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README.md

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+---
+language: "en"
+tags:
+- bert
+- medical
+- clinical
+thumbnail: "https://core.app.datexis.com/static/paper.png"
+---
+
+# CORe Model - BioBERT + Clinical Outcome Pre-Training
+
+## Model description
+
+The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration](https://www.aclweb.org/anthology/2021.eacl-main.75.pdf).
+It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.
+
+#### How to use CORe
+
+You can load the model via the transformers library:
+```
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained("bvanaken/CORe-clinical-outcome-biobert-v1")
+model = AutoModel.from_pretrained("bvanaken/CORe-clinical-outcome-biobert-v1")
+```
+From there, you can fine-tune it on clinical tasks that benefit from patient outcome knowledge.
+
+### Pre-Training Data
+
+The model is based on [BioBERT](https://huggingface.co/dmis-lab/biobert-v1.1) pre-trained on PubMed data.
+The _Clinical Outcome Pre-Training_ included discharge summaries from the MIMIC III training set (specified [here](https://github.com/bvanaken/clinical-outcome-prediction/blob/master/tasks/mimic_train.csv)), medical transcriptions from [MTSamples](https://mtsamples.com/) and clinical notes from the i2b2 challenges 2006-2012. It  further includes ~10k case reports from PubMed Central (PMC), disease articles from Wikipedia and article sections from the [MedQuAd](https://github.com/abachaa/MedQuAD) dataset extracted from NIH websites.
+
+### More Information
+
+For all the details about CORe and contact info, please visit [CORe.app.datexis.com](http://core.app.datexis.com/).
+
+### Cite
+
+```bibtex
+@inproceedings{vanaken21,
+  author    = {Betty van Aken and
+               Jens-Michalis Papaioannou and
+               Manuel Mayrdorfer and
+               Klemens Budde and
+               Felix A. Gers and
+               Alexander Löser},
+  title     = {Clinical Outcome Prediction from Admission Notes using Self-Supervised
+               Knowledge Integration},
+  booktitle = {Proceedings of the 16th Conference of the European Chapter of the
+               Association for Computational Linguistics: Main Volume, {EACL} 2021,
+               Online, April 19 - 23, 2021},
+  publisher = {Association for Computational Linguistics},
+  year      = {2021},
+}
+```

config.json

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+{
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "language": "english",
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "name": "Bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "type_vocab_size": 2,
+  "vocab_size": 28996
+}

flax_model.msgpack

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pytorch_model.bin

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