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llm-data-textbook-quality-classifier-v1

Text Classification
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xlm-roberta
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metadata
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: llm-data-quality-classifer-compare
    results: []

llm-data-quality-classifer-compare

This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2689
  • Accuracy: 0.8833
  • Precision: 0.7551
  • Recall: 0.7598
  • F1: 0.7574

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1

Training results

Training Loss Epoch Step Accuracy F1 Validation Loss Precision Recall
0.4745 0.01 500 0.8076 0.6181 0.4327 0.5898 0.6493
0.4088 0.02 1000 0.8346 0.5522 0.4287 0.7870 0.4254
0.3811 0.02 1500 0.8286 0.6651 0.3741 0.6257 0.7098
0.3762 0.03 2000 0.85 0.6529 0.3413 0.7334 0.5884
0.3647 0.04 2500 0.8427 0.6632 0.3852 0.6815 0.6460
0.3495 0.05 3000 0.8629 0.6987 0.3253 0.7385 0.6631
0.3508 0.06 3500 0.8335 0.6967 0.3605 0.6186 0.7973
0.3342 0.06 4000 0.8553 0.7075 0.3273 0.6865 0.7298
0.341 0.07 4500 0.8602 0.6679 0.3320 0.7759 0.5863
0.3344 0.08 5000 0.8531 0.6916 0.3441 0.6964 0.6868
0.3341 0.09 5500 0.8536 0.7027 0.3265 0.6849 0.7214
0.3319 0.1 6000 0.8599 0.7081 0.3266 0.7076 0.7085
0.3259 0.1 6500 0.8136 0.6907 0.3908 0.5736 0.8678
0.3391 0.11 7000 0.8642 0.6770 0.3338 0.7879 0.5934
0.3207 0.12 7500 0.8668 0.7224 0.3035 0.7221 0.7227
0.3191 0.13 8000 0.8543 0.7153 0.3179 0.6730 0.7631
0.3142 0.14 8500 0.8679 0.7052 0.3101 0.7585 0.6589
0.3195 0.14 9000 0.8636 0.7254 0.3433 0.7012 0.7515
0.3196 0.15 9500 0.8707 0.7191 0.3048 0.7506 0.6902
0.3176 0.16 10000 0.8597 0.7271 0.3177 0.6814 0.7794
0.3218 0.17 10500 0.8723 0.6993 0.3212 0.8031 0.6193
0.3175 0.18 11000 0.8601 0.7239 0.3366 0.6871 0.7648
0.3296 0.18 11500 0.8526 0.7190 0.3218 0.6622 0.7865
0.3249 0.19 12000 0.8731 0.7081 0.2926 0.7896 0.6418
0.3141 0.2 12500 0.8741 0.7215 0.3035 0.7683 0.6802
0.3126 0.21 13000 0.8659 0.7231 0.3127 0.7162 0.7302
0.3204 0.22 13500 0.8665 0.7233 0.3456 0.7190 0.7277
0.3108 0.22 14000 0.8674 0.7214 0.3018 0.7269 0.7160
0.3114 0.23 14500 0.8726 0.7016 0.2967 0.8002 0.6247
0.3071 0.24 15000 0.8768 0.7211 0.2904 0.7886 0.6643
0.2965 0.25 15500 0.8674 0.7310 0.3126 0.7117 0.7515
0.3022 0.26 16000 0.8738 0.7077 0.2887 0.7958 0.6372
0.3101 0.26 16500 0.8559 0.7251 0.3312 0.6683 0.7923
0.3154 0.27 17000 0.8575 0.7304 0.3221 0.6685 0.8048
