RemBERT

## [](#overview)Overview

The RemBERT model was proposed in [Rethinking Embedding Coupling in Pre-trained Language Models](https://arxiv.org/abs/2010.12821) by Hyung Won Chung, Thibault Févry, Henry Tsai, Melvin Johnson, Sebastian Ruder.

The abstract from the paper is the following:

_We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art pre-trained language models. We show that decoupled embeddings provide increased modeling flexibility, allowing us to significantly improve the efficiency of parameter allocation in the input embedding of multilingual models. By reallocating the input embedding parameters in the Transformer layers, we achieve dramatically better performance on standard natural language understanding tasks with the same number of parameters during fine-tuning. We also show that allocating additional capacity to the output embedding provides benefits to the model that persist through the fine-tuning stage even though the output embedding is discarded after pre-training. Our analysis shows that larger output embeddings prevent the model’s last layers from overspecializing to the pre-training task and encourage Transformer representations to be more general and more transferable to other tasks and languages. Harnessing these findings, we are able to train models that achieve strong performance on the XTREME benchmark without increasing the number of parameters at the fine-tuning stage._

Tips:

For fine-tuning, RemBERT can be thought of as a bigger version of mBERT with an ALBERT-like factorization of the embedding layer. The embeddings are not tied in pre-training, in contrast with BERT, which enables smaller input embeddings (preserved during fine-tuning) and bigger output embeddings (discarded at fine-tuning). The tokenizer is also similar to the Albert one rather than the BERT one.

## [](#documentation-resources)Documentation resources

-   [Text classification task guide](../tasks/sequence_classification)
-   [Token classification task guide](../tasks/token_classification)
-   [Question answering task guide](../tasks/question_answering)
-   [Causal language modeling task guide](../tasks/language_modeling)
-   [Masked language modeling task guide](../tasks/masked_language_modeling)
-   [Multiple choice task guide](../tasks/multiple_choice)

## [](#transformers.RemBertConfig)RemBertConfig

### class transformers.RemBertConfig

[](#transformers.RemBertConfig)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/configuration_rembert.py#L32)

( vocab\_size = 250300hidden\_size = 1152num\_hidden\_layers = 32num\_attention\_heads = 18input\_embedding\_size = 256output\_embedding\_size = 1664intermediate\_size = 4608hidden\_act = 'gelu'hidden\_dropout\_prob = 0.0attention\_probs\_dropout\_prob = 0.0classifier\_dropout\_prob = 0.1max\_position\_embeddings = 512type\_vocab\_size = 2initializer\_range = 0.02layer\_norm\_eps = 1e-12use\_cache = Truepad\_token\_id = 0bos\_token\_id = 312eos\_token\_id = 313\*\*kwargs )

This is the configuration class to store the configuration of a [RemBertModel](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertModel). It is used to instantiate an RemBERT model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the RemBERT [google/rembert](https://huggingface.co/google/rembert) architecture.

Configuration objects inherit from [PretrainedConfig](/docs/transformers/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](/docs/transformers/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) for more information.

