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README.md

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+---
+language: "en"
+tags:
+- bert
+- medical
+- clinical
+- assertion
+- negation
+- text-classification
+widget:
+- text: "Patient denies [entity] SOB [entity]."
+
+---
+
+# Clinical Assertion / Negation Classification BERT
+
+## Model description
+
+The Clinical Assertion and Negation Classification BERT is introduced in the paper [Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?
+](https://aclanthology.org/2021.nlpmc-1.5/). The model helps structure information in clinical patient letters by classifying medical conditions mentioned in the letter into PRESENT, ABSENT and POSSIBLE.
+
+The model 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, AutoModelForSequenceClassification, TextClassificationPipeline
+tokenizer = AutoTokenizer.from_pretrained("bvanaken/clinical-assertion-negation-bert")
+model = AutoModelForSequenceClassification.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(0)`, `ABSENT(1)` or `POSSIBLE(2)`. The entity in question is identified with the special token `[entity]` surrounding it.
+
+Example input and inference:
+```
+input = "The patient recovered during the night and now denies any [entity] shortness of breath [entity]."
+
+classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
+
+classification = classifier(input)
+# [{'label': 'ABSENT', 'score': 0.9842607378959656}]
+``` 
+
+### Cite
+
+When working with the model, please cite our paper as follows:
+
+```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"
+}
+```

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "finetuning_task": "text_classification",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "PRESENT",
+    "1": "ABSENT",
+    "2": "POSSIBLE"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "PRESENT": 0,
+    "ABSENT": 1,
+    "POSSIBLE": 2
+  },
+  "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,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.6.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28997
+}

pytorch_model.bin

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