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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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license: mit |
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--- |
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# Model Description |
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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. |
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# Architecture |
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The architecture of this model is identical to [ClinicalMobileBERT](https://huggingface.co/nlpie/clinical-mobilebert). 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. The model has around 25M parameters. |
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# Use Cases |
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This model is useful for NLP tasks in the clinical domain that require identification and classification of problems, treatments, and tests. |
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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.2302.04725, |
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doi = {10.48550/ARXIV.2302.04725}, |
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url = {https://arxiv.org/abs/2302.04725}, |
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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.}, |
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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}, |
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title = {Lightweight Transformers for Clinical Natural Language Processing}, |
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publisher = {arXiv}, |
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year = {2023}, |
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copyright = {arXiv.org perpetual, non-exclusive license} |
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} |
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