Risultati di ricerca
1 lug 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 lug 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.
30 lug 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 giu 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.
23 mag 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 ...
7 dic 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.
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.
18 apr 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 dic 2020 · 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: The original BERT implementation (Devlin et al., 2019) uses a character-level BPE vocabulary of size 30K, which is learned ...
15 feb 2022 · 1. I have an artificial corpus I've built (not a real language) where each document is composed of multiple sentences which again aren't really natural language sentences. 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)
