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nosql-identifier-bert

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
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nosql-identifier-bert / README.md
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metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: nosql-identifier-bert
    results: []

nosql-identifier-bert

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

  • Loss: 0.7566
  • Accuracy: 0.925

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: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 40 0.3987 0.875
No log 2.0 80 0.2198 1.0
No log 3.0 120 0.1294 1.0
No log 4.0 160 0.5029 0.775
No log 5.0 200 0.1562 0.95
No log 6.0 240 0.1672 0.9
No log 7.0 280 0.1928 0.9
No log 8.0 320 0.1154 0.95
No log 9.0 360 0.1780 0.95
No log 10.0 400 0.4642 0.9
No log 11.0 440 0.2898 0.95
No log 12.0 480 0.2842 0.925
0.3022 13.0 520 0.3082 0.95
0.3022 14.0 560 0.3127 0.95
0.3022 15.0 600 0.3421 0.95
0.3022 16.0 640 0.1690 0.975
0.3022 17.0 680 0.2002 0.95
0.3022 18.0 720 0.4938 0.925
0.3022 19.0 760 0.2749 0.95
0.3022 20.0 800 0.2013 0.975
0.3022 21.0 840 0.4775 0.925
0.3022 22.0 880 0.2020 0.975
0.3022 23.0 920 0.2081 0.975
0.3022 24.0 960 0.2603 0.95
0.0784 25.0 1000 0.5710 0.925
0.0784 26.0 1040 0.4450 0.925
0.0784 27.0 1080 0.2669 0.95
0.0784 28.0 1120 0.6181 0.9
0.0784 29.0 1160 0.3211 0.95
0.0784 30.0 1200 0.3222 0.95
0.0784 31.0 1240 0.3238 0.95
0.0784 32.0 1280 0.4617 0.925
0.0784 33.0 1320 0.4142 0.95
0.0784 34.0 1360 0.4887 0.925
0.0784 35.0 1400 0.6418 0.925
0.0784 36.0 1440 0.4104 0.95
0.0784 37.0 1480 0.5617 0.925
0.0605 38.0 1520 0.3141 0.95
0.0605 39.0 1560 0.3596 0.95
0.0605 40.0 1600 0.6732 0.925
0.0605 41.0 1640 0.6785 0.925
0.0605 42.0 1680 0.6904 0.925
0.0605 43.0 1720 0.4378 0.95
0.0605 44.0 1760 0.6341 0.925
0.0605 45.0 1800 0.3517 0.95
0.0605 46.0 1840 0.7044 0.925
0.0605 47.0 1880 0.7259 0.925
0.0605 48.0 1920 0.7297 0.925
0.0605 49.0 1960 0.7293 0.925
0.0363 50.0 2000 0.7248 0.925
0.0363 51.0 2040 0.4813 0.95
0.0363 52.0 2080 0.4647 0.95
0.0363 53.0 2120 0.4684 0.95
0.0363 54.0 2160 0.6032 0.925
0.0363 55.0 2200 0.4853 0.95
0.0363 56.0 2240 0.4840 0.95
0.0363 57.0 2280 0.4827 0.95
0.0363 58.0 2320 0.4400 0.95
0.0363 59.0 2360 0.4886 0.95
0.0363 60.0 2400 0.4966 0.95
0.0363 61.0 2440 0.4897 0.95
0.0363 62.0 2480 0.4682 0.95
0.0299 63.0 2520 0.4750 0.95
0.0299 64.0 2560 0.4844 0.95
0.0299 65.0 2600 0.7955 0.9
0.0299 66.0 2640 0.5219 0.925
0.0299 67.0 2680 0.4882 0.925
0.0299 68.0 2720 0.4505 0.95
0.0299 69.0 2760 0.2627 0.975
0.0299 70.0 2800 0.2633 0.975
0.0299 71.0 2840 0.2667 0.975
0.0299 72.0 2880 0.2955 0.95
0.0299 73.0 2920 0.4848 0.95
0.0299 74.0 2960 0.7284 0.925
0.0334 75.0 3000 0.4787 0.95
0.0334 76.0 3040 0.4838 0.95
0.0334 77.0 3080 0.4995 0.95
0.0334 78.0 3120 0.2645 0.975
0.0334 79.0 3160 0.4928 0.95
0.0334 80.0 3200 0.6753 0.925
0.0334 81.0 3240 0.6419 0.925
0.0334 82.0 3280 0.4380 0.95
0.0334 83.0 3320 0.4723 0.95
0.0334 84.0 3360 0.4748 0.95
0.0334 85.0 3400 0.4657 0.95
0.0334 86.0 3440 0.4651 0.95
0.0334 87.0 3480 0.4647 0.95
0.0319 88.0 3520 0.4570 0.95
0.0319 89.0 3560 0.4539 0.95
0.0319 90.0 3600 0.7459 0.925
0.0319 91.0 3640 0.7451 0.925
0.0319 92.0 3680 0.7484 0.925
0.0319 93.0 3720 0.7533 0.925
0.0319 94.0 3760 0.7536 0.925
0.0319 95.0 3800 0.7547 0.925
0.0319 96.0 3840 0.7566 0.925
0.0319 97.0 3880 0.7556 0.925
0.0319 98.0 3920 0.7563 0.925
0.0319 99.0 3960 0.7565 0.925
0.0281 100.0 4000 0.7566 0.925

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.1
  • Tokenizers 0.11.0