Example:

```
>>> from transformers import RemBertModel, RemBertConfig

>>> 
>>> configuration = RemBertConfig()

>>> 
>>> model = RemBertModel(configuration)

>>> 
>>> configuration = model.config```

## [](#transformers.RemBertTokenizer)RemBertTokenizer

### class transformers.RemBertTokenizer

[](#transformers.RemBertTokenizer)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert.py#L43)

( vocab\_filedo\_lower\_case = Falseremove\_space = Truekeep\_accents = Truebos\_token = '\[CLS\]'eos\_token = '\[SEP\]'unk\_token = '\[UNK\]'sep\_token = '\[SEP\]'pad\_token = '\[PAD\]'cls\_token = '\[CLS\]'mask\_token = '\[MASK\]'\*\*kwargs )

Construct a RemBERT tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).

This tokenizer inherits from [PreTrainedTokenizer](/docs/transformers/v4.30.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizer) which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

#### build\_inputs\_with\_special\_tokens

[](#transformers.RemBertTokenizer.build_inputs_with_special_tokens)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert.py#L172)

( token\_ids\_0: typing.List\[int\]token\_ids\_1: typing.Optional\[typing.List\[int\]\] = None ) → `List[int]`

Parameters

-   [](#transformers.RemBertTokenizer.build_inputs_with_special_tokens.token_ids_0)**token\_ids\_0** (`List[int]`) — List of IDs to which the special tokens will be added.
-   [](#transformers.RemBertTokenizer.build_inputs_with_special_tokens.token_ids_1)**token\_ids\_1** (`List[int]`, _optional_) — Optional second list of IDs for sequence pairs.

List of [input IDs](../glossary#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. A REMBERT sequence has the following format:

-   single sequence: `[CLS] X [SEP]`
-   pair of sequences: `[CLS] A [SEP] B [SEP]`

#### get\_special\_tokens\_mask

[](#transformers.RemBertTokenizer.get_special_tokens_mask)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert.py#L197)

( token\_ids\_0: typing.List\[int\]token\_ids\_1: typing.Optional\[typing.List\[int\]\] = Nonealready\_has\_special\_tokens: bool = False ) → `List[int]`

Parameters

-   [](#transformers.RemBertTokenizer.get_special_tokens_mask.token_ids_0)**token\_ids\_0** (`List[int]`) — List of IDs.
-   [](#transformers.RemBertTokenizer.get_special_tokens_mask.token_ids_1)**token\_ids\_1** (`List[int]`, _optional_) — Optional second list of IDs for sequence pairs.
-   [](#transformers.RemBertTokenizer.get_special_tokens_mask.already_has_special_tokens)**already\_has\_special\_tokens** (`bool`, _optional_, defaults to `False`) — Whether or not the token list is already formatted with special tokens for the model.

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.

#### create\_token\_type\_ids\_from\_sequences

[](#transformers.RemBertTokenizer.create_token_type_ids_from_sequences)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert.py#L228)

( token\_ids\_0: typing.List\[int\]token\_ids\_1: typing.Optional\[typing.List\[int\]\] = None ) → `List[int]`

Parameters

-   [](#transformers.RemBertTokenizer.create_token_type_ids_from_sequences.token_ids_0)**token\_ids\_0** (`List[int]`) — List of IDs.
-   [](#transformers.RemBertTokenizer.create_token_type_ids_from_sequences.token_ids_1)**token\_ids\_1** (`List[int]`, _optional_) — Optional second list of IDs for sequence pairs.

List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).

Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RemBERT

sequence pair mask has the following format:

```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |```

If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).

#### save\_vocabulary

[](#transformers.RemBertTokenizer.save_vocabulary)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert.py#L258)

( save\_directory: strfilename\_prefix: typing.Optional\[str\] = None )

## [](#transformers.RemBertTokenizerFast)RemBertTokenizerFast

### class transformers.RemBertTokenizerFast

[](#transformers.RemBertTokenizerFast)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert_fast.py#L51)

( vocab\_file = Nonetokenizer\_file = Nonedo\_lower\_case = Trueremove\_space = Truekeep\_accents = Falsebos\_token = '\[CLS\]'eos\_token = '\[SEP\]'unk\_token = '<unk>'sep\_token = '\[SEP\]'pad\_token = '<pad>'cls\_token = '\[CLS\]'mask\_token = '\[MASK\]'\*\*kwargs )

Construct a “fast” RemBert tokenizer (backed by HuggingFace’s _tokenizers_ library). Based on [Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). This tokenizer inherits from [PreTrainedTokenizerFast](/docs/transformers/v4.30.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast) which contains most of the main methods. Users should refer to this superclass for more information regarding those methods

#### build\_inputs\_with\_special\_tokens

[](#transformers.RemBertTokenizerFast.build_inputs_with_special_tokens)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert_fast.py#L144)

( token\_ids\_0: typing.List\[int\]token\_ids\_1: typing.Optional\[typing.List\[int\]\] = None ) → `List[int]`

Parameters

-   [](#transformers.RemBertTokenizerFast.build_inputs_with_special_tokens.token_ids_0)**token\_ids\_0** (`List[int]`) — List of IDs to which the special tokens will be added
-   [](#transformers.RemBertTokenizerFast.build_inputs_with_special_tokens.token_ids_1)**token\_ids\_1** (`List[int]`, _optional_, defaults to `None`) — Optional second list of IDs for sequence pairs.

