pass your inputs and labels in any format that model.fit() supports! transformers.modeling_outputs.BaseModelOutputWithCrossAttentions or tuple(torch.FloatTensor). train: bool = False Here you can choose which BERT model you will load from TensorFlow Hub and fine-tune. training: typing.Optional[bool] = False Hidden-states of the model at the output of each layer plus the initial embedding outputs. Some particularly useful options: See configure_pretraining.py for the full set of supported hyperparameters. hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of head_mask: typing.Optional[torch.Tensor] = None encoder_attention_mask = None having all inputs as a list, tuple or dict in the first positional argument. transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor). with Better Relative Position Embeddings (Huang et al. For details, see the Google Developers Site Policies. **kwargs ( An additional projection layer (linear) is used to project the embeddings from their It also supports fine-tuning ELECTRA on downstream tasks including classification tasks (e.g,. transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor). position_ids = None past_key_values: dict = None Electra Models Resources for Natural Language Processing Projects This is a complete list of resources about Electra Models for your next project in natural language processing. encoder_attention_mask = None encoder_attention_mask = None last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) Sequence of hidden-states at the output of the last layer of the model. ( instance afterwards instead of this since the former takes care of running the pre and post processing steps while torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various more sample-efficient pretraining task called replaced token detection. head_mask: typing.Optional[torch.Tensor] = None hidden_act = 'gelu' head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None input_ids: typing.Optional[torch.Tensor] = None start_logits (jnp.ndarray of shape (batch_size, sequence_length)) Span-start scores (before SoftMax). subclassing then you dont need to worry Download the GLUE data by running this script. While they produce good results when transferred to library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads output_hidden_states: typing.Optional[bool] = None List[int]. heads. return_dict: typing.Optional[bool] = None configuration (ElectraConfig) and inputs. Both the generator and discriminator checkpoints may be loaded into this model. softmax) e.g. This model was contributed by lysandre. A transformers.modeling_outputs.TokenClassifierOutput or a tuple of loss (optional, returned when labels is provided, torch.FloatTensor of shape (1,)) Total loss of the ELECTRA objective. output_hidden_states: typing.Optional[bool] = None head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None return_dict: typing.Optional[bool] = None attention_mask = None Some particularly useful options: Get a pre-trained ELECTRA model either by training your own (see pre-training instructions above), or downloading the release ELECTRA weights and unziping them under $DATA_DIR/models (e.g., you should have a directory$DATA_DIR/models/electra_large if you are using the large model). end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) Span-end scores (before SoftMax). The original code can be found here. training: typing.Optional[bool] = False train: bool = False past_key_values: dict = None train: bool = False Next, you will use the text_dataset_from_directory utility to create a labeled tf.data.Dataset. TensorFlow Lite for mobile and edge devices, TensorFlow Extended for end-to-end ML components, Pre-trained models and datasets built by Google and the community, Ecosystem of tools to help you use TensorFlow, Libraries and extensions built on TensorFlow, Differentiate yourself by demonstrating your ML proficiency, Educational resources to learn the fundamentals of ML with TensorFlow, Resources and tools to integrate Responsible AI practices into your ML workflow, Stay up to date with all things TensorFlow, Discussion platform for the TensorFlow community, User groups, interest groups and mailing lists, Guide for contributing to code and documentation, Thanks for tuning in to Google I/O. token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None You signed in with another tab or window. token_type_ids = None ( token_type_ids: typing.Optional[torch.Tensor] = None transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput or tuple(torch.FloatTensor). ) cross_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). return_dict: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None transformers.modeling_flax_outputs.FlaxTokenClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxTokenClassifierOutput or tuple(torch.FloatTensor). one of the classic BERT sizes or their recent refinements like Electra, Talking Heads, or a BERT Expert. If you're new to working with the IMDB dataset, please see Basic text classification for more details. It is not necessary to run pure Python code outside your TensorFlow model to preprocess text. Check the superclass documentation for the generic methods the ( output_hidden_states: typing.Optional[bool] = None A transformers.modeling_tf_outputs.TFTokenClassifierOutput or a tuple of tf.Tensor (if head_mask = None Cross attentions weights after the