ELECTRA

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## [](#overview)Overview

The ELECTRA model was proposed in the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB). ELECTRA is a new pretraining approach which trains two transformer models: the generator and the discriminator. The generator’s role is to replace tokens in a sequence, and is therefore trained as a masked language model. The discriminator, which is the model we’re interested in, tries to identify which tokens were replaced by the generator in the sequence.

The abstract from the paper is the following:

_Masked language modeling (MLM) pretraining methods such as BERT corrupt the input by replacing some tokens with \[MASK\] and then train a model to reconstruct the original tokens. While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a more sample-efficient pretraining task called replaced token detection. Instead of masking the input, our approach corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not. Thorough experiments demonstrate this new pretraining task is more efficient than MLM because the task is defined over all input tokens rather than just the small subset that was masked out. As a result, the contextual representations learned by our approach substantially outperform the ones learned by BERT given the same model size, data, and compute. The gains are particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained using 30x more compute) on the GLUE natural language understanding benchmark. Our approach also works well at scale, where it performs comparably to RoBERTa and XLNet while using less than 1/4 of their compute and outperforms them when using the same amount of compute._

Tips:

-   ELECTRA is the pretraining approach, therefore there is nearly no changes done to the underlying model: BERT. The only change is the separation of the embedding size and the hidden size: the embedding size is generally smaller, while the hidden size is larger. An additional projection layer (linear) is used to project the embeddings from their embedding size to the hidden size. In the case where the embedding size is the same as the hidden size, no projection layer is used.
-   ELECTRA is a transformer model pretrained with the use of another (small) masked language model. The inputs are corrupted by that language model, which takes an input text that is randomly masked and outputs a text in which ELECTRA has to predict which token is an original and which one has been replaced. Like for GAN training, the small language model is trained for a few steps (but with the original texts as objective, not to fool the ELECTRA model like in a traditional GAN setting) then the ELECTRA model is trained for a few steps.
-   The ELECTRA checkpoints saved using [Google Research’s implementation](https://github.com/google-research/electra) contain both the generator and discriminator. The conversion script requires the user to name which model to export into the correct architecture. Once converted to the HuggingFace format, these checkpoints may be loaded into all available ELECTRA models, however. This means that the discriminator may be loaded in the [ElectraForMaskedLM](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraForMaskedLM) model, and the generator may be loaded in the [ElectraForPreTraining](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraForPreTraining) model (the classification head will be randomly initialized as it doesn’t exist in the generator).

This model was contributed by [lysandre](https://huggingface.co/lysandre). The original code can be found [here](https://github.com/google-research/electra).

## [](#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.ElectraConfig)ElectraConfig

### class transformers.ElectraConfig

[](#transformers.ElectraConfig)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/configuration_electra.py#L44)

( vocab\_size = 30522embedding\_size = 128hidden\_size = 256num\_hidden\_layers = 12num\_attention\_heads = 4intermediate\_size = 1024hidden\_act = 'gelu'hidden\_dropout\_prob = 0.1attention\_probs\_dropout\_prob = 0.1max\_position\_embeddings = 512type\_vocab\_size = 2initializer\_range = 0.02layer\_norm\_eps = 1e-12summary\_type = 'first'summary\_use\_proj = Truesummary\_activation = 'gelu'summary\_last\_dropout = 0.1pad\_token\_id = 0position\_embedding\_type = 'absolute'use\_cache = Trueclassifier\_dropout = None\*\*kwargs )

This is the configuration class to store the configuration of a [ElectraModel](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraModel) or a [TFElectraModel](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.TFElectraModel). It is used to instantiate a ELECTRA 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 ELECTRA [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) 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.

