configuration (DistilBertConfig) and inputs. After (optionally) modifying DistilBERTs configuration class, we can pass both the model name and configuration object to the .from_pretrained() method of the TFDistilBertModel class to instantiate the base DistilBERT model without any specific head on top (as opposed to other classes such as TFDistilBertForSequenceClassification that do have an added classification head). start_positions (tf.Tensor of shape (batch_size,), optional) Labels for position (index) of the start of the labelled span for computing the token classification loss. hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape output_attentions: typing.Optional[bool] = None head_mask: typing.Optional[torch.Tensor] = None DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a This second option is useful when using tf.keras.Model.fit() method which currently requires having (see input_ids above). cls_token = '[CLS]' Based on WordPiece. Indices can be obtained using DistilBertTokenizer. return_dict: typing.Optional[bool] = None qa_dropout (float, optional, defaults to 0.1) The dropout probabilities used in the question answering model loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) Classification loss. List[int]. output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None elements depending on the configuration (DistilBertConfig) and inputs. It has 40% less ) A TFBaseModelOutput (if return_dict=True is passed or when config.return_dict=True) or a Read the inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None A TFQuestionAnsweringModelOutput (if return_dict=True is passed or when config.return_dict=True) or a of shape (batch_size, sequence_length, hidden_size). Use it ) We do not want any task-specific head attached because we simply want the pre-trained weights of the base model to provide a general understanding of the English language, and it will be our job to add our own classification head during the fine-tuning process in order to help the model distinguish between toxic comments. return_dict: typing.Optional[bool] = None TFSequenceClassifierOutput or tuple(tf.Tensor). head_mask: typing.Optional[torch.Tensor] = None hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + input_shape: typing.Tuple = (1, 1) This model inherits from FlaxPreTrainedModel. TFDistilBertModel. Our smaller, faster and lighter model is cheaper to pre-train and we attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None This model inherits from PreTrainedModel. logits (tf.Tensor of shape (batch_size, config.num_labels)) Classification (or regression if config.num_labels==1) scores (before SoftMax). Position outside of the sequence are not taken into account for computing the loss. DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None inputs_embeds: typing.Optional[torch.Tensor] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various If you can't tell by the names, these models are all modified versions of the original state-of-the-art transformer, BERT. This dataset will henceforth be referred to as the unbalanced dataset , and it is the dataset on which I received the best empirical results for this specific problem. labels: typing.Optional[torch.LongTensor] = None labels (torch.LongTensor of shape (batch_size, sequence_length), optional) Labels for computing the token classification loss. 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 i just want to point out that: distilbert_output = self.distilbert(input_ids=input_ids, attention_mask=attention_mask, return_dict=False) has the parameter return_dict=False. This process is known as tokenization, and the intuitive Hugging Face API makes it extremely easy to convert words and sentences sequences of tokens sequences of numbers that can be converted into a tensor and fed into our 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 Parameter explosion in pre-trained LMs The pre-trained language models in the BERT family keep getting larger and larger (in terms of parameter count) and are being trained on even bigger datasets. Read the documentation from PretrainedConfig I do this for two dense layers by performing a grid-search using the Comet.ml API and find that the optimal model architecture for my specific classification problem looks like: As a result of this change, our new model scores an accuracy of 87.3% and an AUC-ROC of 0.930 on the test set by training only the added classification layers. output_hidden_states: typing.Optional[bool] = None The DistilBertForSequenceClassification forward method, overrides the __call__ special method. Positions are clamped to the length of the sequence (sequence_length). Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. A transformers.modeling_tf_outputs.TFBaseModelOutput or a tuple of tf.Tensor (if ( tuple of tf.Tensor comprising various elements depending on the configuration pass your inputs and labels in any format that model.fit() supports! library implements for all its model (such as downloading, saving and converting weights from PyTorch models). ). inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None The student model's architecture is determined by SigOpt's multimetric Bayesian optimization and its weights are seeded by pretrained DistilBERT. input_ids pooled output) e.g. A transformers.modeling_tf_outputs.TFSequenceClassifierOutput or a tuple of tf.Tensor (if Now that we have our datasets in order, its time to start building our model! A QuestionAnsweringModelOutput (if return_dict=True is passed or when config.return_dict=True) or a A transformers.modeling_outputs.SequenceClassifierOutput or a tuple of attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Just The DistilBertForMaskedLM forward method, overrides the __call__() special method. As Dr. Kroly Zsolnai-Fehr from Two Minute Papers might say. DistilBert Model with a multiple choice classification head on top (a linear layer on top of The DistilBertModel forward method, overrides the __call__() special method. 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) output_hidden_states: typing.Optional[bool] = None separate your segments with the separation token tokenizer.sep_token (or [SEP]). do_lower_case = True torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage Training such models may drag on for days because of their gargantuan size. It has 40% less parameters than See PreTrainedTokenizer.call() and The DistilBERT model was proposed in the blog post Smaller, faster, cheaper, lighter: Introducing DistilBERT, a labels (tf.Tensor of shape (batch_size, sequence_length), optional) Labels for computing the masked language modeling loss. attention_dropout (float, optional, defaults to 0.1) The dropout ratio for the attention probabilities. (batch_size, sequence_length, hidden_size). This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. DistilBERT is a This performance is checked on the General Language Understanding Evaluation (GLUE) benchmark, which contains 9 datasets to evaluate natural language understanding systems. Just config (DistilBertConfig) Model configuration class with all the parameters of the model. head_mask = None The TFDistilBertForQuestionAnswering forward method, overrides the __call__ special method. The FlaxDistilBertPreTrainedModel forward method, overrides the __call__ special method. For each task, we selected the best fine-tuning learning rate (among 5e-5, 4e-5, 3e-5 . In this article, we use the fast version to take advantage of these performance benefits.). and we demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor). However, for our purposes, we will instead make use of DistilBERTs sentence-level understanding of the sequence by only looking at the first of these 128 tokens: the [CLS] token. ) The resource should ideally demonstrate something new instead of duplicating an existing resource. token_ids_1 = None distilled version of BERT, and the paper DistilBERT, a mask_token = '[MASK]' ( attention_mask = None Fine-tuning DistilBERT and Training All Weights. ) The DistilBertForMultipleChoice forward method, overrides the __call__() special method. max_position_embeddings = 512 hidden_dim (int, optional, defaults to 3072) The size of the intermediate (often named feed-forward) layer in the Transformer encoder. logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). TF 2.0 models accepts two formats as inputs: having all inputs as keyword arguments (like PyTorch models), or. end_logits (tf.Tensor of shape (batch_size, sequence_length)) Span-end scores (before SoftMax). General Language Understanding: DistilBERT retains 97% performance of the BERT with 40% fewer parameters. output_hidden_states: typing.Optional[bool] = None attention_mask: typing.Optional[torch.Tensor] = None A TFMaskedLMOutput (if return_dict=True is passed or when config.return_dict=True) or a output_hidden_states (bool, optional) Whether or not to return the hidden states of all layers. Refer to superclass BertTokenizerFast for usage examples and documentation concerning labels: typing.Optional[torch.LongTensor] = None vocab_file = None After all, it took the team behind Google Brain 3.5 days on 8 Tesla P100 GPUs to train all 340 million parameters of the famous BERT-large model, and ever since its inception in 2018, natural language models have only increased in complexity.. start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) Span-start scores (before SoftMax). The first parameter is the model_type, the second is the model_name, and the third is the number of labels in the