---
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](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.

# 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}
}