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Named Entity Recognition based on FERNET-CC_sk

This model is a fine-tuned version of fav-kky/FERNET-CC_sk on the Slovak wikiann dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1763
  • Precision: 0.9360
  • Recall: 0.9472
  • F1: 0.9416
  • Accuracy: 0.9789

Intended uses & limitation

Supported classes: LOCATION, PERSON, ORGANIZATION

from transformers import pipeline

ner_pipeline = pipeline(task='ner', model='crabz/slovakbert-ner')
input_sentence = "Minister financií a líder mandátovo najsilnejšieho hnutia OĽaNO Igor Matovič upozorňuje, že následky tretej vlny budú na Slovensku veľmi veľké."
classifications = ner_pipeline(input_sentence)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 24
  • eval_batch_size: 24
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10.0

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.1259 1.0 834 0.1095 0.8963 0.9182 0.9071 0.9697
0.071 2.0 1668 0.0974 0.9270 0.9357 0.9313 0.9762
0.0323 3.0 2502 0.1259 0.9257 0.9330 0.9293 0.9745
0.0175 4.0 3336 0.1347 0.9241 0.9360 0.9300 0.9756
0.0156 5.0 4170 0.1407 0.9337 0.9404 0.9370 0.9780
0.0062 6.0 5004 0.1522 0.9267 0.9410 0.9338 0.9774
0.0055 7.0 5838 0.1559 0.9322 0.9429 0.9375 0.9780
0.0024 8.0 6672 0.1733 0.9321 0.9438 0.9379 0.9779
0.0009 9.0 7506 0.1765 0.9347 0.9468 0.9407 0.9784
0.0002 10.0 8340 0.1763 0.9360 0.9472 0.9416 0.9789

Framework versions

  • Transformers 4.14.0.dev0
  • Pytorch 1.10.0
  • Datasets 1.16.1
  • Tokenizers 0.10.3
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Dataset used to train crabz/FERNET-CC_sk-ner

Evaluation results