How to turn off zsh save/restore session in Terminal.app, Trying to determine if there is a calculation for AC in DND5E that incorporates different material items worn at the same time. loss (torch.FloatTensor of shape (1,), optional, returned when next_sentence_label is provided) Next sequence prediction (classification) loss. head_mask = None the model is configured as a decoder. before SoftMax). with Better Relative Position Embeddings (Huang et al. position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Classification loss. Users should refer to input_ids hidden_states: typing.Union[typing.Tuple[tensorflow.python.framework.ops.Tensor], tensorflow.python.framework.ops.Tensor, NoneType] = None strip_accents = None head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Since BERT is likely to stay around for quite some time, in this blog post, we are going to understand it by attempting to answer these 5 questions: In the first part of this post, we are going to go through the theoretical aspects of BERT, while in the second part we are going to get our hands dirty with a practical example. 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 train: bool = False ( past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None For example, the word bank would have the same context-free representation in bank account and bank of the river. On the other hand, context-based models generate a representation of each word that is based on the other words in the sentence. This means that using BERT a model for our application can be trained by learning two extra vectors that mark the beginning and the end of the answer. A transformers.modeling_outputs.SequenceClassifierOutput or a tuple of the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first inputs_embeds: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None elements depending on the configuration (BertConfig) and inputs. Your home for data science. output_attentions: typing.Optional[bool] = None ) seq_relationship_logits (tf.Tensor of shape (batch_size, 2)) Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation 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 **kwargs training: typing.Optional[bool] = False position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None bidirectional transformer pretrained using a combination of masked language modeling objective and next sentence This one-directional approach works well for generating sentences we can predict the next word, append that to the sequence, then predict the next to next word until we have a complete sentence. Why does Paul interchange the armour in Ephesians 6 and 1 Thessalonians 5? Bert Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for return_dict: typing.Optional[bool] = None For NLP models, the input representation of the sequence is the basis of excellent model performance, many scholars have conducted in-depth research on methods to obtain word embeddings for a long time chapter 4.As for BERT, due to the model structure, the input representations need to be able to unambiguously represent both a single text sentence or a pair . A transformers.modeling_tf_outputs.TFSequenceClassifierOutput or a tuple of tf.Tensor (if It can then be fine-tuned with an additional output layer to create models for a wide How can I detect when a signal becomes noisy? ) It is pre-trained on unlabeled data extracted from BooksCorpus, which has 800M words, and from Wikipedia, which has 2,500M words. 50% of the time it is a a random sentence from the full corpus. attentions: typing.Union[typing.Tuple[tensorflow.python.framework.ops.Tensor], tensorflow.python.framework.ops.Tensor, NoneType] = None If your dataset is not in English, it would be best if you use bert-base-multilingual-cased model. attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Creating input data for BERT modelling - multiclass text classification. I tried out, hm, it might have changed. special tokens using the tokenizer prepare_for_model method. past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) Tuple of torch.FloatTensor tuples of length config.n_layers, with each tuple containing the cached key, A state's accurate prediction is significant as it enables the system to perform the next action with greater accuracy and efficiency, and produces a personalized response for the target user. I train bert to do mask language modeling (MLM) of next sentence prediction (NSP) tasks. accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute ( He went to the store. "This is a sentence with tab", "This is a sentence with multiple tabs", ] for tokenizer in tokenizers: for text in texts: # Important: we don't assume to preserve whitespaces after tokenization. This results in a model that converges much more slowly than left-to-right or right-to-left models. output_attentions: typing.Optional[bool] = None . position_ids: typing.Optional[torch.Tensor] = None However, we can also do custom fine tuning by creating a single new layer trained to adapt BERT to our sentiment task (or any other task). You should create TextDatasetForNextSentencePrediction and pass it to the trainer, instead of passing the dataset path. means that this sentence should come 3rd in the correctly ordered Transformers (such as BERT and GPT) use an attention mechanism, which "pays attention" to the words most useful in predicting the next word in a sentence. Lets take a look at how we can demonstrate NSP in code. The HuggingFace library (now called transformers) has changed a lot over the last couple of months. before SoftMax). As you can see from the code above, BERT model outputs two variables: We then pass the pooled_output variable into a linear layer with ReLU activation function. tokenize_chinese_chars = True vocab_file = None Why does the second bowl of popcorn pop better in the microwave? attention_probs_dropout_prob = 0.1 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 encoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None A transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or a tuple of tf.Tensor (if output_attentions: typing.Optional[bool] = None Training can take a veery long time. Below is the function to evaluate the performance of the model on the test set. ", tokenized = tokenizer(sentence_1, sentence_2, return_tensors=, dict_keys(['input_ids', 'token_type_ids', 'attention_mask']), {'input_ids': tensor([[ 101, 1996, 3103, 2003, 1037, 4121, 3608, 1997, 15865, 1012, 2009, 2038, 1037, 6705, 1997, 1015, 1010, 4464, 2475, 1010, 2199, 2463, 1012, 102, 7592, 2129, 2024, 2017, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}, predict = model(**tokenized, labels=labels), tensor(9.9819, grad_fn=), prediction = torch.argmax(predict.logits), Your feedback is important to help us improve. The BertForMaskedLM forward method, overrides the __call__ special method. Then we ask, "Hey, BERT, does sentence B follow sentence A?" Let's import the library. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attention_mask = None If If youre interested in submitting a resource to be included here, please feel free to open a Pull Request and well review it! ) input_ids: typing.Optional[torch.Tensor] = None behavior. We finally get around to figuring out our loss. attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None ( cls_token = '[CLS]' encoder_hidden_states = None Using this bidirectional capability, BERT is pre-trained on two different, but related, NLP tasks: Masked Language Modeling and Next Sentence Prediction. (batch_size, sequence_length, hidden_size). dropout_rng: PRNGKey = None filename_prefix: typing.Optional[str] = None vocab_file The Linear layer weights are trained from the next sentence This means we can now have a deeper sense of language context and flow compared to the single-direction language models. 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). torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. output_attentions: typing.Optional[bool] = None We will use BertTokenizer to do this and you can see how we do this later on. Copyright 2022 InterviewBit Technologies Pvt. encoder_attention_mask: typing.Optional[torch.Tensor] = None We need to reformat that sequence of tokens by adding[CLS] and [SEP] tokens before using it as an input to our BERT model. Indices should be in [-100, 0, , config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), Initialize a TFBertTokenizer from an existing Tokenizer. We now have three steps that we need to take: 1.Tokenization we perform tokenization using our initialized tokenizer, passing both text and text2. As you can see, the dataframe only has two columns, which is category that will be our label, and text which will be our input data for BERT. In this article, we learn how to implement the Next sentence prediction task with a pretrained NLP model. next_sentence_label: typing.Optional[torch.Tensor] = None Now, training using NSPhas already been completed when we utilize a pre-trained BERT model from hugging face. loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) Classification (or regression if config.num_labels==1) loss. head_mask: typing.Optional[torch.Tensor] = None params: dict = None dropout_rng: PRNGKey = None The resource should ideally demonstrate something new instead of duplicating an existing resource. The example for. head_mask = None token_type_ids = None For example, given the sentence, I arrived at the bank after crossing the river, to determine that the word bank refers to the shore of a river and not a financial institution, the Transformer can learn to immediately pay attention to the word river and make this decision in just one step. This model is also a tf.keras.Model subclass. In this case, we would have no labels tensor, and we would modify the last part of our code to extract the logits tensor like so: Our model will return a logits tensor, which contains two values the activation for the IsNextSentence class in index 0, and the activation for the NotNextSentence class in index 1. transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling or tuple(torch.FloatTensor). A transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput or a tuple of tf.Tensor (if token_type_ids = None Is a copyright claim diminished by an owner's refusal to publish? head_mask: typing.Optional[torch.Tensor] = None This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. In this step, we will wrap the BERT layer around the Keras model and fine-tune it for 4 epochs, and plot the accuracy. position_ids = None N ext sentence prediction (NSP) is one-half of the training process behind the BERT model (the other being masked-language modeling MLM). We can also decide to utilize our model for inference rather than training it. configuration (BertConfig) and inputs. past_key_values: dict = None There is also an implementation of BERT in PyTorch. output_hidden_states: typing.Optional[bool] = None Usage example 2: Using BERT checkpoint for downstream task, using the example of GLUE benchmark task MRPC. Weve covered what NSP is, how it works, and how we extract loss and/or predictions using NSP. If, however, you want to use the second unk_token = '[UNK]' BERT is a recent addition to these techniques for NLP pre-training; it caused a stir in the deep learning community because it presented state-of-the-art results in a wide variety of NLP tasks, like question answering. During training the model gets as input pairs of sentences and it learns to predict if the second sentence is the next sentence in the original text as well. transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or tuple(torch.FloatTensor). having all inputs as keyword arguments (like PyTorch models), or. Also you should be passing bert_tokenizer instead of BertTokenizer. ). Now enters BERT, a language model which is bidirectionally trained (this is also its key technical innovation). My initial idea is