CamemBERT¶

Overview¶

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 implementation is the same as RoBERTa. Refer to the documentation of RoBERTa for usage examples as well as the information relative to the inputs and outputs.

This model was contributed by camembert. The original code can be found here.

CamembertConfig¶

class transformers.CamembertConfig(pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs)[source]¶: This class overrides RobertaConfig. Please check the superclass for the appropriate documentation alongside usage examples.

CamembertTokenizer¶

class transformers.CamembertTokenizer(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: Optional[Dict[str, Any]] = None, **kwargs)[source]¶

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.

Parameters

vocab_file (str) – SentencePiece file (generally has a .spm extension) that contains the vocabulary necessary to instantiate a tokenizer.
bos_token (str, optional, defaults to "<s>") –
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

Note

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.
eos_token (str, optional, defaults to "</s>") –
The end of sequence token.

Note

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.
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.
cls_token (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.
unk_token (str, optional, defaults to "<unk>") – The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
pad_token (str, optional, defaults to "<pad>") – The token used for padding, for example when batching sequences of different lengths.
mask_token (str, optional, defaults to "<mask>") – The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.
additional_special_tokens (List[str], optional, defaults to ["<s>NOTUSED", "</s>NOTUSED"]) – Additional special tokens used by the tokenizer.
sp_model_kwargs (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.

sp_model¶

The SentencePiece processor that is used for every conversion (string, tokens and IDs).

Type: SentencePieceProcessor

build_inputs_with_special_tokens(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int][source]¶

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:

single sequence: <s> X </s>
pair of sequences: <s> A </s></s> B </s>

Parameters

token_ids_0 (List[int]) – List of IDs to which the special tokens will be added.
token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs.

Returns

List of input IDs with the appropriate special tokens.

Return type

List[int]

create_token_type_ids_from_sequences(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int][source]¶

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.

Parameters

token_ids_0 (List[int]) – List of IDs.
token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs.

Returns

List of zeros.

Return type

List[int]

get_special_tokens_mask(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False) → List[int][source]¶

Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer prepare_for_model method.

Parameters

token_ids_0 (List[int]) – List of IDs.
token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs.
already_has_special_tokens (bool, optional, defaults to False) – Whether or not the token list is already formatted with special tokens for the model.

Returns

A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.

Return type

List[int]

save_vocabulary(save_directory: str, filename_prefix: Optional[str] = None) → Tuple[str][source]¶

Save only the vocabulary of the tokenizer (vocabulary + added tokens).

This method won’t save the configuration and special token mappings of the tokenizer. Use _save_pretrained() to save the whole state of the tokenizer.

Parameters

save_directory (str) – The directory in which to save the vocabulary.
filename_prefix (str, optional) – An optional prefix to add to the named of the saved files.

Returns

Paths to the files saved.

Return type

Tuple(str)

CamembertTokenizerFast¶

class transformers.CamembertTokenizerFast(vocab_file, 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)[source]¶

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.

Parameters

vocab_file (str) – SentencePiece file (generally has a .spm extension) that contains the vocabulary necessary to instantiate a tokenizer.
bos_token (str, optional, defaults to "<s>") –
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

Note

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.
eos_token (str, optional, defaults to "</s>") –
The end of sequence token.

Note

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.
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.
cls_token (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.
unk_token (str, optional, defaults to "<unk>") – The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
pad_token (str, optional, defaults to "<pad>") – The token used for padding, for example when batching sequences of different lengths.
mask_token (str, optional, defaults to "<mask>") – The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.
additional_special_tokens (List[str], optional, defaults to ["<s>NOTUSED", "</s>NOTUSED"]) – Additional special tokens used by the tokenizer.

build_inputs_with_special_tokens(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int][source]¶

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:

single sequence: <s> X </s>
pair of sequences: <s> A </s></s> B </s>

Parameters

token_ids_0 (List[int]) – List of IDs to which the special tokens will be added.
token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs.

Returns

List of input IDs with the appropriate special tokens.

Return type

List[int]

create_token_type_ids_from_sequences(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int][source]¶

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.

Parameters

token_ids_0 (List[int]) – List of IDs.
token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs.

Returns

List of zeros.

Return type

List[int]

save_vocabulary(save_directory: str, filename_prefix: Optional[str] = None) → Tuple[str][source]¶

Save only the vocabulary of the tokenizer (vocabulary + added tokens).

This method won’t save the configuration and special token mappings of the tokenizer. Use _save_pretrained() to save the whole state of the tokenizer.

Parameters

save_directory (str) – The directory in which to save the vocabulary.
filename_prefix (str, optional) – An optional prefix to add to the named of the saved files.

Returns

Paths to the files saved.

Return type

Tuple(str)

slow_tokenizer_class¶: alias of transformers.models.camembert.tokenization_camembert.CamembertTokenizer

CamembertModel¶

class transformers.CamembertModel(config, add_pooling_layer=True)[source]¶

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.

Parameters: config (CamembertConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

This class overrides RobertaModel. Please check the superclass for the appropriate documentation alongside usage examples.

config_class¶: alias of transformers.models.camembert.configuration_camembert.CamembertConfig

CamembertForCausalLM¶

class transformers.CamembertForCausalLM(config)[source]¶

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.

Parameters: config (CamembertConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

This class overrides RobertaForCausalLM. Please check the superclass for the appropriate documentation alongside usage examples.

config_class¶: alias of transformers.models.camembert.configuration_camembert.CamembertConfig

CamembertForMaskedLM¶

class transformers.CamembertForMaskedLM(config)[source]¶

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.

