Create README.md

README.md

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+# Model Description
+TinyBioBERT is a distilled version of the [BioBERT](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2?text=The+goal+of+life+is+%5BMASK%5D.) which is distilled for 100k training steps using a total batch size of 192 on the PubMed dataset.
+
+# Distillation Procedure
+This model uses a unique distillation method called ‘transformer-layer distillation’ which is applied on each layer of the student to align the attention maps and the hidden states of the student with those of the teacher.
+
+# Architecture and Initialisation
+This model uses 4 hidden layers with a hidden dimension size and an embedding size of 768 resulting in a total of 15M parameters. Due to the small hidden dimension size used in this model, it uses a random initialisation.
+
+# Citation
+```bibtex
+@misc{https://doi.org/10.48550/arxiv.2209.03182,
+  doi = {10.48550/ARXIV.2209.03182},
+  url = {https://arxiv.org/abs/2209.03182},
+  author = {Rohanian, Omid and Nouriborji, Mohammadmahdi and Kouchaki, Samaneh and Clifton, David A.},
+  keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, 68T50},
+  title = {On the Effectiveness of Compact Biomedical Transformers},
+  publisher = {arXiv},
+  year = {2022}, 
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+```