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1 de jul. de 2021 · This way, in BERT, the masking is performed only once at data preparation time, and they basically take each sentence and mask it in 10 different ways. Therefore, at training time, the model will only see those 10 variations of each sentence. On the other hand, in RoBERTa, the masking is done during training. Therefore, each time a sentence is ...
30 de jul. de 2019 · RoBERTa虽然算不上什么惊世骇俗之作，但也绝对是一个造福一方的好东西。 使用起来比BERT除了性能提升，数值上也更稳定。 研究如何更好的修改一个圆形的轮子至少要比牵强附会地造出各种形状“新颖”的轮子有价值太多了!
29 de jun. de 2020 · BERT uses both masked LM and NSP (Next Sentence Prediction) task to train their models. So one of the goals of section 4.2 in the RoBERTa paper is to evaluate the effectiveness of adding NSP tasks and compare it to just using masked LM training. For the sake of completeness, I will briefly describe all the evaluations in the section.
15 de fev. de 2022 · I want to train a language model out of this corpus (to use it later for downstream tasks like classification or clustering with sentence BERT) How to tokenize the documents? Do I need to tokenize the input. like this: <s>sentence1</s><s>sentence2</s>. or <s>the whole document</s>. How to train? Do I need to train an MLM or an NSP or both? By ...
18 de abr. de 2023 · 1. We have lots of domain-specific data (200M+ data points, each document having ~100 to ~500 words) and we wanted to have a domain-specific LM. We took some sample data points (2M+) & fine-tuned RoBERTa-base (using HF-Transformer) using the Mask Language Modelling (MLM) task. So far, we did 4-5 epochs (512 sequence length, batch-size=48) used ...
11 de dez. de 2020 · BERT uses WordPiece, RoBERTa uses BPE. In the original BERT paper, section 'A.2 Pre-training Procedure', it is mentioned: The LM masking is applied after WordPiece tokenization with a uniform masking rate of 15%, and no special consideration given to partial word pieces. And in the RoBERTa paper, section '4.4 Text Encoding' it is mentioned:
4 de fev. de 2024 · In Roberta, I'm not sure if the model use BPE or byte-level BPE tokenization, are these techniques different or the same ?
我们第一次发现通过规模化预训练语言模型，可以让多语言基础模型在高资源（rich-resource）语言（例如英文）上，取得与专门为这些语言设计和训练的单语言预训练模型在对应语言的下游任务上一样好的效果。. 之前的研究曾表明多语言预训练模型在低资源（low ...
23 de jun. de 2021 · 3 个回答. pooler_output – Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining，我的理解是 pooler_output 一般用来做 ...
31 de mar. de 2023 · There are various sources on the internet that claim that BERT has a fixed input size of 512 tokens (e.g. this, this, this, this ...). This magical number also appears in the BERT paper (Devlin et ...
