The CamemBERT model was proposed in CamemBERT: a Tasty French Language Model by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah, and Benoît Sagot. It is based on Facebook’s RoBERTa model released in 2019. It is a model trained on 138GB of French text.
The abstract from the paper is the following:
Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models —in all languages except English— very limited. Aiming to address this issue for French, we release CamemBERT, a French version of the Bi-directional Encoders for Transformers (BERT). We measure the performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging, dependency parsing, named-entity recognition, and natural language inference. CamemBERT improves the state of the art for most of the tasks considered. We release the pretrained model for CamemBERT hoping to foster research and downstream applications for French NLP.
Tips:
This model was contributed by camembert. The original code can be found here.
( pad_token_id = 1 bos_token_id = 0 eos_token_id = 2 **kwargs )
This class overrides RobertaConfig. Please check the superclass for the appropriate documentation alongside usage examples. Instantiating a configuration with the defaults will yield a similar configuration to that of the Camembert camembert-base architecture.
( vocab_file bos_token = '<s>' eos_token = '</s>' sep_token = '</s>' cls_token = '<s>' unk_token = '<unk>' pad_token = '<pad>' mask_token = '<mask>' additional_special_tokens = ['<s>NOTUSED', '</s>NOTUSED'] sp_model_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None **kwargs )
Parameters
str
) —
SentencePiece file (generally has a .spm extension) that
contains the vocabulary necessary to instantiate a tokenizer.
str
, optional, defaults to "<s>"
) —
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the cls_token
.
str
, optional, defaults to "</s>"
) —
The end of sequence token.
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the sep_token
.
str
, optional, defaults to "</s>"
) —
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.
str
, optional, defaults to "<s>"
) —
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.
str
, optional, defaults to "<unk>"
) —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
str
, optional, defaults to "<pad>"
) —
The token used for padding, for example when batching sequences of different lengths.
str
, optional, defaults to "<mask>"
) —
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
List[str]
, optional, defaults to ["<s>NOTUSED", "</s>NOTUSED"]
) —
Additional special tokens used by the tokenizer.
dict
, optional) —
Will be passed to the SentencePieceProcessor.__init__()
method. The Python wrapper for
SentencePiece can be used, among other things,
to set:
enable_sampling
: Enable subword regularization.
nbest_size
: Sampling parameters for unigram. Invalid for BPE-Dropout.
nbest_size = {0,1}
: No sampling is performed.nbest_size > 1
: samples from the nbest_size results.nbest_size < 0
: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.alpha
: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
SentencePieceProcessor
) —
The SentencePiece processor that is used for every conversion (string, tokens and IDs).
Adapted from RobertaTokenizer and XLNetTokenizer. Construct a CamemBERT tokenizer. Based on SentencePiece.
This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters
List[int]
) —
List of IDs to which the special tokens will be added.
List[int]
, optional) —
Optional second list of IDs for sequence pairs.
Returns
List[int]
List of input IDs with the appropriate special tokens.
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An CamemBERT sequence has the following format:
<s> X </s>
<s> A </s></s> B </s>
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
already_has_special_tokens: bool = False
)
→
List[int]
Parameters
List[int]
) —
List of IDs.
List[int]
, optional) —
Optional second list of IDs for sequence pairs.
bool
, optional, defaults to False
) —
Whether or not the token list is already formatted with special tokens for the model.
Returns
List[int]
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer prepare_for_model
method.
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Create a mask from the two sequences passed to be used in a sequence-pair classification task. CamemBERT, like RoBERTa, does not make use of token type ids, therefore a list of zeros is returned.
( vocab_file = None tokenizer_file = None bos_token = '<s>' eos_token = '</s>' sep_token = '</s>' cls_token = '<s>' unk_token = '<unk>' pad_token = '<pad>' mask_token = '<mask>' additional_special_tokens = ['<s>NOTUSED', '</s>NOTUSED'] **kwargs )
Parameters
str
) —
SentencePiece file (generally has a .spm extension) that
contains the vocabulary necessary to instantiate a tokenizer.
str
, optional, defaults to "<s>"
) —
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the cls_token
.
str
, optional, defaults to "</s>"
) —
The end of sequence token.