Examples:

```
>>> from transformers import ElectraConfig, ElectraModel

>>> 
>>> configuration = ElectraConfig()

>>> 
>>> model = ElectraModel(configuration)

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

## [](#transformers.ElectraTokenizer)ElectraTokenizer

### class transformers.ElectraTokenizer

[](#transformers.ElectraTokenizer)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/tokenization_electra.py#L93)

( vocab\_filedo\_lower\_case = Truedo\_basic\_tokenize = Truenever\_split = Noneunk\_token = '\[UNK\]'sep\_token = '\[SEP\]'pad\_token = '\[PAD\]'cls\_token = '\[CLS\]'mask\_token = '\[MASK\]'tokenize\_chinese\_chars = Truestrip\_accents = None\*\*kwargs )

Construct a Electra tokenizer. Based on WordPiece.

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.ElectraTokenizer.build_inputs_with_special_tokens)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/tokenization_electra.py#L223)

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

Parameters

-   [](#transformers.ElectraTokenizer.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.ElectraTokenizer.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 Electra sequence has the following format:

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

Converts a sequence of tokens (string) in a single string.

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

[](#transformers.ElectraTokenizer.create_token_type_ids_from_sequences)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/tokenization_electra.py#L276)

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

Parameters

-   [](#transformers.ElectraTokenizer.create_token_type_ids_from_sequences.token_ids_0)**token\_ids\_0** (`List[int]`) — List of IDs.
-   [](#transformers.ElectraTokenizer.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 Electra

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).

#### get\_special\_tokens\_mask

[](#transformers.ElectraTokenizer.get_special_tokens_mask)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/tokenization_electra.py#L248)

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

Parameters

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

## [](#transformers.ElectraTokenizerFast)ElectraTokenizerFast

### class transformers.ElectraTokenizerFast

[](#transformers.ElectraTokenizerFast)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/tokenization_electra_fast.py#L88)

( vocab\_file = Nonetokenizer\_file = Nonedo\_lower\_case = Trueunk\_token = '\[UNK\]'sep\_token = '\[SEP\]'pad\_token = '\[PAD\]'cls\_token = '\[CLS\]'mask\_token = '\[MASK\]'tokenize\_chinese\_chars = Truestrip\_accents = None\*\*kwargs )

Construct a “fast” ELECTRA tokenizer (backed by HuggingFace’s _tokenizers_ library). Based on WordPiece.

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.ElectraTokenizerFast.build_inputs_with_special_tokens)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/tokenization_electra_fast.py#L176)

( token\_ids\_0token\_ids\_1 = None ) → `List[int]`

Parameters

-   [](#transformers.ElectraTokenizerFast.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.ElectraTokenizerFast.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 ELECTRA sequence has the following format:

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

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

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

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

Parameters

-   [](#transformers.ElectraTokenizerFast.create_token_type_ids_from_sequences.token_ids_0)**token\_ids\_0** (`List[int]`) — List of IDs.
-   [](#transformers.ElectraTokenizerFast.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 ELECTRA

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).

## [](#transformers.models.electra.modeling_electra.ElectraForPreTrainingOutput)Electra specific outputs

### class transformers.models.electra.modeling\_electra.ElectraForPreTrainingOutput

[](#transformers.models.electra.modeling_electra.ElectraForPreTrainingOutput)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L701)

( loss: typing.Optional\[torch.FloatTensor\] = Nonelogits: FloatTensor = Nonehidden\_states: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = Noneattentions: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = None )

Output type of [ElectraForPreTraining](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraForPreTraining).

### class transformers.models.electra.modeling\_tf\_electra.TFElectraForPreTrainingOutput

[](#transformers.models.electra.modeling_tf_electra.TFElectraForPreTrainingOutput)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_tf_electra.py#L786)

( logits: tf.Tensor = Nonehidden\_states: Tuple\[tf.Tensor\] | None = Noneattentions: Tuple\[tf.Tensor\] | None = None )

Output type of [TFElectraForPreTraining](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.TFElectraForPreTraining).

## [](#transformers.ElectraModel)ElectraModel

### class transformers.ElectraModel

[](#transformers.ElectraModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L813)

( config )

Parameters

-   [](#transformers.ElectraModel.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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 Electra Model transformer outputting raw hidden-states without any specific head on top. Identical to the BERT model except that it uses an additional linear layer between the embedding layer and the encoder if the hidden size and embedding size are different. Both the generator and discriminator checkpoints may be loaded into this model.

This model inherits from [PreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

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

#### forward

[](#transformers.ElectraModel.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L840)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Noneencoder\_hidden\_states: typing.Optional\[torch.Tensor\] = Noneencoder\_attention\_mask: typing.Optional\[torch.Tensor\] = Nonepast\_key\_values: typing.Optional\[typing.List\[torch.FloatTensor\]\] = 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.BaseModelOutputWithCrossAttentions](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`

The [ElectraModel](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraModel) 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, ElectraModel
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = ElectraModel.from_pretrained("google/electra-small-discriminator")

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

>>> last_hidden_states = outputs.last_hidden_state```

## [](#transformers.ElectraForPreTraining)ElectraForPreTraining

### class transformers.ElectraForPreTraining

[](#transformers.ElectraForPreTraining)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L1062)

( config )

Parameters

-   [](#transformers.ElectraForPreTraining.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

Electra model with a binary classification head on top as used during pretraining for identifying generated tokens.

It is recommended to load the discriminator checkpoint into that model.

This model inherits from [PreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

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

#### forward

[](#transformers.ElectraForPreTraining.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L1071)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonelabels: typing.Optional\[torch.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.models.electra.modeling\_electra.ElectraForPreTrainingOutput](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.models.electra.modeling_electra.ElectraForPreTrainingOutput) or `tuple(torch.FloatTensor)`