Parameters: config (CamembertConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

This class overrides RobertaForMaskedLM. Please check the superclass for the appropriate documentation alongside usage examples.

config_class¶: alias of transformers.models.camembert.configuration_camembert.CamembertConfig

CamembertForSequenceClassification¶

class transformers.CamembertForSequenceClassification(config)[source]¶

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.

Parameters: config (CamembertConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

This class overrides RobertaForSequenceClassification. Please check the superclass for the appropriate documentation alongside usage examples.

config_class¶: alias of transformers.models.camembert.configuration_camembert.CamembertConfig

CamembertForMultipleChoice¶

class transformers.CamembertForMultipleChoice(config)[source]¶

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.

Parameters: config (CamembertConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

This class overrides RobertaForMultipleChoice. Please check the superclass for the appropriate documentation alongside usage examples.

config_class¶: alias of transformers.models.camembert.configuration_camembert.CamembertConfig

CamembertForTokenClassification¶

class transformers.CamembertForTokenClassification(config)[source]¶

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.

Parameters: config (CamembertConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

This class overrides RobertaForTokenClassification. Please check the superclass for the appropriate documentation alongside usage examples.

config_class¶: alias of transformers.models.camembert.configuration_camembert.CamembertConfig

CamembertForQuestionAnswering¶

class transformers.CamembertForQuestionAnswering(config)[source]¶

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.

Parameters: config (CamembertConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

This class overrides RobertaForQuestionAnswering. Please check the superclass for the appropriate documentation alongside usage examples.

config_class¶: alias of transformers.models.camembert.configuration_camembert.CamembertConfig

TFCamembertModel¶

class transformers.TFCamembertModel(*args, **kwargs)[source]¶

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.

Note

TF 2.0 models accepts two formats as inputs:

having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using tf.keras.Model.fit() method which currently requires having all the tensors in the first argument of the model call function: model(inputs).

If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :

a single Tensor with input_ids only and nothing else: model(inputs_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

Parameters: config (CamembertConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

This class overrides TFRobertaModel. Please check the superclass for the appropriate documentation alongside usage examples.

config_class¶: alias of transformers.models.camembert.configuration_camembert.CamembertConfig

TFCamembertForMaskedLM¶

class transformers.TFCamembertForMaskedLM(*args, **kwargs)[source]¶

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.

Note

TF 2.0 models accepts two formats as inputs:

having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using tf.keras.Model.fit() method which currently requires having all the tensors in the first argument of the model call function: model(inputs).

If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :

a single Tensor with input_ids only and nothing else: model(inputs_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

Parameters: config (CamembertConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

This class overrides TFRobertaForMaskedLM. Please check the superclass for the appropriate documentation alongside usage examples.

config_class¶: alias of transformers.models.camembert.configuration_camembert.CamembertConfig

TFCamembertForSequenceClassification¶

class transformers.TFCamembertForSequenceClassification(*args, **kwargs)[source]¶

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.

Note

TF 2.0 models accepts two formats as inputs:

having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using tf.keras.Model.fit() method which currently requires having all the tensors in the first argument of the model call function: model(inputs).

If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :

a single Tensor with input_ids only and nothing else: model(inputs_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

Parameters: config (CamembertConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

This class overrides TFRobertaForSequenceClassification. Please check the superclass for the appropriate documentation alongside usage examples.

config_class¶: alias of transformers.models.camembert.configuration_camembert.CamembertConfig

TFCamembertForMultipleChoice¶

class transformers.TFCamembertForMultipleChoice(*args, **kwargs)[source]¶

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.

Note

TF 2.0 models accepts two formats as inputs:

having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using tf.keras.Model.fit() method which currently requires having all the tensors in the first argument of the model call function: model(inputs).

If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :

a single Tensor with input_ids only and nothing else: model(inputs_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

Parameters: config (CamembertConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

This class overrides TFRobertaForMultipleChoice. Please check the superclass for the appropriate documentation alongside usage examples.

config_class¶: alias of transformers.models.camembert.configuration_camembert.CamembertConfig

TFCamembertForTokenClassification¶

class transformers.TFCamembertForTokenClassification(*args, **kwargs)[source]¶

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.

Note

TF 2.0 models accepts two formats as inputs:

having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using tf.keras.Model.fit() method which currently requires having all the tensors in the first argument of the model call function: model(inputs).

If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :

a single Tensor with input_ids only and nothing else: model(inputs_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

Parameters: config (CamembertConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

This class overrides TFRobertaForTokenClassification. Please check the superclass for the appropriate documentation alongside usage examples.

config_class¶: alias of transformers.models.camembert.configuration_camembert.CamembertConfig

TFCamembertForQuestionAnswering¶

class transformers.TFCamembertForQuestionAnswering(*args, **kwargs)[source]¶

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.

Note

TF 2.0 models accepts two formats as inputs:

having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using tf.keras.Model.fit() method which currently requires having all the tensors in the first argument of the model call function: model(inputs).

If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :

a single Tensor with input_ids only and nothing else: model(inputs_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

Parameters: config (CamembertConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

This class overrides TFRobertaForQuestionAnswering. Please check the superclass for the appropriate documentation alongside usage examples.

config_class¶: alias of transformers.models.camembert.configuration_camembert.CamembertConfig