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eftnas-s1-bert-base / eftnas-s1-bert-base-qnli /README.md
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metadata
language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: first_try
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE QNLI
          type: glue
          config: qnli
          split: validation
          args: qnli
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8973091707852828

first_try

This model is a fine-tuned version of bert-base-uncased on the GLUE QNLI dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5902
  • Accuracy: 0.8973

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: 128
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 6
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.8032 1.0 3274 0.3192 0.8891 OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 320, 2: 320, 3: 576, 4: 576, 5: 512, 6: 448, 7: 448, 8: 448, 9: 320, 10: 384, 11: 448, 12: 1104, 13: 1066, 14: 1126, 15: 1102, 16: 1067, 17: 1023, 18: 1048, 19: 1061, 20: 984, 21: 772, 22: 609, 23: 205})])
0.8032 1.0 3274 0.2594 0.9059 OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})])
0.5165 2.0 6548 0.3693 0.8925 OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 320, 2: 320, 3: 576, 4: 576, 5: 512, 6: 448, 7: 448, 8: 448, 9: 320, 10: 384, 11: 448, 12: 1104, 13: 1066, 14: 1126, 15: 1102, 16: 1067, 17: 1023, 18: 1048, 19: 1061, 20: 984, 21: 772, 22: 609, 23: 205})])
0.5165 2.0 6548 0.2860 0.9200 OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})])
0.2972 3.0 9822 0.4699 0.8949 OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 320, 2: 320, 3: 576, 4: 576, 5: 512, 6: 448, 7: 448, 8: 448, 9: 320, 10: 384, 11: 448, 12: 1104, 13: 1066, 14: 1126, 15: 1102, 16: 1067, 17: 1023, 18: 1048, 19: 1061, 20: 984, 21: 772, 22: 609, 23: 205})])
0.2972 3.0 9822 0.3910 0.9162 OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})])
0.1611 4.0 13096 0.5542 0.8964 OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 320, 2: 320, 3: 576, 4: 576, 5: 512, 6: 448, 7: 448, 8: 448, 9: 320, 10: 384, 11: 448, 12: 1104, 13: 1066, 14: 1126, 15: 1102, 16: 1067, 17: 1023, 18: 1048, 19: 1061, 20: 984, 21: 772, 22: 609, 23: 205})])
0.1611 4.0 13096 0.4473 0.9160 OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})])
0.1155 5.0 16370 0.5926 0.8969 OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 320, 2: 320, 3: 576, 4: 576, 5: 512, 6: 448, 7: 448, 8: 448, 9: 320, 10: 384, 11: 448, 12: 1104, 13: 1066, 14: 1126, 15: 1102, 16: 1067, 17: 1023, 18: 1048, 19: 1061, 20: 984, 21: 772, 22: 609, 23: 205})])
0.1155 5.0 16370 0.4788 0.9180 OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})])
0.0867 6.0 19644 0.6002 0.8958 OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 320, 2: 320, 3: 576, 4: 576, 5: 512, 6: 448, 7: 448, 8: 448, 9: 320, 10: 384, 11: 448, 12: 1104, 13: 1066, 14: 1126, 15: 1102, 16: 1067, 17: 1023, 18: 1048, 19: 1061, 20: 984, 21: 772, 22: 609, 23: 205})])
0.0867 6.0 19644 0.4831 0.9176 OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})])

Framework versions

  • Transformers 4.29.1
  • Pytorch 1.12.1
  • Datasets 2.13.1
  • Tokenizers 0.13.3