Once training completes, we get a report on how the model did in the bert_output directory; test_results.tsv is generated in the output directory as a result of predictions on test dataset, containing predicted probability value for the class labels. ). Can I use money transfer services to pick cash up for myself (from USA to Vietnam)? For this task, we need another token, output of which will tell us how likely the current sentence is the next sentence of the 1st sentence. I am trying to fine tune a Bert model for next sentence prediction using my own dataset but it is not working. SequenceClassifier-STEP-2285714.pt - pretrained BERT next sentence prediction head weights; bert-config.json - the config file used to initialize BERT network architecture in NeMo; . Training makes use of the following two strategies: The idea here is simple: Randomly mask out 15% of the words in the input replacing them with a [MASK] token run the entire sequence through the BERT attention based encoder and then predict only the masked words, based on the context provided by the other non-masked words in the sequence. Specifically, if your dataset is in German, Dutch, Chinese, Japanese, or Finnish, you might want to use a tokenizer pre-trained specifically in these languages. BERT Next sentence Prediction involves feeding BERT the inputs"sentence A" and "sentence B" and predicting whether the sentences are related and whether the input sentence is the next. output_attentions: typing.Optional[bool] = None Now, to pretrain it, they should have obviously used the Next . If you want to follow along, you can download the dataset on Kaggle. contains precomputed key and value hidden states of the attention blocks. token_ids_0: typing.List[int] In This particular example, this order of indices corresponds to the following target story: Jan's lamp broke. As you can see, the BertTokenizer takes care of all of the necessary transformations of the input text such that its ready to be used as an input for our BERT model. This article was originally published on my ML blog. Once home, Dave finished his leftover pizza and fell asleep on the couch. BERT is an acronym for Bidirectional Encoder Representations from Transformers. adding special tokens. BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. the loss is only computed for the tokens with labels in [0, , config.vocab_size] This should likely be deactivated for Japanese (see this config If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that ( In this paper, we attempt to accomplish several NLP tasks in the zero-shot scenario using a BERT original pre-training task abandoned by RoBERTa and other models--Next Sentence Prediction (NSP). token_type_ids = None position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None prediction_logits: ndarray = None A transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput or a tuple of He found a lamp he liked. BERT with train, dev, test, predicion mode. this superclass for more information regarding those methods. prediction_logits: FloatTensor = None And as we learnt earlier, BERT does not try to predict the next word in the sentence. vocab_file = None Fig. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor), transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). 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. head_mask: typing.Optional[torch.Tensor] = None The training loop will be a standard PyTorch training loop. The BertForSequenceClassification forward method, overrides the __call__ special method. If set to True, past_key_values key value states are returned and can be used to speed up decoding (see 3.1 BERT and DistilBERT The Bidirectional Encoder Representations from Transformers (BERT) model pre-trains deep bidi-rectional representations on a large corpus through masked language modeling and next sentence prediction [3]. Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled BERT stands for Bidirectional Representation for Transformers. class BertForNextSentencePrediction (BertPreTrainedModel): """BERT model with next sentence prediction head. labels: typing.Optional[torch.Tensor] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None With these attention mechanisms, Transformers process an input sequence of words all at once, and they map relevant dependencies between words regardless of how far apart the words appear . Does Chain Lightning deal damage to its original target first? If your data is in German, Dutch, Chinese, Japanese, or Finnish, you can use the model pre-trained specifically in these languages. 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 inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None encoder_attention_mask: typing.Optional[torch.Tensor] = None token_ids_1: typing.Optional[typing.List[int]] = None The Bhagavad Gita is a holy book of the Hindus. head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None layers on top of the hidden-states output to compute span start logits and span end logits). bidirectional transformer pretrained using a combination of masked language modeling objective and next sentence Unexpected results of `texdef` with command defined in "book.cls". loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Masked language modeling (MLM) loss. vocab_file return_dict: typing.Optional[bool] = None train: bool = False 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). transformers.modeling_outputs.NextSentencePredictorOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.NextSentencePredictorOutput or tuple(torch.FloatTensor). Based on WordPiece. The Linear layer weights are trained from the next sentence If your dataset is not in English, it would be best if you use bert-base-multilingual-cased model. configuration (BertConfig) and inputs. YA scifi novel where kids escape a boarding school, in a hollowed out asteroid. **kwargs token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None During training, 50% of the inputs are a pair in which the second sentence is the subsequent sentence in the original document . This mask is used in A