](seeinput_idsdocstring) Tokens with indices set to-100are ignored (masked), theloss is only computed for the tokens with labels in[0, ..., config.vocab_size]
Returns
transformers.modeling_outputs.MaskedLMOutput ortuple(torch.FloatTensor)
Atransformers.modeling_outputs.MaskedLMOutput or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (BertConfig) and inputs.
loss (
torch.FloatTensorof shape(1,),optional, returned whenlabelsis provided) — Masked language modeling (MLM) loss.logits (
torch.FloatTensorof shape(batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).hidden_states (
tuple(torch.FloatTensor),optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings, if the model has an embedding layer, +one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (
tuple(torch.FloatTensor),optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — 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-attentionheads.
TheBertForMaskedLM forward method, overrides the__call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps whilethe latter silently ignores them.
Example:
>>>from transformersimport AutoTokenizer, BertForMaskedLM>>>import torch>>>tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")>>>model = BertForMaskedLM.from_pretrained("google-bert/bert-base-uncased")>>>inputs = tokenizer("The capital of France is <mask>.", return_tensors="pt")>>>with torch.no_grad():... logits = model(**inputs).logits>>># retrieve index of <mask>>>>mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]>>>predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)>>>tokenizer.decode(predicted_token_id)...>>>labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]>>># mask labels of non-<mask> tokens>>>labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)>>>outputs = model(**inputs, labels=labels)>>>round(outputs.loss.item(),2)...
BertForNextSentencePrediction
classtransformers.BertForNextSentencePrediction
<source>(config)
Parameters
- config (BertForNextSentencePrediction) —Model configuration class with all the parameters of the model. Initializing with a config file does notload the weights associated with the model, only the configuration. Check out thefrom_pretrained() method to load the model weights.
Bert Model with anext sentence prediction (classification) head on top.
This model inherits fromPreTrainedModel. Check the superclass documentation for the generic methods thelibrary implements for all its model (such as downloading or saving, resizing the input embeddings, pruning headsetc.)
This model is also a PyTorchtorch.nn.Module subclass.Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usageand behavior.
forward
<source>(input_ids: typing.Optional[torch.Tensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Nonetoken_type_ids: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.Tensor] = Noneinputs_embeds: typing.Optional[torch.Tensor] = Nonelabels: typing.Optional[torch.Tensor] = None**kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs])→transformers.modeling_outputs.NextSentencePredictorOutput ortuple(torch.FloatTensor)
Parameters
- input_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.Indices can be obtained usingAutoTokenizer. SeePreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
- attention_mask (
torch.Tensorof shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that arenot masked,
- 0 for tokens that aremasked.
- token_type_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to asentence A token,
- 1 corresponds to asentence B token.
- position_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.n_positions - 1]. - inputs_embeds (
torch.Tensorof shape(batch_size, sequence_length, hidden_size),optional) —Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. Thisis useful if you want more control over how to convertinput_idsindices into associated vectors than themodel’s internal embedding lookup matrix. - labels (
torch.LongTensorof shape(batch_size,),optional) —Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair(seeinput_idsdocstring). Indices should be in[0, 1]:- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
Returns
transformers.modeling_outputs.NextSentencePredictorOutput ortuple(torch.FloatTensor)
Atransformers.modeling_outputs.NextSentencePredictorOutput or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (BertConfig) and inputs.
loss (
torch.FloatTensorof shape(1,),optional, returned whennext_sentence_labelis provided) — Next sequence prediction (classification) loss.logits (
torch.FloatTensorof shape(batch_size, 2)) — Prediction scores of the next sequence prediction (classification) head (scores of True/False continuationbefore SoftMax).hidden_states (
tuple(torch.FloatTensor),optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings, if the model has an embedding layer, +one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (
tuple(torch.FloatTensor),optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — 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-attentionheads.
TheBertForNextSentencePrediction forward method, overrides the__call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps whilethe latter silently ignores them.