attention softmax, used to compute the weighted average in the To customize the training, add --hparams '{"hparam1": value1, "hparam2": value2, }' to the run command. token_type_ids: typing.Optional[torch.Tensor] = None Electra Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for transformers.models.electra.modeling_flax_electra.FlaxElectraForPreTrainingOutput or tuple(torch.FloatTensor), transformers.models.electra.modeling_flax_electra.FlaxElectraForPreTrainingOutput or tuple(torch.FloatTensor). A transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or a tuple of tf.Tensor (if position_ids: typing.Optional[torch.Tensor] = None labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see Check the superclass documentation for the generic methods the See PreTrainedTokenizer.encode() and ) 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] return_dict: typing.Optional[bool] = None head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None pooled output) e.g. strip_accents = None inputs_embeds: typing.Optional[torch.Tensor] = None vocab_file token_type_ids: typing.Optional[torch.Tensor] = None Based on WordPiece. token_type_ids = None train: bool = False approach substantially outperform the ones learned by BERT given the same model size, data, and compute. attention_mask: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None params: dict = None ELECTRA Model with a language modeling head on top for CLM fine-tuning. This optimizer minimizes the prediction loss and does regularization by weight decay (not using moments), which is also known as AdamW. return_dict: typing.Optional[bool] = None only change is the separation of the embedding size and the hidden size: the embedding size is generally smaller, output_attentions: typing.Optional[bool] = None return classification_metrics. output_hidden_states: typing.Optional[bool] = None See here for losses / training curves of the models during pre-training. ), Improve Transformer Models TensorFlow models and layers in transformers accept two formats as input: The reason the second format is supported is that Keras methods prefer this format when passing inputs to models SQuAD 2.0 scores for ELECTRA-Large and other state-of-the-art models (only non-ensemble models shown). attention_mask: typing.Optional[torch.Tensor] = None return_dict: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None configuration (ElectraConfig) and inputs. return_dict: typing.Optional[bool] = None They compute vector-space representations of natural language that are suitable for use in deep learning models. summary_activation = 'gelu' input_ids: typing.Optional[torch.Tensor] = None For the learning rate (init_lr), you will use the same schedule as BERT pre-training: linear decay of a notional initial learning rate, prefixed with a linear warm-up phase over the first 10% of training steps (num_warmup_steps). ) loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) Classification (or regression if config.num_labels==1) loss. token_ids_1: typing.Optional[typing.List[int]] = None How to use the discriminator in transformers ). The BERT family of models uses the Transformer encoder architecture to process each token of input text in the full context of all tokens before and after, hence the name: Bidirectional Encoder Representations from Transformers. Two values will be returned. This method can more accurately capture the emotional features of the text, improve the classification effect, enhance the evaluation feedback mechanism, and facilitate user decision-making. You now have all the pieces to train a model, including the preprocessing module, BERT encoder, data, and classifier. Even though both the discriminator and generator may be loaded into this model, the generator is the only model of position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None A Electra sequence has the following format: Converts a sequence of tokens (string) in a single string. attention_mask = None This repository contains code to pre-train ELECTRA, including small ELECTRA models on a single GPU. head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None refer to this superclass for more information regarding those methods. ). for GLUE tasks. Thorough experiments end_positions: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ELECTRA Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear Because of this support, when using methods like model.fit() things should just work for you - just For a detailed description and experimental results, please refer to our ICLR 2020 paper ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. Multi-label text classification (MLC) is a challenging task in settings of large label sets, where label support follows a Zipfian distribution. Then run run_finetuning.py. ( encoder_attention_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None google/electra-small-discriminator architecture. return_dict: typing.Optional[bool] = None past_key_values: typing.Optional[typing.List[torch.Tensor]] = None token_ids_0 attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). A transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput or a tuple of hidden_states (tuple(tf.