Dec 29, 2021 · This paper studies if corpus-specific tokenization used for fine-tuning improves the resulting performance of the model.

This paper studies if corpus-specific tokenization used for fine-tuning improves the resulting performance of the model.

Apr 1, 2023 · This paper studies if corpus-specific tokenization used for fine-tuning improves the resulting performance of the model.

This paper studies the effect of dataset-specific tokenization on the fine-tuning of a transformer-based architecture. We carry out experiments that demonstrate ...

This work proposes a new method for model compression that relies on vocabulary transfer that can be effectively used in combination with other compression ...

Implementation of the paper "Fine-Tuning Transformers: Vocabulary Transfer" https://arxiv.org/pdf/2112.14569.pdf - LEYADEV/Vocabulary-Transfer.

Fine-tuning large pre-trained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter ...

The fine-tuning argument.Neil A. Manson - 2009 - Philosophy Compass 4 (1):271-286. Characterising Artificial Intelligence technology for international transfer.

... Fine-tuning an LLM involves refining its abilities and performance in specific tasks or domains by training it further in domain-specific datasets after ...

Fine-tuning transformers involves leveraging the vocabulary learned from a large, diverse dataset to enhance performance on a specific task.