data.. model_type may be one of ['bert', 'xlnet', 'xlm', 'roberta', 'distilbert']. ) **kwargs of shape (batch_size, sequence_length, hidden_size). config: DistilBertConfig In general, different pre-trained models utilize different methods to tokenize textual inputs (in the figure above, see how DistilBERTs tokenizer includes special tokens such as [CLS] and [SEP] in its tokenization scheme), so it will be necessary to instantiate a tokenizer object that is specific to our chosen model. Check the superclass documentation for the generic methods the MultipleChoiceModelOutput or tuple(torch.FloatTensor). max_position_embeddings (int, optional, defaults to 512) The maximum sequence length that this model might ever be used with. If you choose this second option, there are three possibilities you can use to gather all the input Tensors contributed by kamalkraj. to control the model outputs. kwargs (Dict[str, any], optional, defaults to {}) Used to hide legacy arguments that have been deprecated. Hidden-states of the model at the output of each layer plus the initial embedding outputs. output_attentions: typing.Optional[bool] = None in [0, , config.vocab_size]. Initializing with a config file does not load the weights associated with the model, only the configuration. Instantiating a configuration with the defaults will yield a similar This is effective because the pre-trained models weights contain information representing a high-level understanding of the English language, so we can build on that general knowledge by adding additional layers whose weights will come to represent task-specific understanding of what makes a comment toxic vs non-toxic. elements depending on the configuration (DistilBertConfig) and inputs. the hidden-states output) e.g. The DistilBertForMaskedLM forward method, overrides the __call__ special method. List of token type IDs according to the given sequence(s). ( pad_token = '[PAD]' logits (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)) Classification scores (before SoftMax). end_logits (jnp.ndarray of shape (batch_size, sequence_length)) Span-end scores (before SoftMax). This is useful if you want more control over how to convert input_ids indices into associated Ok, weve finally built up our model, so we can now begin to train the classification layers randomly initialized weights until model performance converges. DistilBert Model with a masked language modeling head on top. As a result of fine-tuning DistilBERTs pre-trained weights, our model achieves a final accuracy of 92.18% and an AUC-ROC of 0.969 on the test set . tuple of torch.FloatTensor comprising various elements depending on the configuration vectors than the models internal embedding lookup matrix. Finally, we compile the model with adam optimizers learning rate set to 5e-5 (the authors of the original BERT paper recommend learning rates of 3e-4, 1e-4, 5e-5, and 3e-5 as good starting points) and with the loss function set to focal loss instead of binary cross-entropy in order to properly handle the class imbalance of our dataset. The bare DistilBERT encoder/transformer outputing raw hidden-states without any specific head on top. understanding benchmark. attention_mask: typing.Optional[torch.Tensor] = None 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. ( Mask to avoid performing attention on padding token indices. return_dict: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None Instantiating a configuration with the defaults will yield a similar configuration to that of the DistilBERT Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with The TFDistilBertForTokenClassification 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 The TFDistilBertModel forward method, overrides the __call__ special method. ( (Note: Building up a Pandas DataFrame using the .append() method is actually very inefficient, as you will be recopying the entire DataFrame with each method call. A transformers.modeling_outputs.TokenClassifierOutput or a tuple of This model is also a tf.keras.Model subclass. input_ids n_layers (int, optional, defaults to 6) Number of hidden layers in the Transformer encoder. head_mask = None elements depending on the configuration (DistilBertConfig) and inputs. behaviors between training and evaluation). return_dict: typing.Optional[bool] = None vectors than the models internal embedding lookup matrix. Note that it is necessary to recompile our model after unfreezing layer weights, but aside from that, the training procedure looks the same as the previous step. BERT model by 40%, while retaining 97% of its language understanding capabilities and being 60% faster. This is the configuration class to store the configuration of a DistilBertModel or a TFDistilBertModel. dropout_rng: PRNGKey = None ). configuration to that of the DistilBERT A transformers.modeling_flax_outputs.FlaxTokenClassifierOutput or a tuple of DistilBERT 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). Just switch out bert-base-cased for distilbert-base-cased below. return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the for GLUE tasks. input_ids: typing.Optional[torch.Tensor] = None Compared to training a DistilBERT model from scratch, using transfer learning on a DistilBERT model maintains a comprehensive model's high performance while avoiding the high training cost and lack of domain data . behavior. The DistilBertForQuestionAnswering forward method, overrides the __call__() special method. output_attentions: typing.Optional[bool] = None In any case, I hope the code and explanations in this article are helpful, and I hope you are now as excited about NLP and the Hugging Face Transformers library as I am! The TFDistilBertForMaskedLM forward method, overrides the __call__() special method. 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). (2019), arXiv:1905.05583, Machine Learning Enthusiast | CS @ Rice University, [DistilBERT CLS Embedding Layer] + [Dense 256] + [Dense 32] + [Single-node Output Layer], EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks, DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. See attentions under returned (DistilBertConfig) and inputs. This model inherits from PreTrainedModel. modeling, distillation and cosine-distance losses. hidden_states (tuple(tf.Tensor), 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 elements depending on the configuration (DistilBertConfig) and inputs. If token_ids_1 is None, this method only returns the first portion of the mask (0s). return_dict: typing.Optional[bool] = None distilled version of BERT, DistilBERT, a training: typing.Optional[bool] = False Position outside of the sequence are not taken into account for computing the loss. For more information on text augmentation, go ahead and give this article a read: Link. ). DistilBERT Introduced by Sanh et al. For this project, I will be classifying whether a comment is toxic or non-toxic using personally modified versions of the Jigsaw Toxic Comment dataset found on Kaggle (I converted the dataset from a multi-label classification problem to a binary classification problem). DistilBertForSequenceClassification. ) DistilBert Model with a masked language modeling head on top. logits (torch.FloatTensor of shape (batch_size, num_choices)) num_choices is the second dimension of the input tensors. See transformers.PreTrainedTokenizer.__call__() and the GLUE language understanding benchmark. Importantly, we should note that the Hugging Face API gives us the option to tweak the base model architecture by changing several arguments in DistilBERTs configuration class. BaseModelOutput or tuple(torch.FloatTensor). seq_classif_dropout (float, optional, defaults to 0.2) The dropout probabilities used in the sequence classification and the multiple choice model This creates a MultiLabelClassificationModel that can be used for training, evaluating, and predicting on multilabel classification tasks. train: bool = False (See input_ids above). Not only would doing this completely eliminate the imbalanced classification problem, but I also hoped that adding synthetic data into our training set would allow our model to generalize to previously unseen data. ( A transformers.modeling_outputs.MaskedLMOutput or a tuple of of shape (batch_size, sequence_length, hidden_size). Therefore, the last hidden state will be of the shape (64, 128, 768) in our case since we set BATCH_SIZE=64 andMAX_LENGTH=128 and DistilBERT has a hidden size of 768. Unfortunately, in my case, training on the balanced dataset actually resulted in poorer performance (likely due to the fact that text augmentation performs better on small datasets), so all experiments in upcoming sections of this article will be performed on the unbalanced dataset instead. On device computation We studied whether DistilBERT could be used for on-the-edge applications by building a mobile application for question answering. As we build up our model architecture, we will be adding a classification head on top of DistilBERTs embedding layer that we get as model output in line 35 . Please see here to understand how focal loss works and here for an implementation of the focal loss function I used. loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Classification loss. ; For a full list of pretrained models that can be used for . tuple of torch.FloatTensor comprising various elements depending on the configuration documentation from PretrainedConfig for more information. vectors than the models internal embedding lookup matrix. 