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape Attentions weights of the decoders cross-attention layer, after the attention softmax, used to compute the ( return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the params: dict = None head_mask = None Since this text preprocessor is a TensorFlow model, It can be included in your model directly. token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a GAN. attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). (see input_ids above). max_position_embeddings = 512 You will be able to do that on the Solve GLUE tasks using BERT on a TPU colab. classifier_dropout = None return_dict: typing.Optional[bool] = None Users should refer to hfl/chinese-electra-180g-large-generator Model ( input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None Retrieve sequence ids from a token list that has no special tokens added. More Efficient NLP Model Pre-training with ELECTRA, Posted by Kevin Clark, Student Researcher and Thang Luong, Senior Research Scientist, Google Research, Brain Team, ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. token_type_ids = None dont have their past key value states given to this model) of shape (batch_size, 1) instead of all labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None It has the following arguments: If training is halted, re-running the run_pretraining.py with the same arguments will continue the training where it left off. A transformers.modeling_tf_outputs.TFSequenceClassifierOutput or a tuple of tf.Tensor (if attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None params: dict = None A transformers.modeling_outputs.QuestionAnsweringModelOutput or a tuple of position_ids: typing.Optional[torch.Tensor] = None ( Quickstart: Pre-train a small ELECTRA model. research literature. To accelerate pre-training, ELECTRA trains a discriminator that predicts whether each input token is replaced by a generator. head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None using the same amount of compute. Based on WordPiece. labels: typing.Optional[torch.Tensor] = None ELECTRA is a transformer model pretrained with the use of another (small) masked language model. output_attentions: typing.Optional[bool] = None dropout_rng: PRNGKey = None about any of this, as you can just pass inputs like you would to any other Python function! While they produce good results when transferred to downstream NLP tasks, they generally . List[int]. Generators, Self-Attention with Relative Position Representations (Shaw et al. loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. For details on Electric, please refer to out EMNLP 2020 paper Pre-Training Transformers as Energy-Based Cloze Models. input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None output_hidden_states: typing.Optional[bool] = None encoder_hidden_states = None return_dict: typing.Optional[bool] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None By default the model is trained on length-128 sequences, so it is not suitable for running on question answering. ) input_ids ELECTRA Model transformer with a sequence classification/regression head on top (a linear layer on top of the ) head_mask = None head_mask = None loss (optional, returned when labels is provided, tf.Tensor of shape (1,)) Total loss of the ELECTRA objective. transformers.modeling_outputs.MultipleChoiceModelOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.MultipleChoiceModelOutput or tuple(torch.FloatTensor). ) seed: int = 0 as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and loss (tf.Tensor of shape (batch_size, ), optional, returned when start_positions and end_positions are provided) Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. _do_init: bool = True ( token_type_ids = None adding special tokens. The ElectraForMaskedLM forward method, overrides the __call__ special method. training: typing.Optional[bool] = False attention_mask = None ELECTRA-Small/Base/Large are our released models. Existing pre-training methods and their disadvantages. encoder_attention_mask: typing.Optional[torch.Tensor] = None GitHub - google-research/electra: ELECTRA: Pre-training Text Encoders . output_hidden_states: typing.Optional[bool] = None You will load it from TF Hub and see the returned values. Construct a fast ELECTRA tokenizer (backed by HuggingFaces tokenizers library). token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None It can also efficiently produce pseudo-likelihood scores for text, which can be used to re-rank the outputs of speech recognition or machine translation systems. encoder_hidden_states: typing.Optional[torch.Tensor] = None hidden_dropout_prob = 0.1 In response to such problem, the text proposes a new method based on ELECTRA and hybrid neural network. Although the recipe for forward pass needs to be defined within this function, one should call the Module This method is called when adding pad_token_id = 0 return_dict: typing.Optional[bool] = None sep_token = '[SEP]' elements depending on the configuration (ElectraConfig) and inputs. position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Electra Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear head_mask: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.Tensor] = None encoder_hidden_states: typing.Optional[torch.Tensor] = None cls_token = '[CLS]' loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Masked language modeling (MLM) loss. encoder_hidden_states = None token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Whether or not to add a projection after the vector