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scenario-kd-scr-ner-full_data-univner_full55

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metadata
base_model: haryoaw/scenario-TCR-NER_data-univner_full
library_name: transformers
license: mit
metrics:
  - precision
  - recall
  - f1
  - accuracy
tags:
  - generated_from_trainer
model-index:
  - name: scenario-kd-scr-ner-full_data-univner_full55
    results: []

scenario-kd-scr-ner-full_data-univner_full55

This model is a fine-tuned version of haryoaw/scenario-TCR-NER_data-univner_full on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1087
  • Precision: 0.6202
  • Recall: 0.5509
  • F1: 0.5835
  • Accuracy: 0.9594

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: 55
  • 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
2.7888 0.2910 500 2.4429 0.2941 0.0087 0.0168 0.9242
2.1116 0.5821 1000 2.1355 0.2870 0.1215 0.1707 0.9277
1.9109 0.8731 1500 2.0239 0.2850 0.1580 0.2033 0.9306
1.771 1.1641 2000 1.9268 0.4072 0.1535 0.2230 0.9319
1.633 1.4552 2500 1.8866 0.2862 0.2658 0.2756 0.9336
1.5872 1.7462 3000 1.7527 0.3273 0.2818 0.3028 0.9372
1.4833 2.0373 3500 1.7643 0.3558 0.2464 0.2912 0.9377
1.3965 2.3283 4000 1.6664 0.3675 0.3353 0.3507 0.9394
1.3237 2.6193 4500 1.6537 0.3445 0.3481 0.3463 0.9376
1.2791 2.9104 5000 1.5552 0.3960 0.3758 0.3857 0.9431
1.2023 3.2014 5500 1.5571 0.4338 0.3855 0.4083 0.9442
1.1456 3.4924 6000 1.4999 0.4258 0.3998 0.4124 0.9457
1.1315 3.7835 6500 1.4824 0.4244 0.3741 0.3977 0.9458
1.0693 4.0745 7000 1.4836 0.4407 0.3842 0.4105 0.9461
1.0052 4.3655 7500 1.4413 0.4322 0.4275 0.4298 0.9472
0.9737 4.6566 8000 1.4101 0.4634 0.4161 0.4385 0.9491
0.9521 4.9476 8500 1.3865 0.4476 0.4214 0.4341 0.9491
0.8818 5.2386 9000 1.4115 0.4612 0.4232 0.4414 0.9494
0.8396 5.5297 9500 1.3702 0.4645 0.4470 0.4556 0.9501
0.8456 5.8207 10000 1.3441 0.5076 0.4252 0.4627 0.9508
0.829 6.1118 10500 1.3357 0.4922 0.4718 0.4818 0.9518
0.7611 6.4028 11000 1.3320 0.5100 0.4548 0.4808 0.9522
0.7475 6.6938 11500 1.3570 0.4852 0.4953 0.4902 0.9531
0.7362 6.9849 12000 1.3154 0.5039 0.4929 0.4983 0.9529
0.6776 7.2759 12500 1.3044 0.5099 0.4884 0.4989 0.9534
0.6701 7.5669 13000 1.2921 0.5229 0.4675 0.4936 0.9541
0.6586 7.8580 13500 1.2670 0.5185 0.5067 0.5126 0.9548
0.6284 8.1490 14000 1.2752 0.5346 0.4979 0.5156 0.9548
0.6025 8.4400 14500 1.2738 0.5270 0.4884 0.5070 0.9545
0.5955 8.7311 15000 1.2564 0.5340 0.4895 0.5108 0.9552
0.5784 9.0221 15500 1.2502 0.5406 0.5035 0.5214 0.9546
0.5479 9.3132 16000 1.2339 0.5418 0.5203 0.5308 0.9566
0.54 9.6042 16500 1.2380 0.5473 0.5175 0.5320 0.9564
0.5368 9.8952 17000 1.2403 0.5726 0.5044 0.5363 0.9568
0.5151 10.1863 17500 1.2152 0.5516 0.5445 0.5480 0.9571
0.4959 10.4773 18000 1.2323 0.5657 0.5359 0.5504 0.9570
0.4946 10.7683 18500 1.2150 0.5679 0.5236 0.5449 0.9575
0.499 11.0594 19000 1.2119 0.5637 0.5372 0.5501 0.9576
0.462 11.3504 19500 1.2289 0.5736 0.5294 0.5506 0.9578
0.4631 11.6414 20000 1.2106 0.5661 0.5435 0.5546 0.9576
0.464 11.9325 20500 1.2292 0.5886 0.5087 0.5458 0.9576
0.4463 12.2235 21000 1.2135 0.5823 0.5465 0.5639 0.9578
0.4339 12.5146 21500 1.2098 0.5890 0.5208 0.5528 0.9578
0.4386 12.8056 22000 1.1906 0.5754 0.5387 0.5565 0.9573
0.4249 13.0966 22500 1.1972 0.5873 0.5379 0.5615 0.9580
