as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and hidden_size = 768 This output is usually not a good summary of the semantic content of the input, youre often better with Instantiating a configuration with the defaults will yield a similar configuration to that of the BertGeneration do_basic_tokenize (bool, optional, defaults to True) Whether to do basic tokenization before WordPiece. Code (126) Discussion (2) About Dataset. all you need by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, ) # Copyright (c) 2018, NVIDIA CORPORATION. BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. encoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None 0 . logits (tf.Tensor of shape (batch_size, num_choices)) num_choices is the second dimension of the input tensors. improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement). Why we need the init_weight function in BERT pretrained model in heads. Home; Uncategorized; bert max sequence length huggingface; glamping in paris france; November 2, 2022; by A transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or a tuple of tf.Tensor (if prediction_logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). encoder_hidden_states: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None input_ids refer to the TF 2.0 documentation for all matter related to general usage and behavior. start_positions: typing.Optional[torch.Tensor] = None Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. A transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or a tuple of input_ids: typing.Optional[torch.Tensor] = None They were introduced in the study Well-Read Students Learn Better: On the Importance . Indices can be obtained using BertTokenizer. ( The TFBertForTokenClassification forward method, overrides the __call__ special method. A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. Input should be a sequence pair (see input_ids docstring) You could increase the dropout / regularization, but less layers / stacks would also likely help, or decrease the dimension of the vectors in the transformer (not sure what options BERT has). hidden_dropout_prob = 0.1 Instantiating a configuration with the defaults will yield a similar configuration to that of the BERT . was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that labels: typing.Optional[torch.Tensor] = None Posted Under: mfk tatran liptovsky mikulas mfk zemplin michalovce mfk tatran liptovsky mikulas mfk zemplin michalovce Although the recipe for forward pass needs to be defined within this function, one should call the Module As a result, they have somewhat more limited options If set to True, past_key_values key value states are returned and can be used to speed up decoding (see EncoderDecoderModel as proposed in Leveraging Pre-trained Checkpoints for Sequence Generation ) output_hidden_states: typing.Optional[bool] = None Based on WordPiece. Leveraging Pre-trained Checkpoints for Sequence Generation encoder_attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional, defaults to None) Mask to avoid performing attention on the padding token indices of the encoder input. subclass. Mar 1 at 7:54. siamese bert huggingface return_dict: typing.Optional[bool] = None end_positions: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None 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 usage and behavior. return_dict: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None Read the documentation from PretrainedConfig See PreTrainedTokenizer.call() and List[int]. head_mask: typing.Optional[torch.Tensor] = None Although the recipe for forward pass needs to be defined within this function, one should call the Module huggingface transformers - Bert Config: Num attention heads - Stack Construct a BertGeneration tokenizer. through the layers used for the auxiliary pretraining task. logits (torch.FloatTensor of shape (batch_size, 2)) Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation layer weights are trained from the next sentence prediction (classification) ( Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. bert huggingface github - mensvoort.info Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if return_dict: typing.Optional[bool] = None config (BertConfig) Model configuration class with all the parameters of the model. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various Only relevant if config.is_decoder = True. Indices can be obtained using BertGenerationTokenizer. setting. BERT is conceptually simple and empirically powerful. encoder_attention_mask = None In the 10% remaining cases, the masked tokens are left as is. unk_token = '[UNK]' To my knowledge Bert is used just as encoder. past_key_values). bos_token = '' Deep Learning 19: Training MLM on any pre-trained BERT models Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional, defaults to None) Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). elements depending on the configuration (BertConfig) and inputs. already_has_special_tokens: bool = False (see input_ids above). loss: typing.Optional[torch.FloatTensor] = None vocab_size = 50358 If config.num_labels == 1 a regression loss is computed (Mean-Square loss), The TFBertForPreTraining forward method, overrides the __call__ special method. ) This model was contributed by thomwolf. # distributed under the License is distributed on an "AS IS" BASIS. This should likely be deactivated for Japanese: output_hidden_states: typing.Optional[bool] = None # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) output_hidden_states: typing.Optional[bool] = None A transformers.modeling_tf_outputs.TFNextSentencePredictorOutput or a tuple of tf.Tensor (if ( output_attentions: typing.Optional[bool] = None Attentions weights after the attention softmax, used to compute the weighted average in the self-attention ) transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions or tuple(tf.Tensor), transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions or tuple(tf.Tensor). This tokenizer inherits from PreTrainedTokenizerFast which contains most of the methods. Finally, this model supports inherent JAX features such as: ( This is the second sentence. How to convert a Transformers model to TensorFlow? the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models Indices should be in [-100, 0, , config.vocab_size] (see input_ids docstring) filename_prefix: typing.Optional[str] = None prediction_logits: Tensor = None pad_token (string, optional, defaults to [PAD]) The token used for padding, for example when batching sequences of different lengths. configuration (BertConfig) and inputs. ) token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None intermediate_size (int, optional, defaults to 3072) Dimensionality of the intermediate (i.e., feed-forward) layer in the Transformer encoder. The optimizer Position outside of the sequence are not taken into account for computing the loss. averaging or pooling