The [ElectraForPreTraining](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraForPreTraining) 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.

Examples:

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

>>> discriminator = ElectraForPreTraining.from_pretrained("google/electra-base-discriminator")
>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-base-discriminator")

>>> sentence = "The quick brown fox jumps over the lazy dog"
>>> fake_sentence = "The quick brown fox fake over the lazy dog"

>>> fake_tokens = tokenizer.tokenize(fake_sentence, add_special_tokens=True)
>>> fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
>>> discriminator_outputs = discriminator(fake_inputs)
>>> predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)

>>> fake_tokens
['[CLS]', 'the', 'quick', 'brown', 'fox', 'fake', 'over', 'the', 'lazy', 'dog', '[SEP]']

>>> predictions.squeeze().tolist()
[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]```

## [](#transformers.ElectraForCausalLM)ElectraForCausalLM

### class transformers.ElectraForCausalLM

[](#transformers.ElectraForCausalLM)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L1535)

( config )

Parameters

-   [](#transformers.ElectraForCausalLM.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

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

This model inherits from [PreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

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

#### forward

[](#transformers.ElectraForCausalLM.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L1556)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Noneencoder\_hidden\_states: typing.Optional\[torch.Tensor\] = Noneencoder\_attention\_mask: typing.Optional\[torch.Tensor\] = Nonelabels: typing.Optional\[torch.Tensor\] = Nonepast\_key\_values: typing.Optional\[typing.List\[torch.Tensor\]\] = 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 [ElectraForCausalLM](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraForCausalLM) 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, ElectraForCausalLM, ElectraConfig
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-base-generator")
>>> config = ElectraConfig.from_pretrained("google/electra-base-generator")
>>> config.is_decoder = True
>>> model = ElectraForCausalLM.from_pretrained("google/electra-base-generator", config=config)

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

>>> prediction_logits = outputs.logits```

## [](#transformers.ElectraForMaskedLM)ElectraForMaskedLM

### class transformers.ElectraForMaskedLM

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

( config )

Parameters

-   [](#transformers.ElectraForMaskedLM.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

Electra model with a language modeling head on top.

Even though both the discriminator and generator may be loaded into this model, the generator is the only model of the two to have been trained for the masked language modeling task.

This model inherits from [PreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

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

#### forward

[](#transformers.ElectraForMaskedLM.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L1187)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonelabels: typing.Optional\[torch.Tensor\] = 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 [ElectraForMaskedLM](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraForMaskedLM) 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, ElectraForMaskedLM
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-generator")
>>> model = ElectraForMaskedLM.from_pretrained("google/electra-small-generator")

>>> 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)
>>> tokenizer.decode(predicted_token_id)
'paris'

>>> 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)
>>> round(outputs.loss.item(), 2)
1.22```

## [](#transformers.ElectraForSequenceClassification)ElectraForSequenceClassification

### class transformers.ElectraForSequenceClassification

[](#transformers.ElectraForSequenceClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L964)

( config )

Parameters

-   [](#transformers.ElectraForSequenceClassification.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

ELECTRA Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.

This model inherits from [PreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

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

#### forward

[](#transformers.ElectraForSequenceClassification.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L975)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonelabels: typing.Optional\[torch.Tensor\] = 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 [ElectraForSequenceClassification](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraForSequenceClassification) 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, ElectraForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/electra-base-emotion")
>>> model = ElectraForSequenceClassification.from_pretrained("bhadresh-savani/electra-base-emotion")

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

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

>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
'joy'

>>> 
>>> num_labels = len(model.config.id2label)
>>> model = ElectraForSequenceClassification.from_pretrained("bhadresh-savani/electra-base-emotion", num_labels=num_labels)

>>> labels = torch.tensor([1])
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
0.06```

Example of multi-label classification:

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

>>> tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/electra-base-emotion")
>>> model = ElectraForSequenceClassification.from_pretrained("bhadresh-savani/electra-base-emotion", problem_type="multi_label_classification")

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

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

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

>>> 
>>> num_labels = len(model.config.id2label)
>>> model = ElectraForSequenceClassification.from_pretrained(
...     "bhadresh-savani/electra-base-emotion", 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.ElectraForMultipleChoice)ElectraForMultipleChoice

### class transformers.ElectraForMultipleChoice

[](#transformers.ElectraForMultipleChoice)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L1448)

( config )

Parameters

-   [](#transformers.ElectraForMultipleChoice.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

ELECTRA Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.

This model inherits from [PreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

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

#### forward

[](#transformers.ElectraForMultipleChoice.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L1459)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonelabels: typing.Optional\[torch.Tensor\] = 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 [ElectraForMultipleChoice](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraForMultipleChoice) 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, ElectraForMultipleChoice
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = ElectraForMultipleChoice.from_pretrained("google/electra-small-discriminator")