list of [input IDs](../glossary#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. A RemBERT sequence has the following format:

-   single sequence: `[CLS] X [SEP]`
-   pair of sequences: `[CLS] A [SEP] B [SEP]`

#### get\_special\_tokens\_mask

[](#transformers.RemBertTokenizerFast.get_special_tokens_mask)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert_fast.py#L169)

( token\_ids\_0: typing.List\[int\]token\_ids\_1: typing.Optional\[typing.List\[int\]\] = Nonealready\_has\_special\_tokens: bool = False ) → `List[int]`

Parameters

-   [](#transformers.RemBertTokenizerFast.get_special_tokens_mask.token_ids_0)**token\_ids\_0** (`List[int]`) — List of ids.
-   [](#transformers.RemBertTokenizerFast.get_special_tokens_mask.token_ids_1)**token\_ids\_1** (`List[int]`, _optional_, defaults to `None`) — Optional second list of IDs for sequence pairs.
-   [](#transformers.RemBertTokenizerFast.get_special_tokens_mask.already_has_special_tokens)**already\_has\_special\_tokens** (`bool`, _optional_, defaults to `False`) — Set to True if the token list is already formatted with special tokens for the model

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

Retrieves 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.

#### create\_token\_type\_ids\_from\_sequences

[](#transformers.RemBertTokenizerFast.create_token_type_ids_from_sequences)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert_fast.py#L200)

( token\_ids\_0: typing.List\[int\]token\_ids\_1: typing.Optional\[typing.List\[int\]\] = None ) → `List[int]`

Parameters

-   [](#transformers.RemBertTokenizerFast.create_token_type_ids_from_sequences.token_ids_0)**token\_ids\_0** (`List[int]`) — List of ids.
-   [](#transformers.RemBertTokenizerFast.create_token_type_ids_from_sequences.token_ids_1)**token\_ids\_1** (`List[int]`, _optional_, defaults to `None`) — Optional second list of IDs for sequence pairs.

List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).

Creates a mask from the two sequences passed to be used in a sequence-pair classification task. A RemBERT

