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scenario-kd-scr-ner-full_data-univner_full44

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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_full44
    results: []

scenario-kd-scr-ner-full_data-univner_full44

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: 1.1033
  • Precision: 0.6036
  • Recall: 0.5441
  • F1: 0.5723
  • Accuracy: 0.9593

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: 44
  • 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
2.8555 0.2910 500 2.5101 0.1888 0.0407 0.0669 0.9233
2.1564 0.5821 1000 2.1629 0.2303 0.1529 0.1838 0.9272
1.9176 0.8731 1500 2.0551 0.2484 0.1326 0.1729 0.9290
1.7645 1.1641 2000 1.9667 0.2914 0.1539 0.2015 0.9319
1.6699 1.4552 2500 1.8455 0.2770 0.2391 0.2566 0.9342
1.5836 1.7462 3000 1.8413 0.3525 0.2388 0.2847 0.9377
1.4981 2.0373 3500 1.6842 0.3698 0.2897 0.3249 0.9398
1.3481 2.3283 4000 1.6502 0.3867 0.3320 0.3573 0.9405
1.3432 2.6193 4500 1.6815 0.3763 0.3334 0.3536 0.9382
1.2954 2.9104 5000 1.5667 0.4099 0.3443 0.3742 0.9431
1.1925 3.2014 5500 1.5220 0.4337 0.3594 0.3931 0.9441
1.1588 3.4924 6000 1.5354 0.4016 0.3821 0.3916 0.9428
1.107 3.7835 6500 1.4844 0.4572 0.3718 0.4101 0.9456
1.0738 4.0745 7000 1.4503 0.4181 0.4041 0.4110 0.9465
0.9979 4.3655 7500 1.4415 0.4712 0.3787 0.4199 0.9472
0.9833 4.6566 8000 1.4303 0.4696 0.4158 0.4411 0.9475
0.9564 4.9476 8500 1.4136 0.4310 0.4250 0.4280 0.9481
0.8779 5.2386 9000 1.3971 0.4514 0.4465 0.4489 0.9495
0.8511 5.5297 9500 1.3609 0.4661 0.4161 0.4397 0.9495
0.849 5.8207 10000 1.3403 0.5161 0.4224 0.4646 0.9508
0.8182 6.1118 10500 1.3515 0.4611 0.4581 0.4596 0.9507
0.76 6.4028 11000 1.3629 0.4871 0.4484 0.4670 0.9515
0.7494 6.6938 11500 1.3188 0.4907 0.4812 0.4859 0.9526
0.7363 6.9849 12000 1.3117 0.4692 0.4425 0.4555 0.9518
0.6894 7.2759 12500 1.3309 0.5193 0.4308 0.4709 0.9526
0.6688 7.5669 13000 1.3295 0.5067 0.4526 0.4781 0.9529
0.6628 7.8580 13500 1.2883 0.5212 0.4920 0.5062 0.9542
0.6274 8.1490 14000 1.2968 0.5284 0.4878 0.5073 0.9544
0.6066 8.4400 14500 1.2572 0.5216 0.4942 0.5075 0.9543
0.6126 8.7311 15000 1.2623 0.5105 0.5167 0.5136 0.9548
0.5929 9.0221 15500 1.2700 0.5223 0.4940 0.5077 0.9548
0.5614 9.3132 16000 1.2609 0.5307 0.5054 0.5177 0.9552
0.5557 9.6042 16500 1.2618 0.5590 0.4766 0.5145 0.9552
0.5526 9.8952 17000 1.2479 0.5451 0.4975 0.5202 0.9556
0.526 10.1863 17500 1.2370 0.5432 0.5211 0.5320 0.9566
0.5102 10.4773 18000 1.2283 0.5435 0.5232 0.5331 0.9563
0.5163 10.7683 18500 1.2373 0.5523 0.5279 0.5398 0.9562
0.4977 11.0594 19000 1.2457 0.5610 0.5028 0.5303 0.9564
0.4829 11.3504 19500 1.2389 0.5677 0.5268 0.5465 0.9572
0.4692 11.6414 20000 1.2132 0.5599 0.5198 0.5391 0.9570
0.4749 11.9325 20500 1.2180 0.5531 0.5377 0.5453 0.9565
0.4533 12.2235 21000 1.2167 0.5562 0.5193 0.5371 0.9569