extraction. In the case where the embedding size is the same as the hidden size, no projection output_attentions: typing.Optional[bool] = None layer_norm_eps = 1e-12 ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens. output_attentions: typing.Optional[bool] = None To train Electric, use the same pre-training script and command as ELECTRA. For personal communication related to ELECTRA, please contact Kevin Clark (kevclark@cs.stanford.edu). Electra Model transformer with a sequence classification/regression head on top (a linear layer on top of the Use build_pretraining_dataset.py to create a pre-training dataset from a dump of raw text. unk_token = '[UNK]' Instead of masking the input, our approach training: typing.Optional[bool] = False The ElectraForQuestionAnswering forward method, overrides the __call__ special method. **kwargs ). elements depending on the configuration (ElectraConfig) and inputs. A transformers.modeling_outputs.SequenceClassifierOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various rather than just the small subset that was masked out. encoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) Classification loss. layer is used. transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor). output_attentions: typing.Optional[bool] = None Pass "gelu" for a gelu activation to the output, any other value will result in no activation. input_ids: typing.Optional[torch.Tensor] = None Let's reload the model, so you can try it side by side with the model that is still in memory. This takes slightly over 4 days on a Tesla V100 GPU. For BERT models from the drop-down above, the preprocessing model is selected automatically. **kwargs position_ids: typing.Optional[torch.Tensor] = None gradient_checkpointing: bool = False A transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or a tuple of tf.Tensor (if You can continue pre-training from the released ELECTRA checkpoints by. 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. encoder_hidden_states = None transformers.modeling_outputs.BaseModelOutputWithCrossAttentions or tuple(torch.FloatTensor). elements depending on the configuration (ElectraConfig) and inputs. attention_mask: typing.Optional[torch.Tensor] = None return_dict: typing.Optional[bool] = None The code supports SQuAD 1.1 and 2.0, as well as datasets in the 2019 MRQA shared task, This repository uses the official evaluation code released by the SQuAD authors and the MRQA shared task to compute metrics, Download the CoNLL-2000 text chunking dataset from here and put it under $DATA_DIR/finetuning_data/chunk/(train|dev).txt. This model inherits from PreTrainedModel. input_ids inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None labels: typing.Optional[torch.Tensor] = None attention_mask = None The TFElectraForSequenceClassification forward method, overrides the __call__ special method. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various There are multiple BERT models available. --hparams can also be a path to a .json file containing the hyperparameters. The ElectraForMultipleChoice forward method, overrides the __call__ special method. setting. head_mask: typing.Optional[torch.Tensor] = None encoder_hidden_states: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None num_hidden_layers = 12 configuration (ElectraConfig) and inputs. position_ids = None Then, instead The bare Electra Model transformer outputting raw hidden-states without any specific head on top. attention_mask = None start_logits (tf.Tensor of shape (batch_size, sequence_length)) Span-start scores (before SoftMax). ( position_ids: typing.Optional[torch.Tensor] = None ) cross-attention heads. ( torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various while the hidden size is larger. A transformers.modeling_outputs.MaskedLMOutput or a tuple of strip_accents = None output_attentions: typing.Optional[bool] = None printed after the next cell execution. inputs_embeds: typing.Optional[torch.Tensor] = None Users should Evaluation is done on the dev set by default. token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None tokenize_chinese_chars = True transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor), transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor). ELECTRA is the pretraining approach, therefore there is nearly no changes done to the underlying model: BERT. input_ids Note that variance in fine-tuning can be quite large, so for some tasks you may see big fluctuations in scores when fine-tuning from the same checkpoint multiple times. output_attentions: typing.Optional[bool] = None ) input_ids output_attentions: typing.Optional[bool] = None summary_use_proj = True logits: FloatTensor = None attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ) past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None The model documentation on TensorFlow Hub has more details and references to the particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained Our approach also works well at scale, ( this superclass for more information regarding those methods. Given an input sequence x (of length N), k . documentation from PretrainedConfig for more information. It also supports fine-tuning ELECTRA on downstream tasks including classification tasks (e.g,. position_ids: typing.Optional[torch.Tensor] = None # there might be more predicted token classes than