Instructions to use sultan/BioM-ELECTRA-Base-Generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sultan/BioM-ELECTRA-Base-Generator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sultan/BioM-ELECTRA-Base-Generator")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sultan/BioM-ELECTRA-Base-Generator") model = AutoModelForMaskedLM.from_pretrained("sultan/BioM-ELECTRA-Base-Generator") - Notebooks
- Google Colab
- Kaggle
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README.md
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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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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 500k steps with a batch size of 1024 on TPUv3-32 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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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 500k steps with a batch size of 1024 on TPUv3-32 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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