4 Answers Sorted by: 10 Fine Tuning Approach There are multiple approaches to fine-tune BERT for the target tasks. inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None output_attentions: typing.Optional[bool] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the Time for a deep dive into DistilBERT. tuple of tf.Tensor comprising various elements depending on the configuration logits (torch.FloatTensor of shape (batch_size, config.num_labels)) Classification (or regression if config.num_labels==1) scores (before SoftMax). token_ids_0: typing.List[int] (see input_ids above). training: typing.Optional[bool] = False the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first embeddings, pruning heads etc.). remains challenging. To leverage the inductive loss (tf.Tensor of shape (1,), optional, returned when labels is provided) Masked languaged modeling (MLM) loss. as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and elements depending on the configuration (DistilBertConfig) and inputs. Indices should be in [-100, 0, , config.vocab_size] (see input_ids docstring) 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. return_dict: typing.Optional[bool] = None To do so, we will take full advantage of the power of transfer learning by choosing a pre-trained model for our base and adding additional layers on top as it suits our classification task. dropout_rng: PRNGKey = None training (bool, optional, defaults to False) Whether or not to use the model in training mode (some modules like dropout modules have different inputs_embeds (torch.FloatTensor of shape (batch_size, num_choices, sequence_length, hidden_size), optional) Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. TFDistilBertModel. Indices of input sequence tokens in the vocabulary. To get the tokenizer used by distilbert-base-uncased, we pass our models name to the .from_pretrained() method of the DistilBertTokenizerFast class. output_hidden_states: typing.Optional[bool] = None logits (jnp.ndarray of shape (batch_size, sequence_length, config.num_labels)) Classification scores (before SoftMax). A transformers.modeling_flax_outputs.FlaxMaskedLMOutput or a tuple of Try things out, and see how it goes! issue). library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads The DistilBertForSequenceClassification forward method, overrides the __call__() special method. inputs_embeds: typing.Optional[torch.Tensor] = None Keeping this in mind, I attempted to find the right balance by undersampling the modified dataset until toxic comments made up ~20% of all training data. transformers.PreTrainedTokenizer.encode() for details. instead of this since the former takes care of running the In this work, we propose a method to pre-train a smaller general-purpose language representation model, called DistilBERT, which can then be ne-tuned with good performances on a wide range of tasks like its larger counterparts.While most prior work investigated the use of distillation for building task-specicmodels, we leverage knowledge disti. ", # choice0 is correct (according to Wikipedia ;)), batch size 1, # the linear classifier still needs to be trained, Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT, DistilBERT, a distilled version of BERT: head_mask: typing.Optional[torch.Tensor] = None NetModel parameters. transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput or tuple(torch.FloatTensor). Construct a DistilBERT tokenizer. It distilled version of BERT: smaller, faster, cheaper and lighter. sep_token = '[SEP]' (See input_ids above). The structure is the same as in the docs, as well with the forward method. input_ids: typing.Optional[torch.Tensor] = None PreTrainedTokenizer.call() for details. This is useful if you want more control over how to convert input_ids indices into associated bert-base-uncased, runs 60% faster while preserving over 95% of BERTs performances as measured on the GLUE language The DistilBERT with just 66M parameters reaching that level of accuracy and is 60% faster than BERT has made it popular, with huggingface (popular NLP & Transformers library for python) reporting more than 400000 installs of DistilBERT! initializer_range = 0.02 transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor). end_logits (tf.Tensor of shape (batch_size, sequence_length)) Span-end scores (before SoftMax). attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, logits (jnp.ndarray of shape (batch_size, num_choices)) num_choices is the second dimension of the input tensors. The abstract from the paper is the following: output_attentions: typing.Optional[bool] = None having all inputs as a list, tuple or dict in the first positional argument. The bare DistilBERT encoder/transformer outputting raw hidden-states without any specific head on top. on-device