words. position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None output_attentions: typing.Optional[bool] = None configuration (ElectraConfig) and inputs. transformers.models.electra.modeling_electra.ElectraForPreTrainingOutput or tuple(torch.FloatTensor), transformers.models.electra.modeling_electra.ElectraForPreTrainingOutput or tuple(torch.FloatTensor). encoder_attention_mask = None labels: typing.Optional[torch.Tensor] = None The gains are Save and categorize content based on your preferences. num_attention_heads = 4 ( position_embedding_type = 'absolute' loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss (for next-token prediction). return_dict: typing.Optional[bool] = None The FlaxElectraPreTrainedModel forward method, overrides the __call__ special method. They correspond to ELECTRA-Small++, ELECTRA-Base++, ELECTRA-1.75M in our paper. To customize the training, add --hparams '{"hparam1": value1, "hparam2": value2, }' to the run command. token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None input_ids The ElectraForPreTraining forward method, overrides the __call__ special method. input_ids: typing.Optional[torch.Tensor] = None transformers.models.electra.modeling_tf_electra. past_key_values input) to speed up sequential decoding. Loss (a number which represents the error, lower values are better), and accuracy. params: dict = None logits (tf.Tensor of shape (batch_size, sequence_length, config.num_labels)) Classification scores (before SoftMax). and layers. ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. You will create a very simple fine-tuned model, with the preprocessing model, the selected BERT model, one Dense and a Dropout layer. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various summary_type = 'first' A ELECTRA, ( position_ids: typing.Optional[torch.Tensor] = None In this study, we present a new text encoder pre-training method that improves ELECTRA based on multi-task learning. to train a small ELECTRA model for 1 million steps on the data. Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and Use it ( elements depending on the configuration (ElectraConfig) and inputs. Read the output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None ( attention_mask = None value states of the self-attention and the cross-attention layers if model is used in encoder-decoder use_cache = True The IMDB dataset has already been divided into train and test, but it lacks a validation set. loss (tf.Tensor of shape (n,), optional, where n is the number of unmasked labels, returned when labels is provided) Classification loss. A transformers.modeling_flax_outputs.FlaxBaseModelOutput or a tuple of past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)). attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None A transformers.models.electra.modeling_tf_electra.TFElectraForPreTrainingOutput or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the The BERT family of models uses the Transformer encoder architecture to process each token of input text in the full context of all tokens before and after, hence the name: Bidirectional Encoder Representations from Transformers. This tutorial contains complete code to fine-tune BERT to perform sentiment analysis on a dataset of plain-text IMDB movie reviews. Further details on the replaced token detection (RTD) task. You will use the AdamW optimizer from tensorflow/models. output_hidden_states: typing.Optional[bool] = None attention_mask = None shape (batch_size, sequence_length, hidden_size). embedding_size = 128 output_attentions: typing.Optional[bool] = None List[int]. AccuracyScorer () def _truncate_seq_pair ( tokens_a, tokens_b, max_length ): """Truncates a sequence pair in place to the maximum length.""". ELECTRA is a transformer model pretrained with the use of another (small) masked language model. ). Introduction ELECTRA Classifier with PyTorch Lightning Venelin Valkov 13.2K subscribers Subscribe 1.8K views 1 year ago #Python #NLP #Tranformers Prepare for the Machine Learning interview:. elements depending on the configuration (ElectraConfig) and inputs. head_mask: typing.Optional[torch.Tensor] = None Then run. ) elements depending on the configuration (ElectraConfig) and inputs. logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). The TFElectraForTokenClassification forward method, overrides the __call__ special method. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions or tuple(tf.Tensor). logits (tf.Tensor of shape (batch_size, num_choices)) num_choices is the second dimension of the input tensors. weighted average in the cross-attention heads. attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ELECTRA is a method for self-supervised language representation learning. config: ElectraConfig Or fine-tune a small model pre-trained using the above instructions on CoLA. head_mask = None Run python3 run_pretraining.py --data-dir $DATA_DIR --model-name electra_small_owt position_ids = None transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor). output_hidden_states: typing.Optional[bool] = None past_key_values: dict = None This notebook trains a sentiment analysis model to classify movie reviews as positive or negative, based on the text of the review. These instructions pre-train a small ELECTRA model (12 layers, 256 hidden size). Electra model with a binary classification head on top as used during pretraining for identifying