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the sep_token
.
str
, optional, defaults to "</s>"
) —
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.
str
, optional, defaults to "<s>"
) —
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.
str
, optional, defaults to "<unk>"
) —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
str
, optional, defaults to "<pad>"
) —
The token used for padding, for example when batching sequences of different lengths.
str
, optional, defaults to "<mask>"
) —
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
List[str]
, optional, defaults to ["<s>NOTUSED", "</s>NOTUSED"]
) —
Additional special tokens used by the tokenizer.
Construct a “fast” CamemBERT tokenizer (backed by HuggingFace’s tokenizers library). Adapted from RobertaTokenizer and XLNetTokenizer. Based on BPE.
This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters
List[int]
) —
List of IDs to which the special tokens will be added.
List[int]
, optional) —
Optional second list of IDs for sequence pairs.
Returns
List[int]
List of input IDs with the appropriate special tokens.
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An CamemBERT sequence has the following format:
<s> X </s>
<s> A </s></s> B </s>
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Create a mask from the two sequences passed to be used in a sequence-pair classification task. CamemBERT, like RoBERTa, does not make use of token type ids, therefore a list of zeros is returned.
( config add_pooling_layer = True )
Parameters
The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in Attention is all you need_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as a decoder the model needs to be initialized with the is_decoder
argument of the configuration set to
True
. To be used in a Seq2Seq model, the model needs to initialized with both is_decoder
argument and
add_cross_attention
set to True
; an encoder_hidden_states
is then expected as an input to the forward pass.
.. _Attention is all you need: https://arxiv.org/abs/1706.03762
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
encoder_hidden_states: typing.Optional[torch.Tensor] = None
encoder_attention_mask: typing.Optional[torch.Tensor] = None
past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using CamembertTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]
:
tuple(tuple(torch.FloatTensor))
of length config.n_layers
with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)
) —
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values
are used, the user can optionally input only the last decoder_input_ids
(those that
don’t have their past key value states given to this model) of shape (batch_size, 1)
instead of all
decoder_input_ids
of shape (batch_size, sequence_length)
.
bool
, optional) —
If set to True
, past_key_values
key value states are returned and can be used to speed up decoding (see
past_key_values
).
Returns
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (CamembertConfig) and inputs.
last_hidden_state (torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (torch.FloatTensor
of shape (batch_size, hidden_size)
) — Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.
hidden_states (tuple(torch.FloatTensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of torch.FloatTensor
(one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size)
.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of torch.FloatTensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(torch.FloatTensor)
, optional, returned when output_attentions=True
and config.add_cross_attention=True
is passed or when config.output_attentions=True
) — Tuple of torch.FloatTensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
past_key_values (tuple(tuple(torch.FloatTensor))
, optional, returned when use_cache=True
is passed or when config.use_cache=True
) — Tuple of tuple(torch.FloatTensor)
of length config.n_layers
, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)
) and optionally if
config.is_encoder_decoder=True
2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head)
.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
config.is_encoder_decoder=True
in the cross-attention blocks) that can be used (see past_key_values
input) to speed up sequential decoding.
The CamembertModel forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import CamembertTokenizer, CamembertModel
>>> import torch
>>> tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
>>> model = CamembertModel.from_pretrained("camembert-base")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
( config )
Parameters
CamemBERT 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.
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
encoder_hidden_states: typing.Optional[torch.FloatTensor] = None
encoder_attention_mask: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
past_key_values: typing.Tuple[typing.Tuple[torch.FloatTensor]] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using CamembertTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
[-100, 0, ..., config.vocab_size]
(see input_ids
docstring) Tokens with indices set to -100
are
ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size]
tuple(tuple(torch.FloatTensor))
of length config.n_layers
with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)
) —
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values
are used, the user can optionally input only the last decoder_input_ids
(those that
don’t have their past key value states given to this model) of shape (batch_size, 1)
instead of all
decoder_input_ids
of shape (batch_size, sequence_length)
.
bool
, optional) —
If set to True
, past_key_values
key value states are returned and can be used to speed up decoding (see
past_key_values
).