0.3041 0.28 17500 0.8754 0.7248 0.2864 0.7704 0.6843
0.3093 0.29 18000 0.8603 0.7292 0.3101 0.6813 0.7844
0.3006 0.3 18500 0.8753 0.7111 0.3008 0.7999 0.6401
0.3108 0.3 19000 0.8689 0.7316 0.2911 0.7185 0.7452
0.3071 0.31 19500 0.8793 0.7366 0.2839 0.7725 0.7039
0.3002 0.32 20000 0.852 0.7239 0.3391 0.6550 0.8090
0.301 0.33 20500 0.8769 0.7396 0.2896 0.7505 0.7289
0.3075 0.34 21000 0.8785 0.7402 0.2891 0.7595 0.7219
0.2922 0.34 21500 0.8393 0.7164 0.4094 0.6210 0.8465
0.2973 0.35 22000 0.8787 0.7416 0.2962 0.7579 0.7260
0.2987 0.36 22500 0.8711 0.7430 0.2983 0.7119 0.7769
0.3071 0.37 23000 0.8739 0.7407 0.3167 0.7306 0.7510
0.2846 0.38 23500 0.8801 0.7401 0.2901 0.7707 0.7118
0.2924 0.38 24000 0.863 0.7299 0.3155 0.6922 0.7719
0.2938 0.39 24500 0.8724 0.7368 0.2973 0.7290 0.7448
0.2917 0.4 25000 0.8772 0.7436 0.2939 0.7446 0.7427
0.294 0.41 25500 0.8772 0.7394 0.2944 0.7528 0.7264
0.2979 0.42 26000 0.8774 0.7421 0.2819 0.7487 0.7356
0.2884 0.42 26500 0.873 0.7394 0.2932 0.7278 0.7515
0.2992 0.43 27000 0.8655 0.7419 0.3053 0.6872 0.8061
0.3018 0.44 27500 0.8788 0.7296 0.2781 0.7845 0.6818
0.305 0.45 28000 0.8785 0.7408 0.2760 0.7584 0.7239
0.2918 0.46 28500 0.8788 0.7381 0.2826 0.7659 0.7123
0.2998 0.46 29000 0.874 0.7403 0.2893 0.7319 0.7490
0.2875 0.47 29500 0.8803 0.7422 0.2891 0.7675 0.7185
0.2946 0.48 30000 0.2781 0.8798 0.7415 0.7656 0.7534
0.2907 0.49 30500 0.2860 0.8752 0.7280 0.7656 0.7463
0.2981 0.5 31000 0.3012 0.8732 0.7276 0.7531 0.7402
0.2948 0.5 31500 0.2777 0.8792 0.7894 0.6768 0.7288
0.2933 0.51 32000 0.2839 0.8773 0.7428 0.7469 0.7449
0.2891 0.52 32500 0.2774 0.8795 0.7678 0.7131 0.7395
0.2869 0.53 33000 0.2790 0.8764 0.7405 0.7460 0.7432
0.2907 0.54 33500 0.2889 0.8764 0.7580 0.7118 0.7342
0.2912 0.54 34000 0.2887 0.8807 0.7464 0.7611 0.7537
0.283 0.55 34500 0.2754 0.8816 0.7847 0.6977 0.7386
0.2877 0.56 35000 0.3036 0.8727 0.7221 0.7627 0.7418
0.2923 0.57 35500 0.2853 0.8783 0.7693 0.7035 0.7349
0.2902 0.58 36000 0.2881 0.8772 0.7462 0.7394 0.7428
0.2863 0.58 36500 0.2886 0.8768 0.7303 0.7711 0.7501
0.2837 0.59 37000 0.2753 0.8801 0.7503 0.7494 0.7498
0.3021 0.6 37500 0.2848 0.8775 0.7330 0.7694 0.7508
0.291 0.61 38000 0.2793 0.88 0.7423 0.7652 0.7536
0.2821 0.62 38500 0.2867 0.88 0.7429 0.7640 0.7533
0.2867 0.62 39000 0.2851 0.8796 0.7367 0.7748 0.7553
0.2846 0.63 39500 0.2813 0.8828 0.7661 0.7360 0.7507
0.2836 0.64 40000 0.2842 0.8793 0.7406 0.7644 0.7523
