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BioM-Transformers: Building Large Biomedical Language Models with
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BERT, ALBERT and ELECTRA
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Abstract
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The impact of design choices on the performance
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of biomedical language models recently
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improving the performance of biomedical language
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models.
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This model was pre-trained on PubMed Abstracts only with biomedical domain vocabulary for 434K steps with a batch size of 4096 on TPUv3-512 unit.
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Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints.
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Acknowledgment
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We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units.
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```bibtex
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@inproceedings{alrowili-shanker-2021-biom,
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# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
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# Abstract
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The impact of design choices on the performance
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of biomedical language models recently
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improving the performance of biomedical language
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models.
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# Model Description
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This model was pre-trained on PubMed Abstracts only with biomedical domain vocabulary for 434K steps with a batch size of 4096 on TPUv3-512 unit.
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Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints.
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# Acknowledgment
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We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units.
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# Citation
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```bibtex
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@inproceedings{alrowili-shanker-2021-biom,
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