>>> 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.ElectraForTokenClassification)ElectraForTokenClassification

### class transformers.ElectraForTokenClassification

[](#transformers.ElectraForTokenClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L1259)

( config )

Parameters

-   [](#transformers.ElectraForTokenClassification.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

Electra model with a token classification head on top.

Both the discriminator and generator may be loaded into this model.

This model inherits from [PreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

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

#### forward

[](#transformers.ElectraForTokenClassification.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L1273)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonelabels: typing.Optional\[torch.Tensor\] = 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 [ElectraForTokenClassification](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraForTokenClassification) 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, ElectraForTokenClassification
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/electra-base-discriminator-finetuned-conll03-english")
>>> model = ElectraForTokenClassification.from_pretrained("bhadresh-savani/electra-base-discriminator-finetuned-conll03-english")

>>> inputs = tokenizer(
...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
... )

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

>>> predicted_token_class_ids = logits.argmax(-1)

>>> 
>>> 
>>> 
>>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
>>> predicted_tokens_classes
['B-LOC', 'B-ORG', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-LOC', 'I-LOC']

>>> labels = predicted_token_class_ids
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
0.11```

## [](#transformers.ElectraForQuestionAnswering)ElectraForQuestionAnswering

### class transformers.ElectraForQuestionAnswering

[](#transformers.ElectraForQuestionAnswering)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L1340)

( config )

Parameters

-   [](#transformers.ElectraForQuestionAnswering.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

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

This model inherits from [PreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

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

#### forward

[](#transformers.ElectraForQuestionAnswering.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_electra.py#L1354)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonestart\_positions: typing.Optional\[torch.Tensor\] = Noneend\_positions: typing.Optional\[torch.Tensor\] = 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 [ElectraForQuestionAnswering](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraForQuestionAnswering) 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, ElectraForQuestionAnswering
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/electra-base-squad2")
>>> model = ElectraForQuestionAnswering.from_pretrained("bhadresh-savani/electra-base-squad2")

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()

>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)
'a nice puppet'

>>> 
>>> target_start_index = torch.tensor([11])
>>> target_end_index = torch.tensor([12])

>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
>>> loss = outputs.loss
>>> round(loss.item(), 2)
2.64```

## [](#transformers.TFElectraModel)TFElectraModel

### class transformers.TFElectraModel

[](#transformers.TFElectraModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_tf_electra.py#L911)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFElectraModel.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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 Electra Model transformer outputting raw hidden-states without any specific head on top. Identical to the BERT model except that it uses an additional linear layer between the embedding layer and the encoder if the hidden size and embedding size are different. Both the generator and discriminator checkpoints may be loaded into this model.

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.TFElectraModel.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_tf_electra.py#L917)

( 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.TFBaseModelOutputWithPastAndCrossAttentions](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions) or `tuple(tf.Tensor)`

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

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = TFElectraModel.from_pretrained("google/electra-small-discriminator")

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

>>> last_hidden_states = outputs.last_hidden_state```

## [](#transformers.TFElectraForPreTraining)TFElectraForPreTraining

### class transformers.TFElectraForPreTraining

[](#transformers.TFElectraForPreTraining)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_tf_electra.py#L990)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFElectraForPreTraining.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

Electra model with a binary classification head on top as used during pretraining for identifying generated tokens.

Even though both the discriminator and generator may be loaded into this model, the discriminator is the only model of the two to have the correct classification head to be used for this model.

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.TFElectraForPreTraining.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_tf_electra.py#L997)

( 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\[bool\] = Nonetraining: Optional\[bool\] = False ) → [transformers.models.electra.modeling\_tf\_electra.TFElectraForPreTrainingOutput](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.models.electra.modeling_tf_electra.TFElectraForPreTrainingOutput) or `tuple(tf.Tensor)`

The [TFElectraForPreTraining](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.TFElectraForPreTraining) 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.

Examples:

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

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = TFElectraForPreTraining.from_pretrained("google/electra-small-discriminator")
>>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  
>>> outputs = model(input_ids)
>>> scores = outputs[0]```

## [](#transformers.TFElectraForMaskedLM)TFElectraForMaskedLM

### class transformers.TFElectraForMaskedLM

[](#transformers.TFElectraForMaskedLM)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_tf_electra.py#L1099)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFElectraForMaskedLM.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

Electra model with a language modeling head on top.

Even though both the discriminator and generator may be loaded into this model, the generator is the only model of the two to have been trained for the masked language modeling task.

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.TFElectraForMaskedLM.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_tf_electra.py#L1121)

( 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 [TFElectraForMaskedLM](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.TFElectraForMaskedLM) 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, TFElectraForMaskedLM
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-generator")
>>> model = TFElectraForMaskedLM.from_pretrained("google/electra-small-generator")

>>> 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)
>>> tokenizer.decode(predicted_token_id)
'paris'```

