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Auf unserer Open-Source-Plattform »Open Roberta Lab« erstellst du im Handumdrehen deine ersten Programme per drag and drop. Dabei hilft dir (NEPO), unsere grafische Programmiersprache.
Overview. The RoBERTa model was proposed in RoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. It is based on Google’s BERT model released in 2018.
Jan 10, 2023 · ROBERTa is a variant of BERT that improves its performance by training on a larger dataset and using dynamic masking. Learn about its architecture, datasets, and achievements on various NLP tasks.
Jul 26, 2019 · RoBERTa: A Robustly Optimized BERT Pretraining Approach. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke...
- arXiv:1907.11692 [cs.CL]
- 2019
- Computation and Language (cs.CL)
- Open Source Bert by Google
- Architecture
- Beginning of The Optimization of Bert: Introduction to Roberta
- Altering The Training Procedure
Bidirectional Encoder Representations from Transformers, or BERT, is a self-supervised method released by Google in 2018.
Transformer model — a foundational concept for BERT
BERT is based on the Transformer model architecture Examining the model as if it were a single black box, a machine translation application would take a sentence in one language and translate it into a different language. 1. Basic Transformer consists of an encoder to read the text input and a decoder to produce a prediction for the task. 1. Since BERT’s goal is to generate a language representation model, it only needs the encoder part. hence, BERT is basically a trained Transformer Encoder...
Training of BERT
During pretraining, BERT uses two objectives: masked language modeling and next sentence prediction. 1. Masked Language Modeling(MLM) basically masks80% of the 15% of the randomly selected input tokens and uses the other tokens to attempt to predict the mask (missing word). 1. Next Sentence Prediction(NSP)is a binary classification lossfor predicting whether two segments follow each other or are from a different document to create a semantic meaning.
1. Masking in BERT training:
The masking is done only once during data preprocessing, resulting in a single static mask. Hence, the same input masks were fed to the model on every single epoch.
2. Next Sentence Prediction:
1. The original input format used in BERT is SEGMENT-PAIR+NSP LOSS. 2. In this, each input has a pair of segments, which can each contain multiple natural sentences, but the total combined length must be less than 512 tokens. 3. It is noticed that individual sentences hurt performance on downstream tasks, which according to the hypothesis is because the model was not able to learn long-range dependencies, hence the authors could experiment with removing/adding NSP loss to see the effect in th...
3. Text Encoding:
1. The original BERT implementation uses a character-level BPE vocabulary of size 30K. 2. BERT uses the WordPiece method a language-modeling-based variant of Byte Pair Encoding.
1. Replacing Static masking with Dynamic Masking :
To avoid masking the same word multiple times, Facebook used Dynamic masking; the training data was repeated 10 times and every next time, the masked word would be different, meaning the sentence would be the same but the words masked would be different.
2. Removing NSP :
TEST 1: Feeding the following alternate training formats. 2.1. Retain NSP Loss: 1. SENTENCE-PAIR+NSP:Each input contains a pair of natural sentences, sampled from a contiguous portion of one document or separate documents. The NSP loss is retained. 2.2. Remove NSP loss: 1. FULL-SENTENCES: Each input is packed with full sentences sampled contiguously from single or cross documents, such that the total length is at most 512 tokens. We remove the NSP loss. 2. DOC-SENTENCES: Inputs are constructe...
Results —
1. Removing the NSP loss matches or slightly improves downstream task performance, in contrast to the original BERT with NSP loss. 2. The sequences from a single document (DOC-SENTENCES) perform slightly better than packing sequences from multiple documents (FULL-SENTENCES) as shown inTable 1.
- Aastha Singh
RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.
Sep 24, 2023 · RoBERTa is a large language model that improves BERT with dynamic masking, next sentence prediction, batch size, and vocabulary size. Learn about the key techniques and advantages of RoBERTa over BERT and XLNet.
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