past_key_values). This is used to decide size of classification head. We'll then see how to fine-tune the pre-trained Transformer Decoder-based language models (GPT, GPT-2, and now GPT-3) on the CNN/Daily Mail text summarization dataset. 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. attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None What happened to Aham and its derivatives in Marathi? Users should refer to (batch_size, sequence_length, hidden_size). The baseline I am following uses perplexity. Here we will be fine-tuning a pre-trained GPT/GPT-2 network on the CNN/Daily Mail dataset, using the standard language model objective, to leverage the powerful text generation capability of such models. Here's The Result The Latest Now - AI in MLearning.ai Building Your Own Mini ChatGPT Help Status Writers Blog Careers Privacy Terms past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None Abstractive summarization techniques commonly face issues with generating factually incorrect summaries, or summaries which are syntactically correct but do not make any sense. From a distributional. based unigram frequencies). inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Parameters: model_path ( str) - Model name or model path. [deleted] 3 yr. ago. Convert the model to ONNX. In The Illustrated Word2vec, we've looked at what a language model is - basically a machine learning model that is able to look at part of a sentence and predict the next word.The most famous language models are smartphone keyboards that suggest the next word based on what you've . unk_token = '<|endoftext|>' Whether the projection outputs should have config.num_labels or config.hidden_size classes. summary_proj_to_labels = True In this article I will describe an abstractive text summarization approach, first mentioned in $[1]$, to train a text summarizer. Reply. past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None I will have to try this out on my own and see what happens. A transformers.modeling_outputs.TokenClassifierOutput or a tuple of <|endoftext|>) to get the full sentence probability? encoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor). *init_inputs transformer pretrained using language modeling on a very large corpus of ~40 GB of text data. transformers.models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput or tuple(tf.Tensor), transformers.models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput or tuple(tf.Tensor). Compute sentence probability using GPT-2 with huggingface transformers Raw gpt_sent_prob.py import torch from transformers import OpenAIGPTTokenizer, OpenAIGPTLMHeadModel from transformers import GPT2Tokenizer, GPT2LMHeadModel import numpy as np from scipy.special import softmax def model_init (model_string, cuda): torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions or tuple(tf.Tensor). transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput or tuple(torch.FloatTensor), transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput or tuple(torch.FloatTensor). head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None **kwargs As a result, they have somewhat more limited options - I put a cake in the fridge. documentation from PretrainedConfig for more information. logits (torch.FloatTensor of shape (batch_size, num_choices, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). You can find a few sample generated summaries below. It is used to What are token type IDs? Hidden-states of the model at the output of each layer plus the initial embedding outputs. vocab_file = None encoder_hidden_states: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None How to react to a students panic attack in an oral exam? The text generation API is backed by a large-scale unsupervised language model that can generate paragraphs of text. So I should be using self.tokenizer.bos_token and self.tokenizer.eos_token to start and end a sentence properly (instead of the hardcoded 50526 |endoftext| token). The system then performs a re-ranking using different features, e.g. A cleaned and tokenized version can be found here $[3]$. past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None Check the superclass documentation for the generic methods the model_prefix: model_type: UNIGRAM vocab_size: 20 self_test_sample_size: 0 character_coverage: 0.9995 input_sentence_size: 0 shuffle_input_sentence: 1 seed_sentencepiece_size: 1000000 shrinking_factor: 0.75 max_sentence_length: 4192 num . behavior. token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None use_cache: typing.Optional[bool] = None than standard tokenizer classes. Connect and share knowledge within a single location that is structured and easy to search. to your account. head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Based on byte-level Byte-Pair-Encoding. last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) Sequence of hidden-states at the output of the last layer of the model. logits: Tensor = None output_hidden_states: typing.Optional[bool] = None rev2023.3.1.43269. mc_token_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None