Returns
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (CamembertConfig) and inputs.
loss (torch.FloatTensor
of shape (1,)
, optional, returned when labels
is provided) — Language modeling loss (for next-token prediction).
logits (torch.FloatTensor
of shape (batch_size, sequence_length, config.vocab_size)
) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(torch.FloatTensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of torch.FloatTensor
(one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size)
.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of torch.FloatTensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(torch.FloatTensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of torch.FloatTensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads.
past_key_values (tuple(tuple(torch.FloatTensor))
, optional, returned when use_cache=True
is passed or when config.use_cache=True
) — Tuple of torch.FloatTensor
tuples of length config.n_layers
, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if config.is_decoder = True
.
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values
input) to speed up sequential decoding.
The CamembertForCausalLM forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import CamembertTokenizer, CamembertForCausalLM, CamembertConfig
>>> import torch
>>> tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
>>> config = CamembertConfig.from_pretrained("camembert-base")
>>> config.is_decoder = True
>>> model = CamembertForCausalLM.from_pretrained("camembert-base", config=config)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits
( config )
Parameters
CamemBERT 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.
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
encoder_hidden_states: typing.Optional[torch.FloatTensor] = None
encoder_attention_mask: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using CamembertTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size]
(see input_ids
docstring) Tokens with indices set to -100
are ignored (masked), the
loss is only computed for the tokens with labels in [0, ..., config.vocab_size]
Dict[str, any]
, optional, defaults to {}) —
Used to hide legacy arguments that have been deprecated.
Returns
transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.MaskedLMOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (CamembertConfig) and inputs.
loss (torch.FloatTensor
of shape (1,)
, optional, returned when labels
is provided) — Masked language modeling (MLM) loss.
logits (torch.FloatTensor
of shape (batch_size, sequence_length, config.vocab_size)
) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(torch.FloatTensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of torch.FloatTensor
(one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size)
.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of torch.FloatTensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The CamembertForMaskedLM forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import CamembertTokenizer, CamembertForMaskedLM
>>> import torch
>>> tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
>>> model = CamembertForMaskedLM.from_pretrained("camembert-base")
>>> inputs = tokenizer("The capital of France is <mask>.", return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # retrieve index of <mask>
>>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
>>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
>>> tokenizer.decode(predicted_token_id)
' Paris'
( config )
Parameters
CamemBERT 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.
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using CamembertTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size,)
, optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]
. If config.num_labels == 1
a regression loss is computed (Mean-Square loss), If
config.num_labels > 1
a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.SequenceClassifierOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (CamembertConfig) and inputs.
loss (torch.FloatTensor
of shape (1,)
, optional, returned when labels
is provided) — Classification (or regression if config.num_labels==1) loss.
logits (torch.FloatTensor
of shape (batch_size, config.num_labels)
) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of torch.FloatTensor
(one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size)
.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of torch.FloatTensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The CamembertForSequenceClassification forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example of single-label classification:
>>> import torch
>>> from transformers import CamembertTokenizer, CamembertForSequenceClassification
>>> tokenizer = CamembertTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
>>> model = CamembertForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
>>> 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]
'optimism'
>>> # 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 = CamembertForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion", num_labels=num_labels)
>>> labels = torch.tensor([1])
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
0.08
Example of multi-label classification:
>>> import torch
>>> from transformers import CamembertTokenizer, CamembertForSequenceClassification
>>> tokenizer = CamembertTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
>>> model = CamembertForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion", 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_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
'optimism'
>>> # 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 = CamembertForSequenceClassification.from_pretrained(
... "cardiffnlp/twitter-roberta-base-emotion", num_labels=num_labels, problem_type="multi_label_classification"
... )
>>> labels = torch.nn.functional.one_hot(torch.tensor([predicted_class_id]), num_classes=num_labels).to(
... torch.float
... )
>>> loss = model(**inputs, labels=labels).loss
>>> loss.backward()
( config )
Parameters
CamemBERT 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.
(
input_ids: typing.Optional[torch.LongTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.MultipleChoiceModelOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, num_choices, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using CamembertTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor
of shape (batch_size, num_choices, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, num_choices, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, num_choices, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
torch.FloatTensor
of shape (batch_size, num_choices, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size,)
, optional) —
Labels for computing the multiple choice classification loss. Indices should be in [0, ..., num_choices-1]
where num_choices
is the size of the second dimension of the input tensors. (See
input_ids
above)
Returns
transformers.modeling_outputs.MultipleChoiceModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.MultipleChoiceModelOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (CamembertConfig) and inputs.
loss (torch.FloatTensor
of shape (1,), optional, returned when labels
is provided) — Classification loss.
logits (torch.FloatTensor
of shape (batch_size, num_choices)
) — num_choices is the second dimension of the input tensors. (see input_ids above).
