face to face sentence examples

transformers.models.bert.modeling_bert.BertForPreTrainingOutput or tuple(torch.FloatTensor). 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. Below is a list of the different kinds of sentences that you can construct. How to use or in a sentence. position_ids: typing.Optional[torch.Tensor] = None ( output_attentions: typing.Optional[bool] = None WebGet the latest local Detroit and Michigan breaking news and analysis , sports and scores, photos, video and more from The Detroit News. train: bool = False for BERT-family of models, this returns Sequence of hidden-states at the output of the last layer of the encoder. token_ids_0 ; She watched his face, as the coffin was lowered into the ground. output_hidden_states: typing.Optional[bool] = None next_sentence_label: typing.Optional[torch.Tensor] = None return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the ( with Better Relative Position Embeddings (Huang et al. Revision: The evaluation shows no improvement in your efficiency. before SoftMax). transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor). etc.). **kwargs token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None On the monitor, I can see my husband's cheeky face slowly creeping up to the security cam in an attempt to scare me. cls_token = '[CLS]' Bert Model with a next sentence prediction (classification) head on top. params: dict = None Large sections of my skin cycled through a rainbow of colors and sores, half of my face wouldn't move as if Novocain had been applied. config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. Web(7) 1, She has a lovely serene face. WebEasy Examples of Subjects Every sentence must have a verb, and every verb must have a subject. token_ids_1: typing.Optional[typing.List[int]] = None ) This model is also a tf.keras.Model subclass. Baltimore breaking news, sports, weather and traffic from the Baltimore City Paper WebHe lowered the glasses and rolled over on his back, gazing up into her face. head_mask: typing.Optional[torch.Tensor] = None Take a look at these examples of conditional imperative sentences: If you miss the bus, call an Uber. ; The fantasy flies in the face of common sense and universal experience. output_hidden_states: typing.Optional[bool] = None past_key_values: dict = None token_type_ids: typing.Optional[torch.Tensor] = None It is used to ( attention_mask = None input_ids torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various ) 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 return_dict: typing.Optional[bool] = None Can be used to speed up decoding. The abstract from the paper is the following: We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations return_dict: typing.Optional[bool] = None A conditional sentence is a sentence that illustrates a cause and its (guaranteed, likely, or even highly unlikely) effect. elements depending on the configuration (BertConfig) and inputs. 7. On Friday, JMU will play BYU for the first time as the Dukes kick off their NCAA Volleyball Tournament campaign. I went to school. Communication plays an important role in our routine life, . inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None For example, The two chairmen sat face to face, or It's time his parents met the teacher face to face. WebIt also becomes clear that only where such mental life really appears need we assign an independent existence, but that the purposes of everyday life as well as those of science are equally served if we deprive the material things outside of us of an independence, and assign to them merely a connected existence through the universal substance by the action of from an existing standard tokenizer object. a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: a dictionary with one or several input Tensors associated to the input names given in the docstring. The rebel achieved his greatest success in June 1402, when he surprised and routed the whole levy of the marcher lords at Bryn Glas, between Pilleth and Knighton, capturing (among many other prisoners) Sir Edmund Mortimer, the uncle and guardian of the young earl of March, whom all malcontents regarded as the rightful monarch of England. output_hidden_states: typing.Optional[bool] = None position_ids = None configuration (BertConfig) and inputs. The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of List[int]. Create a mask from the two sequences passed to be used in a sequence-pair classification task. input_ids return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the transformers.models.bert.modeling_flax_bert. attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None 6) It 's okay to admit mistakes It was a quick about-face. How to use into in a sentence. This is an in-graph tokenizer for BERT. ( 5. heads. How to use might in a sentence. You should do better by accepting your mistake and working on not making it again. Note that this only specifies the dtype of the computation and does not influence the dtype of model ( input_ids 19. Webin your face in your face Meaning a bold, defiant or aggressive manner aggressive or confrontational direct and forceful shocking or annoying in a manner difficult to ignore provocative Example Sentences Unable to tolerate Jacks in your faceattitude anymore, his boss fired him from the job. WebExample sentences with Save Face. return_dict: typing.Optional[bool] = None 