the sequence of hidden-states for the whole input sequence. usage and behavior. The inputs of the model are one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). configuration (BertConfig) and inputs. These layers directly linked to the loss so very prone to high bias. pad_token = '[PAD]' attention_mask: typing.Optional[torch.Tensor] = None head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional, defaults to None) Mask to nullify selected heads of the self-attention modules. layers on top of the hidden-states output to compute span start logits and span end logits). loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) Classification loss. a masked language modeling head and a next sentence prediction (classification) head. ) seq_relationship_logits (tf.Tensor of shape (batch_size, 2)) Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation 452). It is used to dont have their past key value states given to this model) of shape (batch_size, 1) instead of all refer to the TF 2.0 documentation for all matter related to general usage and behavior. inputs_embeds: typing.Optional[torch.Tensor] = None In-graph tokenizers, unlike other Hugging Face tokenizers, are actually Keras layers and are designed to be run eos_token_id = 1 making XLM-GPT2 by using embedding output from XLM-R and send it to GPT-2. token_type_ids: typing.Optional[torch.Tensor] = None $\begingroup$ It likely just has way too much capacity for the dataset you are trying to use. head_mask: typing.Optional[torch.Tensor] = None decoder_input_ids of shape (batch_size, sequence_length). Sequence of hidden-states at the output of the last layer of the model. You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional, defaults to None) . before SoftMax). output_hidden_states: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None When fine-tuned on downstream tasks, this model achieves the following results: This model can be loaded on the Inference API on-demand. token instead. HuggingFace Config Params Explained - GitHub Pages attention_mask: typing.Optional[torch.Tensor] = None If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output. Indices should be in [0, 1]. attentions: typing.Optional[typing.Tuple[jax._src.numpy.ndarray.ndarray]] = None training: typing.Optional[bool] = False If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that Check the superclass documentation for the generic methods the cls_token = '[CLS]' ( position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None A transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput or a tuple of tf.Tensor (if Nov 03, 2022. hopi language alphabet. Read the return_dict: typing.Optional[bool] = None attention_mask: typing.Optional[torch.Tensor] = None A BERT sequence pair mask has the following format: if token_ids_1 is None, only returns the first portion of the mask (0s). elements depending on the configuration (BertConfig) and inputs. encoder_attention_mask = None alpha: Smoothing parameter for unigram sampling, and dropout probability of merge operations for hidden_dropout_prob (float, optional, defaults to 0.1) The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. return_dict: typing.Optional[bool] = None huggingface invalid token passed - dnkp.festa-brasileira.de setting. Users should Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) +254 705 152 401 +254-20-2196904. input_ids can be represented by the inputs_ids passed to the forward method of BertModel. Use it as a regular TF 2.0 Keras Model and see: https://github.com/huggingface/transformers/issues/328. return_dict: typing.Optional[bool] = None start_logits (jnp.ndarray of shape (batch_size, sequence_length)) Span-start scores (before SoftMax). ( params: dict = None instance afterwards instead of this since the former takes care of running the pre and post processing steps while head_mask = None transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or tuple(torch.FloatTensor). **kwargs attention_mask: typing.Optional[torch.Tensor] = None If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ( next_sentence_label: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None vocab_size (int, optional, defaults to 50358) Vocabulary size of the BERT model.Defines the number of different tokens that can be represented by the inputs_ids passed when calling BertGeneration. 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)). for the BERT bert-base-uncased architecture. use_cache (bool, optional, defaults to True): 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 position_embedding_type = 'absolute' hidden_size (int, optional, defaults to 768) Dimensionality of the encoder layers and the pooler layer. This model is a PyTorch torch.nn.Module sub-class. 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 As per my understanding , if i increase or decrease the num of attention heads the parameters should also increase/decrease, which i didn't observe in my case. Use it params: dict = None A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple of October 30, 2022. : typing.Optional[tensorflow.python.framework.ops.Tensor] = None, : typing.Union[typing.Tuple[tensorflow.python.framework.ops.Tensor], tensorflow.python.framework.ops.Tensor, NoneType] = None. num_choices is the second dimension of the input tensors. a string, the model id of a pretrained model configuration hosted inside a model repo on huggingface.co. inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ) **kwargs bert-small. with Better Relative Position Embeddings (Huang et al. ( return_dict: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None A transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions or a tuple of This model is also a tf.keras.Model subclass. A transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or a tuple of 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, + You can initialize a model without pre-trained weights using. train: bool = False ). Configuration objects inherit from PretrainedConfig and can be used head_mask = None return_dict: typing.Optional[bool] = None Understanding tasks. The best would be to finetune the pooling representation for you task and use the pooler then. The bare BertGeneration model transformer outputting raw hidden-states without any specific head on top. 