sequence pair mask has the following format:

```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |```

if token\_ids\_1 is None, only returns the first portion of the mask (0s).

#### save\_vocabulary

[](#transformers.RemBertTokenizerFast.save_vocabulary)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert_fast.py#L230)

( save\_directory: strfilename\_prefix: typing.Optional\[str\] = None )

## [](#transformers.RemBertModel)RemBertModel

### class transformers.RemBertModel

[](#transformers.RemBertModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L745)

( configadd\_pooling\_layer = True )

Parameters

-   [](#transformers.RemBertModel.config)**config** ([RemBertConfig](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare RemBERT Model transformer outputting raw hidden-states without any specific head on top. This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. 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](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.

To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set 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.

#### forward

[](#transformers.RemBertModel.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L784)

( input\_ids: LongTensor = Noneattention\_mask: typing.Optional\[torch.LongTensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.LongTensor\] = Noneposition\_ids: typing.Optional\[torch.LongTensor\] = Nonehead\_mask: typing.Optional\[torch.FloatTensor\] = Noneinputs\_embeds: typing.Optional\[torch.FloatTensor\] = Noneencoder\_hidden\_states: typing.Optional\[torch.FloatTensor\] = Noneencoder\_attention\_mask: typing.Optional\[torch.FloatTensor\] = Nonepast\_key\_values: typing.Optional\[typing.Tuple\[typing.Tuple\[torch.FloatTensor\]\]\] = Noneuse\_cache: typing.Optional\[bool\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.BaseModelOutputWithPastAndCrossAttentions](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions) or `tuple(torch.FloatTensor)`

The [RemBertModel](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertModel) 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 AutoTokenizer, RemBertModel
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> model = RemBertModel.from_pretrained("google/rembert")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state```

## [](#transformers.RemBertForCausalLM)RemBertForCausalLM

### class transformers.RemBertForCausalLM

[](#transformers.RemBertForCausalLM)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1016)

( config )

Parameters

-   [](#transformers.RemBertForCausalLM.config)**config** ([RemBertConfig](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

RemBERT Model with a `language modeling` head on top for CLM fine-tuning. This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

#### forward

[](#transformers.RemBertForCausalLM.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1037)

( input\_ids: LongTensor = Noneattention\_mask: typing.Optional\[torch.LongTensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.LongTensor\] = Noneposition\_ids: typing.Optional\[torch.LongTensor\] = Nonehead\_mask: typing.Optional\[torch.FloatTensor\] = Noneinputs\_embeds: typing.Optional\[torch.FloatTensor\] = Noneencoder\_hidden\_states: typing.Optional\[torch.FloatTensor\] = Noneencoder\_attention\_mask: typing.Optional\[torch.FloatTensor\] = Nonepast\_key\_values: typing.Optional\[typing.Tuple\[typing.Tuple\[torch.FloatTensor\]\]\] = Nonelabels: typing.Optional\[torch.LongTensor\] = Noneuse\_cache: typing.Optional\[bool\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.CausalLMOutputWithCrossAttentions](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`

The [RemBertForCausalLM](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertForCausalLM) 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 AutoTokenizer, RemBertForCausalLM, RemBertConfig
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> config = RemBertConfig.from_pretrained("google/rembert")
>>> config.is_decoder = True
>>> model = RemBertForCausalLM.from_pretrained("google/rembert", config=config)

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> prediction_logits = outputs.logits```

## [](#transformers.RemBertForMaskedLM)RemBertForMaskedLM

### class transformers.RemBertForMaskedLM

[](#transformers.RemBertForMaskedLM)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L914)

( config )

Parameters

-   [](#transformers.RemBertForMaskedLM.config)**config** ([RemBertConfig](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

RemBERT Model with a `language modeling` head on top. This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

#### forward

[](#transformers.RemBertForMaskedLM.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L936)

( input\_ids: LongTensor = Noneattention\_mask: typing.Optional\[torch.LongTensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.LongTensor\] = Noneposition\_ids: typing.Optional\[torch.LongTensor\] = Nonehead\_mask: typing.Optional\[torch.FloatTensor\] = Noneinputs\_embeds: typing.Optional\[torch.FloatTensor\] = Noneencoder\_hidden\_states: typing.Optional\[torch.FloatTensor\] = Noneencoder\_attention\_mask: typing.Optional\[torch.FloatTensor\] = Nonelabels: typing.Optional\[torch.LongTensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.MaskedLMOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput) or `tuple(torch.FloatTensor)`

The [RemBertForMaskedLM](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertForMaskedLM) 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 AutoTokenizer, RemBertForMaskedLM
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> model = RemBertForMaskedLM.from_pretrained("google/rembert")

>>> inputs = tokenizer("The capital of France is [MASK].", return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> 
>>> 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)

>>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
>>> 
>>> labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)

>>> outputs = model(**inputs, labels=labels)```

## [](#transformers.RemBertForSequenceClassification)RemBertForSequenceClassification

### class transformers.RemBertForSequenceClassification

[](#transformers.RemBertForSequenceClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1168)

( config )

Parameters

-   [](#transformers.RemBertForSequenceClassification.config)**config** ([RemBertConfig](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

RemBERT 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 is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

#### forward

[](#transformers.RemBertForSequenceClassification.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1179)

( input\_ids: FloatTensor = Noneattention\_mask: typing.Optional\[torch.FloatTensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.LongTensor\] = Noneposition\_ids: typing.Optional\[torch.FloatTensor\] = Nonehead\_mask: typing.Optional\[torch.FloatTensor\] = Noneinputs\_embeds: typing.Optional\[torch.FloatTensor\] = Nonelabels: typing.Optional\[torch.LongTensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.SequenceClassifierOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or `tuple(torch.FloatTensor)`

The [RemBertForSequenceClassification](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertForSequenceClassification) 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 AutoTokenizer, RemBertForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> model = RemBertForSequenceClassification.from_pretrained("google/rembert")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_class_id = logits.argmax().item()

>>> 
>>> num_labels = len(model.config.id2label)
>>> model = RemBertForSequenceClassification.from_pretrained("google/rembert", num_labels=num_labels)

>>> labels = torch.tensor([1])
>>> loss = model(**inputs, labels=labels).loss```

Example of multi-label classification:

```
>>> import torch
>>> from transformers import AutoTokenizer, RemBertForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> model = RemBertForSequenceClassification.from_pretrained("google/rembert", problem_type="multi_label_classification")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]

>>> 
>>> num_labels = len(model.config.id2label)
>>> model = RemBertForSequenceClassification.from_pretrained(
...     "google/rembert", num_labels=num_labels, problem_type="multi_label_classification"
... )

>>> labels = torch.sum(
...     torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
... ).to(torch.float)
>>> loss = model(**inputs, labels=labels).loss```

## [](#transformers.RemBertForMultipleChoice)RemBertForMultipleChoice

### class transformers.RemBertForMultipleChoice

[](#transformers.RemBertForMultipleChoice)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1264)

( config )

Parameters

-   [](#transformers.RemBertForMultipleChoice.config)**config** ([RemBertConfig](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

RemBERT 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 is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

#### forward

[](#transformers.RemBertForMultipleChoice.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1275)

( input\_ids: FloatTensor = Noneattention\_mask: typing.Optional\[torch.FloatTensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.LongTensor\] = Noneposition\_ids: typing.Optional\[torch.FloatTensor\] = Nonehead\_mask: typing.Optional\[torch.FloatTensor\] = Noneinputs\_embeds: typing.Optional\[torch.FloatTensor\] = Nonelabels: typing.Optional\[torch.LongTensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.MultipleChoiceModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput) or `tuple(torch.FloatTensor)`

The [RemBertForMultipleChoice](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertForMultipleChoice) 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 AutoTokenizer, RemBertForMultipleChoice
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> model = RemBertForMultipleChoice.from_pretrained("google/rembert")

>>> 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)  

>>> 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)  

>>> 
>>> loss = outputs.loss
>>> logits = outputs.logits```

## [](#transformers.RemBertForTokenClassification)RemBertForTokenClassification

### class transformers.RemBertForTokenClassification

[](#transformers.RemBertForTokenClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1355)

( config )

Parameters

-   [](#transformers.RemBertForTokenClassification.config)**config** ([RemBertConfig](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

RemBERT 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 is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

#### forward

[](#transformers.RemBertForTokenClassification.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1367)

( input\_ids: FloatTensor = Noneattention\_mask: typing.Optional\[torch.FloatTensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.LongTensor\] = Noneposition\_ids: typing.Optional\[torch.FloatTensor\] = Nonehead\_mask: typing.Optional\[torch.FloatTensor\] = Noneinputs\_embeds: typing.Optional\[torch.FloatTensor\] = Nonelabels: typing.Optional\[torch.LongTensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.TokenClassifierOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or `tuple(torch.FloatTensor)`

The [RemBertForTokenClassification](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertForTokenClassification) 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 AutoTokenizer, RemBertForTokenClassification
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> model = RemBertForTokenClassification.from_pretrained("google/rembert")

>>> 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)

>>> 
>>> 
>>> 
>>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]

>>> labels = predicted_token_class_ids
>>> loss = model(**inputs, labels=labels).loss```

## [](#transformers.RemBertForQuestionAnswering)RemBertForQuestionAnswering

### class transformers.RemBertForQuestionAnswering

[](#transformers.RemBertForQuestionAnswering)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1433)

( config )

Parameters

-   [](#transformers.RemBertForQuestionAnswering.config)**config** ([RemBertConfig](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

RemBERT 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 is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

#### forward

[](#transformers.RemBertForQuestionAnswering.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1445)

( input\_ids: FloatTensor = Noneattention\_mask: typing.Optional\[torch.FloatTensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.LongTensor\] = Noneposition\_ids: typing.Optional\[torch.FloatTensor\] = Nonehead\_mask: typing.Optional\[torch.FloatTensor\] = Noneinputs\_embeds: typing.Optional\[torch.FloatTensor\] = Nonestart\_positions: typing.Optional\[torch.LongTensor\] = Noneend\_positions: typing.Optional\[torch.LongTensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.QuestionAnsweringModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput) or `tuple(torch.FloatTensor)`

The [RemBertForQuestionAnswering](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertForQuestionAnswering) 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 AutoTokenizer, RemBertForQuestionAnswering
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> model = RemBertForQuestionAnswering.from_pretrained("google/rembert")

>>> 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]

>>> 
>>> target_start_index = torch.tensor([14])
>>> target_end_index = torch.tensor([15])

>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
>>> loss = outputs.loss```

## [](#transformers.TFRemBertModel)TFRemBertModel

### class transformers.TFRemBertModel

[](#transformers.TFRemBertModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L916)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFRemBertModel.config)**config** ([RemBertConfig](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare RemBERT Model transformer outputing raw hidden-states without any specific head on top.