```
>>> 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)
>>> round(float(outputs.loss), 2)
1.22```

## [](#transformers.TFElectraForSequenceClassification)TFElectraForSequenceClassification

### class transformers.TFElectraForSequenceClassification

[](#transformers.TFElectraForSequenceClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_tf_electra.py#L1218)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFElectraForSequenceClassification.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

ELECTRA Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.

This model inherits from [TFPreTrainedModel](/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.TFElectraForSequenceClassification.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_tf_electra.py#L1225)

( 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 [TFElectraForSequenceClassification](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.TFElectraForSequenceClassification) 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, TFElectraForSequenceClassification
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/electra-base-emotion")
>>> model = TFElectraForSequenceClassification.from_pretrained("bhadresh-savani/electra-base-emotion")

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

>>> logits = model(**inputs).logits

>>> predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
>>> model.config.id2label[predicted_class_id]
'joy'```

```
>>> 
>>> num_labels = len(model.config.id2label)
>>> model = TFElectraForSequenceClassification.from_pretrained("bhadresh-savani/electra-base-emotion", num_labels=num_labels)

>>> labels = tf.constant(1)
>>> loss = model(**inputs, labels=labels).loss
>>> round(float(loss), 2)
0.06```

## [](#transformers.TFElectraForMultipleChoice)TFElectraForMultipleChoice

### class transformers.TFElectraForMultipleChoice

[](#transformers.TFElectraForMultipleChoice)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_tf_electra.py#L1289)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFElectraForMultipleChoice.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

ELECTRA 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.TFElectraForMultipleChoice.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_tf_electra.py#L1301)

( 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 [TFElectraForMultipleChoice](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.TFElectraForMultipleChoice) 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, TFElectraForMultipleChoice
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = TFElectraForMultipleChoice.from_pretrained("google/electra-small-discriminator")

>>> 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.TFElectraForTokenClassification)TFElectraForTokenClassification

### class transformers.TFElectraForTokenClassification

[](#transformers.TFElectraForTokenClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_tf_electra.py#L1382)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFElectraForTokenClassification.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

Electra model with a token classification head on top.

Both the discriminator and generator may be loaded into this model.

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.TFElectraForTokenClassification.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_tf_electra.py#L1395)

( 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 [TFElectraForTokenClassification](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.TFElectraForTokenClassification) 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, TFElectraForTokenClassification
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/electra-base-discriminator-finetuned-conll03-english")
>>> model = TFElectraForTokenClassification.from_pretrained("bhadresh-savani/electra-base-discriminator-finetuned-conll03-english")

>>> inputs = tokenizer(
...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="tf"
... )

>>> logits = model(**inputs).logits
>>> predicted_token_class_ids = tf.math.argmax(logits, axis=-1)

>>> 
>>> 
>>> 
>>> predicted_tokens_classes = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()]
>>> predicted_tokens_classes
['B-LOC', 'B-ORG', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-LOC', 'I-LOC']```