Classification scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of torch.FloatTensor
(one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size)
.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of torch.FloatTensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The CamembertForMultipleChoice forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import CamembertTokenizer, CamembertForMultipleChoice
>>> import torch
>>> tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
>>> model = CamembertForMultipleChoice.from_pretrained("camembert-base")
>>> 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
( config )
Parameters
CamemBERT 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.
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using CamembertTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Labels for computing the token classification loss. Indices should be in [0, ..., config.num_labels - 1]
.
Returns
transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.TokenClassifierOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (CamembertConfig) and inputs.
loss (torch.FloatTensor
of shape (1,)
, optional, returned when labels
is provided) — Classification loss.
logits (torch.FloatTensor
of shape (batch_size, sequence_length, config.num_labels)
) — Classification scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of torch.FloatTensor
(one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size)
.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of torch.FloatTensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The CamembertForTokenClassification forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import CamembertTokenizer, CamembertForTokenClassification
>>> import torch
>>> tokenizer = CamembertTokenizer.from_pretrained("Jean-Baptiste/roberta-large-ner-english")
>>> model = CamembertForTokenClassification.from_pretrained("Jean-Baptiste/roberta-large-ner-english")
>>> inputs = tokenizer(
... "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
... )
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_token_class_ids = logits.argmax(-1)
>>> # Note that tokens are classified rather then input words which means that
>>> # there might be more predicted token classes than words.
>>> # Multiple token classes might account for the same word
>>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
>>> predicted_tokens_classes
['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']
( config )
Parameters
CamemBERT 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.
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
start_positions: typing.Optional[torch.LongTensor] = None
end_positions: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using CamembertTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size,)
, optional) —
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length
). Position outside of the sequence
are not taken into account for computing the loss.
torch.LongTensor
of shape (batch_size,)
, optional) —
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length
). Position outside of the sequence
are not taken into account for computing the loss.
Returns
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.QuestionAnsweringModelOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (CamembertConfig) and inputs.
loss (torch.FloatTensor
of shape (1,)
, optional, returned when labels
is provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (torch.FloatTensor
of shape (batch_size, sequence_length)
) — Span-start scores (before SoftMax).
end_logits (torch.FloatTensor
of shape (batch_size, sequence_length)
) — Span-end scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of torch.FloatTensor
(one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size)
.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of torch.FloatTensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The CamembertForQuestionAnswering forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import CamembertTokenizer, CamembertForQuestionAnswering
>>> import torch
>>> tokenizer = CamembertTokenizer.from_pretrained("deepset/roberta-base-squad2")
>>> model = CamembertForQuestionAnswering.from_pretrained("deepset/roberta-base-squad2")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> tokenizer.decode(predict_answer_tokens)
' puppet'
( *args **kwargs )
Parameters
The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers
accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit()
things should “just work” for you - just
pass your inputs and labels in any format that model.fit()
supports! If, however, you want to use the second
format outside of Keras methods like fit()
and predict()
, such as when creating your own layers or models with
the Keras Functional
API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
input_ids
only and nothing else: model(input_ids)
model([input_ids, attention_mask])
or model([input_ids, attention_mask, token_type_ids])
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
encoder_hidden_states: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
encoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
training: typing.Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions or tuple(tf.Tensor)
Parameters
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using CamembertTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
Numpy array
or tf.Tensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
tf.Tensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
bool
, optional, defaults to False
) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
tf.Tensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]
:
Tuple[Tuple[tf.Tensor]]
of length config.n_layers
) —
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values
are used, the user can optionally input only the last decoder_input_ids
(those that
don’t have their past key value states given to this model) of shape (batch_size, 1)
instead of all
decoder_input_ids
of shape (batch_size, sequence_length)
.
bool
, optional, defaults to True
) —
If set to True
, past_key_values
key value states are returned and can be used to speed up decoding (see
past_key_values
). Set to False
during training, True
during generation
Returns
transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (CamembertConfig) and inputs.
last_hidden_state (tf.Tensor
of shape (batch_size, sequence_length, hidden_size)
) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (tf.Tensor
of shape (batch_size, hidden_size)
) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.