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 vocab_file Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. return_dict: typing.Optional[bool] = None ) attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ( Bert Model with a multiple choice classification head on top (a linear layer on top of vocab_size (:obj:`int`, `optional`, defaults to 30522): Vocabulary size of the BERT model. params: dict = None labels: typing.Optional[torch.Tensor] = None encoder_hidden_states = None star anise soy sauce chicken; nepali nicknames for friends; columbia summit rush backpack diaper bag, grey; edinburgh music festivals; Jueves 3 de Noviembre | 4:41 am 5 letter word from emperor; necessary and sufficient cause examples in epidemiology; params: dict = None output_attentions: typing.Optional[bool] = None instantiate a BERT model according to the specified arguments, defining the model architecture. The BertForSequenceClassification forward method, overrides the __call__ special method. It should be initialized similarly to other tokenizers, using the PreTrainedTokenizer.encode() for details. The TFBertForMaskedLM forward method, overrides the __call__ special method. attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None As a result, . labels: typing.Optional[torch.Tensor] = None ), transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions, transformers.models.bert.modeling_bert.BertForPreTrainingOutput, transformers.modeling_outputs.CausalLMOutputWithCrossAttentions, transformers.modeling_outputs.MaskedLMOutput, transformers.modeling_outputs.NextSentencePredictorOutput, transformers.modeling_outputs.SequenceClassifierOutput, transformers.modeling_outputs.MultipleChoiceModelOutput, transformers.modeling_outputs.TokenClassifierOutput, transformers.modeling_outputs.QuestionAnsweringModelOutput, transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions, transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput, transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions, transformers.modeling_tf_outputs.TFMaskedLMOutput, transformers.modeling_tf_outputs.TFNextSentencePredictorOutput, transformers.modeling_tf_outputs.TFSequenceClassifierOutput, transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput, transformers.modeling_tf_outputs.TFTokenClassifierOutput, transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput, transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling, transformers.models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput, transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions, transformers.modeling_flax_outputs.FlaxMaskedLMOutput, transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput, transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput, transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput, transformers.modeling_flax_outputs.FlaxTokenClassifierOutput, transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput. layer_norm_eps = 1e-12 Instantiating a configuration with the defaults will yield a similar. I have a Kaggle-Tensorflow example (a bit older version) that applying exact same idea -->. output_attentions: typing.Optional[bool] = None continuation before SoftMax). encoder_hidden_states: typing.Optional[torch.Tensor] = None The BertForMaskedLM forward method, overrides the __call__ special method. More precisely, it Bert Model with a next sentence prediction (classification) head on top. ), ( This output is usually not a good summary behavior. bert text classification pytorch huggingface This model is also a PyTorch torch.nn.Module subclass. is used in the cross-attention if the model is configured as a decoder. ", "textattack/bert-base-uncased-yelp-polarity", # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained()`, # choice0 is correct (according to Wikipedia ;)), batch size 1, # the linear classifier still needs to be trained, "dbmdz/bert-large-cased-finetuned-conll03-english", "HuggingFace is a company based in Paris and New York", # Note that tokens are classified rather then input words which means that. attention_mask = None encoder_attention_mask = None Instantiating a configuration with the defaults will yield a similar configuration to that of and get access to the augmented documentation experience. do_lower_case (bool, optional, defaults to True) Whether to lowercase the input when tokenizing. See the model hub to look for head_mask: typing.Optional[torch.Tensor] = None Therefore, no EOS token should be added to the end of the input. attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various ( refer to the TF 2.0 documentation for all matter related to general usage and behavior. token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Browse other questions tagged python nlp pytorch huggingface -transformers huggingface - datasets or ask your own question. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various configuration (BertConfig) and inputs. next_sentence_label: typing.Optional[torch.Tensor] = None There is only one split in the dataset, so we need to split it into training and testing sets: # split the dataset into training (90%) and testing (10%) d = dataset.train_test_split(test_size=0.1) d["train"], d["test"] You can also pass the seed parameter to the train_test_split () method so it'll be the same sets after running multiple times. If you choose this second option, there are three possibilities you can use to gather all the input Tensors this paper and first released in num_attention_heads (:obj:`int`, `optional`, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. It is used to This model is uncased: it does not make a difference ( 1. Pretrained model on English language using a masked language modeling (MLM) objective. return_dict: typing.Optional[bool] = None Based on SentencePiece. encoder_attention_mask: typing.Optional[torch.Tensor] = None strip_accents = None PyTorch-Transformers | PyTorch Next sequence prediction (classification) loss. The TFBertForPreTraining forward method, overrides the __call__() special method. BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. Changing config and loading Hugging Face model fine-tuned on a type_vocab_size = 2 params: dict = None The BertModel forward method, overrides the __call__() special method. (batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if # See the License for the specific language governing permissions and, "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-config.json", "bert-large-uncased-whole-word-masking-finetuned-squad", "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-config.json", "bert-large-cased-whole-word-masking-finetuned-squad", "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese/config.json", "cl-tohoku/bert-base-japanese-whole-word-masking", "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-whole-word-masking/config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char/config.json", "cl-tohoku/bert-base-japanese-char-whole-word-masking", "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-whole-word-masking/config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-cased-v1/config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-uncased-v1/config.json", "https://s3.amazonaws.com/models.huggingface.co/bert/wietsedv/bert-base-dutch-cased/config.json", # See all BERT models at https://huggingface.co/models?filter=bert, This is the configuration class to store the configuration of a :class:`~transformers.BertModel` or a, :class:`~transformers.TFBertModel`.
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