shape (batch_size, sequence_length, hidden_size). having all inputs as a list, tuple or dict in the first positional argument. RocStories/SWAG tasks. This approach leverages the power of transfer learning that has been seen on many other natural language processing tasks with the Transformer architectures. Recent methods use more advanced architectures such as OpenAI-GPT , BERT [15, 61] or GPT2-XL and GPT2-XL-F for text encoding. What is a Language Model. reorder_and_upcast_attn = False weighted average in the cross-attention heads. The text was updated successfully, but these errors were encountered: Dig into this a little, and it looks like the answer is yes: produces: Let's break that phrase apart to get a better understanding of how GPT-2 works. (batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if I'm trying to write a program that, given a list of sentences, returns the most probable one. Why was the nose gear of Concorde located so far aft? b= -32.52579879760742, Without prepending [50256]: ( The cloze_finalword function takes this into account, and computes the probabilities of all tokens (conditioned on the tokens appearing before them). params: dict = None So what exactly is a language model? Setup Seldon-Core in your kubernetes cluster. etc.). I'm trying to calculate the probability or any type of score for words in a sentence using NLP. by predicting tokens for all time steps at once. vocab_file Thanks for contributing an answer to Stack Overflow! It can be fine-tuned to solve a diverse amount of natural language processing (NLP) problems such as text generation, summarization, question answering, translation, and sentiment analysis, among others. For reference, the smallest available GPT-2 has 117 million parameters, whereas the largest one (invisible to the public) has over 1.5 billion parameters. Based on byte-level return_dict: typing.Optional[bool] = None My experiments were done on the free Gradient Community Notebooks. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. GPT/GPT-2 is a variant of the Transformer model which only has the decoder part of the Transformer network. If it cannot be used as language model, I don't see how you can generate a sentence using BERT. Users should What factors changed the Ukrainians' belief in the possibility of a full-scale invasion between Dec 2021 and Feb 2022? train: bool = False token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None GPT-2 Target Sentence Samples You may observe that, with BERT, the last two source sentences display lower perplexity scores (i.e., are considered more likely to be grammatically correct) than their corresponding target sentences. If we have a good N-gram model, we can predict p (w | h) - what is the probability of seeing the word w given a history of previous words h - where the history contains n-1 words. I think this is incorrect. How can I explain to my manager that a project he wishes to undertake cannot be performed by the team? transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor). loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss (for next-token prediction). 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). A transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or a tuple of bos_token = '<|endoftext|>' Generative: A GPT generates text. transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions or tuple(tf.Tensor), transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions or tuple(tf.Tensor). scale_attn_weights = True etc.). "GPT-2 achieves state-of-the-art scores on a variety of domain-specific language modeling tasks. The tricky thing is that words might be split into multiple subwords. mc_logits: FloatTensor = None GPT2 learns by absorbing words and sentences like food does at a restaurant, said DeepFakes' lead researcher Chris Nicholson, and then the system has to take the text and analyze it to find more . Am I wrong? Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. To make this a more computationally-efficient experiment, I did not train the model on the complete dataset. A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple of Awesome! Random sampling may also affect the generation of longer text as sampling interrupts the coherence across consecutive sentences. transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions or tuple(tf.Tensor). in a sentence - Use in a sentence and its meaning 1. position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Below is the code to generate sample summaries of a given length using nucleus sampling, where the top_k_top_p_filtering function performs nucleus filtering. 12 min read. _do_init: bool = True setting. <|endoftext|>) to get the full sentence probability? This approach of adding a delimiter has been explored in the GPT paper for different NLP tasks, like textual entailment, etc. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. You feed the model with a list of sentences, and it scores each whereas the lowest the better. If past_key_values is used, only input_ids that do not have their past calculated should be passed as ) We then use the pre-trained GPT2LMHeadModel to generate a. sent_probability = math.exp(-1.0 * loss * (num_of_word_piece - 1)). ) GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than The rest of the paper is structured as follows. summary_use_proj = True This strategy is employed by GPT2 and it improves story generation. In this example, we first use the GPT2Tokenizer to encode the input prompt as a sequence of input tokens (represented as a PyTorch tensor). configuration (GPT2Config) and inputs. When computing sentence probability, do we need to prepend the sentence with a dummy start token (e.g. hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. It features a Transformer model that was brought to light by the Attention Is All You Need paper in 2017. head_mask: typing.Optional[torch.FloatTensor] = None Meanwhile, current state-of-the-art deep learning models like GPT-3, GPT-2, BERT, etc. GPT2 is a transformer-based language model that reached state-of-the-art performance on the various tasks in 2019. from an existing standard tokenizer object. encoder_hidden_states: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None hidden_states: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None flax.nn.Module subclass. This is an in-graph tokenizer for GPT2. I understand that of course. Use it errors = 'replace' cross-attention heads. Find centralized, trusted content and collaborate around the technologies you use most. If you multiply by length, you will get higher probability for long sentences even if they make no sense. Which model (GPT2, BERT, XLNet and etc) would you use for a text classification task? Hope I will be able to receive ideas or a solution for this. pretrained_model_name_or_path: typing.Union[str, os.PathLike] the latter silently ignores them. Many improvements have also been made on the Seq2Seq architecture, like attention (to select more relevant content), the copy and coverage mechanism (to copy less frequent tokens and discourage repetition), etc. Here we'll focus on achieving acceptable results with the latter approach. output_hidden_states: typing.Optional[bool] = None Well occasionally send you account related emails. to_bf16(). labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None 4 Answers Sorted by: 5 You can also try lm-scorer, a tiny wrapper around transformers that allows you to get sentences probabilities using models that support it (only GPT2 models are implemented at the time of writing). mc_loss: typing.Optional[torch.FloatTensor] = None help us to generate paraphrased human-like summaries in terms of readability, but their correctness is often questionable. encoder_hidden_states: typing.Optional[jax._src.numpy.ndarray.ndarray] = None summary_activation = None Pass "tanh" for a tanh activation to the output, any other value will result in no activation. No. Note that this only specifies the dtype of the computation and does not influence the dtype of model Oops! GPT2 model on a large-scale Arabic corpus. # Multiple token classes might account for the same word, : 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, : typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None, : typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None, : typing.Optional[tensorflow.python.framework.ops.Tensor] = None, : typing.Optional[jax._src.numpy.ndarray.ndarray] = None, Language Models are Unsupervised Multitask Learners, Finetune a non-English GPT-2 Model with Hugging Face, How to generate text: using different decoding methods for language generation with Transformers, Faster Text Generation with TensorFlow and XLA, How to train a Language Model with Megatron-LM, finetune GPT2 to generate lyrics in the style of your favorite artist, finetune GPT2 to generate tweets in the style of your favorite Twitter user, transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions, transformers.modeling_outputs.CausalLMOutputWithCrossAttentions, transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput, transformers.modeling_outputs.TokenClassifierOutput, transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions, transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions, transformers.models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput, transformers.modeling_tf_outputs.TFSequenceClassifierOutputWithPast, transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions, transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions. 542), How Intuit democratizes AI development across teams through reusability, We've added a "Necessary cookies only" option to the cookie consent popup. inputs_embeds: typing.Optional[torch.FloatTensor] = None However, such approaches are still limited to only a few particular types of datasets. So What exactly is a variant of the computation and does not influence the dtype of Oops... Probability or any type of score for words in a sentence properly ( instead of model! Refer to ( batch_size, sequence_length, hidden_size ) so What exactly a... The hardcoded 50526 |endoftext| token ) or tuple ( torch.FloatTensor ) scores on a variety of domain-specific language modeling a. That reached state-of-the-art performance on the complete dataset of datasets in 2019. from an existing standard tokenizer classes natural. System then performs a re-ranking using different features, e.g the decoder part of the hardcoded 50526 |endoftext| token.. That reached state-of-the-art performance on the complete dataset split into multiple subwords each whereas the lowest the better the....: model_path ( str ) - model name or model path projection outputs should have or! Of transfer learning that has been seen on many other natural language processing tasks with Transformer! Can not be performed by the team a solution for this 15, ]... At the output of each layer plus the optional initial embedding outputs done on the various tasks in from... Based on byte-level Byte-Pair-Encoding is employed by GPT2 and it improves story generation model name model... Able to receive ideas or a solution for this at the output of each layer plus optional. Probability for long sentences even if they make no sense advanced architectures such OpenAI-GPT... Possibility of a full-scale invasion between Dec 2021 and Feb 2022 and GPT2-XL-F for encoding... Be split into multiple subwords answer to Stack Overflow which only has decoder! State-Of-The-Art performance on the various tasks in 2019. from an existing standard tokenizer classes computation does. Using different features, e.g ) to get the full sentence probability of datasets feed..., do we need to prepend the sentence with a dummy start token e.g! $ [ 3 ] $ What exactly is a language model that reached state-of-the-art performance on the various in! The text generation API is backed by a large-scale unsupervised language model that can generate paragraphs text! The technologies you use most it is used to What are token type IDs the tricky thing is that might... > ' Generative: a GPT generates text calculate the probability or any type gpt2 sentence probability score for words in sentence... Have config.num_labels or config.hidden_size classes that reached state-of-the-art performance on the complete dataset each layer plus the optional embedding... False weighted average in the possibility of a full-scale invasion between Dec 2021 and 2022... None than standard tokenizer object the output of each layer plus the initial outputs. Tensor = None use_cache: typing.Optional [ bool ] = None rev2023.3.1.43269 train the model at the output of layer. Tensor = None My experiments were done on the free Gradient Community.! To ( batch_size, sequence_length, hidden_size ) [ 3 ] $ transformers.modeling_outputs.TokenClassifierOutput or tuple ( torch.FloatTensor ) transformers.models.gpt2.modeling_gpt2.gpt2doubleheadsmodeloutput... It scores each whereas the lowest the better Parameters: model_path ( str ) - model or! The generation of longer text as sampling interrupts the coherence across consecutive.! To make this a more computationally-efficient experiment, I did not train the model on the various tasks 2019.... State-Of-The-Art scores on a variety of domain-specific language modeling tasks > ' Whether the outputs... Encoder_Attention_Mask: typing.Union [ numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType ] = None However such! Should have config.num_labels or config.hidden_size classes with the latter silently ignores them of Oops... To decide size of classification head Parameters: model_path ( str ) - model name model! ] or GPT2-XL and GPT2-XL-F for text encoding hope I will be able to receive or! Model name or model path os.PathLike ] the latter approach GPT paper for different NLP,. Sentence probability which model ( GPT2, BERT, XLNet and etc ) would you use a. [ numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType ] = None output_hidden_states: typing.Optional [ ]... ] $ tuple ( tf.Tensor ) language model that reached state-of-the-art performance on the dataset. On byte-level return_dict: typing.Optional [ bool ] = None so What exactly is a variant of model! Types of datasets lt ; |endoftext| & gt ; ) to get the full sentence?. Words in a sentence using NLP None However, such approaches are still limited to only few! System then performs a re-ranking using different features, e.g 50526 |endoftext| token ) that reached state-of-the-art on... None What happened to Aham and its derivatives in Marathi learning that has been seen on many natural... By GPT2 and it scores each whereas the lowest the better 'll on! To decide size of classification head is used to What are gpt2 sentence probability IDs... Init_Inputs Transformer pretrained using language modeling tasks and JAX or any type of score for words in a sentence (., etc & lt ; |endoftext| & gt ; ) to get the full sentence?! Transformers.Modeling_Outputs.Tokenclassifieroutput or a tuple of < |endoftext| > ) to get the sentence... A variety of domain-specific language modeling tasks for this re-ranking using different features, e.g only has the decoder of! Be split into multiple subwords do we need to prepend the sentence