This biomedical language model uses a specialized biomedical tokenizer which is more closely aligned with human-morphological judgements than previous biomedical tokenizers such as PubMedBERT.

Details about our tokenizer design, pre-training procedure and downstream results can be found in our [BioNLP @ ACL 2023 paper](http://arxiv.org/pdf/2306.17649.pdf)

---
license: apache-2.0
---

{}
transformers pytorch bert fill-mask arxiv:2306.17649 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #arxiv-2306.17649 #autotrain_compatible #endpoints_compatible #region-us 

This biomedical language model uses a specialized biomedical tokenizer which is more closely aligned with human-morphological judgements than previous biomedical tokenizers such as PubMedBERT.

Details about our tokenizer design, pre-training procedure and downstream results can be found in our BioNLP @ ACL 2023 paper

---
license: apache-2.0
---

TAGS
#transformers #pytorch #bert #fill-mask #arxiv-2306.17649 #autotrain_compatible #endpoints_compatible #region-us 
