Stephanie
Search results
Jul 1, 2021 · 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 static mask.
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! 编辑于 2020-05-13 01:28. 知乎用户 ...
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 ...
Jul 30, 2020 · Some examples of tasks where RoBERTa is useful are sentiment classification, part-of-speech (POS) tagging and named entity recognition (NER). GPT-3 is meant for text generation tasks. Its paradigm is very different, normally referred to as "priming". You basically take GPT-3, give it some text as context and let it generate more text.
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 ...
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 ...
Dec 7, 2021 · I'm running an experiment investigating the internal structure of large pre-trained models (BERT and RoBERTa, to be specific). Part of this experiment involves fine-tuning the models on a made-up new word in a specific sentential context and observing its predictions for that novel word in other contexts post-tuning.
Dec 11, 2020 · The original BERT implementation (Devlin et al., 2019) uses a character-level BPE vocabulary of size 30K, which is learned after preprocessing the input with heuristic tokenization rules. I appreciate if someone can clarify why in the RoBERTa paper it is said that BERT uses BPE? bert. transfer-learning. transformer. language-model. tokenization.
Is it possible to feed embeddings from XLM- RoBERTa to transformer seq2seq model? I'm working on NMT that translates verbal language sentences to sign language sentences (e.g Input: He sells food. Output (sign language sentence): Food he sells). But I have a very small dataset of sentence pairs - around 1000.
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 ...