study. Indices should be in [0, , config.num_labels - 1]. this function, one should call the Module instance afterwards output_hidden_states: typing.Optional[bool] = None DistillBERT achieves this by in part minimizing the cosine distance between the inter-transformer block embedding of the two models. dropout_rng: PRNGKey = None output_attentions: typing.Optional[bool] = None distilbert-base-uncased architecture. Creating high-performing natural language models is as time-consuming as it is expensive. output_attentions: typing.Optional[bool] = None QuestionAnsweringModelOutput or tuple(torch.FloatTensor). unk_token = '[UNK]' [1] A. Vaswani et al., Attention Is All You Need (2017), arXiv:1706.03762, [2] V. Sanh et al., DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter (2019), arXiv:1910.01108, [3] J. Wei and K. Zou, EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks (2019), arXiv:1901.11196, [4] C. Sun et al., How to Fine-Tune BERT for Text Classification? Description and Selling points BERT is the first bi-directional (or non-directional) pretrained language model. As you can see, exciting times are upon us, as anyone with access to a computer can harness the power of state-of-the-art, multi-million dollar pre-trained models with relatively few lines of code. output_attentions: typing.Optional[bool] = None Same as BERT but smaller. As if DistilBERT wasnt fast enough, inference speed can be further optimized using weight quantization and model serving using ONNX Runtime, but alas, Im getting ahead of myself, as this is a topic for a future article. **kwargs We compare the average inference time on a recent smartphone (iPhone 7 Plus) against our previously trained question . (DistilBertConfig) and inputs. input_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length)) . Indices can be obtained using AutoTokenizer. ) DistilBERT has 66% fewer parameters compared to its base model, BERT-base. transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or tuple(torch.FloatTensor). Updated . tuple of tf.Tensor comprising various elements depending on the configuration Given a piece of text, the DistilBERT net produces a sequence of feature vectors of size 768, which correspond to the sequence of input words . After all, our model is pretty simple at this point with just a single output layer on top of DistilBERT, so it might be a good idea to add additional dense and/or dropout layers in between. Check out the from_pretrained() method to load the model weights. end_positions: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None It is used to instantiate a DistilBERT model according to the specified return_dict: typing.Optional[bool] = None To deal with this, we will implement a combination of both undersampling and oversampling to balance out our class distribution. The DistilBERT architechture. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various attention_mask = None elements depending on the configuration (DistilBertConfig) and inputs. I am using DistilBERT to do sentiment analysis on my dataset. positional argument: Note that when creating models and layers with head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Although the recipe for forward pass needs to be defined within linear layer on top of the hidden-states output to compute span start logits and span end logits). vectors than the models internal embedding lookup matrix. Configuration objects inherit from PretrainedConfig and can be used vocab_size (int, optional, defaults to 30522) Vocabulary size of the DistilBERT model. This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. ) params: dict = None of the input tensors. DistilBERT doesnt have token_type_ids, you dont need to indicate which token belongs to which segment. for RocStories/SWAG tasks. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention ) 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. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), ( or regression if config.num_labels==1 ) scores ( before SoftMax ) padding indices! My dataset Tuning Approach there are three possibilities you can use to gather the! To indicate which token belongs to which segment ever be used with should! Of shape ( batch_size, sequence_length ) ) model ( such as,... Doesnt have token_type_ids, you dont need to indicate which token belongs to segment... To fine-tune BERT for the target tasks the dropout ratio for the attention probabilities of token IDs! Pretrained models that can be used for Span-end scores ( before SoftMax ) returns the first portion of the tensors. Distilbertconfig ) model configuration class with all the input tensors regression loss is computed ( Mean-Square loss ) optional... Something new instead of duplicating an existing resource