This model inherits from [TFPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.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](https://www.tensorflow.org/api_docs/python/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:

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

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:

-   a single Tensor with `input_ids` only and nothing else: `model(input_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})`

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_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!

#### call

[](#transformers.TFRemBertModel.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L922)

( input\_ids: TFModelInputType | None = Noneattention\_mask: np.ndarray | tf.Tensor | None = Nonetoken\_type\_ids: np.ndarray | tf.Tensor | None = Noneposition\_ids: np.ndarray | tf.Tensor | None = Nonehead\_mask: np.ndarray | tf.Tensor | None = Noneinputs\_embeds: np.ndarray | tf.Tensor | None = Noneencoder\_hidden\_states: np.ndarray | tf.Tensor | None = Noneencoder\_attention\_mask: np.ndarray | tf.Tensor | None = Nonepast\_key\_values: Optional\[Tuple\[Tuple\[Union\[np.ndarray, tf.Tensor\]\]\]\] = Noneuse\_cache: Optional\[bool\] = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonetraining: Optional\[bool\] = False ) → [transformers.modeling\_tf\_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions) or `tuple(tf.Tensor)`

The [TFRemBertModel](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.TFRemBertModel) 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 AutoTokenizer, TFRemBertModel
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> model = TFRemBertModel.from_pretrained("google/rembert")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)

>>> last_hidden_states = outputs.last_hidden_state```

## [](#transformers.TFRemBertForMaskedLM)TFRemBertForMaskedLM

### class transformers.TFRemBertForMaskedLM

[](#transformers.TFRemBertForMaskedLM)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L987)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFRemBertForMaskedLM.config)**config** ([RemBertConfig](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

RemBERT Model with a `language modeling` head on top.

This model inherits from [TFPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.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](https://www.tensorflow.org/api_docs/python/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:

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

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:

-   a single Tensor with `input_ids` only and nothing else: `model(input_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})`

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_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!

#### call

[](#transformers.TFRemBertForMaskedLM.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1003)

( input\_ids: TFModelInputType | None = Noneattention\_mask: np.ndarray | tf.Tensor | None = Nonetoken\_type\_ids: np.ndarray | tf.Tensor | None = Noneposition\_ids: np.ndarray | tf.Tensor | None = Nonehead\_mask: np.ndarray | tf.Tensor | None = Noneinputs\_embeds: np.ndarray | tf.Tensor | None = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonelabels: np.ndarray | tf.Tensor | None = Nonetraining: Optional\[bool\] = False ) → [transformers.modeling\_tf\_outputs.TFMaskedLMOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput) or `tuple(tf.Tensor)`

The [TFRemBertForMaskedLM](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.TFRemBertForMaskedLM) 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 AutoTokenizer, TFRemBertForMaskedLM
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> model = TFRemBertForMaskedLM.from_pretrained("google/rembert")

>>> inputs = tokenizer("The capital of France is [MASK].", return_tensors="tf")
>>> logits = model(**inputs).logits