Example:
>>>from transformersimport AutoTokenizer, BertForNextSentencePrediction>>>import torch>>>tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")>>>model = BertForNextSentencePrediction.from_pretrained("google-bert/bert-base-uncased")>>>prompt ="In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.">>>next_sentence ="The sky is blue due to the shorter wavelength of blue light.">>>encoding = tokenizer(prompt, next_sentence, return_tensors="pt")>>>outputs = model(**encoding, labels=torch.LongTensor([1]))>>>logits = outputs.logits>>>assert logits[0,0] < logits[0,1]# next sentence was random
BertForSequenceClassification
classtransformers.BertForSequenceClassification
<source>(config)
Parameters
- config (BertForSequenceClassification) —Model configuration class with all the parameters of the model. Initializing with a config file does notload the weights associated with the model, only the configuration. Check out thefrom_pretrained() method to load the model weights.
Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooledoutput) e.g. for GLUE tasks.
This model inherits fromPreTrainedModel. Check the superclass documentation for the generic methods thelibrary implements for all its model (such as downloading or saving, resizing the input embeddings, pruning headsetc.)
This model is also a PyTorchtorch.nn.Module subclass.Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usageand behavior.
forward
<source>(input_ids: typing.Optional[torch.Tensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Nonetoken_type_ids: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.Tensor] = Noneinputs_embeds: typing.Optional[torch.Tensor] = Nonelabels: typing.Optional[torch.Tensor] = None**kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs])→transformers.modeling_outputs.SequenceClassifierOutput ortuple(torch.FloatTensor)
Parameters
- input_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.Indices can be obtained usingAutoTokenizer. SeePreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
- attention_mask (
torch.Tensorof shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that arenot masked,
- 0 for tokens that aremasked.
- token_type_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to asentence A token,
- 1 corresponds to asentence B token.
- position_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.n_positions - 1]. - inputs_embeds (
torch.Tensorof shape(batch_size, sequence_length, hidden_size),optional) —Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. Thisis useful if you want more control over how to convertinput_idsindices into associated vectors than themodel’s internal embedding lookup matrix. - labels (
torch.LongTensorof shape(batch_size,),optional) —Labels for computing the sequence classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]. Ifconfig.num_labels == 1a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.SequenceClassifierOutput ortuple(torch.FloatTensor)
Atransformers.modeling_outputs.SequenceClassifierOutput or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (BertConfig) and inputs.
loss (
torch.FloatTensorof shape(1,),optional, returned whenlabelsis provided) — Classification (or regression if config.num_labels==1) loss.logits (
torch.FloatTensorof shape(batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).hidden_states (
tuple(torch.FloatTensor),optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings, if the model has an embedding layer, +one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (
tuple(torch.FloatTensor),optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — 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-attentionheads.
TheBertForSequenceClassification forward method, overrides the__call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps whilethe latter silently ignores them.
Example of single-label classification:
>>>import torch>>>from transformersimport AutoTokenizer, BertForSequenceClassification>>>tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")>>>model = BertForSequenceClassification.from_pretrained("google-bert/bert-base-uncased")>>>inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")>>>with torch.no_grad():... logits = model(**inputs).logits>>>predicted_class_id = logits.argmax().item()>>>model.config.id2label[predicted_class_id]...>>># To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`>>>num_labels =len(model.config.id2label)>>>model = BertForSequenceClassification.from_pretrained("google-bert/bert-base-uncased", num_labels=num_labels)>>>labels = torch.tensor([1])>>>loss = model(**inputs, labels=labels).loss>>>round(loss.item(),2)...