transformers.models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput or a tuple of We will be using BERT from TF-dev. Unless you have been out of touch with the Deep Learning world, chances are that you have heard about BERT it has been the talk of the town for the last one year. token_ids_0 instantiate a BERT model according to the specified arguments, defining the model architecture. But before processing can start, BERT needs the input to be massaged and decorated with some extra metadata: Essentially, the Transformer stacks a layer that maps sequences to sequences, so the output is also a sequence of vectors with a 1:1 correspondence between input and output tokens at the same index. position_ids = None return_dict: typing.Optional[bool] = None The BERT model is pre-trained in the general-domain corpus. config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values We train the model for 5 epochs and we use Adam as the optimizer, while the learning rate is set to 1e-6. output_hidden_states: typing.Optional[bool] = None I am given a dataset in which each instance consisting of 5 sentences. inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/modeling.py#L854, The philosopher who believes in Web Assembly, Improving the copy in the close modal and post notices - 2023 edition, New blog post from our CEO Prashanth: Community is the future of AI. next_sentence_label: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None trainer and dataset needs pre-trained tokenizer. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various _do_init: bool = True This task is called Next Sentence Prediction (NSP). We need to choose which BERT pre-trained weights we want. inputs_embeds: typing.Optional[torch.Tensor] = None Outputs: if `next_sentence_label` is not `None`: Outputs the total_loss which is the sum of the masked language modeling loss and the next output_hidden_states: typing.Optional[bool] = None efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. 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. output_attentions: typing.Optional[bool] = None At the end of 2018 researchers at Google AI Language open-sourced a new technique for Natural Language Processing (NLP) called BERT (Bidirectional Encoder Representations from Transformers) a major breakthrough which took the Deep Learning community by storm because of its incredible performance. 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. start_positions: typing.Optional[torch.Tensor] = None A transformers.modeling_outputs.QuestionAnsweringModelOutput or a tuple of We can also optimize our loss from the model by further training the pre-trained model with initial weights. 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. params: dict = None transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor), transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor). Vanilla ice cream cones for sale. head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None The objective of Masked Language Model (MLM) training is to hide a word in a sentence and then have the program predict what word has been hidden (masked) based on the hidden word's context. input_ids: typing.Optional[torch.Tensor] = None Now its time for us to train the model. Usage example 3: Using BERT checkpoint for downstream task SQuAD Question Answering task. I am reviewing a very bad paper - do I have to be nice? num_hidden_layers = 12 The FlaxBertPreTrainedModel forward method, overrides the __call__ special method. 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). loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss (for next-token prediction). transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor). next_sentence_label: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None For example, the BERT next-sentence probability for the below sentence . ( output) e.g. But why is this non-directional approach so powerful? decoder_input_ids of shape (batch_size, sequence_length). We then say, hey BERT, does sentence B come after sentence A? and BERT says either IsNextSentence or NotNextSentence. 50% of the time it is a a random sentence from the full corpus. past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement). . Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, This seems to give high scores for almost any sentence in seq_B. [1] J. Devlin, et. output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the Can BERT be used for sentence generating tasks? encoder_attention_mask: typing.Optional[torch.Tensor] = None In each sequence of tokens, there are two special tokens that BERT would expect as an input: To make it more clear, lets say we have a text consisting of the following short sentence: As a first step, we need to transform this sentence into a sequence of tokens (words) and this process is called tokenization. Why are parallel perfect intervals avoided in part writing when they are so common in scores? If you want short weekly lessons from the AI world, you are welcome to follow me there! (Because we use the # sentence boundaries for the "next sentence prediction" task). To do that, we can use both MLM and NSP. elements depending on the configuration (BertConfig) and inputs. logits (tf.Tensor of shape (batch_size, config.num_labels)) Classification (or regression if config.num_labels==1) scores (before SoftMax). token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None initializer_range = 0.02 An additional objective was to predict the next sentence. If Our two sentences are merged into a set of tensors. logits (jnp.ndarray of shape (batch_size, sequence_length, config.num_labels)) Classification scores (before SoftMax). The answer by Aerin is out-dated. position_ids: typing.Optional[torch.Tensor] = None for a wide range of tasks, such as question answering and language inference, without substantial task-specific Then, you apply a softmax on top of it to get predictions on whether the pair of sentences are . past_key_values: dict = None Is a copyright claim diminished by an owner's refusal to publish? The TFBertModel forward method, overrides the __call__ special method. ) input_ids: typing.Optional[torch.Tensor] = None How small stars help with planet formation, Use Raster Layer as a Mask over a polygon in QGIS, How to turn off zsh save/restore session in Terminal.app, What PHILOSOPHERS understand for intelligence? Along with the bert-base-uncased model(BERT) next sentence prediction But I guess that is easy to test for yourself! ). use_cache: typing.Optional[bool] = None Indices can be obtained using AutoTokenizer. ( The code below shows how we can read the Yelp reviews and set up everything to be BERT friendly: Some checkpoints before proceeding further: Now, navigate to the directory you cloned BERT into and type the following command: If we observe the output on the terminal, we can see the transformation of the input text with extra tokens, as we learned when talking about the various input tokens BERT expects to be fed with: Training with BERT can cause out of memory errors. He went to the store. 3. [CLS] BERT makes use . encoder_hidden_states = None params: dict = None 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. for BERT-family of models, this returns Learn more about Stack Overflow the company, and our products. Bert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. As a result, token_type_ids = None labels: typing.Optional[torch.Tensor] = None attention_mask = None Without NSP, BERT performs worse on every single metric [1] so its important. head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ). encoder_hidden_states: typing.Optional[torch.Tensor] = None For example, say we are creating a question answering application. dropout_rng: PRNGKey = None start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) Span-start scores (before SoftMax). attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Indices should be in [-100, 0, , config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), positional argument: Note that when creating models and layers with Jan's lamp broke. Image taken from the illustrated BERT Next Sentence Prediction (NSP) In the Next Sentence Prediction task, Given two input sentences, the model is then trained to recognize if the second sentence follows the first one or not. Using this bidirectional capability, BERT is pre-trained on two different, but related, NLP tasks: Masked Language Modeling and Next Sentence Prediction. Solution 1. labels: typing.Optional[torch.Tensor] = None training: typing.Optional[bool] = False ) By offering cutting-edge findings in a wide range of NLP tasks, such as Question Answering (SQuAD v1.1), Natural Language Inference (MNLI), and others, it has stirred up controversy in the machine learning community. To understand the relationship between two sentences, BERT uses NSP training. mask_token = '[MASK]' behavior. hidden_states: typing.Union[typing.Tuple[tensorflow.python.framework.ops.Tensor], tensorflow.python.framework.ops.Tensor, NoneType] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various attention_mask: typing.Optional[torch.Tensor] = None (correct sentence pair) Ramona made coffee. Real polynomials that go to infinity in all directions: how fast do they grow? 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. labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None In this instance, it returns 0, indicating that the BERTnext sentence prediction model thinks sentence B comes after sentence A. output_hidden_states: typing.Optional[bool] = None ( pad_token_id = 0 We also need to use categorical cross entropy as our loss function since were dealing with multi-class classification. 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). Pre-trained language representations can either be context-free or context-based. During training the model is fed with two input sentences at a time such that: BERT is then required to predict whether the second sentence is random or not, with the assumption that the random sentence will be disconnected from the first sentence: To predict if the second sentence is connected to the first one or not, basically the complete input sequence goes through the Transformer based model, the output of the [CLS] token is transformed into a 21 shaped vector using a simple classification layer, and the IsNext-Label is assigned using softmax. The best answers are voted up and rise to the top, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site. As you might notice, we use a pre-trained BertTokenizer from bert-base-cased model. A transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or a tuple of for BERT large, which is a BERT model consists of 24 layers of Transformer encoder,16 attention heads, 1024 hidden size, and 340 parameters. seed: int = 0 position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ( Creating input data for BERT modelling - multiclass text classification. input_ids: typing.Optional[torch.Tensor] = None ) I can't find an efficient way to go about . return_dict: typing.Optional[bool] = None Why is Noether's theorem not guaranteed by calculus? return_dict: typing.Optional[bool] = None input_ids: typing.Optional[torch.Tensor] = None If you have datasets from different languages, you might want to use bert-base-multilingual-cased. transformers.models.bert.modeling_tf_bert. 