Fine-Tuned BERT Model: Difference between revisions
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(Created page with "A Fine-Tuned BERT Model is a BERT Model that is a fine-tuned NNet model. * <B>Context:</B> ** It can (typically) leverage Transfer Learning to apply learned language representations to a new domain. ** It can (often) utilize a Specific Dataset such as LEDGAR for legal texts or SQuAD for question answering. ** It can range from being a BERT Model for Sentiment Analysis to being a BERT Model for Legal Contract Review. **...") |
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Revision as of 16:46, 25 April 2024
A Fine-Tuned BERT Model is a BERT Model that is a fine-tuned NNet model.
- Context:
- It can (typically) leverage Transfer Learning to apply learned language representations to a new domain.
- It can (often) utilize a Specific Dataset such as LEDGAR for legal texts or SQuAD for question answering.
- It can range from being a BERT Model for Sentiment Analysis to being a BERT Model for Legal Contract Review.
- It can enhance Natural Language Understanding by fine-tuning the model on task-specific data, increasing its predictive accuracy.
- It can improve Text Classification tasks by focusing on the nuanced features of the dataset.
- ...
- Example(s):
- a LegalPro-BERT that showcases enhancements in identifying and classifying Legal Provisions within contracts.
- a BioBERT that demonstrates advancements in processing biomedical literature.
- ...
- Counter-Example(s):
- General BERT Models, which have not been adapted to specific datasets or tasks.
- Non-BERT Models, such as GPT-3 or ELMo, which use different architectures and training methods.
- ...
- See: Transfer Learning, Text Classification, Natural Language Understanding