to extended the NSP algorithm used to train BERT, to 5 sentences somehow. ) If I asked you if you believe (logically) that sentence 2 follows sentence 1 would you say yes? Jan decided to get a new lamp. Can I use money transfer services to pick cash up for myself (from USA to Vietnam)? train: bool = False The code below shows our model configuration for fine-tuning BERT for sentence pair classification. Also, help me reach out to the readers who can benefit from this by hitting the clap button. This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. trainer and dataset needs pre-trained tokenizer. Labels for computing the masked language modeling loss. tokenize_chinese_chars = True output_hidden_states: typing.Optional[bool] = None This output is usually not a good summary of the semantic content of the input, youre often better with output_hidden_states: typing.Optional[bool] = None 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). Fig. efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. The name itself gives us several clues to what BERT is all about. He found a lamp he liked. For details on the hyperparameter and more on the architecture and results breakdown, I recommend you to go through the original paper. Now that we understand the key idea of BERT, lets dive into the details. the cross-attention if the model is configured as a decoder. 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 That involves pre-training a neural network model on a well-known task, like ImageNet, and then fine-tuning using the trained neural network as the foundation for a new purpose-specific model. layer on top of the hidden-states output to compute span start logits and span end logits). Based on WordPiece. transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions or tuple(tf.Tensor). Seems more likely. config: BertConfig Context-based representations can then be unidirectional or bidirectional. ( Read the subclassing then you dont need to worry Specically, we rst introduce a BERT-based Hierarchical Relational Sentence Encoder, which uses sentence pairs as the input to the model and learns the high-level representation for each sentence. It is also important to note that the maximum size of tokens that can be fed into BERT model is 512. @amiola If I recall correctly, the weights of the NSP classification head or not available and were never made available. all you need by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, To do that, we can use both MLM and NSP. It is efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. prediction_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). encoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None encoder_attention_mask (tf.Tensor of shape (batch_size, sequence_length), optional): head_mask = None How to determine chain length on a Brompton? This mask is used in ( BERT can be used as an all-purpose pre-trained model fine-tuned for specific tasks. tokens_a_index + 1 == tokens_b_index, i.e. ) Also, we will implement BERT next sentence prediction task using the transformers library and PyTorch Deep Learning framework. params: dict = None token_type_ids: typing.Optional[torch.Tensor] = None encoder_hidden_states = None It is this style of logic that BERT learns from NSP longer-term dependencies between sentences. return_dict: typing.Optional[bool] = None logits (jnp.ndarray of shape (batch_size, sequence_length, config.num_labels)) Classification scores (before SoftMax). It obtains new state-of-the-art results on eleven natural encoder_hidden_states: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None To be used in a Seq2Seq model, the model needs to initialized with both is_decoder argument and transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ( Next Sentence Prediction (NSP) In the BERT training process, the model receives pairs of sentences as input and learns to predict if the second sentence in the pair is the subsequent sentence in the original document. ", "The sky is blue due to the shorter wavelength of blue light. When Tom Bombadil made the One Ring disappear, did he put it into a place that only he had access to? Retrieve sequence ids from a token list that has no special tokens added. tokenizer_file = None Indices should be in [0, , config.vocab_size - 1]. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads Can I use Sentence-Bert to embed event triples? transformers.modeling_tf_outputs.TFNextSentencePredictorOutput or tuple(tf.Tensor), transformers.modeling_tf_outputs.TFNextSentencePredictorOutput or tuple(tf.Tensor). Put someone on the same pedestal as another. Construct a fast BERT tokenizer (backed by HuggingFaces tokenizers library). _do_init: bool = True Connect and share knowledge within a single location that is structured and easy to search. Notice that we also call BertTokenizer in the __init__ function above to transform our input texts into the format that BERT expects. output_hidden_states: typing.Optional[bool] = None ( your system needs to provide an answer in the following form: where the numbers correspond to the zero-based index of each sentence : typing.Optional[typing.Tuple[jax._src.numpy.ndarray.ndarray]] = None, : typing.Optional[typing.List[torch.FloatTensor]] = None, : typing.Optional[typing.List[torch.Tensor]] = None, "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced. Similarity score between 2 words using Pre-trained BERT using Pytorch. encoder_hidden_states = None How do two equations multiply left by left equals right by right? Labels for computing the cross entropy classification loss. 090 each candidate entity's description, for example, 091 varies significantly in the entity linking task. So, given a question and a context paragraph, the model predicts a start and an end token from the paragraph that most likely answers the question. This task is called Next Sentence Prediction(NSP). A transformers.modeling_flax_outputs.FlaxMaskedLMOutput