DeepFrog aims to be a (partial) successor of the Dutch-NLP suite Frog. Whereas the various NLP modules in Frog wre built on k-NN classifiers, DeepFrog builds on deep learning techniques and can use a variety of neural transformers.
Our deliverables are multi-faceted:
training
).We aim to make available various models for Dutch NLP.
Model page with instructions: https://huggingface.co/proycon/robbert-pos-cased-deepfrog-nld
Uses pre-trained model RobBERT (a Roberta model), fine-tuned on part-of-speech tags with the full corpus as also used by Frog. Uses the tag set of Corpus Gesproken Nederlands (CGN), this corpus constitutes a subset of the training data.
Test Evaluation:
f1 = 0.9708171206225681
loss = 0.07882563415198372
precision = 0.9708171206225681
recall = 0.9708171206225681
Model page with instructions: https://huggingface.co/proycon/robbert2-pos-cased-deepfrog-nld
Uses pre-trained model RobBERT v2 (a Roberta model), fine-tuned on part-of-speech tags with the full corpus as also used by Frog. Uses the tag set of Corpus Gesproken Nederlands (CGN), this corpus constitutes a subset of the training data.
f1 = 0.9664560038891591
loss = 0.09085878504153627
precision = 0.9659863945578231
recall = 0.9669260700389105
Model page with instructions: https://huggingface.co/proycon/bert-pos-cased-deepfrog-nld
Uses pre-trained model BERTje (a BERT model), fine-tuned on part-of-speech tags with the full corpus as also used by Frog. Uses the tag set of Corpus Gesproken Nederlands (CGN), this corpus constitutes a subset of the training data.
Test Evaluation:
f1 = 0.9737354085603113
loss = 0.0647074995296342
precision = 0.9737354085603113
recall = 0.9737354085603113
Model page with instructions: https://huggingface.co/proycon/robbert-ner-cased-sonar1-nld
Uses pre-trained model RobBERT (a Roberta model), fine-tuned on Named Entities from the SoNaR1 corpus (as also used by Frog). Provides basic PER,LOC,ORG,PRO,EVE,MISC tags.
Test Evaluation (note: this is a simple token-based evaluation rather than entity based!)
f1 = 0.9170731707317074
loss = 0.023864904676364467
precision = 0.9306930693069307
recall = 0.9038461538461539
Note: the tokenisation in this model is English rather than Dutch
Model page with instructions: https://huggingface.co/proycon/robbert2-ner-cased-sonar1-nld
Uses pre-trained model RobBERT (v2) (a Roberta model), fine-tuned on Named Entities from the SoNaR1 corpus (as also used by Frog). Provides basic PER,LOC,ORG,PRO,EVE,MISC tags.
f1 = 0.8878048780487806
loss = 0.03555946223787032
precision = 0.900990099009901
recall = 0.875
Model page with instructions: https://huggingface.co/proycon/bert-ner-cased-sonar1-nld
Uses pre-trained model BERTje (a BERT model), fine-tuned on Named Entities from the SoNaR1 corpus (as also used by Frog). Provides basic PER,LOC,ORG,PRO,EVE,MISC tags.
Test Evaluation (note: this is a simple token-based evaluation rather than entity based!)
f1 = 0.9519230769230769
loss = 0.02323892477299803
precision = 0.9519230769230769
recall = 0.9519230769230769