Example of multi-label classification:
>>>import torch>>>from transformersimport AutoTokenizer, BertForSequenceClassification>>>tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")>>>model = BertForSequenceClassification.from_pretrained("google-bert/bert-base-uncased", problem_type="multi_label_classification")>>>inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")>>>with torch.no_grad():... logits = model(**inputs).logits>>>predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) >0.5]>>># To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`>>>num_labels =len(model.config.id2label)>>>model = BertForSequenceClassification.from_pretrained(..."google-bert/bert-base-uncased", num_labels=num_labels, problem_type="multi_label_classification"...)>>>labels = torch.sum(... torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1...).to(torch.float)>>>loss = model(**inputs, labels=labels).loss
BertForMultipleChoice
classtransformers.BertForMultipleChoice
<source>(config)
Parameters
- config (BertForMultipleChoice) —Model configuration class with all the parameters of the model. Initializing with a config file does notload the weights associated with the model, only the configuration. Check out thefrom_pretrained() method to load the model weights.
The Bert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and asoftmax) e.g. for RocStories/SWAG tasks.
This model inherits fromPreTrainedModel. Check the superclass documentation for the generic methods thelibrary implements for all its model (such as downloading or saving, resizing the input embeddings, pruning headsetc.)
This model is also a PyTorchtorch.nn.Module subclass.Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usageand behavior.
forward
<source>(input_ids: typing.Optional[torch.Tensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Nonetoken_type_ids: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.Tensor] = Noneinputs_embeds: typing.Optional[torch.Tensor] = Nonelabels: typing.Optional[torch.Tensor] = None**kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs])→transformers.modeling_outputs.MultipleChoiceModelOutput ortuple(torch.FloatTensor)
Parameters
- input_ids (
torch.LongTensorof shape(batch_size, num_choices, sequence_length)) —Indices of input sequence tokens in the vocabulary.Indices can be obtained usingAutoTokenizer. SeePreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
- attention_mask (
torch.Tensorof shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that arenot masked,
- 0 for tokens that aremasked.
- token_type_ids (
torch.LongTensorof shape(batch_size, num_choices, sequence_length),optional) —Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to asentence A token,
- 1 corresponds to asentence B token.
- position_ids (
torch.LongTensorof shape(batch_size, num_choices, sequence_length),optional) —Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. - inputs_embeds (
torch.FloatTensorof shape(batch_size, num_choices, sequence_length, hidden_size),optional) —Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. Thisis useful if you want more control over how to convertinput_idsindices into associated vectors than themodel’s internal embedding lookup matrix. - labels (
torch.LongTensorof shape(batch_size,),optional) —Labels for computing the multiple choice classification loss. Indices should be in[0, ..., num_choices-1]wherenum_choicesis the size of the second dimension of the input tensors. (Seeinput_idsabove)
Returns
transformers.modeling_outputs.MultipleChoiceModelOutput ortuple(torch.FloatTensor)
Atransformers.modeling_outputs.MultipleChoiceModelOutput or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (BertConfig) and inputs.
loss (
torch.FloatTensorof shape(1,),optional, returned whenlabelsis provided) — Classification loss.logits (
torch.FloatTensorof shape(batch_size, num_choices)) —num_choices is the second dimension of the input tensors. (seeinput_ids above).Classification scores (before SoftMax).
hidden_states (
tuple(torch.FloatTensor),optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings, if the model has an embedding layer, +one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (
tuple(torch.FloatTensor),optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — 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-attentionheads.
TheBertForMultipleChoice forward method, overrides the__call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps whilethe latter silently ignores them.
Example:
>>>from transformersimport AutoTokenizer, BertForMultipleChoice>>>import torch>>>tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")>>>model = BertForMultipleChoice.from_pretrained("google-bert/bert-base-uncased")>>>prompt ="In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.">>>choice0 ="It is eaten with a fork and a knife.">>>choice1 ="It is eaten while held in the hand.">>>labels = torch.tensor(0).unsqueeze(0)# choice0 is correct (according to Wikipedia ;)), batch size 1>>>encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)>>>outputs = model(**{k: v.unsqueeze(0)for k, vin encoding.items()}, labels=labels)# batch size is 1>>># the linear classifier still needs to be trained>>>loss = outputs.loss>>>logits = outputs.logits
BertForTokenClassification
classtransformers.BertForTokenClassification
<source>(config)
Parameters
- config (BertForTokenClassification) —Model configuration class with all the parameters of the model. Initializing with a config file does notload the weights associated with the model, only the configuration. Check out thefrom_pretrained() method to load the model weights.
