id: CardioNER model --medication
name: CardioNER model --medication
description: >-
Finetuned CardioBERTa.nl model for detection of medication spans. This model
is a mulilabel model using BCE loss.
license: mit
language: nl
tags:
- span classification
- lexical semantic
- biology
- biomedical
- clinical ner
- science
- bionlp
base_model: UMCU/CardioBERTa.nl_clinical
pipeline_tag: token-classification
Model Card for Cardioner Model --Medication
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 model --medication is a muticlass span classification model. The classes that can be predicted are ['medication'].
Extracting span classification from CardioNER model --medication
The following script converts a string of <512 tokens to a list of span predictions.
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.
Alternatively you might try
named_ents = le_pipe(SOME_TEXT, stride=256)
Data description
50/50 Train/validation split on CardioCCC, a manually labeled cardiology corpus
Acknowledgement
This is part of the DT4H project.
Doi and reference
For more details about training/eval and other scripts, see CardioNER github repo. and for more information on the background, see Datatools4Heart Huggingface/Website