Vocabulary/Tokenizer extensions
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Model Description
This checkpoint contains the original Bioformer-16L model with an adapted tokenizer designed to better support domain-specific vocabulary. The model weights remain identical to the original pretrained Bioformer-16l checkpoint; only the tokenizer has been modified.
No continued pretraining (e.g., Domain-Adaptive Pretraining (DAPT) or Task-Adaptive Pretraining (TAPT)) or downstream task fine-tuning has been performed. Consequently, newly added vocabulary tokens have not yet learned meaningful representations, as their corresponding embedding vectors remain at their initialization values.
This checkpoint is intended to serve as the starting point for continued pretraining, enabling the model to learn representations for the newly introduced vocabulary before being fine-tuned on downstream biomedical NLP tasks.
Current Status
- ✅ Original Bioformer-16l pretrained weights
- ✅ Adapted tokenizer with additional domain-specific vocabulary
- ❌ No continued pretraining (DAPT/TAPT)
- ❌ No downstream task fine-tuning
Intended Use
This checkpoint is primarily intended for researchers and practitioners who wish to:
- Perform continued pretraining using the adapted tokenizer.
- Investigate the impact of vocabulary adaptation on biomedical language models.
- Fine-tune a Bioformer model with an expanded tokenizer on downstream biomedical NLP tasks.
It is not recommended for direct inference, as the newly added tokens have not yet been trained and therefore do not provide meaningful representations.
Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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