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  • What is purpose of the [CLS] token and why is its encoding output . . .
    (1)[CLS] appears at the very beginning of each sentence, it has a fixed embedding and a fix positional embedding, thus this token contains no information itself (2)However, the output of [CLS] is inferred by all other words in this sentence, so [CLS] contains all information in other words
  • Bert-Transformer : Why Bert transformer uses [CLS] token for . . .
    The CLS token helps with the NSP task on which BERT is trained (apart from MLM) The authors found it convenient to create a new hidden state at the start of a sentence, rather than taking the sentence average or other types of pooling However this does not mean that the BERT authors recommend using the CLS token as a sentence embedding
  • What is the vector value of [CLS] [SEP] tokens in BERT
    About the value of the embedded vectors of [CLS] and [SEP]: they are not filled with 0's but contain numerical representations like any of the other vectors in the embedding table From the point of view of the model, there is nothing special about their embedded vectors, so they are trained normally like the others
  • What information does output of [SEP] token captures in BERT?
    $\begingroup$ Yeah I was working with BERTSUM for extractive text summarization and in the paper the [CLS] token embedding were used to predict if that sentence will remain in the final summary The inference I got from your answer was that the [CLS] token has a meaning only because of the way it was used during training
  • nlp - How does BERT produce CLS token? Internally does it do max . . .
    The output at the first position (which is the position the special token [CLS] is at the input sequence and is what you call the "CLS token") is neither computed with max-pooling or average pooling, but it is computed with self-attention, like the other output positions The difference with the other output positions is that the first position
  • deep learning - How special tokens in BERT-Transformers work? - Data . . .
    In conclusion, special tokens are defined by a convention, and the 2 main ones are [CLS] and [SEP] which delimit the 2 main types of vectors necessary for the Bert model for the question answer process Note: You can define [CLS] or [SEP] with other names in the Pretrained tokenizer from HuggingFace with the sep_token and the cls_token attributes
  • machine learning - What is the use of [SEP] in paper BERT? - Data . . .
    I know that [CLS] means the start of a sentence and [SEP] makes BERT know the second sentence has begun However, I have a question If I have 2 sentences, which are s1 and s2, and our fine-tuning task is the same In one way, I add special tokens and the input looks like [CLS]+s1+[SEP] + s2 + [SEP]
  • object detection - Yolov8 - box_loss and dfl_loss stays at 0. cls_loss . . .
    I'm having trouble using Yolov8 to work properly I have my own custom dataset and an online dataset that I am using Yolov8 trains on these datasets However, the only metric that converges is the cls_loss It seems like the model isn't giving me any sort of bounding boxes to compute box_loss
  • Class token in ViT and BERT - Data Science Stack Exchange
    Stack Exchange Network Stack Exchange network consists of 183 Q A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers
  • why YOLO models multiple the loss by batch size in detection head?
    return loss sum() * batch_size, loss detach() # loss(box, cls, dfl) This line is from yolov8, but I saw similar thing in v5 too So far I only see this kind of practice in YOLO I wonder what is the reason behind it





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