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metadata
license: cc-by-4.0
tags:
  - generated_from_trainer
datasets:
  - wikiann
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: small-e-czech-finetuned-ner-wikiann
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wikiann
          type: wikiann
          args: cs
        metrics:
          - name: Precision
            type: precision
            value: 0.8713322894683097
          - name: Recall
            type: recall
            value: 0.8970423324922905
          - name: F1
            type: f1
            value: 0.8840004144075699
          - name: Accuracy
            type: accuracy
            value: 0.9557089381093997

small-e-czech-finetuned-ner-wikiann

This model is a fine-tuned version of Seznam/small-e-czech on the wikiann dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2547
  • Precision: 0.8713
  • Recall: 0.8970
  • F1: 0.8840
  • Accuracy: 0.9557

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.2924 1.0 2500 0.2449 0.7686 0.8088 0.7882 0.9320
0.2042 2.0 5000 0.2137 0.8050 0.8398 0.8220 0.9400
0.1699 3.0 7500 0.1912 0.8236 0.8593 0.8411 0.9466
0.1419 4.0 10000 0.1931 0.8349 0.8671 0.8507 0.9488
0.1316 5.0 12500 0.1892 0.8470 0.8776 0.8620 0.9519
0.1042 6.0 15000 0.2058 0.8433 0.8811 0.8618 0.9508
0.0884 7.0 17500 0.2020 0.8602 0.8849 0.8724 0.9531
0.0902 8.0 20000 0.2118 0.8551 0.8837 0.8692 0.9528
0.0669 9.0 22500 0.2171 0.8634 0.8906 0.8768 0.9550
0.0529 10.0 25000 0.2228 0.8638 0.8912 0.8773 0.9545
0.0613 11.0 27500 0.2293 0.8626 0.8898 0.8760 0.9544
0.0549 12.0 30000 0.2276 0.8694 0.8958 0.8824 0.9554
0.0516 13.0 32500 0.2384 0.8717 0.8940 0.8827 0.9552
0.0412 14.0 35000 0.2443 0.8701 0.8931 0.8815 0.9554
0.0345 15.0 37500 0.2464 0.8723 0.8958 0.8839 0.9557
0.0412 16.0 40000 0.2477 0.8705 0.8948 0.8825 0.9552
0.0363 17.0 42500 0.2525 0.8742 0.8973 0.8856 0.9559
0.0341 18.0 45000 0.2529 0.8727 0.8962 0.8843 0.9561
0.0194 19.0 47500 0.2533 0.8699 0.8966 0.8830 0.9557
0.0247 20.0 50000 0.2547 0.8713 0.8970 0.8840 0.9557

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.4
  • Tokenizers 0.11.6