The Bert transformer with a token classification head on top (a linear layer on top of the hidden-statesoutput) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits fromPreTrainedModel. Check the superclass documentation for the generic methods thelibrary implements for all its model (such as downloading or saving, resizing the input embeddings, pruning headsetc.)
This model is also a PyTorchtorch.nn.Module subclass.Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usageand behavior.
forward
<source>(input_ids: typing.Optional[torch.Tensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Nonetoken_type_ids: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.Tensor] = Noneinputs_embeds: typing.Optional[torch.Tensor] = Nonelabels: typing.Optional[torch.Tensor] = None**kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs])→transformers.modeling_outputs.TokenClassifierOutput ortuple(torch.FloatTensor)
Parameters
- input_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.Indices can be obtained usingAutoTokenizer. SeePreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
- attention_mask (
torch.Tensorof shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that arenot masked,
- 0 for tokens that aremasked.
- token_type_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to asentence A token,
- 1 corresponds to asentence B token.
- position_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.n_positions - 1]. - inputs_embeds (
torch.Tensorof shape(batch_size, sequence_length, hidden_size),optional) —Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. Thisis useful if you want more control over how to convertinput_idsindices into associated vectors than themodel’s internal embedding lookup matrix. - labels (
torch.LongTensorof shape(batch_size, sequence_length),optional) —Labels for computing the token classification loss. Indices should be in[0, ..., config.num_labels - 1].
Returns
transformers.modeling_outputs.TokenClassifierOutput ortuple(torch.FloatTensor)
Atransformers.modeling_outputs.TokenClassifierOutput or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (BertConfig) and inputs.
loss (
torch.FloatTensorof shape(1,),optional, returned whenlabelsis provided) — Classification loss.logits (
torch.FloatTensorof shape(batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax).hidden_states (
tuple(torch.FloatTensor),optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings, if the model has an embedding layer, +one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (
tuple(torch.FloatTensor),optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — 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-attentionheads.
TheBertForTokenClassification forward method, overrides the__call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps whilethe latter silently ignores them.
Example:
>>>from transformersimport AutoTokenizer, BertForTokenClassification>>>import torch>>>tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")>>>model = BertForTokenClassification.from_pretrained("google-bert/bert-base-uncased")>>>inputs = tokenizer(..."HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"...)>>>with torch.no_grad():... logits = model(**inputs).logits>>>predicted_token_class_ids = logits.argmax(-1)>>># Note that tokens are classified rather then input words which means that>>># there might be more predicted token classes than words.>>># Multiple token classes might account for the same word>>>predicted_tokens_classes = [model.config.id2label[t.item()]for tin predicted_token_class_ids[0]]>>>predicted_tokens_classes...>>>labels = predicted_token_class_ids>>>loss = model(**inputs, labels=labels).loss>>>round(loss.item(),2)...
BertForQuestionAnswering
classtransformers.BertForQuestionAnswering
<source>(config)
Parameters
- config (BertForQuestionAnswering) —Model configuration class with all the parameters of the model. Initializing with a config file does notload the weights associated with the model, only the configuration. Check out thefrom_pretrained() method to load the model weights.
The Bert transformer with a span classification head on top for extractive question-answering tasks likeSQuAD (a linear layer on top of the hidden-states output to computespan start logits andspan end logits).
This model inherits fromPreTrainedModel. Check the superclass documentation for the generic methods thelibrary implements for all its model (such as downloading or saving, resizing the input embeddings, pruning headsetc.)
This model is also a PyTorchtorch.nn.Module subclass.Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usageand behavior.
forward
<source>(input_ids: typing.Optional[torch.Tensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Nonetoken_type_ids: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.Tensor] = Noneinputs_embeds: typing.Optional[torch.Tensor] = Nonestart_positions: typing.Optional[torch.Tensor] = Noneend_positions: typing.Optional[torch.Tensor] = None**kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs])→transformers.modeling_outputs.QuestionAnsweringModelOutput ortuple(torch.FloatTensor)
Parameters
- input_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.Indices can be obtained usingAutoTokenizer. SeePreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
- attention_mask (
torch.Tensorof shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that arenot masked,
- 0 for tokens that aremasked.
