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Related Questions
- How does WordPiece tokenization compare to other tokenization techniques, such as subwording or character-level tokenization?
- Can you explain the concept of 'out-of-vocabulary' words and how WordPiece tokenization addresses this limitation?
- What are the benefits of using subword units in the WordPiece tokenization approach?
- How does the choice of subword units, such as WordPiece or BPE, impact the performance of language models?
- What are some common applications where WordPiece tokenization is particularly useful, such as in low-resource languages or in domains with limited vocabulary?
- Can you provide an example of how WordPiece tokenization would handle an out-of-vocabulary word, such as a proper noun or a technical term?
- How does WordPiece tokenization impact the overall computational efficiency of language models, particularly in terms of memory usage and inference speed?
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