0.2835 0.65 40500 0.2797 0.8792 0.7382 0.7690 0.7533
0.2833 0.66 41000 0.2763 0.8821 0.7895 0.6931 0.7382
0.2743 0.66 41500 0.2852 0.8833 0.7717 0.7289 0.7497
0.2921 0.67 42000 0.2780 0.8791 0.7561 0.7319 0.7438
0.279 0.68 42500 0.2759 0.8827 0.7882 0.6985 0.7407
0.2752 0.69 43000 0.2795 0.8796 0.7642 0.7202 0.7415
0.2902 0.7 43500 0.2735 0.8809 0.7824 0.6972 0.7374
0.2832 0.7 44000 0.2742 0.8815 0.7690 0.7231 0.7453
0.2783 0.71 44500 0.2773 0.8815 0.7692 0.7227 0.7452
0.2879 0.72 45000 0.2716 0.8838 0.7766 0.7235 0.7491
0.2898 0.73 45500 0.2728 0.8804 0.7513 0.7494 0.7503
0.2771 0.74 46000 0.2795 0.877 0.7370 0.7573 0.7470
0.2743 0.74 46500 0.2833 0.8707 0.7013 0.8028 0.7486
0.2868 0.75 47000 0.2719 0.8821 0.7575 0.7477 0.7526
0.2771 0.76 47500 0.2784 0.8833 0.7636 0.7435 0.7534
0.2824 0.77 48000 0.2778 0.8772 0.7291 0.7765 0.7520
0.2819 0.78 48500 0.2772 0.8825 0.7532 0.7585 0.7559
0.2781 0.78 49000 0.2747 0.881 0.7502 0.7552 0.7527
0.2844 0.79 49500 0.2877 0.8762 0.7215 0.7877 0.7532
0.2732 0.8 50000 0.2738 0.8809 0.7511 0.7527 0.7519
0.2681 0.81 50500 0.2832 0.8761 0.7191 0.7932 0.7543
0.2795 0.82 51000 0.2755 0.8856 0.7876 0.7160 0.7501
0.2649 0.82 51500 0.2797 0.8805 0.7360 0.7823 0.7584
0.2776 0.83 52000 0.2671 0.8833 0.7627 0.7452 0.7538
0.2762 0.84 52500 0.2745 0.8812 0.7416 0.7744 0.7576
0.2803 0.85 53000 0.2766 0.8847 0.7694 0.7415 0.7551
0.2675 0.86 53500 0.2742 0.8785 0.7392 0.7623 0.7506
0.2725 0.86 54000 0.2720 0.8826 0.7576 0.7506 0.7541
0.2693 0.87 54500 0.2739 0.8836 0.7650 0.7427 0.7537
0.2745 0.88 55000 0.2751 0.8792 0.7348 0.7765 0.7551
0.273 0.89 55500 0.2762 0.8812 0.7388 0.7807 0.7591
0.2645 0.9 56000 0.2664 0.8828 0.7647 0.7385 0.7514
0.2698 0.9 56500 0.2728 0.8814 0.7467 0.7648 0.7557
0.2771 0.91 57000 0.2681 0.8839 0.7635 0.7473 0.7553
0.2663 0.92 57500 0.2715 0.885 0.7617 0.7573 0.7595
0.2546 0.93 58000 0.2836 0.8796 0.7323 0.7848 0.7576
0.2752 0.94 58500 0.2747 0.8801 0.7363 0.7790 0.7570
0.2645 0.94 59000 0.2733 0.8834 0.7484 0.7740 0.7610
0.2561 0.95 59500 0.2765 0.8828 0.7508 0.7652 0.7580
0.2753 0.96 60000 0.2721 0.8815 0.7483 0.7623 0.7552
0.251 0.97 60500 0.2735 0.8822 0.7546 0.7540 0.7543
0.2742 0.98 61000 0.2721 0.8831 0.7497 0.7694 0.7594
0.2734 0.98 61500 0.2712 0.8836 0.7512 0.7694 0.7602
0.2713 0.99 62000 0.2690 0.8836 0.7556 0.7606 0.7581
0.2764 1.0 62500 0.2689 0.8833 0.7551 0.7598 0.7574

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0