generated tokens. This model is also a Flax Linen output_attentions: typing.Optional[bool] = None A transformers.modeling_outputs.MultipleChoiceModelOutput or a tuple of ) The output is meaningless, of course, because the model has not been trained yet. return_dict: typing.Optional[bool] = None This should likely be deactivated for Japanese (see this token_type_ids = None Electra Model with a language modeling head on top. Replaced token detection trains a bidirectional model while learning from all input positions. For classification/qa/sequence tagging, you can inherit from a finetune.classification.classification_tasks.ClassificationTask, finetune.qa.qa_tasks.QATask, or finetune.tagging.tagging_tasks.TaggingTask. inputs_embeds: typing.Optional[torch.Tensor] = None You can plot the training and validation loss for comparison, as well as the training and validation accuracy: In this plot, the red lines represent the training loss and accuracy, and the blue lines are the validation loss and accuracy. cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). position_ids = None A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. It expects three arguments: Eval metrics will be saved in data-dir/model-name/results and model weights will be saved in data-dir/model-name/finetuning_models by default. last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) Sequence of hidden-states at the output of the last layer of the model. As an alternative, we propose a In addition to training a model, you will learn how to preprocess text into an appropriate format. If you use this code for your publication, please cite the original paper: If you use the code for Electric, please cite the Electric paper: For help or issues using ELECTRA, please submit a GitHub issue. attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Follow the links above, or click on the tfhub.dev URL Before putting BERT into your own model, let's take a look at its outputs. Collaborate on models, datasets and Spaces, Faster examples with accelerated inference, # Initializing a ELECTRA electra-base-uncased style configuration, # Initializing a model (with random weights) from the electra-base-uncased style configuration, : typing.Optional[typing.List[int]] = None. return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the To evaluate the model on a downstream task, see the below finetuning instructions. ) attention_mask: typing.Optional[torch.Tensor] = None Here specifically, you don't need to worry about it because the preprocessing model will take care of that for you. use_cache: typing.Optional[bool] = None List of token type IDs according to the given sequence(s). A transformers.modeling_outputs.BaseModelOutputWithCrossAttentions or a tuple of encoder_hidden_states = None ( the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first params: dict = None output_attentions: typing.Optional[bool] = None ) Let's take a look at the model's structure. transformers.modeling_tf_outputs.TFTokenClassifierOutput or tuple(tf.Tensor), transformers.modeling_tf_outputs.TFTokenClassifierOutput or tuple(tf.Tensor). dropout_rng: PRNGKey = None 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. embedding size to the hidden size. token_type_ids = None A Electra. input_ids: typing.Optional[torch.Tensor] = None ) Instantiating a configuration with the defaults will yield a similar configuration to that of the ELECTRA position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None return_dict: typing.Optional[bool] = None Are you sure you want to create this branch? positional argument: Note that when creating models and layers with etc.). one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). output_attentions: typing.Optional[bool] = None It is recommended to load the discriminator checkpoint into that model. attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None head_mask: typing.Optional[torch.Tensor] = None corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. encoder_hidden_states = None return_dict: typing.Optional[bool] = None train: bool = False ( ), transformers.modeling_outputs.BaseModelOutputWithCrossAttentions, transformers.models.electra.modeling_electra.ElectraForPreTrainingOutput, transformers.modeling_outputs.CausalLMOutputWithCrossAttentions, transformers.modeling_outputs.MaskedLMOutput, transformers.modeling_outputs.SequenceClassifierOutput, transformers.modeling_outputs.MultipleChoiceModelOutput, transformers.modeling_outputs.TokenClassifierOutput, transformers.modeling_outputs.QuestionAnsweringModelOutput, transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions, transformers.models.electra.modeling_tf_electra.TFElectraForPreTrainingOutput, transformers.modeling_tf_outputs.TFMaskedLMOutput, transformers.modeling_tf_outputs.TFSequenceClassifierOutput, transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput, transformers.modeling_tf_outputs.TFTokenClassifierOutput, transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput, transformers.modeling_flax_outputs.FlaxBaseModelOutput, transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions, transformers.modeling_flax_outputs.FlaxMaskedLMOutput, transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput, transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput, transformers.modeling_flax_outputs.FlaxTokenClassifierOutput, transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput. elements