0.4076 13.3877 23000 1.1994 0.5680 0.5585 0.5632 0.9576
0.4122 13.6787 23500 1.2129 0.5894 0.5331 0.5598 0.9580
0.4156 13.9697 24000 1.1865 0.5779 0.5485 0.5628 0.9580
0.3926 14.2608 24500 1.1828 0.5974 0.5397 0.5671 0.9589
0.3966 14.5518 25000 1.1764 0.5959 0.5390 0.5660 0.9586
0.3861 14.8428 25500 1.1769 0.5869 0.5307 0.5574 0.9581
0.3847 15.1339 26000 1.1997 0.5829 0.5406 0.5610 0.9581
0.3703 15.4249 26500 1.1809 0.5736 0.5543 0.5638 0.9582
0.3747 15.7159 27000 1.1896 0.5871 0.5320 0.5582 0.9577
0.3713 16.0070 27500 1.1700 0.5965 0.5422 0.5681 0.9589
0.3558 16.2980 28000 1.1922 0.5970 0.5416 0.5680 0.9586
0.3582 16.5891 28500 1.1507 0.5831 0.5470 0.5644 0.9586
0.3571 16.8801 29000 1.1405 0.5899 0.5418 0.5648 0.9584
0.3522 17.1711 29500 1.1610 0.6046 0.5517 0.5769 0.9588
0.3414 17.4622 30000 1.1670 0.6042 0.5485 0.5750 0.9590
0.3488 17.7532 30500 1.1502 0.5904 0.5624 0.5761 0.9586
0.34 18.0442 31000 1.1595 0.6091 0.5304 0.5670 0.9585
0.3336 18.3353 31500 1.1553 0.6025 0.5439 0.5717 0.9589
0.3295 18.6263 32000 1.1683 0.5916 0.5337 0.5611 0.9580
0.3345 18.9173 32500 1.1478 0.5825 0.5536 0.5677 0.9585
0.3263 19.2084 33000 1.1415 0.6093 0.5369 0.5708 0.9589
0.3206 19.4994 33500 1.1410 0.5888 0.5637 0.5760 0.9593
0.3234 19.7905 34000 1.1371 0.6072 0.5490 0.5766 0.9591
0.3212 20.0815 34500 1.1401 0.6006 0.5478 0.5730 0.9587
0.3154 20.3725 35000 1.1505 0.6165 0.5400 0.5758 0.9591
0.3081 20.6636 35500 1.1512 0.5977 0.5393 0.5670 0.9591
0.3137 20.9546 36000 1.1477 0.6185 0.5357 0.5741 0.9590
0.3048 21.2456 36500 1.1344 0.6070 0.5416 0.5724 0.9593
0.3028 21.5367 37000 1.1308 0.6192 0.5481 0.5815 0.9594
0.3039 21.8277 37500 1.1492 0.6167 0.5318 0.5711 0.9591
0.3013 22.1187 38000 1.1340 0.6139 0.5393 0.5742 0.9592
0.2966 22.4098 38500 1.1176 0.6073 0.5561 0.5806 0.9594
0.2956 22.7008 39000 1.1156 0.6100 0.5627 0.5854 0.9593
0.2982 22.9919 39500 1.1282 0.6162 0.5553 0.5842 0.9596
0.2915 23.2829 40000 1.1359 0.6048 0.5510 0.5766 0.9593
0.2882 23.5739 40500 1.1194 0.6075 0.5517 0.5783 0.9592
0.2906 23.8650 41000 1.1256 0.6058 0.5442 0.5734 0.9590
0.2852 24.1560 41500 1.1115 0.6143 0.5465 0.5785 0.9596
0.2864 24.4470 42000 1.1214 0.6103 0.5441 0.5753 0.9594
0.2829 24.7381 42500 1.1333 0.6267 0.5346 0.5770 0.9592
0.2836 25.0291 43000 1.1195 0.6067 0.5550 0.5797 0.9591
0.2795 25.3201 43500 1.1260 0.6332 0.5315 0.5779 0.9593
0.2779 25.6112 44000 1.1119 0.6164 0.5457 0.5789 0.9597
0.2787 25.9022 44500 1.1094 0.6103 0.5640 0.5862 0.9600
0.2765 26.1932 45000 1.1104 0.6166 0.5474 0.5799 0.9596
0.2743 26.4843 45500 1.1164 0.6172 0.5553 0.5846 0.9596
0.2731 26.7753 46000 1.1246 0.6158 0.5578 0.5854 0.9594
0.2705 27.0664 46500 1.1110 0.6153 0.5468 0.5790 0.9593
0.2707 27.3574 47000 1.1101 0.6207 0.5586 0.5880 0.9602
0.2713 27.6484 47500 1.1131 0.6203 0.5455 0.5805 0.9596
0.2704 27.9395 48000 1.1122 0.6193 0.5494 0.5823 0.9596
0.2669 28.2305 48500 1.1127 0.6139 0.5519 0.5812 0.9596
0.2696 28.5215 49000 1.1148 0.6233 0.5449 0.5815 0.9597
0.2658 28.8126 49500 1.1130 0.6182 0.5451 0.5794 0.9597
0.2663 29.1036 50000 1.1070 0.6170 0.5475 0.5802 0.9593
0.2625 29.3946 50500 1.1055 0.6172 0.5498 0.5816 0.9599
0.2652 29.6857 51000 1.1010 0.6332 0.5516 0.5896 0.9603
0.2662 29.9767 51500 1.1087 0.6202 0.5509 0.5835 0.9594

Framework versions

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