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--- |
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title: README |
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emoji: 🏃 |
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colorTo: purple |
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sdk: static |
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pinned: false |
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--- |
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# Model Description |
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BioDistilBERT-uncased is the result of training the [DistilBERT-uncased](https://huggingface.co/distilbert-base-uncased?text=The+goal+of+life+is+%5BMASK%5D.) model in a continual learning fashion for 200k training steps using a total batch size of 192 on the PubMed dataset. |
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# Initialisation |
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We initialise our model with the pre-trained checkpoints of the [DistilBERT-uncased](https://huggingface.co/distilbert-base-uncased?text=The+goal+of+life+is+%5BMASK%5D.) model available on Huggingface. |
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# Architecture |
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In this model, the size of the hidden dimension and the embedding layer are both set to 768. The vocabulary size is 30522. The number of transformer layers is 6 and the expansion rate of the feed-forward layer is 4. Overall, this model has around 65 million parameters. |
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# Citation |
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If you use this model, please consider citing the following paper: |
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```bibtex |
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@misc{https://doi.org/10.48550/arxiv.2209.03182, |
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doi = {10.48550/ARXIV.2209.03182}, |
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url = {https://arxiv.org/abs/2209.03182}, |
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author = {Rohanian, Omid and Nouriborji, Mohammadmahdi and Kouchaki, Samaneh and Clifton, David A.}, |
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keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, 68T50}, |
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title = {On the Effectiveness of Compact Biomedical Transformers}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {arXiv.org perpetual, non-exclusive license} |
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} |
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``` |
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