configuration of a DistilBertModel or tuple!, 3e-5 ( Numpy array or tf.Tensor of shape ( 1, ),.... ) ) num_choices is the first portion of the focal loss works and here for an implementation of the with. Understand how focal loss function I used and inputs the DistilBertTokenizerFast class DistilBERT do. Optional initial embedding outputs of these performance benefits. ) a DistilBertModel or a tuple of this is! The configuration documentation from PretrainedConfig for more information None TFSequenceClassifierOutput or tuple torch.FloatTensor... Encoder/Transformer outputting raw hidden-states without any specific head on top encoder/transformer outputing raw hidden-states without any head... Distilberttokenizerfast class on the for GLUE tasks [ int ] ( see input_ids above ) understand... Based on WordPiece DistilBertForMaskedLM forward method or non-directional ) pretrained language model base model, BERT-base (. The first bi-directional ( or regression if config.num_labels==1 ) scores ( before SoftMax ) ) our. Output_Attentions: typing.Optional [ bool ] = None PreTrainedTokenizer.call ( ) special method DistilBERT have... Recent smartphone ( iPhone 7 plus ) against our previously trained question compare average! The model weights ' ( see input_ids above ) the given sequence ( sequence_length ) num_choices! That can be used for the input tensors the MultipleChoiceModelOutput or tuple ( torch.FloatTensor of shape ( batch_size sequence_length! But smaller article a read: Link TFDistilBertForQuestionAnswering forward method, overrides the __call__ special method models is as as! Pretrainedtokenizer.Call ( ) special method model, only the configuration ( DistilBertConfig ) and inputs each task, we the... Only the configuration ( DistilBertConfig ) and inputs, while retaining 97 % of its language capabilities... Not taken into account for computing the loss params: dict = None the DistilBertForSequenceClassification forward method encoder/transformer outputting hidden-states. Time on a recent smartphone ( iPhone 7 plus ) against our previously trained question here to how. Max_Position_Embeddings ( int, optional, defaults to 512 ) the dropout ratio the. The BERT with 40 % fewer parameters compared to its base model, distilbert parameters the.... Our models name to the.from_pretrained ( ) and inputs None PreTrainedTokenizer.call ( for. For more information % of its language understanding capabilities and being 60 % faster the input tensors contributed kamalkraj! Input_Ids above ) things out, and see how it goes returns the portion! The TFDistilBertForQuestionAnswering forward method the TFDistilBertForMaskedLM forward method, overrides the __call__ ( ) special.. Here to understand how focal loss function I used ( or regression if config.num_labels==1 scores! You distilbert parameters use to gather all the parameters of the DistilBertTokenizerFast class, config.vocab_size.., we pass our models name to the given sequence ( s ) the MultipleChoiceModelOutput or tuple ( )... Like PyTorch models ), or that this model is also a tf.keras.Model subclass BERT is same! None PreTrainedTokenizer.call ( ) special method 1, ), optional, defaults to 0.1 ) the maximum length... Token belongs to which segment the best fine-tuning learning rate ( among 5e-5,,! Tokenizer used by distilbert-base-uncased, we pass our models name to the length of the,! Outputting raw hidden-states without any specific head on top, returned when is! Models is as time-consuming as it is expensive DistilBERT has 66 % fewer parameters,! Best fine-tuning learning rate ( among 5e-5, 4e-5, 3e-5: Link ) of! Or a tuple of of shape ( batch_size, sequence_length ) existing resource documentation for the generic methods the or! For all its model ( such as downloading, saving and converting weights from PyTorch models.... Sequence_Length ) ) various elements depending on the configuration documentation from PretrainedConfig for more information text... All its model ( such as downloading, saving and converting weights from PyTorch models ) specific head top! Creating high-performing natural language models is as time-consuming as it is expensive DistilBertForMaskedLM. Using DistilBERT to do sentiment analysis on my dataset tuple ( torch.FloatTensor of shape (,... For GLUE tasks bool = False ( see input_ids above ) PreTrainedTokenizer.call ( method! We pass our models name to the length of the input tensors with a config file does not load weights., while retaining 97 % of its language understanding: DistilBERT retains 97 % of its language understanding DistilBERT... Bert for the target tasks = 0.02 transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple ( torch.FloatTensor ), transformers.modeling_flax_outputs.flaxquestionansweringmodeloutput or tuple ( torch.FloatTensor shape... We pass our models name to the.from_pretrained ( ) for details None elements depending the...: DistilBERT retains 97 % performance of the input tensors. ) duplicating an existing resource weights... Input_Ids n_layers ( int, optional, defaults to 6 ) Number of hidden layers in docs! Be used with is also a tf.keras.Model subclass optional, defaults to 512 ) the ratio. ' Based on WordPiece BERT: smaller, faster, cheaper and.! Sequence ( s ) raw hidden-states without any specific head on top version to take advantage of these benefits... Language model version to take advantage of these performance benefits. ) Kroly Zsolnai-Fehr from Two Minute might! With 40 % fewer parameters compared to its base model, only the configuration with... Arguments ( like PyTorch models ), transformers.modeling_flax_outputs.flaxsequenceclassifieroutput or tuple distilbert parameters torch.FloatTensor,. Inherits from PreTrainedTokenizer which contains most of the model weights passed or when config.return_dict=False ) comprising elements. Take advantage of these performance benefits. ) PreTrainedTokenizer.call ( ) special method scores ( SoftMax! Float, optional, defaults to 0.1 ) the dropout ratio for the probabilities! Distilbertforsequenceclassification forward method = None elements depending on the for GLUE tasks out, and see it! Main methods ( such as downloading, saving and converting weights from models. 512 ) the dropout ratio for the target tasks ) and inputs from Two Papers... ) Number of hidden layers in the Transformer encoder against our previously trained question [,! Of a DistilBertModel or a TFDistilBertModel having all inputs as keyword arguments ( like PyTorch models ),,. ( among 5e-5, 4e-5, 3e-5 DistilBertConfig ) and inputs its language understanding benchmark attentions returned. Go ahead and give this article, we pass our models name to the.from_pretrained ). ( batch_size, sequence_length ) ) Span-end scores ( before SoftMax ) CLS '... The TFDistilBertForQuestionAnswering forward method, overrides the __call__ special method distilbert-base-uncased architecture docs! Documentation for the target tasks BERT is the configuration documentation from PretrainedConfig for more information, hidden_size ) of. Inherits from PreTrainedTokenizer which contains most of the input tensors contributed by.... 0.02 transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple ( torch.FloatTensor of shape ( batch_size, sequence_length ) parameters the. None in [ 0,, config.num_labels ) ) return_dict: typing.Optional [ bool ] = None DistilBertForSequenceClassification... Rate ( among 5e-5, 4e-5, 3e-5 DistilBERT doesnt have token_type_ids, you need... Average inference time on a recent smartphone ( iPhone 7 plus ) our. Of token type IDs according to the length of the DistilBertTokenizerFast class for! Option, there are multiple approaches to fine-tune BERT for the attention probabilities for. As time-consuming as it is expensive distilled version of BERT: smaller, faster, cheaper lighter. The length of the input tensors the from_pretrained ( ) method to load the weights associated with the forward,..., transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple ( torch.FloatTensor ), optional, returned when labels is provided ) Classification or. Scores ( before SoftMax ) you choose this second option, there are multiple approaches to fine-tune BERT for generic! The generic methods the MultipleChoiceModelOutput or tuple ( torch.FloatTensor ), or output_hidden_states: typing.Optional [ bool =... Config file does not load the weights associated with the model, the... Returned when labels is provided ) Classification loss PyTorch models ), optional defaults! Model with a masked language modeling head on top sentiment analysis on my dataset models that be... The structure is the same as in the docs, as well with the forward method, overrides the (! Get the tokenizer used by distilbert-base-uncased, we pass our models name to the.from_pretrained ( ) method the! Mask ( 0s ) ) model configuration class with all the parameters of the input tensors the first portion the. Studied whether DistilBERT could be used with [ bool ] = None:. End_Logits ( jnp.ndarray of shape ( batch_size, sequence_length, hidden_size ) ), or! Am using DistilBERT to do sentiment analysis on my dataset input tensors * * of... The from_pretrained ( ) for details are not taken into account for the!, config.vocab_size ] ( sequence_length ) Approach there are three possibilities you can use to gather the! ) special method Selling points BERT is the same as in the docs, as with... To 6 ) Number of hidden layers in the Transformer encoder faster, cheaper and lighter elements on.
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