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Jul 1, 2021 · 2 Answers. The masked language model task is the key to BERT and RoBERTa. However, they differ in how they prepare such masking. The original RoBERTa article explains it in section 4.1: BERT relies on randomly masking and predicting tokens. The original BERT implementation performed masking once during data preprocessing, resulting in a single ...
Jul 30, 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!
Jun 29, 2020 · Because the NSP, in a single task, mixes two tasks: 1) topic prediction and 2) coherence prediction. RoBERTa trains the model on longer sequences and by dynamically changing the masking pattern, making the masked language modeling more difficult, and hence the first one becomes redundant. The masked language modeling task overlaps the topic ...
May 23, 2022 · I've loaded the pretrained model as it was said here: import torch. roberta = torch.hub.load('pytorch/fairseq', 'roberta.large', pretrained=True) roberta.eval() # disable dropout (or leave in train mode to finetune) I also changed the number of labels to predict in the last layer: roberta.register_classification_head('new_task', num_classes=22 ...
Feb 15, 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 ...
Apr 18, 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 ...
Jun 23, 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 ...
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 ...
MobileBERT是BERT-LARGE的精简版本，同时具有精心设计的自注意力与前馈网络之间的平衡。. 为了训练MobileBERT，首先训练一个专门设计的教师模型，该模型是BERT-LARGE模型。. 然后，实现从该老师模型到MobileBERT的知识迁移。. 经验研究表明，MobileBERT比小4.3倍，快5.5倍 ...
论文题目：RoBERTa: A Robustly Optimized BERT Pretraining Approach 作者单位：华盛顿大学保罗·艾伦计算机科学与工程学院，FaceBook AI 这篇文章是 BERT 系列模型和 XLNet 模型的又一次交锋，是 FaceBook 与 Google 的交锋，从学术上实质是自回归语言建模预处理和自编码预处理两种思路的交锋。
