|
--- |
|
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} |
|
} |
|
|