- token_type_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to asentence A token,
- 1 corresponds to asentence B token.
- position_ids (
torch.Tensorof shape(batch_size, sequence_length),optional) —Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.n_positions - 1]. - inputs_embeds (
torch.Tensorof shape(batch_size, sequence_length, hidden_size),optional) —Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. Thisis useful if you want more control over how to convertinput_idsindices into associated vectors than themodel’s internal embedding lookup matrix. - start_positions (
torch.Tensorof shape(batch_size,),optional) —Labels for position (index) of the start of the labelled span for computing the token classification loss.Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequenceare not taken into account for computing the loss. - end_positions (
torch.Tensorof shape(batch_size,),optional) —Labels for position (index) of the end of the labelled span for computing the token classification loss.Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequenceare not taken into account for computing the loss.
Returns
transformers.modeling_outputs.QuestionAnsweringModelOutput ortuple(torch.FloatTensor)
Atransformers.modeling_outputs.QuestionAnsweringModelOutput or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (BertConfig) and inputs.
loss (
torch.FloatTensorof shape(1,),optional, returned whenlabelsis provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.start_logits (
torch.FloatTensorof shape(batch_size, sequence_length)) — Span-start scores (before SoftMax).end_logits (
torch.FloatTensorof shape(batch_size, sequence_length)) — Span-end scores (before SoftMax).hidden_states (
tuple(torch.FloatTensor),optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings, if the model has an embedding layer, +one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (
tuple(torch.FloatTensor),optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — 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-attentionheads.
TheBertForQuestionAnswering forward method, overrides the__call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps whilethe latter silently ignores them.
Example:
>>>from transformersimport AutoTokenizer, BertForQuestionAnswering>>>import torch>>>tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")>>>model = BertForQuestionAnswering.from_pretrained("google-bert/bert-base-uncased")>>>question, text ="Who was Jim Henson?","Jim Henson was a nice puppet">>>inputs = tokenizer(question, text, return_tensors="pt")>>>with torch.no_grad():... outputs = model(**inputs)>>>answer_start_index = outputs.start_logits.argmax()>>>answer_end_index = outputs.end_logits.argmax()>>>predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index +1]>>>tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)...>>># target is "nice puppet">>>target_start_index = torch.tensor([14])>>>target_end_index = torch.tensor([15])>>>outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)>>>loss = outputs.loss>>>round(loss.item(),2)...
Bert specific outputs
classtransformers.models.bert.modeling_bert.BertForPreTrainingOutput
<source>(loss: typing.Optional[torch.FloatTensor] = Noneprediction_logits: typing.Optional[torch.FloatTensor] = Noneseq_relationship_logits: typing.Optional[torch.FloatTensor] = Nonehidden_states: typing.Optional[tuple[torch.FloatTensor]] = Noneattentions: typing.Optional[tuple[torch.FloatTensor]] = None)
Parameters
- loss (
*optional*, returned whenlabelsis provided,torch.FloatTensorof shape(1,)) —Total loss as the sum of the masked language modeling loss and the next sequence prediction(classification) loss. - prediction_logits (
torch.FloatTensorof shape(batch_size, sequence_length, config.vocab_size)) —Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). - seq_relationship_logits (
torch.FloatTensorof shape(batch_size, 2)) —Prediction scores of the next sequence prediction (classification) head (scores of True/False continuationbefore SoftMax). - hidden_states (
tuple[torch.FloatTensor],optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) —Tuple oftorch.FloatTensor(one for the output of the embeddings, if the model has an embedding layer, +one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- attentions (
tuple[torch.FloatTensor],optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) —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-attentionheads.
Output type ofBertForPreTraining.