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End of training
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metadata
license: mit
base_model: roberta-large
tags:
  - generated_from_trainer
datasets:
  - fursov/gec_ner_val3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner-gec-roberta-large-v4
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: fursov/gec_ner_val3
          type: fursov/gec_ner_val3
        metrics:
          - name: Precision
            type: precision
            value: 0.643409688321442
          - name: Recall
            type: recall
            value: 0.5775246056357017
          - name: F1
            type: f1
            value: 0.6086894738711854
          - name: Accuracy
            type: accuracy
            value: 0.9614897122818877

ner-gec-roberta-large-v4

This model is a fine-tuned version of roberta-large on the fursov/gec_ner_val3 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2489
  • Precision: 0.6434
  • Recall: 0.5775
  • F1: 0.6087
  • Accuracy: 0.9615

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: 5e-05
  • train_batch_size: 64
  • eval_batch_size: 8
  • 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 Accuracy F1 Validation Loss Precision Recall
0.2536 0.58 500 0.9347 0.0376 0.2469 0.0814 0.0245
0.2316 1.15 1000 0.9359 0.1365 0.2339 0.2272 0.0975
0.2175 1.73 1500 0.9392 0.1823 0.2172 0.2842 0.1342
0.1757 2.3 2000 0.9438 0.3123 0.1979 0.4011 0.2556
0.1682 2.88 2500 0.9502 0.3911 0.1817 0.4787 0.3307
0.121 3.46 3000 0.9537 0.4504 0.1753 0.5310 0.3910
0.0982 4.03 3500 0.9556 0.4980 0.1807 0.5606 0.4480
0.0858 4.61 4000 0.9577 0.5304 0.1732 0.5867 0.4839
0.0563 5.18 4500 0.1839 0.6007 0.5155 0.5548 0.9585
0.0586 5.76 5000 0.1804 0.6231 0.5237 0.5691 0.9605
0.0404 6.34 5500 0.1948 0.6214 0.5423 0.5792 0.9599
0.0397 6.91 6000 0.1994 0.6309 0.5458 0.5852 0.9610
0.0281 7.49 6500 0.2131 0.6345 0.5568 0.5931 0.9610
0.0182 8.06 7000 0.2249 0.6507 0.5649 0.6047 0.9625
0.0188 8.64 7500 0.2322 0.6413 0.5782 0.6081 0.9612
0.0123 9.22 8000 0.2473 0.6506 0.5777 0.6120 0.9622
0.0123 9.79 8500 0.2491 0.6427 0.5771 0.6081 0.9614

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.16.1
  • Tokenizers 0.15.0