Create README.md

README.md

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+---
+license: mit
+---
+<h1 align="center">
+  MedTok: Multimodal Medical Code Tokenizer
+</h1>
+
+## Overview of MedTok
+MEDTOK is a multimodal tokenizer of medical codes that combines text descriptions of codes with graph-based representations of dependencies between codes derived from clinical ontologies and standard medical terminologies. MEDTOK is a general-purpose tokenizer that can be integrated into any transformer-based model or system that requires tokenization.
+
+## How to use MedTok?
+```bash
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("mims-harvard/MedTok")
+tokens = tokenizer.tokenize("E11.9")
+ids = tokenizer.encode("E11.9")
+embed = tokenizer.embed("E11.9")
+```
+
+If you want to use the tokenized embedding for each medical code, please download it from [mims-harvard/MedTok](https://huggingface.co/mims-harvard/MedTok) or [code2embeddings.json.zip](https://doi.org/10.7910/DVN/7XNT3M) directly. And the downloaded embedding file could be put into 'MedTok/embedding.npy' to run EHR or QA tasks based on MedTok.
+
+### 🏥MedTok for EHR & MedicalQA
+Please reference our github repo [MedTok](https://github.com/mims-harvard/MedTok)
+
+## Citation
+```bash
+@article{su2025multimodal,
+  title={Multimodal Medical Code Tokenizer},
+  author={Su, Xiaorui and Messica, Shvat and Huang, Yepeng and Johnson, Ruth and Fesser, Lukas and Gao, Shanghua and Sahneh, Faryad and Zitnik, Marinka},
+  journal={International Conference on Machine Learning, ICML},
+  year={2025}
+}
+```
+</details>