>>> 
>>> 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)```

```
>>> labels = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
>>> 
>>> labels = tf.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)

>>> outputs = model(**inputs, labels=labels)```

## [](#transformers.TFRemBertForCausalLM)TFRemBertForCausalLM

### class transformers.TFRemBertForCausalLM

[](#transformers.TFRemBertForCausalLM)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1061)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFRemBertForCausalLM.config)**config** ([RemBertConfig](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

RemBERT Model with a `language modeling` head on top for CLM fine-tuning.

This model inherits from [TFPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.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](https://www.tensorflow.org/api_docs/python/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:

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

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:

-   a single Tensor with `input_ids` only and nothing else: `model(input_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})`

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_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!

#### call

[](#transformers.TFRemBertForCausalLM.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1087)

( input\_ids: TFModelInputType | None = Noneattention\_mask: np.ndarray | tf.Tensor | None = Nonetoken\_type\_ids: np.ndarray | tf.Tensor | None = Noneposition\_ids: np.ndarray | tf.Tensor | None = Nonehead\_mask: np.ndarray | tf.Tensor | None = Noneinputs\_embeds: np.ndarray | tf.Tensor | None = Noneencoder\_hidden\_states: np.ndarray | tf.Tensor | None = Noneencoder\_attention\_mask: np.ndarray | tf.Tensor | None = Nonepast\_key\_values: Optional\[Tuple\[Tuple\[Union\[np.ndarray, tf.Tensor\]\]\]\] = Noneuse\_cache: Optional\[bool\] = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonelabels: np.ndarray | tf.Tensor | None = Nonetraining: Optional\[bool\] = False ) → [transformers.modeling\_tf\_outputs.TFCausalLMOutputWithCrossAttentions](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions) or `tuple(tf.Tensor)`

encoder\_hidden\_states (`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. encoder\_attention\_mask (`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]`:

-   1 for tokens that are **not masked**,
-   0 for tokens that are **masked**.

past\_key\_values (`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)`. use\_cache (`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 labels (`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]`.

Example:

```
>>> from transformers import AutoTokenizer, TFRemBertForCausalLM
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> model = TFRemBertForCausalLM.from_pretrained("google/rembert")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)
>>> logits = outputs.logits```

## [](#transformers.TFRemBertForSequenceClassification)TFRemBertForSequenceClassification

### class transformers.TFRemBertForSequenceClassification

[](#transformers.TFRemBertForSequenceClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1180)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFRemBertForSequenceClassification.config)**config** ([RemBertConfig](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

RemBERT Model transformer with a sequence classification/regression head on top e.g., for GLUE tasks.

This model inherits from [TFPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.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](https://www.tensorflow.org/api_docs/python/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:

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

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:

-   a single Tensor with `input_ids` only and nothing else: `model(input_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})`

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_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!

#### call

[](#transformers.TFRemBertForSequenceClassification.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1194)

( input\_ids: TFModelInputType | None = Noneattention\_mask: np.ndarray | tf.Tensor | None = Nonetoken\_type\_ids: np.ndarray | tf.Tensor | None = Noneposition\_ids: np.ndarray | tf.Tensor | None = Nonehead\_mask: np.ndarray | tf.Tensor | None = Noneinputs\_embeds: np.ndarray | tf.Tensor | None = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonelabels: np.ndarray | tf.Tensor | None = Nonetraining: Optional\[bool\] = False ) → [transformers.modeling\_tf\_outputs.TFSequenceClassifierOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput) or `tuple(tf.Tensor)`

The [TFRemBertForSequenceClassification](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.TFRemBertForSequenceClassification) 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 AutoTokenizer, TFRemBertForSequenceClassification
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> model = TFRemBertForSequenceClassification.from_pretrained("google/rembert")

