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README.md

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+---
+id: CardioNER.nl_128xtokenWindow
+name: CardioNER.nl_128xtokenWindow
+description: CardioBERTa.nl_clinical finetuned for multilabel NER task with tokenwindow
+  of 128
+license: gpl-3.0
+language: nl
+tags:
+- lexical semantic
+- span classification
+- science
+- biology
+- clinical ner
+- biomedical
+- ner,medical
+- bionlp
+base_model: UMCU/CardioBERTa.nl_clinical
+pipeline_tag: token-classification
+---
+
+# Model Card for Cardioner.Nl 128Xtokenwindow
+
+
+
+
+This a UMCU/CardioBERTa.nl_clinical base model finetuned for span classification. For this model
+we used IOB-tagging. Using the IOB-tagging schema facilitates the aggregation of predictions
+over sequences. This specific model is trained on a batch of 240 span-labeled documents.
+
+### Expected input and output
+The input should be a string with **Dutch** cardio clinical text.
+
+CardioNER.nl_128xtokenWindow is a muticlass span classification model.
+The classes that can be predicted are ['procedure,medication,diseasae,symptom'].
+
+#### Extracting span classification from CardioNER.nl_128xtokenWindow
+
+The following script converts a string of <512 tokens to a list of span predictions.
+```python
+from transformers import pipeline
+
+le_pipe = pipeline('ner',
+                    model=model,
+                    tokenizer=model, aggregation_strategy="simple",
+                    device=-1)
+
+named_ents = le_pipe(SOME_TEXT)
+```
+
+To process a string of arbitrary length you can split the string into sentences or paragraphs
+using e.g. pysbd or spacy(sentencizer) and iteratively parse the list of with the span-classification pipe.
+You can also use the strider built in the transformer pipeline, although this is limited to non-overlapping strides plus it requires a FastTokenizer and it does not work for aggregation_strategy=None;
+```python
+named_ents = le_pipe(SOME_TEXT, stride=256)
+```
+
+
+
+
+# Data description
+
+CardioCCC; manually labeled cardiology discharge letters; procedure, medication, disease, symptom
+
+
+# Acknowledgement
+
+This is part of the [DT4H project](https://www.datatools4heart.eu/).
+
+# Doi and reference
+
+
+
+For more details about training/eval and other scripts, see CardioNER [github repo](https://github.com/DataTools4Heart/CardioNER).
+and for more information on the background, see Datatools4Heart [Huggingface](https://huggingface.co/DT4H)/[Website](https://www.datatools4heart.eu/)
+
+