performs comparably to RoBERTa and XLNet while using less than 1/4 of their end_positions (tf.Tensor of shape (batch_size,), optional) – Labels for position (index) of the end of the labelled span for computing the token classification loss. Install Apex if you are using fp16 training. Mask values selected in [0, 1]: token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) –. Indices should be in [0, ..., config.num_labels - 1]. logits (torch.FloatTensor of shape (batch_size, num_choices)) – num_choices is the second dimension of the input tensors. the input by replacing some tokens with [MASK] and then train a model to ELECTRA Model transformer with a sequence classification/regression head on top (a linear layer on top of You have tons of configuration options that you can use when performing any NLP task in Simple Transformers, although you don’t need to set each one (sensible defaults are used wherever possible). Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids 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: logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) – Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). intermediate_size (int, optional, defaults to 1024) – Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder. Comparatively, learning from all input tokens seems to have a much bigger impact than resolving the pre-train/fine-tune mismatch of masked tokens. tuple of torch.FloatTensor comprising various elements depending on the configuration Description: Fine tune pretrained BERT from HuggingFace Transformers on SQuAD. You can find all the architecture configuration options and their default values here. It is used to instantiate a ELECTRA model according to the specified A MultipleChoiceModelOutput (if return_dict=True is passed or when config.return_dict=True) or a Used in the sequence classification and multiple choice models. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention Position outside of the sequence are not taken into account for computing the loss. I am excited by the possibilities this opens up as ELECTRA should significantly lower the compute resource barrier to training your own language models. Even though both the discriminator and generator may be loaded into this model, the generator is If you open one of the files, you’ll notice that they have two columns, with the first column containing the index and the second column containing the text. transformers.PreTrainedTokenizer.encode() for details. Indices should be in [0, ..., num_choices-1] where num_choices is the size of the second dimension Indices should be in [0, ..., config.num_labels - 1]. Specifically, I downloaded the following datasets; You should be able to improve the results by using a bigger dataset. logits (torch.FloatTensor of shape (batch_size, config.num_labels)) – Classification (or regression if config.num_labels==1) scores (before SoftMax). for more information. of shape (batch_size, sequence_length, hidden_size). The (ElectraConfig) and inputs. for GLUE tasks. The ELECTRA paper recommends using a generator model that is 0.25–0.5 of the size of the discriminator. summary_use_proj (bool, optional, defaults to True) –. In Simple Transformers, all language modelling tasks are handled with the LanguageModelingModel class. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), One huge advantage of the ELECTRA pre-training approach is that it’s possible to train your own language models on a single GPU! The ElectraForQuestionAnswering forward method, overrides the __call__() special method. 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. Refer to superclass BertTokenizerFast for usage examples and documentation concerning (ElectraConfig) and inputs. labels (torch.LongTensor of shape (batch_size, sequence_length), optional) – Labels for computing the masked language modeling loss. in [0, ..., config.vocab_size]. Electra model with a token classification head on top. Hidden-states of the model at the output of each layer plus the initial embedding outputs. input_ids (Numpy array or tf.Tensor of shape (batch_size, num_choices, sequence_length)) –, attention_mask (Numpy array or tf.Tensor of shape (batch_size, num_choices, sequence_length), optional) –, position_ids (Numpy array or tf.Tensor of shape (batch_size, num_choices, sequence_length), optional) –. pre and post processing steps while the latter silently ignores them.

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