```
>>> labels = predicted_token_class_ids
>>> loss = tf.math.reduce_mean(model(**inputs, labels=labels).loss)
>>> round(float(loss), 2)
0.11```

## [](#transformers.TFElectraForQuestionAnswering)TFElectraForQuestionAnswering

### class transformers.TFElectraForQuestionAnswering

[](#transformers.TFElectraForQuestionAnswering)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_tf_electra.py#L1459)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFElectraForQuestionAnswering.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

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

This model inherits from [TFPreTrainedModel](/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.TFElectraForQuestionAnswering.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_tf_electra.py#L1469)

( 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 [TFElectraForQuestionAnswering](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.TFElectraForQuestionAnswering) 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, TFElectraForQuestionAnswering
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/electra-base-squad2")
>>> model = TFElectraForQuestionAnswering.from_pretrained("bhadresh-savani/electra-base-squad2")

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

>>> inputs = tokenizer(question, text, return_tensors="tf")
>>> outputs = model(**inputs)

>>> answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
>>> answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])

>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> tokenizer.decode(predict_answer_tokens)
'a nice puppet'```

```
>>> 
>>> target_start_index = tf.constant([11])
>>> target_end_index = tf.constant([12])

>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
>>> loss = tf.math.reduce_mean(outputs.loss)
>>> round(float(loss), 2)
2.64```

## [](#transformers.FlaxElectraModel)FlaxElectraModel

### class transformers.FlaxElectraModel

[](#transformers.FlaxElectraModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L920)

( config: ElectraConfiginput\_shape: typing.Tuple = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>\_do\_init: bool = Truegradient\_checkpointing: bool = False\*\*kwargs )

Parameters

-   [](#transformers.FlaxElectraModel.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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 Electra Model transformer outputting raw hidden-states without any specific head on top.

This model inherits from [FlaxPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

-   [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-   [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-   [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-   [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

#### \_\_call\_\_

[](#transformers.FlaxElectraModel.__call__)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L770)

( input\_idsattention\_mask = Nonetoken\_type\_ids = Noneposition\_ids = Nonehead\_mask = Noneencoder\_hidden\_states = Noneencoder\_attention\_mask = Noneparams: dict = Nonedropout\_rng: PRNGKey = Nonetrain: bool = Falseoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonepast\_key\_values: dict = None ) → [transformers.modeling\_flax\_outputs.FlaxBaseModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput) or `tuple(torch.FloatTensor)`

The `FlaxElectraPreTrainedModel` 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, FlaxElectraModel

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = FlaxElectraModel.from_pretrained("google/electra-small-discriminator")

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

>>> last_hidden_states = outputs.last_hidden_state```

## [](#transformers.FlaxElectraForPreTraining)FlaxElectraForPreTraining

### class transformers.FlaxElectraForPreTraining

[](#transformers.FlaxElectraForPreTraining)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L1071)

( config: ElectraConfiginput\_shape: typing.Tuple = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>\_do\_init: bool = Truegradient\_checkpointing: bool = False\*\*kwargs )

Parameters

-   [](#transformers.FlaxElectraForPreTraining.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

Electra model with a binary classification head on top as used during pretraining for identifying generated tokens.

It is recommended to load the discriminator checkpoint into that model.

This model inherits from [FlaxPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

-   [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-   [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-   [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-   [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

#### \_\_call\_\_

[](#transformers.FlaxElectraForPreTraining.__call__)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L770)

( input\_idsattention\_mask = Nonetoken\_type\_ids = Noneposition\_ids = Nonehead\_mask = Noneencoder\_hidden\_states = Noneencoder\_attention\_mask = Noneparams: dict = Nonedropout\_rng: PRNGKey = Nonetrain: bool = Falseoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonepast\_key\_values: dict = None ) → `transformers.models.electra.modeling_flax_electra.FlaxElectraForPreTrainingOutput` or `tuple(torch.FloatTensor)`

The `FlaxElectraPreTrainedModel` 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, FlaxElectraForPreTraining

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = FlaxElectraForPreTraining.from_pretrained("google/electra-small-discriminator")

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

>>> prediction_logits = outputs.logits```

