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Librarian Bot: Add base_model information to model (#2)
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - finer-139
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: google/bert_uncased_L-2_H-128_A-2
model-index:
  - name: bertiny-finetuned-finer-full
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: finer-139
          type: finer-139
          args: finer-139
        metrics:
          - type: precision
            value: 0.555368475586064
            name: Precision
          - type: recall
            value: 0.5164398410213176
            name: Recall
          - type: f1
            value: 0.5351972041937094
            name: F1
          - type: accuracy
            value: 0.988733187308122
            name: Accuracy

bertiny-finetuned-finer-full

This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the 10% of finer-139 dataset for 40 epochs according to paper. It achieves the following results on the evaluation set:

  • Loss: 0.0788
  • Precision: 0.5554
  • Recall: 0.5164
  • F1: 0.5352
  • Accuracy: 0.9887

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: 40

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.0852 1.0 11255 0.0929 1.0 0.0001 0.0002 0.9843
0.08 2.0 22510 0.0840 0.4626 0.0730 0.1261 0.9851
0.0759 3.0 33765 0.0750 0.5113 0.2035 0.2912 0.9865
0.0569 4.0 45020 0.0673 0.4973 0.3281 0.3953 0.9872
0.0488 5.0 56275 0.0635 0.5289 0.3749 0.4388 0.9878
0.0422 6.0 67530 0.0606 0.5258 0.4068 0.4587 0.9880
0.0364 7.0 78785 0.0600 0.5588 0.4186 0.4787 0.9883
0.0307 8.0 90040 0.0589 0.5223 0.4916 0.5065 0.9883
0.0284 9.0 101295 0.0595 0.5588 0.4813 0.5171 0.9887
0.0255 10.0 112550 0.0597 0.5606 0.4944 0.5254 0.9888
0.0223 11.0 123805 0.0600 0.5533 0.4998 0.5252 0.9888
0.0228 12.0 135060 0.0608 0.5290 0.5228 0.5259 0.9885
0.0225 13.0 146315 0.0612 0.5480 0.5111 0.5289 0.9887
0.0204 14.0 157570 0.0634 0.5646 0.5120 0.5370 0.9890
0.0176 15.0 168825 0.0639 0.5611 0.5135 0.5363 0.9889
0.0167 16.0 180080 0.0647 0.5631 0.5120 0.5363 0.9888
0.0161 17.0 191335 0.0665 0.5607 0.5081 0.5331 0.9889
0.0145 18.0 202590 0.0673 0.5437 0.5280 0.5357 0.9887
0.0166 19.0 213845 0.0687 0.5722 0.5008 0.5341 0.9889
0.0155 20.0 225100 0.0685 0.5325 0.5337 0.5331 0.9885
0.0142 21.0 236355 0.0705 0.5626 0.5166 0.5386 0.9890
0.0127 22.0 247610 0.0694 0.5426 0.5358 0.5392 0.9887
0.0112 23.0 258865 0.0721 0.5591 0.5129 0.5351 0.9888
0.0123 24.0 270120 0.0733 0.5715 0.5081 0.5380 0.9889
0.0116 25.0 281375 0.0735 0.5621 0.5123 0.5361 0.9888
0.0112 26.0 292630 0.0739 0.5634 0.5181 0.5398 0.9889
0.0108 27.0 303885 0.0753 0.5548 0.5155 0.5344 0.9887
0.0125 28.0 315140 0.0746 0.5507 0.5221 0.5360 0.9886
0.0093 29.0 326395 0.0762 0.5602 0.5156 0.5370 0.9888
0.0094 30.0 337650 0.0762 0.5625 0.5157 0.5381 0.9889
0.0117 31.0 348905 0.0767 0.5519 0.5195 0.5352 0.9887
0.0091 32.0 360160 0.0772 0.5501 0.5198 0.5345 0.9887
0.0109 33.0 371415 0.0775 0.5635 0.5097 0.5353 0.9888
0.0094 34.0 382670 0.0776 0.5467 0.5216 0.5339 0.9887
0.009 35.0 393925 0.0782 0.5601 0.5139 0.5360 0.9889
0.0093 36.0 405180 0.0780 0.5568 0.5156 0.5354 0.9888
0.0087 37.0 416435 0.0783 0.5588 0.5143 0.5356 0.9888
0.009 38.0 427690 0.0785 0.5483 0.5178 0.5326 0.9887
0.0094 39.0 438945 0.0787 0.5541 0.5154 0.5340 0.9887
0.0088 40.0 450200 0.0788 0.5554 0.5164 0.5352 0.9887

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.12.0+cu113
  • Datasets 2.3.2
  • Tokenizers 0.12.1