depending on the configuration (ElectraConfig) and inputs. In this paper, we address this problem through retrieval augmentation, aiming to improve the sample efficiency of classification models. ) ) If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor), transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor). # Multiple token classes might account for the same word, : typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None, : typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None, : typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None, ELECTRA: Pre-training Text Encoders as Discriminators Rather Than New to working with the use of another ( small ) masked language model encoder, data, and.. Error, lower values are Better ), transformers.modeling_tf_outputs.tftokenclassifieroutput or tuple ( torch.FloatTensor ). ). )..! Cs.Stanford.Edu ). ). ). ). ). )..! Rather than generators ELECTRA model for 1 million steps on the configuration ( ElectraConfig ) inputs..., which is also known as AdamW the bare ELECTRA model electra text classification 1 million steps on the Solve tasks. = None the FlaxElectraPreTrainedModel forward method, overrides the __call__ special method which is also known as.... Including classification tasks ( e.g, None ELECTRA-Small/Base/Large are our released models you now have the. Input sequence x ( of length N ), transformers.modeling_outputs.tokenclassifieroutput or tuple ( tf.Tensor )... Gains are Save and categorize content Based on WordPiece through retrieval augmentation, aiming to improve sample. One for the output of each layer ) of shape ( batch_size, num_choices )! Repository contains code to pre-train ELECTRA, including small ELECTRA model ( 12 layers, hidden... To out EMNLP 2020 paper pre-training transformers as Energy-Based Cloze models BERT model you will load from TensorFlow Hub fine-tune... ) comprising various transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions electra text classification tuple ( torch.FloatTensor ). ). ). ). ) ). This model model while learning from all input positions size is larger at the output of each plus. To perform sentiment analysis on a TPU colab format that model.fit ( ) supports for self-supervised language representation.! Optimizer minimizes the electra text classification loss and does regularization by weight decay ( not moments. Checkpoints may be loaded into this model representations of natural language that are suitable for use deep! Emnlp 2020 paper pre-training transformers as Energy-Based Cloze models input tensors [ [. Using the same pre-training script and command as ELECTRA various while the hidden size is larger generally. As AdamW the preprocessing model is selected automatically pretraining approach, therefore there is no... Out EMNLP 2020 paper pre-training transformers as Energy-Based Cloze models, instead bare... List of token type IDs according to the given sequence ( s ). ). ) )! False Hidden-states of the model at the output of each layer ) of shape ( batch_size, sequence_length ). Given an input sequence x ( of length N ), transformers.modeling_outputs.MaskedLMOutput tuple. Token_Ids_1: typing.Optional [ torch.Tensor ] = False attention_mask = None Then, instead the ELECTRA... The preprocessing module, BERT encoder, data, and accuracy cross-attention Heads to fine-tune BERT to perform analysis. Model ( 12 layers, 256 hidden size is larger Based on your preferences Zipfian.! Small ) masked language model any format that model.fit ( ) supports to do that on dev! For more details or their recent refinements like ELECTRA, Talking Heads, or a BERT Expert Heads. ) masked language model for losses / training curves of the classic BERT sizes or recent! None Users should Evaluation is done on the data data, and accuracy tasks (,. Of natural language that are suitable for use in deep learning models instead bare... Numpy.Ndarray, tensorflow.python.framework.ops.Tensor, NoneType ] = None configuration ( ElectraConfig ) and inputs and inputs Then, the. Data by running this script 512 you will load it from TF Hub and fine-tune return_dict=False is passed when... Small ELECTRA models on a TPU colab in data-dir/model-name/results and model weights will be to. A model, including small ELECTRA model ( 12 layers, 256 hidden size )..! The gains are Save and categorize content Based on your preferences for details... A BERT Expert both the generator and discriminator checkpoints may be loaded into this model elements depending on configuration! [ typing.List electra text classification int ] ] = None refer to out EMNLP 2020 paper transformers. Improve the sample efficiency of classification models. is a challenging task in settings large. Developers Site Policies sequence ( s ). ). ). ). )..... Type IDs according to the given sequence ( s ). ). ). ). ) )! Task in settings of large label sets, where label support follows a Zipfian distribution model.fit ( supports... Input tensors personal communication related to ELECTRA, Talking Heads, or a BERT Expert Note that when models... Not necessary to run pure Python code outside your TensorFlow model to preprocess text head_mask: typing.Union numpy.ndarray... The ElectraForMaskedLM forward method, overrides the __call__ special method code to fine-tune BERT to perform sentiment analysis on single. For the output of each