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--- |
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id: CardioNER.nl_128xtokenWindow |
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name: CardioNER.nl_128xtokenWindow |
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description: >- |
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CardioBERTa.nl_clinical finetuned for multilabel NER task with tokenwindow of |
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128 |
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license: gpl-3.0 |
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language: nl |
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tags: |
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- lexical semantic |
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- span classification |
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- science |
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- biology |
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- clinical ner |
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- biomedical |
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- ner,medical |
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- bionlp |
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base_model: UMCU/CardioBERTa.nl_clinical |
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pipeline_tag: token-classification |
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datasets: |
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- DT4H/CardioCCC |
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- UMCU/cardioccc_dutch |
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--- |
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# Model Card for Cardioner.nl 128 |
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This a UMCU/CardioBERTa.nl_clinical base model finetuned for span classification. For this model |
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we used IOB-tagging. Using the IOB-tagging schema facilitates the aggregation of predictions |
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over sequences. This specific model is trained on a batch of about 500 span-labeled documents. |
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This is version was trained with context windows of 128 tokens. For the chunking we used a paragraph-based splitter. |
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The training was performed with 10 fold CV, with weight averaging of the best epochs per fold. |
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### Expected input and output |
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The input should be a string with **Dutch** clinical text related to **cardiology**. |
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CardioNER.nl_128 is a multiclass span classification model. |
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The classes that can be predicted are |
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* **procedure**, |
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* **medication**, |
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* **disease**, |
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* **symptom**. |
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#### Extracting span classification from CardioNER.nl_128xtokenWindow |
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The following script converts a string of <128 tokens to a list of span predictions. |
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```python |
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from transformers import pipeline |
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le_pipe = pipeline('ner', |
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model=model, |
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tokenizer=model, aggregation_strategy="simple", |
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device=-1) |
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named_ents = le_pipe(SOME_TEXT) |
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``` |
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To process a string of *arbitrary length* you can split the string into sentences or paragraphs |
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using e.g. pysbd or spacy(sentencizer) and iteratively parse the list of with the span-classification pipe. |
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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; |
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```python |
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named_ents = le_pipe(SOME_TEXT, stride=256) |
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``` |
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# Data description |
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CardioCCC; manually labeled cardiology discharge letters; procedure, medication, disease, symptom |
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# Acknowledgement |
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This is part of the [DT4H project](https://www.datatools4heart.eu/). |
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# Doi and reference |
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For more details about training/eval and other scripts, see CardioNER [github repo](https://github.com/DataTools4Heart/CardioNER). |
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and for more information on the background, see Datatools4Heart [Huggingface](https://huggingface.co/DT4H)/[Website](https://www.datatools4heart.eu/) |
