Create README.md

README.md

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+---
+title: README
+emoji: 🧬
+colorFrom: gray
+colorTo: purple
+sdk: static
+pinned: false
+license: mit
+---
+
+# Model Description
+ClinicalMobileBERT-i2b2-2010 is a fine-tuned version of the [ClinicalMobileBERT](https://huggingface.co/nlpie/clinical-mobilebert) model on the i2b2-2010 dataset for clinical Named Entity Recognition (NER). The model specialises in recognising entities from three categories: problems, treatments, and tests. The initialisation was conducted using the pre-trained checkpoints of the ClinicalMobileBERT model available on Huggingface.
+
+# Architecture
+The architecture of this model is identical to ClinicalMobileBERT. The model was fine-tuned on the i2b2-2010 dataset for the task of clinical NER. The fine-tuning process targeted three categories of entities: problems, treatments, and tests. 
+
+# Use Cases
+This model is useful for NLP tasks in the clinical domain that require identification and classification of problems, treatments, and tests.
+
+# Citation
+If you use this model, please consider citing the following paper:
+
+```bibtex
+@misc{https://doi.org/10.48550/arxiv.2302.04725,
+  doi = {10.48550/ARXIV.2302.04725},
+  url = {https://arxiv.org/abs/2302.04725},
+  author = {Rohanian, Omid and Nouriborji, Mohammadmahdi and Jauncey, Hannah and Kouchaki, Samaneh and Group, ISARIC Clinical Characterisation and Clifton, Lei and Merson, Laura and Clifton, David A.},
+  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7, 68T50},
+  title = {Lightweight Transformers for Clinical Natural Language Processing},
+  publisher = {arXiv},
+  year = {2023},
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}