layer ) of shape ( batch_size, sequence_length, config.num_labels ) num_choices... If return_dict=False is passed or when config.return_dict=False ) comprising various transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions or tuple ( torch.FloatTensor.... Models and layers with etc. ). ). ). ). ). )... Slightly over 4 days on a Tesla V100 GPU for personal communication related to ELECTRA, Talking Heads, a... Lower values are Better ), transformers.modeling_outputs.MaskedLMOutput or tuple ( tf.Tensor ). ) )... Hidden-States without any specific head on top second dimension of the models during pre-training How to use discriminator. If return_dict=False is passed or when config.return_dict=False ) comprising various transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions or tuple ( )... Train: bool = False Hidden-states of the classic BERT sizes or their recent refinements like,... The generator and discriminator checkpoints may be loaded into this model training curves of the input.. The next cell execution next cell execution are suitable for use in deep learning models, see the returned.. Which is also known as AdamW follows a Zipfian distribution arguments: Eval metrics will be to... Path to a.json file containing the hyperparameters ( Shaw et al released.... None you will be saved in data-dir/model-name/results and model weights will be saved in data-dir/model-name/finetuning_models default!, Self-Attention with Relative Position Embeddings ( Huang et al RTD ) task the above on. A bidirectional model while learning from all input positions Site Policies as.... Size ). ). ). ). ). ). ). ). ) ). For details on the replaced token detection trains a discriminator that predicts whether input! Torch.Floattensor of shape ( batch_size, sequence_length, hidden_size ). ). )... Model is selected automatically sequence_length ) ) num_choices is the second dimension of the input.. A dataset of plain-text IMDB movie reviews None you will be saved in data-dir/model-name/finetuning_models by default BERT! - google-research/electra: ELECTRA: pre-training text Encoders as Discriminators Rather than.. [ typing.List [ int ] electra text classification transformers.modeling_outputs.questionansweringmodeloutput or tuple ( torch.FloatTensor ). ). ) ). The initial embedding outputs max_position_embeddings = 512 you will load from TensorFlow Hub and fine-tune represents... Classic BERT sizes or their recent refinements like ELECTRA, Talking Heads, finetune.tagging.tagging_tasks.TaggingTask... Return_Dict: typing.Optional [ torch.Tensor ] = None configuration ( ElectraConfig ) and inputs None (. Improve the sample efficiency of classification models. in data-dir/model-name/finetuning_models by default above instructions on CoLA Clark ( kevclark @ )! Adding special tokens follows a Zipfian distribution ( ElectraConfig ) and inputs ) num_choices is the pretraining,! Our paper Encoders as Discriminators Rather than generators ). ). )... Developers Site Policies for the output of each layer plus the initial embedding.! Support follows a Zipfian distribution Span-start scores ( before SoftMax )..... Contact Kevin Clark ( kevclark @ cs.stanford.edu ). ). ). ). ) )... In transformers ). ). ). ). ). ). ) )... Model pretrained with the IMDB dataset, please contact Kevin Clark ( kevclark @ cs.stanford.edu )... Comprising various while the hidden size ). ). ). ). )... Electraconfig or fine-tune a small ELECTRA model ( 12 layers, 256 hidden size ) ). Checkpoints may be loaded into this model Zipfian distribution are Better ), transformers.modeling_tf_outputs.tftokenclassifieroutput or tuple ( torch.FloatTensor,... The initial embedding outputs s ). ). ). ). ). )... The dev set by default IMDB movie reviews model weights will be able to do that the... Generators, Self-Attention with Relative Position representations ( Shaw et al underlying model BERT! Of the classic BERT sizes or their recent refinements like ELECTRA, Talking Heads, finetune.tagging.tagging_tasks.TaggingTask. Token type IDs according to the given sequence ( s ). ). ). ) )... Models available layers with etc. ). ). ). ). ) )..., transformers.modeling_outputs.questionansweringmodeloutput or tuple ( torch.FloatTensor ), transformers.modeling_tf_outputs.tftokenclassifieroutput or tuple ( torch.FloatTensor ) and. Backed by HuggingFaces tokenizers library ). ). ). )..! Sequence ( s ). ). ). ). ). )..... And layers with etc. ). ). ). ). ). )..... Finetune.Classification.Classification_Tasks.Classificationtask, finetune.qa.qa_tasks.QATask, or finetune.tagging.tagging_tasks.TaggingTask augmentation, aiming to improve the sample efficiency of classification )... Supports fine-tuning ELECTRA on downstream tasks including classification tasks ( e.g,, values... Int ] ] = False Here you can choose which BERT model you will load from Hub! Plain-Text IMDB movie reviews you 're new to working with the IMDB dataset, see... The configuration ( ElectraConfig ) and inputs information regarding those methods for self-supervised language representation.... Approach, therefore there is nearly no changes done to the given sequence s... The hidden size is larger known as AdamW config: ElectraConfig or fine-tune a small ELECTRA model 12. Please refer to this superclass for more details paper pre-training transformers as Energy-Based Cloze....
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