This output is usually not a good summary of the semantic content of the input, you’re often better with averaging or pooling the sequence of hidden-states for the whole input sequence.
past_key_values (List[tf.Tensor]
, optional, returned when use_cache=True
is passed or when config.use_cache=True
) — List of tf.Tensor
of length config.n_layers
, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)
).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values
input) to speed up sequential decoding.
hidden_states (tuple(tf.Tensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of tf.Tensor
(one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size)
.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
The TFCamembertModel forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import CamembertTokenizer, TFCamembertModel
>>> import tensorflow as tf
>>> tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
>>> model = TFCamembertModel.from_pretrained("camembert-base")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)
>>> last_hidden_states = outputs.last_hidden_state
( *args **kwargs )
Parameters
CamemBERT Model with a language modeling
head on top for CLM fine-tuning.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers
accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit()
things should “just work” for you - just
pass your inputs and labels in any format that model.fit()
supports! If, however, you want to use the second
format outside of Keras methods like fit()
and predict()
, such as when creating your own layers or models with
the Keras Functional
API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
input_ids
only and nothing else: model(input_ids)
model([input_ids, attention_mask])
or model([input_ids, attention_mask, token_type_ids])
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
encoder_hidden_states: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
encoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
training: typing.Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions or tuple(tf.Tensor)
Parameters
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using CamembertTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
Numpy array
or tf.Tensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
tf.Tensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
bool
, optional, defaults to False
) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
tf.Tensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]
:
Tuple[Tuple[tf.Tensor]]
of length config.n_layers
) —
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values
are used, the user can optionally input only the last decoder_input_ids
(those that
don’t have their past key value states given to this model) of shape (batch_size, 1)
instead of all
decoder_input_ids
of shape (batch_size, sequence_length)
.
bool
, optional, defaults to True
) —
If set to True
, past_key_values
key value states are returned and can be used to speed up decoding (see
past_key_values
). Set to False
during training, True
during generation
tf.Tensor
or np.ndarray
of shape (batch_size, sequence_length)
, optional) —
Labels for computing the cross entropy classification loss. Indices should be in [0, ..., config.vocab_size - 1]
.
Returns
transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (CamembertConfig) and inputs.
loss (tf.Tensor
of shape (n,)
, optional, where n is the number of non-masked labels, returned when labels
is provided) — Language modeling loss (for next-token prediction).
logits (tf.Tensor
of shape (batch_size, sequence_length, config.vocab_size)
) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(tf.Tensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of tf.Tensor
(one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size)
.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
past_key_values (List[tf.Tensor]
, optional, returned when use_cache=True
is passed or when config.use_cache=True
) — List of tf.Tensor
of length config.n_layers
, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)
).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values
input) to speed up sequential decoding.
The TFCamembertForCausalLM forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import CamembertTokenizer, TFCamembertForCausalLM
>>> import tensorflow as tf
>>> tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
>>> model = TFCamembertForCausalLM.from_pretrained("camembert-base")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)
>>> logits = outputs.logits
( *args **kwargs )
Parameters
CamemBERT Model with a language modeling
head on top.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers
accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit()
things should “just work” for you - just
pass your inputs and labels in any format that model.fit()
supports! If, however, you want to use the second
format outside of Keras methods like fit()
and predict()
, such as when creating your own layers or models with
the Keras Functional
API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
input_ids
only and nothing else: model(input_ids)
model([input_ids, attention_mask])
or model([input_ids, attention_mask, token_type_ids])
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
training: typing.Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFMaskedLMOutput or tuple(tf.Tensor)
Parameters
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using CamembertTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
Numpy array
or tf.Tensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
tf.Tensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
bool
, optional, defaults to False
) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size]
(see input_ids
docstring) Tokens with indices set to -100
are ignored (masked), the
loss is only computed for the tokens with labels in [0, ..., config.vocab_size]
Returns
transformers.modeling_tf_outputs.TFMaskedLMOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFMaskedLMOutput or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (CamembertConfig) and inputs.