>>> 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])```

```
>>> 
>>> num_labels = len(model.config.id2label)
>>> model = TFRemBertForSequenceClassification.from_pretrained("google/rembert", num_labels=num_labels)

>>> labels = tf.constant(1)
>>> loss = model(**inputs, labels=labels).loss```

## [](#transformers.TFRemBertForMultipleChoice)TFRemBertForMultipleChoice

### class transformers.TFRemBertForMultipleChoice

[](#transformers.TFRemBertForMultipleChoice)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1257)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFRemBertForMultipleChoice.config)**config** ([RemBertConfig](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

RemBERT 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](/docs/transformers/v4.30.0/en/main_classes/model#transformers.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](https://www.tensorflow.org/api_docs/python/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:

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

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:

-   a single Tensor with `input_ids` only and nothing else: `model(input_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})`

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_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!

#### call

[](#transformers.TFRemBertForMultipleChoice.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1267)

( input\_ids: TFModelInputType | None = Noneattention\_mask: np.ndarray | tf.Tensor | None = Nonetoken\_type\_ids: np.ndarray | tf.Tensor | None = Noneposition\_ids: np.ndarray | tf.Tensor | None = Nonehead\_mask: np.ndarray | tf.Tensor | None = Noneinputs\_embeds: np.ndarray | tf.Tensor | None = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonelabels: np.ndarray | tf.Tensor | None = Nonetraining: Optional\[bool\] = False ) → [transformers.modeling\_tf\_outputs.TFMultipleChoiceModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput) or `tuple(tf.Tensor)`

The [TFRemBertForMultipleChoice](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.TFRemBertForMultipleChoice) 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 AutoTokenizer, TFRemBertForMultipleChoice
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> model = TFRemBertForMultipleChoice.from_pretrained("google/rembert")

>>> 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)  

>>> 
>>> logits = outputs.logits```

## [](#transformers.TFRemBertForTokenClassification)TFRemBertForTokenClassification

### class transformers.TFRemBertForTokenClassification

[](#transformers.TFRemBertForTokenClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1353)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFRemBertForTokenClassification.config)**config** ([RemBertConfig](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

RemBERT 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](/docs/transformers/v4.30.0/en/main_classes/model#transformers.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](https://www.tensorflow.org/api_docs/python/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:

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

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:

-   a single Tensor with `input_ids` only and nothing else: `model(input_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})`

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_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!

#### call

[](#transformers.TFRemBertForTokenClassification.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1365)

( input\_ids: TFModelInputType | None = Noneattention\_mask: np.ndarray | tf.Tensor | None = Nonetoken\_type\_ids: np.ndarray | tf.Tensor | None = Noneposition\_ids: np.ndarray | tf.Tensor | None = Nonehead\_mask: np.ndarray | tf.Tensor | None = Noneinputs\_embeds: np.ndarray | tf.Tensor | None = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonelabels: np.ndarray | tf.Tensor | None = Nonetraining: Optional\[bool\] = False ) → [transformers.modeling\_tf\_outputs.TFTokenClassifierOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFTokenClassifierOutput) or `tuple(tf.Tensor)`

The [TFRemBertForTokenClassification](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.TFRemBertForTokenClassification) 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 AutoTokenizer, TFRemBertForTokenClassification
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> model = TFRemBertForTokenClassification.from_pretrained("google/rembert")

>>> 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)

>>> 
>>> 
>>> 
>>> predicted_tokens_classes = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()]```

```
>>> labels = predicted_token_class_ids
>>> loss = tf.math.reduce_mean(model(**inputs, labels=labels).loss)```

## [](#transformers.TFRemBertForQuestionAnswering)TFRemBertForQuestionAnswering

### class transformers.TFRemBertForQuestionAnswering

[](#transformers.TFRemBertForQuestionAnswering)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1426)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFRemBertForQuestionAnswering.config)**config** ([RemBertConfig](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

RemBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).

This model inherits from [TFPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.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](https://www.tensorflow.org/api_docs/python/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:

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

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:

-   a single Tensor with `input_ids` only and nothing else: `model(input_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})`

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_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!

#### call

[](#transformers.TFRemBertForQuestionAnswering.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1437)

( input\_ids: TFModelInputType | None = Noneattention\_mask: np.ndarray | tf.Tensor | None = Nonetoken\_type\_ids: np.ndarray | tf.Tensor | None = Noneposition\_ids: np.ndarray | tf.Tensor | None = Nonehead\_mask: np.ndarray | tf.Tensor | None = Noneinputs\_embeds: np.ndarray | tf.Tensor | None = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonestart\_positions: np.ndarray | tf.Tensor | None = Noneend\_positions: np.ndarray | tf.Tensor | None = Nonetraining: Optional\[bool\] = False ) → [transformers.modeling\_tf\_outputs.TFQuestionAnsweringModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput) or `tuple(tf.Tensor)`

The [TFRemBertForQuestionAnswering](/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.TFRemBertForQuestionAnswering) 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 AutoTokenizer, TFRemBertForQuestionAnswering
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> model = TFRemBertForQuestionAnswering.from_pretrained("google/rembert")

>>> 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]```

```
>>> 
>>> target_start_index = tf.constant([14])
>>> target_end_index = tf.constant([15])

>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
>>> loss = tf.math.reduce_mean(outputs.loss)```