Contents
What tokenizer does RoBERTa use?
RoBERTa has the same architecture as BERT, but uses a byte-level BPE as a tokenizer (same as GPT-2) and uses a different pretraining scheme.
What are BPE tokens?
BPE ensures that the most common words will be represented in the new vocabulary as a single token, while less common words will be broken down into two or more subword tokens. To achieve this, BPE will go through every potential option at each step and pick the tokens to merge based on the highest frequency.
How does WordPiece tokenizer work?
1 Answer. WordPiece and BPE are two similar and commonly used techniques to segment words into subword-level in NLP tasks. Generate a new word unit by combining two units out of the current word inventory to increment the word unit inventory by one.
Is RoBERTa cased or uncased?
| Rank | Model | BLURB Score (Macro Avg.) |
|---|---|---|
| 8 | RoBERTa (cased) | 76.46 |
| 9 | BlueBERT (cased) | 76.27 |
| 10 | BERT base (uncased) | 76.11 |
| 11 | BERT base (cased) | 75.86 |
What does RoBERTa mean?
r(o)-ber-ta, rob(e)-rta. Origin:German. Popularity:6394. Meaning:bright fame.
Why do we use byte pair encoding?
Byte-pair encoding allows us to define tokens automatically from data, instead of precpecifying character or word boundaries. This is especially useful in dealing with unkown words.
What is WordPiece vocabulary?
WordPiece is a subword segmentation algorithm used in natural language processing. The vocabulary is initialized with individual characters in the language, then the most frequent combinations of symbols in the vocabulary are iteratively added to the vocabulary.
How is the wordpiece tokenizer used in Bert?
Bert model uses WordPiece tokenizer. Any word that does not occur in the WordPiece vocabulary is broken down into sub-words greedily. For example, ‘RTX’ is broken into ‘R’, ‘##T’ and ‘##X’ where ## indicates it is a subtoken. a) how BPE tokenizer works? b) what does G represents in each of tokens?
How big is the vocabulary of Roberta base?
The original BERT implementation uses a WordPiece tokenizer with a vocabulary of 32K subword units. This method, however, can introduce “unknown” tokens when processing rare words. In this implementation, we use a byte-level BPE tokenizer with a vocabulary of 50,265 subword units (same as RoBERTa-base).
Which is better to train Roberta or Bert base?
Note the batch size when training RoBERTa is 8000. Therefore, although RoBERTa-base was trained for 500K steps, its training computational cost is 16 times that of BERT-base. In the RoBERTa paper, it is shown that training with large batches improves perplexity for the masked language modeling objective, as well as end-task accuracy.
Which is the tokenization algorithm used for Bert?
WordPiece is the subword tokenization algorithm used for BERT, DistilBERT, and Electra. The algorithm was outlined in Japanese and Korean Voice Search (Schuster et al., 2012) and is very similar to BPE.