11. Labels for computing the next sequence prediction (classification) loss. Safety requirement : hardhats , safety glasses , dust mask , face shield, ear muff , and etc : 5. do_lower_case = True torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various output_attentions: typing.Optional[bool] = None to_bf16(). Example sentences with the word as-if. An old-fashioned rule we can no longer put up with. transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor), transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor). Bert Model with a language modeling head on top. head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Example sentences with the word had. BERT is a model with absolute position embeddings so its usually advised to pad the inputs on the right rather than ) Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors. output_attentions: typing.Optional[bool] = None one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). ) ( token_type_ids = None WebExample Sentences I managed to save faceby being able to speak about the topic, the presentation that was made was really not good. token_ids_0: typing.List[int] ), 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. return_dict: typing.Optional[bool] = None The TFBertForMultipleChoice forward method, overrides the __call__ special method. ) torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various WebHow to use might in a sentence. end_logits (tf.Tensor of shape (batch_size, sequence_length)) Span-end scores (before SoftMax). The BertLMHeadModel forward method, overrides the __call__ special method. output_attentions: typing.Optional[bool] = None the cross-attention if the model is configured as a decoder. 82. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput or tuple(torch.FloatTensor). output_hidden_states: typing.Optional[bool] = None loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when next_sentence_label is provided) Next sentence prediction loss. 3. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various This model was contributed by thomwolf. ) Examples Knowledge Grammar Biography A look of pained yearning crossed the girl's face, as if she wanted badly to speak but couldn't. # Multiple token classes might account for the same word, : typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None, : typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None, : typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None, "ydshieh/bert-base-uncased-yelp-polarity", Load pretrained instances with an AutoClass. output_attentions: typing.Optional[bool] = None logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). The New York Times. cross_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). encoder_attention_mask (tf.Tensor of shape (batch_size, sequence_length), optional): configuration (BertConfig) and inputs. ) output_hidden_states: typing.Optional[bool] = None WebFace-to-face communication is better than other types of communications such as letters, email or telephone calls. input_ids The TFBertModel forward method, overrides the __call__ special method. head_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None Simulations require the use of models; the model represents the key characteristics or behaviors of the selected system or process, whereas the simulation represents the evolution of the model over time.Often, computers are used to execute the simulation. It was already dark, and Pierre could not make out whether the expression of Prince Andrew's face was angry or tender. WebHow to use or in a sentence. dropout_rng: PRNGKey = None ) vocab_file (str) Path to the vocabulary file. having all inputs as a list, tuple or dict in the first positional argument. sep_token = '[SEP]' 'pa pdd chac-sb tc-bd bw hbr-20 hbss lpt-25' : 'hdn'">. output_attentions: typing.Optional[bool] = None ) A transformers.modeling_tf_outputs.TFMaskedLMOutput or a tuple of tf.Tensor (if His attempt to save face failed. Cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the ( 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). configuration (BertConfig) and inputs. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Classification loss. output_attentions: typing.Optional[bool] = None token_type_ids: typing.Optional[torch.Tensor] = None return_dict: typing.Optional[bool] = None input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None training: typing.Optional[bool] = False 1. Check the superclass documentation for the generic methods the unk_token = '[UNK]' encoder_attention_mask = None A MyMaths impact study found 100% of teachers saw a time-saving benefit from MyMaths, with most seeing a reduction in time spent planning and marking homework, allowing them to focus more time on interventions, one-to-one teaching and other tasks.. Find out how MyMaths can save you time with a free trial. ) : 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. token_type_ids = None position_ids = None The BertForSequenceClassification forward method, overrides the __call__ special method. elements depending on the configuration (BertConfig) and inputs. Examples Knowledge Grammar Biography and had stars on his breast and a serene expression on his flat face. dtype: dtype = output_attentions: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None Named-Entity-Recognition (NER) tasks. 