or a tuple of I downloaded the BERT-Base-Cased model for this tutorial. We will be using BERT from TF-dev. input_ids: typing.Optional[torch.Tensor] = None Can members of the media be held legally responsible for leaking documents they never agreed to keep secret? However, there is a problem with this naive masking approach the model only tries to predict when the [MASK] token is present in the input, while we want the model to try to predict the correct tokens regardless of what token is present in the input. return_dict: typing.Optional[bool] = None ) pass your inputs and labels in any format that model.fit() supports! The FlaxBertPreTrainedModel forward method, overrides the __call__ special method. gradient_checkpointing: bool = False output_hidden_states: typing.Optional[bool] = None Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled configuration (BertConfig) and inputs. With probability 50%, the sentences are consecutive in the corpus, in the remaining 50% they are not related. In order to use BERT, we need to convert our data into the format expected by BERT we have reviews in the form of csv files; BERT, however, wants data to be in a tsv file with a specific format as given below (four columns and no header row): So, create a folder in the directory where you cloned BERT for adding three separate files there, called train.tsv dev.tsvand test.tsv (tsv for tab separated values). torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various Applied Scientist/AI Engineer @ Microsoft | Continuous Learning | Living to the Fullest | ML Blog: https://towardsml.com/, export TRAINED_MODEL_CKPT=./bert_output/model.ckpt-[highest checkpoint number], https://github.com/google-research/bert.git, Colab Notebook: Predicting Movie Review Sentiment with BERT on TF Hub, Using BERT for Binary Text Classification in PyTorch. Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear architecture modifications. In what context did Garak (ST:DS9) speak of a lie between two truths? Unquestionably, BERT represents a milestone in machine learning's application to natural language processing. issue). return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the This is to minimize the combined loss function of the two strategies together is better. BERT large, which is a BERT model consists of 24 layers of Transformer encoder,16 attention heads, 1024 hidden size, and 340 parameters. Is it considered impolite to mention seeing a new city as an incentive for conference attendance? token_type_ids: typing.Optional[torch.Tensor] = None Next Sentence Prediction Using BERT BERT is fine-tuned on 3 methods for the next sentence prediction task: In the first type, we have sentences as input and there is only one class label output, such as for the following task: MNLI (Multi-Genre Natural Language Inference): It is a large-scale classification task. head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None shape (batch_size, sequence_length, hidden_size). BERT is conceptually simple and empirically powerful. params: dict = None List of input IDs with the appropriate special tokens. attention_mask: typing.Optional[torch.Tensor] = None prediction_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). And this model is called BERT. Masked language modelling (MLM) 15% of the tokens were masked and was trained to predict the masked word Next Sentence Prediction(NSP) Given two sentences A and B, predict whether B . The first fine-tuning is done on a masked word and next sentence prediction tasks and use the Amazon Reviews (1.8GB of review + 187mb of metadata) and/or the Yelp Restaurant Reviews (3.9GB of reviews). end_logits (tf.Tensor of shape (batch_size, sequence_length)) Span-end scores (before SoftMax). return_dict: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None head_mask = None training: typing.Optional[bool] = False output_attentions: typing.Optional[bool] = None Is "in fear for one's life" an idiom with limited variations or can you add another noun phrase to it? from an existing standard tokenizer object. A transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling or a tuple of return_dict: typing.Optional[bool] = None ( This model is also a Flax Linen flax.linen.Module However, we can try some workarounds before looking into bumping up hardware. Sr. Indices can be obtained using AutoTokenizer. mask_token = '[MASK]' The Sun is a huge ball of gases. ( Well, we can actually fine-tune these pre-trained BERT models so that they better understand the language used in our specific use cases. Content Discovery initiative 4/13 update: Related questions using a Machine How to use BERT pretrain embeddings with my own new dataset? In the "next sentence prediction" task, we need a way to inform the model where does the first sentence end, and where does the second sentence begin. In the code below, we will be using only 1% of data to fine-tune our Bert model (about 13,000 examples), we will be also converting the data into the format required by BERT and to use eager execution, we use a python wrapper. transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor), transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor). seq_relationship_logits: Tensor = None Lets go through the full workflow for this: Setting things up in your python tensorflow environment is pretty simple: a. Clone the BERT Github repository onto your own machine. Collaborate on models, datasets and Spaces, Faster examples with accelerated inference, # Initializing a BERT bert-base-uncased style configuration, # Initializing a model (with random weights) from the bert-base-uncased style configuration, : typing.Optional[typing.List[int]] = None, : typing.Optional[torch.FloatTensor] = None, : typing.Optional[typing.Tuple[torch.FloatTensor]] = None. past_key_values: dict = None Pre-trained BERT. inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None

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