Create README.md

README.md

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+---
+language: "en"
+tags:
+- bert
+- medical
+- clinical
+- assertion
+- negation
+
+---
+
+# Clinical Assertion / Negation Classification BERT
+
+## Model description
+
+The model is introduced in the paper [Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?
+](https://aclanthology.org/2021.nlpmc-1.5/). 
+It is based on the [ClinicalBERT - Bio + Discharge Summary BERT Model](https://huggingface.co/emilyalsentzer/Bio_Discharge_Summary_BERT) by Alsentzer et al. and fine-tuned on assertion data from the [2010 i2b2 challenge](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168320/).
+
+
+#### How to use the model
+
+You can load the model via the transformers library:
+```
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained("bvanaken/clinical-assertion-negation-bert")
+model = AutoModel.from_pretrained("bvanaken/clinical-assertion-negation-bert")
+```
+The model expects input in the form of spans/sentences with one marked entity to classify as `PRESENT`, `ABSENT` or `POSSIBLE`. The entity in question is identified with the special token `[entity]` surrounding it.
+
+Example input:
+```
+The patient recovered during the night and now denies any [entity] shortness of breath [entity].
+``` 
+
+### Cite
+
+```bibtex
+@inproceedings{van-aken-2021-assertion,
+    title = "Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?",
+    author = "van Aken, Betty  and
+      Trajanovska, Ivana  and
+      Siu, Amy  and
+      Mayrdorfer, Manuel  and
+      Budde, Klemens  and
+      Loeser, Alexander",
+    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations",
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.nlpmc-1.5",
+    doi = "10.18653/v1/2021.nlpmc-1.5"
+}
+```