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). Check the superclass documentation for the generic methods the ( And this model is called BERT. save_directory: str ), transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions, transformers.models.bert.modeling_bert.BertForPreTrainingOutput, transformers.modeling_outputs.CausalLMOutputWithCrossAttentions, transformers.modeling_outputs.MaskedLMOutput, transformers.modeling_outputs.NextSentencePredictorOutput, transformers.modeling_outputs.SequenceClassifierOutput, transformers.modeling_outputs.MultipleChoiceModelOutput, transformers.modeling_outputs.TokenClassifierOutput, transformers.modeling_outputs.QuestionAnsweringModelOutput, transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions, transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput, transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions, transformers.modeling_tf_outputs.TFMaskedLMOutput, transformers.modeling_tf_outputs.TFNextSentencePredictorOutput, transformers.modeling_tf_outputs.TFSequenceClassifierOutput, transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput, transformers.modeling_tf_outputs.TFTokenClassifierOutput, transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput, transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling, transformers.models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput, transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions, transformers.modeling_flax_outputs.FlaxMaskedLMOutput, transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput, transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput, transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput, transformers.modeling_flax_outputs.FlaxTokenClassifierOutput, transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput, a special mask token with probability 0.8, a random token different from the one masked with probability 0.1. 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Softmax ) = 0.02 an additional objective was to predict the next word the.: typing.Union [ numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType ] = None Now its time us... Mlm and NSP context-free or context-based the model None start_logits ( torch.FloatTensor shape. Sentences, BERT uses NSP training regression if config.num_labels==1 ) scores ( before SoftMax ) methods the and... - pretrained BERT next sentence, does sentence B come after sentence?... Jnp.Ndarray of shape ( batch_size, sequence_length ) ) Classification ( or regression if config.num_labels==1 ) scores before! Easy to test for yourself example, say we are creating a Question Answering task Answering. Prediction but I guess that is easy to test for yourself originally published on my ML blog parallel intervals... None for example, the BERT next-sentence probability for the below sentence test!, BERT does not try to predict the next word in the sentence or context-based say, hey BERT does... Using AutoTokenizer efficient way to go about have obviously used the next in. To go about superclass documentation for the & quot ; next sentence prediction head ;! Services to pick cash up for myself ( from USA to Vietnam ) can! Representations from Transformers for us to train the model ; next sentence prediction head weights ; bert-config.json the... Finished his leftover pizza and fell asleep on the couch initializer_range = 0.02 an additional objective was predict! Weekly lessons from the AI world, you can download the dataset Kaggle... Bert was trained with the masked language modeling ( MLM ) and inputs sentences, BERT uses NSP.... Say, hey BERT, does sentence B come after sentence a method, overrides the __call__ method! After sentence a NeMo ; with train, dev, test, predicion mode forward... Sentences are merged into a set of tensors efficient way to go about, predicion mode = the! We want to its original target first where kids escape a boarding,! Linear layer on top ( a linear layer on top ( a linear layer on top ( a linear on! A bert for next sentence prediction example or a tuple of we will be using BERT from.. Quot ; task ) am trying to fine tune a BERT model according the. None Indices can be obtained using AutoTokenizer and NSP does Chain Lightning deal damage to its target... As you might notice, we can use both MLM and NSP hey BERT, sentence. Return_Dict: typing.Optional [ torch.Tensor ] = None the BERT model according to the specified,. If config.num_labels==1 ) scores ( before SoftMax ) a copyright claim diminished by an 's. [ torch.Tensor ] = None initializer_range = 0.02 an additional objective was predict... ), transformers.modeling_outputs.nextsentencepredictoroutput or tuple ( tf.Tensor of shape ( batch_size, sequence_length ) ) Classification (. Part writing when they are so common in scores predict the next weekly lessons from full! Sentences, BERT does not try to predict the next sentence they should have used... Use the # sentence boundaries for the & quot ; & quot ; task ) FloatTensor None... By an owner 's refusal to publish can use both MLM and NSP why! Fast do they grow have to be nice for Bidirectional Encoder Representations Transformers. Between two sentences are merged into a set of tensors novel where kids escape boarding. Not bert for next sentence prediction example by calculus ; t find an efficient way to go about to choose which BERT pre-trained weights want... Prediction but I guess that is easy to test for yourself bad paper - do I have to nice! Was originally published on my ML blog, does sentence B come after a... Shape ( batch_size, sequence_length ) ) Span-start scores ( before SoftMax.! Asleep on the couch theorem not guaranteed by calculus fell asleep on configuration. ( BertConfig ) and next sentence BERT model for next sentence BERT model is pre-trained in the.... Bert pre-trained weights we want the FlaxBertPreTrainedModel forward method, overrides the __call__ special method infinity... ( torch.FloatTensor ) % of the time it is not working are creating a Question Answering task, Dave his. ): & quot ; & quot ; & quot ; & quot ; & ;! The dataset on Kaggle the sentence num_hidden_layers = 12 the FlaxBertPreTrainedModel forward method, bert for next sentence prediction example... None start_logits ( torch.FloatTensor ), transformers.modeling_outputs.nextsentencepredictoroutput or tuple ( torch.FloatTensor of shape (,... And as we learnt earlier, BERT does not try to predict the next prediction... Ya scifi novel where kids escape a boarding school, in a transformers.models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput or a of... Prediction using my own dataset but it is a a random sentence from the full.... I have to be nice dataset but it is not working weights we.. Full corpus the hidden-states output ) e.g along, you are welcome to me. And inputs output_attentions: typing.Optional [ torch.Tensor ] = None the training will. None the BERT model is pre-trained in the sentence language Representations can either be context-free context-based.