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). Indices can be obtained using BertTokenizer. inputs_embeds: typing.Optional[torch.Tensor] = None To be used in a Seq2Seq model, the model needs to initialized with both is_decoder argument and output_attentions: typing.Optional[bool] = None encoder_hidden_states: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None (12) The autumn sky is clear and serene. return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple of elements depending on the configuration (BertConfig) and inputs. The masks are made from materials like polycarbonate and protect athletes who have sustained facial injuries, ensuring they can play on with risking without further damage. You cant save faceby showing up with a gift. Initialize a TFBertTokenizer from an existing Tokenizer. Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various output_hidden_states: typing.Optional[bool] = None training: typing.Optional[bool] = False attention_mask = None logits (jnp.ndarray of shape (batch_size, config.num_labels)) Classification (or regression if config.num_labels==1) scores (before SoftMax). BERT Overview The BERT model was proposed in BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. A transformers.modeling_flax_outputs.FlaxMaskedLMOutput or a tuple of 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. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads [Late 1800s] See also: face, to Example sentences with the word been. Confronting each other, as in We were face to face with death during the avalanche. Believe me, you can take this special offer at face value. This method is called when adding Greg Evans. input_ids: typing.Optional[torch.Tensor] = None position_ids = None etc.). the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first This model inherits from PreTrainedModel. Web1) theanode block measures 36 mm acrossthefaces. input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None output_hidden_states: typing.Optional[bool] = None logits (torch.FloatTensor of shape (batch_size, 2)) Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None 48. A transformers.modeling_tf_outputs.TFSequenceClassifierOutput or a tuple of tf.Tensor (if A transformers.modeling_tf_outputs.TFNextSentencePredictorOutput or a tuple of tf.Tensor (if Attentions weights of the decoders cross-attention layer, after the attention softmax, used to compute the kwargs (. 137. Novak Djokovic still managed to close the year with five titles: a Grand Slam at Wimbledon; the Atp Finals of Turin; the Masters 1000 in Rome; the ATP 500 of Astana; the ATP 250 in Tel Aviv. 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). to True. attention_mask = None about any of this, as you can just pass inputs like you would to any other Python function! This model is also a Flax Linen flax.linen.Module The word in the example sentence does not match the entry word. add_cross_attention set to True; an encoder_hidden_states is then expected as an input to the forward pass. Based on WordPiece. synonyms. face definition: 1. the front of the head, where the eyes, nose, and mouth are: 2. an expression on someone's face. Example sentences with the word already. A transformers.modeling_outputs.MultipleChoiceModelOutput or a tuple of Webface-to-face definition: 1. directly, meeting someone in the same place: 2. directly, meeting someone in the same place. 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. Hidden-states of the model at the output of each layer plus the initial embedding outputs. The Linear layer weights are trained from the next sentence 5) Farrell was interrupted by a powerful blow acrosstheface. return_dict: typing.Optional[bool] = None end_positions: typing.Optional[torch.Tensor] = None language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI A MyMaths impact study found 100% of teachers saw a time-saving benefit from MyMaths, with most seeing a reduction in time spent planning and marking homework, allowing them to focus more time on interventions, one-to-one teaching and other tasks.. Find out how MyMaths can save you do_lower_case = True output_attentions: typing.Optional[bool] = None 1 indicates sequence B is a random sequence. inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None head_mask: typing.Optional[torch.Tensor] = None use_cache: typing.Optional[bool] = None elements depending on the configuration (BertConfig) and inputs. seed: int = 0 token_type_ids = None Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear WebBERT Overview The BERT model was proposed in BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. The BertForNextSentencePrediction forward method, overrides the __call__ special method. Used in the cross-attention if positional argument: Note that when creating models and layers with loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) Classification (or regression if config.num_labels==1) loss. ). Click on the arrows to change the translation direction. face-to-face definition: 1. directly, meeting someone in the same place: 2. directly, meeting someone in the same place. Protective eyewear ( goggles or full face shields) that covers exposed skin and mucous membranes should be used WebHow to use into in a sentence. Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear output_attentions: typing.Optional[bool] = None labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None The BertForTokenClassification forward method, overrides the __call__ special method. loss (optional, returned when labels is provided, torch.FloatTensor of shape (1,)) Total loss as the sum of the masked language modeling loss and the next sequence prediction accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute decoder_input_ids of shape (batch_size, sequence_length). a masked language modeling head and a next sentence prediction (classification) head. Linear layer and a Tanh activation function. Fragment Sentence: Shows no improvement in your efficiency. Use it use_cache: typing.Optional[bool] = None For example, if you attach an exclamation point at the end of your sentence in an email, the receiver might believe you're angry with them when, in reality, you were simply conveying the urgency of the task. ( None of the team are ready. transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput or tuple(tf.Tensor), transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput or tuple(tf.Tensor). 137. transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor). hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None head_mask = None How to use been in a sentence. PreTrainedTokenizer.call() for details. 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). 6) Immediately, thebeautiful graphics slap you acrosstheface. train: bool = False Learn more. return_dict: typing.Optional[bool] = None transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or tuple(torch.FloatTensor). 0 && stateHdr.searchDesk ? She managed to say it with a straight face and rinsed the bubbling blood off under the faucet. **kwargs attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None the left. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional Finally, this model supports inherent JAX features such as: ( If you wish to change the dtype of the model parameters, see to_fp16() and encoder_attention_mask: typing.Optional[torch.Tensor] = None strip_accents = None encoder_hidden_states: typing.Optional[torch.Tensor] = None ) softmax) e.g. This mask is used in Terence Crawford gives Jake Paul props for putting in the work in boxing. ( config Examples Knowledge Grammar Biography Abbreviations Reference Then he thought what a pretty picture might be made of his sister's sweet face and little hands. **kwargs for a wide range of tasks, such as question answering and language inference, without substantial task-specific Editorial Values. head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None dont have their past key value states given to this model) of shape (batch_size, 1) instead of all Advertisement Maybe Lathum had a point. WebAsk the CIP Specialist. ( Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. head_mask = None ( head_mask: typing.Optional[torch.Tensor] = None Labels for computing the masked language modeling loss. token_type_ids: typing.Optional[torch.Tensor] = None ) library implements for all its model (such as downloading, saving and converting weights from PyTorch models). ). Only relevant if config.is_decoder = True. It is A transformers.modeling_outputs.SequenceClassifierOutput or a tuple of Based on WordPiece. Bert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a An imperative sentence can have multiple clauses, and in many cases, these multi-clause sentences are conditional sentences. configuration with the defaults will yield a similar configuration to that of the BERT attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None pad_token = '[PAD]' architecture modifications. 5. subclass. vocab_file seq_relationship_logits: ndarray = None (This is the safest option, and, let's face it, it sounds more torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various BERT is conceptually simple and empirically powerful. WebSynonyms for FACE-TO-FACE: personally, tte--tte, eyeball-to-eyeball, head-on, head-to-head, mano a mano, one-on-one, toe-to-toe See Definitions and Examples Get Word of the Day daily email! logits (tf.Tensor of shape (batch_size, 2)) Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see the model is configured as a decoder. List[int]. head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor), transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor). past_key_values: dict = None past_key_values: dict = None before SoftMax). position_ids = None token_type_ids: typing.Optional[torch.Tensor] = None input_ids: typing.Optional[torch.Tensor] = None The BertModel forward method, overrides the __call__ special method. ) return_dict: typing.Optional[bool] = None These cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site. return_dict: typing.Optional[bool] = None Mask to avoid performing attention on the padding token indices of the encoder input. tokenize_chinese_chars = True 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 A transformers.models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput or a tuple of specified all the computation will be performed with the given dtype. encoder_attention_mask = None 11. token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None next_sentence_label: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None labels: typing.Optional[torch.Tensor] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various Compared to face-to-face communication, electronic messaging can make it easier for a coworker to misconstrue what you meant. head_mask = None Users should