loss (tf.Tensor
of shape (n,)
, optional, where n is the number of non-masked labels, returned when labels
is provided) — Masked language modeling (MLM) loss.
logits (tf.Tensor
of shape (batch_size, sequence_length, config.vocab_size)
) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(tf.Tensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of tf.Tensor
(one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size)
.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The TFCamembertForMaskedLM forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import CamembertTokenizer, TFCamembertForMaskedLM
>>> import tensorflow as tf
>>> tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
>>> model = TFCamembertForMaskedLM.from_pretrained("camembert-base")
>>> inputs = tokenizer("The capital of France is <mask>.", return_tensors="tf")
>>> logits = model(**inputs).logits
>>> # retrieve index of <mask>
>>> mask_token_index = tf.where((inputs.input_ids == tokenizer.mask_token_id)[0])
>>> selected_logits = tf.gather_nd(logits[0], indices=mask_token_index)
>>> predicted_token_id = tf.math.argmax(selected_logits, axis=-1)
>>> tokenizer.decode(predicted_token_id)
' Paris'
( *args **kwargs )
Parameters
CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers
accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit()
things should “just work” for you - just
pass your inputs and labels in any format that model.fit()
supports! If, however, you want to use the second
format outside of Keras methods like fit()
and predict()
, such as when creating your own layers or models with
the Keras Functional
API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
input_ids
only and nothing else: model(input_ids)
model([input_ids, attention_mask])
or model([input_ids, attention_mask, token_type_ids])
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
training: typing.Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)
Parameters
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using CamembertTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
Numpy array
or tf.Tensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
tf.Tensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
bool
, optional, defaults to False
) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
tf.Tensor
of shape (batch_size,)
, optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]
. If config.num_labels == 1
a regression loss is computed (Mean-Square loss), If
config.num_labels > 1
a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFSequenceClassifierOutput or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (CamembertConfig) and inputs.
loss (tf.Tensor
of shape (batch_size, )
, optional, returned when labels
is provided) — Classification (or regression if config.num_labels==1) loss.
logits (tf.Tensor
of shape (batch_size, config.num_labels)
) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(tf.Tensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of tf.Tensor
(one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size)
.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The TFCamembertForSequenceClassification forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import CamembertTokenizer, TFCamembertForSequenceClassification
>>> import tensorflow as tf
>>> tokenizer = CamembertTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
>>> model = TFCamembertForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> logits = model(**inputs).logits
>>> predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
>>> model.config.id2label[predicted_class_id]
'optimism'
>>> # 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 = TFCamembertForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion", num_labels=num_labels)
>>> labels = tf.constant(1)
>>> loss = model(**inputs, labels=labels).loss
>>> round(float(loss), 2)
0.08
( *args **kwargs )
Parameters
CamemBERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers
accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit()
things should “just work” for you - just
pass your inputs and labels in any format that model.fit()
supports! If, however, you want to use the second
format outside of Keras methods like fit()
and predict()
, such as when creating your own layers or models with
the Keras Functional
API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
input_ids
only and nothing else: model(input_ids)
model([input_ids, attention_mask])
or model([input_ids, attention_mask, token_type_ids])
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
training: typing.Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or tuple(tf.Tensor)
Parameters
Numpy array
or tf.Tensor
of shape (batch_size, num_choices, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using CamembertTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
Numpy array
or tf.Tensor
of shape (batch_size, num_choices, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
Numpy array
or tf.Tensor
of shape (batch_size, num_choices, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
Numpy array
or tf.Tensor
of shape (batch_size, num_choices, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
Numpy array
or tf.Tensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
tf.Tensor
of shape (batch_size, num_choices, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
bool
, optional, defaults to False
) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
tf.Tensor
of shape (batch_size,)
, optional) —
Labels for computing the multiple choice classification loss. Indices should be in [0, ..., num_choices]
where num_choices
is the size of the second dimension of the input tensors. (See input_ids
above)
Returns
transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (CamembertConfig) and inputs.
loss (tf.Tensor
of shape (batch_size, ), optional, returned when labels
is provided) — Classification loss.
logits (tf.Tensor
of shape (batch_size, num_choices)
) — num_choices is the second dimension of the input tensors. (see input_ids above).
