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scenario-non-kd-scr-ner-full-xlmr_data-univner_full66

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xlm-roberta
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metadata
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: scenario-non-kd-scr-ner-full-xlmr_data-univner_full66
    results: []

scenario-non-kd-scr-ner-full-xlmr_data-univner_full66

This model is a fine-tuned version of FacebookAI/xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3798
  • Precision: 0.5821
  • Recall: 0.5864
  • F1: 0.5842
  • Accuracy: 0.9606

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: 3e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 66
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.3415 0.2910 500 0.2817 0.4535 0.1387 0.2124 0.9301
0.2629 0.5821 1000 0.2498 0.3527 0.1915 0.2482 0.9340
0.2318 0.8731 1500 0.2322 0.3468 0.2285 0.2755 0.9366
0.2056 1.1641 2000 0.2234 0.3522 0.2851 0.3151 0.9380
0.1827 1.4552 2500 0.2226 0.3487 0.3443 0.3465 0.9395
0.1777 1.7462 3000 0.2044 0.4135 0.3391 0.3726 0.9419
0.1627 2.0373 3500 0.2053 0.3898 0.3799 0.3848 0.9423
0.1372 2.3283 4000 0.2020 0.4118 0.3959 0.4037 0.9434
0.1294 2.6193 4500 0.1890 0.4132 0.4460 0.4289 0.9467
0.1224 2.9104 5000 0.1756 0.4334 0.4686 0.4503 0.9480
0.0947 3.2014 5500 0.1864 0.4487 0.5070 0.4761 0.9491
0.0861 3.4924 6000 0.1806 0.5010 0.4972 0.4991 0.9533
0.0815 3.7835 6500 0.1839 0.5021 0.4905 0.4963 0.9533
0.0746 4.0745 7000 0.1943 0.4900 0.5006 0.4953 0.9534
0.0574 4.3655 7500 0.2050 0.5221 0.4881 0.5045 0.9544
0.0585 4.6566 8000 0.1908 0.5093 0.5351 0.5219 0.9552
0.0565 4.9476 8500 0.1902 0.5107 0.5334 0.5218 0.9552
0.0425 5.2386 9000 0.2168 0.5241 0.5351 0.5296 0.9565
0.0412 5.5297 9500 0.2073 0.5717 0.5116 0.5400 0.9568
0.0392 5.8207 10000 0.2052 0.5331 0.5406 0.5368 0.9561
0.0362 6.1118 10500 0.2221 0.4957 0.5700 0.5303 0.9548
0.0291 6.4028 11000 0.2298 0.5156 0.5431 0.5290 0.9559
0.0274 6.6938 11500 0.2378 0.5018 0.5409 0.5206 0.9548
0.0296 6.9849 12000 0.2342 0.5433 0.5408 0.5420 0.9571
0.0201 7.2759 12500 0.2525 0.5167 0.5777 0.5455 0.9573
0.0211 7.5669 13000 0.2469 0.5491 0.5413 0.5452 0.9577
0.0218 7.8580 13500 0.2429 0.5509 0.5454 0.5481 0.9570
0.0178 8.1490 14000 0.2578 0.5129 0.5468 0.5293 0.9559
0.0153 8.4400 14500 0.2565 0.5540 0.5607 0.5573 0.9584
0.0157 8.7311 15000 0.2661 0.5652 0.5317 0.5479 0.9579
0.0158 9.0221 15500 0.2706 0.5403 0.5604 0.5501 0.9579
0.012 9.3132 16000 0.2912 0.5543 0.5359 0.5449 0.9580
0.0123 9.6042 16500 0.2804 0.5234 0.5784 0.5496 0.9573
0.0124 9.8952 17000 0.2640 0.5384 0.5659 0.5518 0.9576
0.0106 10.1863 17500 0.2812 0.5626 0.5481 0.5552 0.9582
0.0094 10.4773 18000 0.2928 0.5317 0.5869 0.5579 0.9574
0.0094 10.7683 18500 0.2820 0.5695 0.5377 0.5532 0.9583
0.0098 11.0594 19000 0.2896 0.5526 0.5574 0.5550 0.9581
0.0072 11.3504 19500 0.2952 0.5509 0.5832 0.5666 0.9584
0.0078 11.6414 20000 0.2940 0.5288 0.5957 0.5603 0.9574
0.0078 11.9325 20500 0.2972 0.5439 0.5634 0.5535 0.9579
0.0061 12.2235 21000 0.3019 0.5683 0.5861 0.5770 0.9596
0.0062 12.5146 21500 0.3057 0.5477 0.5640 0.5557 0.9582
0.0065 12.8056 22000 0.3010 0.5546 0.5703 0.5623 0.9581
0.0058 13.0966 22500 0.3143 0.5460 0.5836 0.5642 0.9589
0.0051 13.3877 23000 0.3061 0.5576 0.5776 0.5674 0.9591