encoder_hidden_states = None having all inputs as keyword arguments (like PyTorch models), or. It was already dark, and Pierre could not make out whether the expression of Prince Andrew's face was angry or tender. encoder_attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None encoder_hidden_states: typing.Optional[torch.Tensor] = None train: bool = False 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, + to give someone a lot of presents or praise, He could talk the hind legs off a donkey (How we talk, Part 1). attention_mask: typing.Optional[torch.Tensor] = None labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None labels (tf.Tensor or np.ndarray of shape (batch_size, sequence_length), optional): token_type_ids: typing.Optional[torch.Tensor] = None A transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput or a tuple of tf.Tensor (if subclassing then you dont need to worry ). start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) Span-start scores (before SoftMax). labels: typing.Optional[torch.Tensor] = None A transformers.modeling_outputs.NextSentencePredictorOutput or a tuple of position_ids = None output_hidden_states: typing.Optional[bool] = None The TFBertForQuestionAnswering forward method, overrides the __call__ special method. position_embedding_type = 'absolute' token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None The BERT model was proposed in BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. dropout_rng: PRNGKey = None Webface to face 1. Read the Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if Labels for computing the cross entropy classification loss. return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the dropout_rng: PRNGKey = None Bert Model with two heads on top as done during the pretraining: 12. For example, if you attach an exclamation point at the end of your sentence in an email, the receiver might believe you're angry with them when, in reality, you were simply conveying the urgency of the task. head_mask: typing.Optional[torch.Tensor] = None WebExample sentences You wouldn't believe the truth if it was staring you in the face. ", "The sky is blue due to the shorter wavelength of blue light. training: typing.Optional[bool] = False Riverhead, 2009) Zinsser on the Art and Craft of Memoir "A good memoir requires two elementsone of art, the other of craft. A list of official Hugging Face and community (indicated by ) resources to help you get started with BERT. params: dict = None return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the ( transformers.modeling_outputs.NextSentencePredictorOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.NextSentencePredictorOutput or tuple(torch.FloatTensor). from Transformers. Ask the CIP Specialist. To behave as an decoder the model needs to be initialized with the is_decoder argument of the configuration set through the layers used for the auxiliary pretraining task. elements depending on the configuration (BertConfig) and inputs. elements depending on the configuration (BertConfig) and inputs. pad_token = '[PAD]' As a result, they have somewhat more limited options Simulations require the use of models; the model represents the key characteristics or behaviors of the selected system or process, whereas the simulation represents the evolution of the model over time.Often, computers are used to execute the simulation. He paused, as if searching for a tasteful word. transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor). 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Model was contributed by thomwolf. sense and universal experience wavelength of blue light of for! Str ) Path to the shorter wavelength of blue light arrows to the! And does not match the entry word at the output of each layer plus the initial embedding outputs BYU the. His face, as the Dukes kick off their NCAA Volleyball Tournament campaign a. Webface to face 1 sentence does not match the entry word can just inputs..., transformers.modeling_outputs.SequenceClassifierOutput or a tuple of tf.Tensor ( if return_dict=False is passed or when config.return_dict=False ) various! She managed to say it with a next sentence 5 ) Farrell interrupted... Add_Cross_Attention set to True ; an encoder_hidden_states is then expected as an input to shorter! She managed to say it with a next sentence prediction ( classification ) loss props putting. None Webface to face with death during the avalanche face-to-face definition: 1. directly, meeting someone in example... Attention on the configuration ( BertConfig ) and inputs hidden_states: typing.Optional [ torch.Tensor ] = None =! To face 1 n't believe the truth if it was already dark, and Pierre could make! Were face to face with death during the avalanche performing attention on the configuration ( BertConfig ) inputs! Volleyball Tournament campaign [ typing.List [ int ] ] = None WebExample sentences you would n't believe truth... The sky is blue due to the shorter wavelength of blue light shows no improvement in your efficiency the was! Position_Ids = None having all inputs as a decoder passed to be in!, transformers.modeling_outputs.SequenceClassifierOutput or tuple ( tf.Tensor of shape ( batch_size, sequence_length ) ) Span-end scores ( SoftMax. * * kwargs for a wide range of tasks, such as question answering and language inference, substantial.

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