## [](#transformers.FlaxElectraForCausalLM)FlaxElectraForCausalLM

### class transformers.FlaxElectraForCausalLM

[](#transformers.FlaxElectraForCausalLM)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L1565)

( config: ElectraConfiginput\_shape: typing.Tuple = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>\_do\_init: bool = Truegradient\_checkpointing: bool = False\*\*kwargs )

Parameters

-   [](#transformers.FlaxElectraForCausalLM.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

Electra Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for autoregressive tasks.

This model inherits from [FlaxPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

-   [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-   [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-   [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-   [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

#### \_\_call\_\_

[](#transformers.FlaxElectraForCausalLM.__call__)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L770)

( input\_idsattention\_mask = Nonetoken\_type\_ids = Noneposition\_ids = Nonehead\_mask = Noneencoder\_hidden\_states = Noneencoder\_attention\_mask = Noneparams: dict = Nonedropout\_rng: PRNGKey = Nonetrain: bool = Falseoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonepast\_key\_values: dict = None ) → [transformers.modeling\_flax\_outputs.FlaxCausalLMOutputWithCrossAttentions](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`

The `FlaxElectraPreTrainedModel` 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, FlaxElectraForCausalLM

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = FlaxElectraForCausalLM.from_pretrained("google/electra-small-discriminator")

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

>>> 
>>> next_token_logits = outputs.logits[:, -1]```

## [](#transformers.FlaxElectraForMaskedLM)FlaxElectraForMaskedLM

### class transformers.FlaxElectraForMaskedLM

[](#transformers.FlaxElectraForMaskedLM)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L1007)

( config: ElectraConfiginput\_shape: typing.Tuple = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>\_do\_init: bool = Truegradient\_checkpointing: bool = False\*\*kwargs )

Parameters

-   [](#transformers.FlaxElectraForMaskedLM.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

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

This model inherits from [FlaxPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

-   [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-   [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-   [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-   [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

#### \_\_call\_\_

[](#transformers.FlaxElectraForMaskedLM.__call__)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L770)

( input\_idsattention\_mask = Nonetoken\_type\_ids = Noneposition\_ids = Nonehead\_mask = Noneencoder\_hidden\_states = Noneencoder\_attention\_mask = Noneparams: dict = Nonedropout\_rng: PRNGKey = Nonetrain: bool = Falseoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonepast\_key\_values: dict = None ) → [transformers.modeling\_flax\_outputs.FlaxMaskedLMOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput) or `tuple(torch.FloatTensor)`

The `FlaxElectraPreTrainedModel` 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, FlaxElectraForMaskedLM

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = FlaxElectraForMaskedLM.from_pretrained("google/electra-small-discriminator")

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

>>> outputs = model(**inputs)
>>> logits = outputs.logits```

## [](#transformers.FlaxElectraForSequenceClassification)FlaxElectraForSequenceClassification

### class transformers.FlaxElectraForSequenceClassification

[](#transformers.FlaxElectraForSequenceClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L1481)

( config: ElectraConfiginput\_shape: typing.Tuple = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>\_do\_init: bool = Truegradient\_checkpointing: bool = False\*\*kwargs )

Parameters

-   [](#transformers.FlaxElectraForSequenceClassification.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

Electra Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.

This model inherits from [FlaxPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

-   [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-   [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-   [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-   [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

#### \_\_call\_\_

[](#transformers.FlaxElectraForSequenceClassification.__call__)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L770)

( input\_idsattention\_mask = Nonetoken\_type\_ids = Noneposition\_ids = Nonehead\_mask = Noneencoder\_hidden\_states = Noneencoder\_attention\_mask = Noneparams: dict = Nonedropout\_rng: PRNGKey = Nonetrain: bool = Falseoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonepast\_key\_values: dict = None ) → [transformers.modeling\_flax\_outputs.FlaxSequenceClassifierOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput) or `tuple(torch.FloatTensor)`

The `FlaxElectraPreTrainedModel` 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, FlaxElectraForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = FlaxElectraForSequenceClassification.from_pretrained("google/electra-small-discriminator")

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

>>> outputs = model(**inputs)
>>> logits = outputs.logits```

## [](#transformers.FlaxElectraForMultipleChoice)FlaxElectraForMultipleChoice

### class transformers.FlaxElectraForMultipleChoice

[](#transformers.FlaxElectraForMultipleChoice)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L1313)

