
# 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}
}

{"license": "mit", "title": "README", "emoji": "\ud83e\uddec", "colorFrom": "gray", "colorTo": "purple", "sdk": "static", "pinned": false}
transformers pytorch mobilebert token-classification arxiv:2302.04725 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mobilebert #token-classification #arxiv-2302.04725 #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# Model Description
ClinicalMobileBERT-i2b2-2010 is a fine-tuned version of the ClinicalMobileBERT 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. 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.

If you use this model, please consider citing the following paper:

'''bibtex
@misc{URL
  doi = {10.48550/ARXIV.2302.04725},
  url = {URL
  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 = {URL perpetual, non-exclusive license}
}

# Model Description
ClinicalMobileBERT-i2b2-2010 is a fine-tuned version of the ClinicalMobileBERT 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. 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.

If you use this model, please consider citing the following paper:

'''bibtex
@misc{URL
  doi = {10.48550/ARXIV.2302.04725},
  url = {URL
  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 = {URL perpetual, non-exclusive license}
}
TAGS
#transformers #pytorch #mobilebert #token-classification #arxiv-2302.04725 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # Model Description
ClinicalMobileBERT-i2b2-2010 is a fine-tuned version of the ClinicalMobileBERT 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. 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.

If you use this model, please consider citing the following paper:

'''bibtex
@misc{URL
  doi = {10.48550/ARXIV.2302.04725},
  url = {URL
  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 = {URL perpetual, non-exclusive license}
}
TAGS
#transformers #pytorch #mobilebert #token-classification #arxiv-2302.04725 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# Model Description
ClinicalMobileBERT-i2b2-2010 is a fine-tuned version of the ClinicalMobileBERT 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. 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.

If you use this model, please consider citing the following paper:

'''bibtex
@misc{URL
  doi = {10.48550/ARXIV.2302.04725},
  url = {URL
  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 = {URL perpetual, non-exclusive license}
}