Classification scores (before SoftMax).
hidden_states (tuple(tf.Tensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of tf.Tensor
(one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size)
.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The TFCamembertForMultipleChoice forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import CamembertTokenizer, TFCamembertForMultipleChoice
>>> import tensorflow as tf
>>> tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
>>> model = TFCamembertForMultipleChoice.from_pretrained("camembert-base")
>>> 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
( *args **kwargs )
Parameters
CamemBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers
accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit()
things should “just work” for you - just
pass your inputs and labels in any format that model.fit()
supports! If, however, you want to use the second
format outside of Keras methods like fit()
and predict()
, such as when creating your own layers or models with
the Keras Functional
API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
input_ids
only and nothing else: model(input_ids)
model([input_ids, attention_mask])
or model([input_ids, attention_mask, token_type_ids])
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
training: typing.Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFTokenClassifierOutput or tuple(tf.Tensor)
Parameters
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using CamembertTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
Numpy array
or tf.Tensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
tf.Tensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
bool
, optional, defaults to False
) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Labels for computing the token classification loss. Indices should be in [0, ..., config.num_labels - 1]
.
Returns
transformers.modeling_tf_outputs.TFTokenClassifierOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFTokenClassifierOutput or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (CamembertConfig) and inputs.
loss (tf.Tensor
of shape (n,)
, optional, where n is the number of unmasked labels, returned when labels
is provided) — Classification loss.
logits (tf.Tensor
of shape (batch_size, sequence_length, config.num_labels)
) — Classification scores (before SoftMax).
hidden_states (tuple(tf.Tensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of tf.Tensor
(one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size)
.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The TFCamembertForTokenClassification forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import CamembertTokenizer, TFCamembertForTokenClassification
>>> import tensorflow as tf
>>> tokenizer = CamembertTokenizer.from_pretrained("ydshieh/roberta-large-ner-english")
>>> model = TFCamembertForTokenClassification.from_pretrained("ydshieh/roberta-large-ner-english")
>>> inputs = tokenizer(
... "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="tf"
... )
>>> logits = model(**inputs).logits
>>> predicted_token_class_ids = tf.math.argmax(logits, axis=-1)
>>> # Note that tokens are classified rather then input words which means that
>>> # there might be more predicted token classes than words.
>>> # Multiple token classes might account for the same word
>>> predicted_tokens_classes = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()]
>>> predicted_tokens_classes
['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']
( *args **kwargs )
Parameters
CamemBERT 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 TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers
accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit()
things should “just work” for you - just
pass your inputs and labels in any format that model.fit()
supports! If, however, you want to use the second
format outside of Keras methods like fit()
and predict()
, such as when creating your own layers or models with
the Keras Functional
API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
input_ids
only and nothing else: model(input_ids)
model([input_ids, attention_mask])
or model([input_ids, attention_mask, token_type_ids])
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
start_positions: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
end_positions: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
training: typing.Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor)
Parameters
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using CamembertTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
Numpy array
or tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
Numpy array
or tf.Tensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
tf.Tensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
bool
, optional, defaults to False
) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
tf.Tensor
of shape (batch_size,)
, optional) —
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length
). Position outside of the sequence
are not taken into account for computing the loss.
tf.Tensor
of shape (batch_size,)
, optional) —
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length
). Position outside of the sequence
are not taken into account for computing the loss.
Returns
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (CamembertConfig) and inputs.
loss (tf.Tensor
of shape (batch_size, )
, optional, returned when start_positions
and end_positions
are provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (tf.Tensor
of shape (batch_size, sequence_length)
) — Span-start scores (before SoftMax).
end_logits (tf.Tensor
of shape (batch_size, sequence_length)
) — Span-end scores (before SoftMax).
hidden_states (tuple(tf.Tensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of tf.Tensor
(one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size)
.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The TFCamembertForQuestionAnswering forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import CamembertTokenizer, TFCamembertForQuestionAnswering
>>> import tensorflow as tf
>>> tokenizer = CamembertTokenizer.from_pretrained("ydshieh/roberta-base-squad2")
>>> model = TFCamembertForQuestionAnswering.from_pretrained("ydshieh/roberta-base-squad2")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="tf")
>>> outputs = model(**inputs)
>>> answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
>>> answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> tokenizer.decode(predict_answer_tokens)
' puppet'