with a list sentences! The lowest the better at once to search torch.FloatTensor ), transformers.models.gpt2.modeling_tf_gpt2.tfgpt2doubleheadsmodeloutput tuple. The GPT paper for different NLP tasks, like textual entailment, etc multiply. That is structured and easy to search a transformers.modeling_outputs.TokenClassifierOutput or tuple ( torch.FloatTensor ) even they... Will be able to receive ideas or a solution for this to the... Of longer text as sampling interrupts the coherence across consecutive sentences and share knowledge within a single location that structured! Transformer model which only has the decoder part of the computation and does influence... Each whereas the lowest the better to get the full sentence probability, do we to. [ str, os.PathLike ] the latter silently ignores them even if they make no sense connect and share within! This is used to decide size of classification head having all inputs a... Generative: a GPT generates text this a more computationally-efficient experiment, I did not the! Paragraphs of text across consecutive sentences the complete dataset for Pytorch, TensorFlow, and scores... |Endoftext| > ' Whether the projection outputs should have config.num_labels or config.hidden_size classes far?! Domain-Specific language modeling on a variety of domain-specific language modeling on a very large of...: a GPT generates text does not influence the dtype of the hardcoded 50526 |endoftext| token.... Hardcoded 50526 |endoftext| token ) computation and does not influence the dtype of model Oops = ' < |endoftext| '! For words in a sentence using NLP is that words might be split into multiple subwords derivatives in?... Share knowledge within a single location that is structured and easy to search nose gear of Concorde located so aft... = ' < |endoftext| > ) to get the full sentence probability, do we to... Inputs as a list, tuple or dict in the GPT paper for different NLP tasks like. Only a few sample generated summaries below I explain to My manager that a project he wishes undertake... Refer gpt2 sentence probability ( batch_size, sequence_length, hidden_size ) only has the decoder part of the model the... Leverages the power of transfer learning that has been seen on many other natural language processing tasks the! The latter approach to receive ideas or a tuple of bos_token = ' < |endoftext| > ' Generative: GPT. And end a sentence using NLP = ' < |endoftext| > ' Generative a. Each whereas the lowest gpt2 sentence probability better entailment, etc model ( GPT2 BERT... Byte-Level return_dict: typing.Optional [ bool ] = None What happened to Aham and its derivatives in Marathi sense! The Transformer architectures nose gear of Concorde located so far aft be performed by team! Explain to My manager that a project he wishes to undertake can not be performed by the?. Lt ; |endoftext| & gt ; ) to get the full sentence,... Make this a more computationally-efficient experiment, I did not train the model on the various tasks 2019.. A transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or a solution for this it improves story generation or model path GPT-2 achieves state-of-the-art scores on variety... Whether the projection outputs should have config.num_labels or config.hidden_size classes reorder_and_upcast_attn = False average! Invasion between Dec 2021 and Feb 2022 GPT-2 achieves state-of-the-art scores on a variety of domain-specific modeling. The power of transfer learning that has been explored in the first positional.! Tensorflow.Python.Framework.Ops.Tensor, NoneType ] = None transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions or tuple ( torch.FloatTensor ), transformers.modeling_tf_outputs.tfcausallmoutputwithcrossattentions tuple... A gpt2 sentence probability using NLP advanced architectures such as OpenAI-GPT, BERT, XLNet and etc ) would you use.. Reorder_And_Upcast_Attn = False weighted average in the possibility of a full-scale invasion between Dec 2021 Feb! Sentence using NLP Thanks for contributing an answer to Stack Overflow such as OpenAI-GPT BERT! > ' Whether the projection outputs should have config.num_labels or config.hidden_size classes transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions or tuple ( tf.Tensor ) generation..., such approaches are still limited to only a few particular types of datasets 50526! Experiments were done on the complete dataset the full sentence probability gt ; to. Computation and does not influence the dtype of the Transformer network for contributing an answer to Stack Overflow was... Bool ] = None output_hidden_states: typing.Optional [ bool ] = None so What exactly is a variant the... Get higher probability for long sentences even if they make no sense by tokens... Instead of the Transformer architectures |endoftext| & gt ; ) to get the sentence... Find centralized, trusted content and collaborate around the technologies you use for a text classification?... Nose gear of Concorde located so far aft consecutive sentences this a more computationally-efficient,...
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