( config: ElectraConfiginput\_shape: typing.Tuple = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>\_do\_init: bool = Truegradient\_checkpointing: bool = False\*\*kwargs )

Parameters

-   [](#transformers.FlaxElectraForMultipleChoice.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

ELECTRA 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 [FlaxPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

-   [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-   [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-   [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-   [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

#### \_\_call\_\_

[](#transformers.FlaxElectraForMultipleChoice.__call__)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L770)

( input\_idsattention\_mask = Nonetoken\_type\_ids = Noneposition\_ids = Nonehead\_mask = Noneencoder\_hidden\_states = Noneencoder\_attention\_mask = Noneparams: dict = Nonedropout\_rng: PRNGKey = Nonetrain: bool = Falseoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonepast\_key\_values: dict = None ) → [transformers.modeling\_flax\_outputs.FlaxMultipleChoiceModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput) or `tuple(torch.FloatTensor)`

The `FlaxElectraPreTrainedModel` 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, FlaxElectraForMultipleChoice

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = FlaxElectraForMultipleChoice.from_pretrained("google/electra-small-discriminator")

>>> 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="jax", padding=True)
>>> outputs = model(**{k: v[None, :] for k, v in encoding.items()})

>>> logits = outputs.logits```

## [](#transformers.FlaxElectraForTokenClassification)FlaxElectraForTokenClassification

### class transformers.FlaxElectraForTokenClassification

[](#transformers.FlaxElectraForTokenClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L1166)

( config: ElectraConfiginput\_shape: typing.Tuple = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>\_do\_init: bool = Truegradient\_checkpointing: bool = False\*\*kwargs )

Parameters

-   [](#transformers.FlaxElectraForTokenClassification.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

Electra model with a token classification head on top.

Both the discriminator and generator may be loaded into this model.

This model inherits from [FlaxPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

-   [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-   [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-   [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-   [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

#### \_\_call\_\_

[](#transformers.FlaxElectraForTokenClassification.__call__)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L770)

( input\_idsattention\_mask = Nonetoken\_type\_ids = Noneposition\_ids = Nonehead\_mask = Noneencoder\_hidden\_states = Noneencoder\_attention\_mask = Noneparams: dict = Nonedropout\_rng: PRNGKey = Nonetrain: bool = Falseoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonepast\_key\_values: dict = None ) → [transformers.modeling\_flax\_outputs.FlaxTokenClassifierOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput) or `tuple(torch.FloatTensor)`

The `FlaxElectraPreTrainedModel` 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, FlaxElectraForTokenClassification

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = FlaxElectraForTokenClassification.from_pretrained("google/electra-small-discriminator")

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

>>> outputs = model(**inputs)
>>> logits = outputs.logits```

## [](#transformers.FlaxElectraForQuestionAnswering)FlaxElectraForQuestionAnswering

### class transformers.FlaxElectraForQuestionAnswering

[](#transformers.FlaxElectraForQuestionAnswering)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L1388)

( config: ElectraConfiginput\_shape: typing.Tuple = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>\_do\_init: bool = Truegradient\_checkpointing: bool = False\*\*kwargs )

Parameters

-   [](#transformers.FlaxElectraForQuestionAnswering.config)**config** ([ElectraConfig](/docs/transformers/v4.30.0/en/model_doc/electra#transformers.ElectraConfig)) — 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.

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

This model inherits from [FlaxPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

-   [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-   [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-   [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-   [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

#### \_\_call\_\_

[](#transformers.FlaxElectraForQuestionAnswering.__call__)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/electra/modeling_flax_electra.py#L770)

( input\_idsattention\_mask = Nonetoken\_type\_ids = Noneposition\_ids = Nonehead\_mask = Noneencoder\_hidden\_states = Noneencoder\_attention\_mask = Noneparams: dict = Nonedropout\_rng: PRNGKey = Nonetrain: bool = Falseoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonepast\_key\_values: dict = None ) → [transformers.modeling\_flax\_outputs.FlaxQuestionAnsweringModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput) or `tuple(torch.FloatTensor)`

The `FlaxElectraPreTrainedModel` 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, FlaxElectraForQuestionAnswering

>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = FlaxElectraForQuestionAnswering.from_pretrained("google/electra-small-discriminator")

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="jax")

>>> outputs = model(**inputs)
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logits```