0.0056 13.6787 23500 0.3028 0.5428 0.5813 0.5614 0.9582
0.0054 13.9697 24000 0.3043 0.5553 0.5726 0.5638 0.9581
0.0037 14.2608 24500 0.3197 0.5485 0.5901 0.5685 0.9588
0.0043 14.5518 25000 0.3163 0.5730 0.5585 0.5656 0.9586
0.0047 14.8428 25500 0.3160 0.5476 0.5813 0.5640 0.9587
0.0036 15.1339 26000 0.3385 0.6042 0.5382 0.5693 0.9594
0.0036 15.4249 26500 0.3352 0.5462 0.5786 0.5619 0.9591
0.0033 15.7159 27000 0.3312 0.5505 0.5758 0.5629 0.9587
0.0036 16.0070 27500 0.3457 0.5735 0.5553 0.5642 0.9594
0.0027 16.2980 28000 0.3351 0.5602 0.5703 0.5652 0.9589
0.0031 16.5891 28500 0.3375 0.5714 0.5451 0.5579 0.9589
0.0033 16.8801 29000 0.3349 0.5621 0.5814 0.5716 0.9590
0.0024 17.1711 29500 0.3422 0.5545 0.5869 0.5703 0.9595
0.0024 17.4622 30000 0.3313 0.5552 0.6018 0.5775 0.9588
0.0025 17.7532 30500 0.3302 0.5683 0.5832 0.5757 0.9595
0.0023 18.0442 31000 0.3387 0.5555 0.5845 0.5696 0.9591
0.0022 18.3353 31500 0.3519 0.5757 0.5497 0.5624 0.9591
0.0019 18.6263 32000 0.3471 0.5574 0.5888 0.5727 0.9592
0.0022 18.9173 32500 0.3429 0.5632 0.5882 0.5754 0.9597
0.0017 19.2084 33000 0.3576 0.5673 0.5765 0.5719 0.9599
0.0019 19.4994 33500 0.3459 0.5637 0.5791 0.5713 0.9593
0.0017 19.7905 34000 0.3516 0.5643 0.5686 0.5664 0.9593
0.0015 20.0815 34500 0.3632 0.5790 0.5764 0.5777 0.9599
0.0015 20.3725 35000 0.3528 0.5731 0.5791 0.5761 0.9598
0.0015 20.6636 35500 0.3560 0.5582 0.5788 0.5684 0.9589
0.0015 20.9546 36000 0.3525 0.5698 0.5770 0.5734 0.9593
0.0012 21.2456 36500 0.3562 0.5723 0.5741 0.5732 0.9597
0.0013 21.5367 37000 0.3584 0.5690 0.5679 0.5684 0.9595
0.0013 21.8277 37500 0.3598 0.5547 0.6047 0.5786 0.9593
0.0011 22.1187 38000 0.3639 0.5676 0.5814 0.5744 0.9598
0.0008 22.4098 38500 0.3594 0.5576 0.5881 0.5724 0.9590
0.001 22.7008 39000 0.3661 0.5696 0.5786 0.5740 0.9597
0.001 22.9919 39500 0.3595 0.5621 0.5905 0.5760 0.9597
0.0008 23.2829 40000 0.3634 0.5700 0.5813 0.5756 0.9603
0.0008 23.5739 40500 0.3619 0.5790 0.5700 0.5745 0.9597
0.001 23.8650 41000 0.3704 0.5839 0.5666 0.5751 0.9599
0.001 24.1560 41500 0.3639 0.5679 0.5923 0.5798 0.9597
0.0008 24.4470 42000 0.3688 0.5703 0.5773 0.5738 0.9594
0.0007 24.7381 42500 0.3794 0.5712 0.5803 0.5757 0.9600
0.0007 25.0291 43000 0.3754 0.5807 0.5662 0.5733 0.9597
0.0006 25.3201 43500 0.3732 0.5809 0.5866 0.5837 0.9602
0.0007 25.6112 44000 0.3795 0.5940 0.5550 0.5739 0.9598
0.0008 25.9022 44500 0.3721 0.5729 0.5843 0.5786 0.9599
0.0004 26.1932 45000 0.3773 0.5790 0.5734 0.5762 0.9601
0.0005 26.4843 45500 0.3811 0.5788 0.5713 0.5750 0.9600
0.0005 26.7753 46000 0.3787 0.5757 0.5901 0.5828 0.9603
0.0005 27.0664 46500 0.3766 0.5740 0.5817 0.5779 0.9601
0.0004 27.3574 47000 0.3786 0.5758 0.5800 0.5779 0.9602
0.0004 27.6484 47500 0.3800 0.5755 0.5866 0.5810 0.9603
0.0004 27.9395 48000 0.3824 0.5843 0.5752 0.5798 0.9603
0.0004 28.2305 48500 0.3836 0.5829 0.5739 0.5784 0.9603
0.0002 28.5215 49000 0.3813 0.5894 0.5804 0.5849 0.9607
0.0004 28.8126 49500 0.3817 0.5824 0.5852 0.5838 0.9604
0.0004 29.1036 50000 0.3816 0.5814 0.5816 0.5815 0.9604
0.0003 29.3946 50500 0.3800 0.5804 0.5865 0.5834 0.9605
0.0003 29.6857 51000 0.3794 0.5831 0.5864 0.5847 0.9606
0.0002 29.9767 51500 0.3798 0.5821 0.5864 0.5842 0.9606

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.1.1+cu121
  • Datasets 2.14.5
  • Tokenizers 0.19.1