transformers safetensors bert fill-mask autotrain_compatible endpoints_compatible region:us
---
library_name: transformers
---
license: apache-2.0
tags:
- medical
- domain-adaptation
- transformer
- NLP
datasets:
- custom
language: en
---

# Medical Domain Transformer

This repository hosts a transformer model adapted for medical text processing. The model was trained using a specialized corpus of medical texts and is optimized for natural language processing tasks such as named entity recognition, text classification, and more.

## Model Details
- **Training Hardware:** NVIDIA A30 GPU
- **Final Perplexity:** 3.39
- **Tasks:** Masked language modeling for medical text
- **Applications:** Clinical text processing, medical research

## How to Use the Model
You can load and use this model with Hugging Face's Transformers library:

```python
from transformers import AutoModelForMaskedLM, AutoTokenizer

model_name = "cgkinyua/Medical-Domain-Transformer"
model = AutoModelForMaskedLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

input_text = "The patient was prescribed [MASK] for hypertension."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model(**inputs)
print(outputs)

## Citation

If you use this model in your research or applications, please cite it as:
@misc{cgkinyua2024medicaltransformer, title={Medical Domain Transformer}, author={Charles Kinyua Gitonga}, year={2024}, publisher={Hugging Face}, howpublished={\url{https://huggingface.co/cgkinyua/Medical-Domain-Transformer}}, }

