plantl-gob-es/lm-biomedical-clinical-es

Official source for Spanish pretrained biomedical and clinical language models and resources made @ BSC-TEMU within the "Plan de las Tecnologías del Lenguaje" (Plan-TL).

lm-biomedical-clinical-es's Issues

NER training code

Is the training code for the pharmaconner results published somewhere?

NER Example

Why there is a tag Ġ in the output?

Expected output with the predicted entities:

[
{'word': 'Ġcalcio', 'score': 0.9963880181312561, 'entity': 'B-NORMALIZABLES', 'index': 24, 'start': 137, 'end': 143}, {'word': 'Ġcalcio', 'score': 0.9965023398399353, 'entity': 'B-NORMALIZABLES', 'index': 29, 'start': 163, 'end': 169}, {'word': 'Ġmagnesio', 'score': 0.996299147605896, 'entity': 'B-NORMALIZABLES', 'index': 32, 'start': 178, 'end': 186}, {'word': 'ĠPTH', 'score': 0.9950509667396545, 'entity': 'B-PROTEINAS', 'index': 34, 'start': 189, 'end': 192}
]

Fine-tune it for multiclass or multilabel text classification

I have medical reports, and I try to predict the disease associated with each report :

1. both medical reports and disease to predict are written by humans -> mistakes, inconsistency in label names (same disease different ways to write it and reverse)
2. Should I use RobertaForSequenceClassifier or AutomodelForSequenceClassifier ?
3. what-s the best way to handle imperfect labels ? Embed them in the same roberta tokenized space, and predict the mean of the vector, or predict the whole vector (it becomes then a multilabel task).
   best

Dataset

Hi,

Is the dataset used for training the models available?

Thanks for your work.