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End of training
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metadata
license: cc-by-nc-sa-4.0
base_model: ufal/robeczech-base
tags:
  - generated_from_trainer
datasets:
  - cnec
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: CNEC_1_1_ext_robeczech-base
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: cnec
          type: cnec
          config: default
          split: validation
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.8638931689779148
          - name: Recall
            type: recall
            value: 0.8989845002672368
          - name: F1
            type: f1
            value: 0.8810895756940808
          - name: Accuracy
            type: accuracy
            value: 0.963311432325887

CNEC_1_1_ext_robeczech-base

This model is a fine-tuned version of ufal/robeczech-base on the cnec dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1985
  • Precision: 0.8639
  • Recall: 0.8990
  • F1: 0.8811
  • Accuracy: 0.9633

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.2585 6.85 1000 0.1912 0.8276 0.8696 0.8481 0.9550
0.1224 13.7 2000 0.1807 0.8455 0.8894 0.8669 0.9586
0.0788 20.55 3000 0.1715 0.8624 0.8974 0.8795 0.9643
0.0562 27.4 4000 0.1782 0.8650 0.9043 0.8842 0.9633
0.0432 34.25 5000 0.1856 0.8598 0.9017 0.8803 0.9640
0.0346 41.1 6000 0.1975 0.8622 0.8963 0.8789 0.9630
0.0306 47.95 7000 0.1985 0.8639 0.8990 0.8811 0.9633

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0