FastPDN FastPolDeepNer is model for Named Entity Recognition, designed for easy use, training and configuration. The forerunner of this project is PolDeepNer2. The model implements a pipeline consisting of data processing and training using: hydra, pytorch, pytorch lightning, transformers. Source code: https://gitlab.clarin pl.eu/grupa wieszcz/ner/fast pdn How to use Here is how to use this model to get Named Entities in text: Here is how to use this model to get the logits for every token in text: Training data The FastPDN model was trained on datasets (with 82 class versions) of kpwr and cen. Annotation guidelines are specified here. Pretraining FastPDN models have been fine tuned, thanks to pretrained models: herbert base case distiluse base multilingual cased v1 Evaluation Runs trained on cen n82 and kpwr n82 : name test/f1 test/pdn2 f1 test/acc test/precision test/recall distiluse 0.53 0.61 0.95 0.55 0.54 herbert 0.68 0.78 0.97 0.7 0.69 Authors Grupa Wieszcze CLARIN PL Wiktor Walentynowicz Contact Norbert Ropiak (norbert.ropiak@pwr.edu.pl)
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