0.4446 12.5146 21500 1.2015 0.5489 0.5331 0.5409 0.9568
0.447 12.8056 22000 1.2149 0.5598 0.5258 0.5422 0.9566
0.4394 13.0966 22500 1.1838 0.5620 0.5376 0.5495 0.9570
0.4245 13.3877 23000 1.1928 0.5910 0.5269 0.5571 0.9582
0.4152 13.6787 23500 1.1844 0.5571 0.5331 0.5449 0.9570
0.4164 13.9697 24000 1.2065 0.5649 0.5304 0.5471 0.9572
0.397 14.2608 24500 1.1761 0.5779 0.5304 0.5531 0.9572
0.3952 14.5518 25000 1.1791 0.5659 0.5379 0.5515 0.9575
0.3973 14.8428 25500 1.2108 0.5868 0.5262 0.5548 0.9575
0.3906 15.1339 26000 1.1774 0.5684 0.5568 0.5625 0.9581
0.3781 15.4249 26500 1.1943 0.5897 0.5337 0.5603 0.9579
0.3723 15.7159 27000 1.1733 0.5578 0.5519 0.5548 0.9571
0.3705 16.0070 27500 1.1785 0.5711 0.5403 0.5553 0.9578
0.3563 16.2980 28000 1.1837 0.5760 0.5356 0.5551 0.9580
0.3542 16.5891 28500 1.1748 0.5918 0.5195 0.5533 0.9580
0.3603 16.8801 29000 1.1675 0.5874 0.5429 0.5643 0.9585
0.3518 17.1711 29500 1.1536 0.5918 0.5444 0.5671 0.9582
0.343 17.4622 30000 1.1422 0.5899 0.5333 0.5602 0.9583
0.3399 17.7532 30500 1.1619 0.5854 0.5258 0.5540 0.9581
0.3355 18.0442 31000 1.1395 0.5846 0.5423 0.5627 0.9584
0.3317 18.3353 31500 1.1426 0.5920 0.5465 0.5683 0.9584
0.3284 18.6263 32000 1.1357 0.5834 0.5496 0.5660 0.9590
0.3262 18.9173 32500 1.1450 0.5751 0.5403 0.5572 0.9583
0.3215 19.2084 33000 1.1376 0.5822 0.5500 0.5656 0.9587
0.3188 19.4994 33500 1.1514 0.5885 0.5442 0.5655 0.9587
0.3172 19.7905 34000 1.1390 0.5908 0.5416 0.5651 0.9585
0.3102 20.0815 34500 1.1351 0.5968 0.5367 0.5652 0.9587
0.3109 20.3725 35000 1.1356 0.5980 0.5392 0.5671 0.9586
0.3039 20.6636 35500 1.1291 0.6160 0.5187 0.5632 0.9587
0.307 20.9546 36000 1.1421 0.6046 0.5423 0.5718 0.9588
0.3043 21.2456 36500 1.1416 0.6020 0.5553 0.5777 0.9591
0.2936 21.5367 37000 1.1234 0.6004 0.5475 0.5727 0.9591
0.2984 21.8277 37500 1.1317 0.5894 0.5423 0.5649 0.9582
0.2986 22.1187 38000 1.1294 0.5961 0.5468 0.5704 0.9590
0.2885 22.4098 38500 1.1287 0.6151 0.5385 0.5742 0.9591
0.2897 22.7008 39000 1.1261 0.5981 0.5507 0.5734 0.9590
0.2897 22.9919 39500 1.1226 0.6051 0.5393 0.5703 0.9589
0.2857 23.2829 40000 1.1248 0.6082 0.5353 0.5694 0.9591
0.2819 23.5739 40500 1.1162 0.6230 0.5413 0.5793 0.9594
0.2867 23.8650 41000 1.1145 0.6027 0.5435 0.5716 0.9591
0.2796 24.1560 41500 1.1193 0.6025 0.5488 0.5744 0.9592
0.2758 24.4470 42000 1.1114 0.6073 0.5529 0.5788 0.9593
0.2779 24.7381 42500 1.1022 0.6036 0.5530 0.5772 0.9596
0.2782 25.0291 43000 1.1104 0.6155 0.5434 0.5772 0.9593
0.2723 25.3201 43500 1.1179 0.6139 0.5275 0.5674 0.9590
0.276 25.6112 44000 1.1176 0.6099 0.5445 0.5753 0.9592
0.2694 25.9022 44500 1.1124 0.6043 0.5386 0.5696 0.9591
0.2723 26.1932 45000 1.1185 0.6068 0.5421 0.5726 0.9591
0.2664 26.4843 45500 1.1090 0.6026 0.5455 0.5727 0.9592
0.2675 26.7753 46000 1.1077 0.6022 0.5571 0.5787 0.9594
0.2687 27.0664 46500 1.1059 0.6146 0.5353 0.5722 0.9593
0.2635 27.3574 47000 1.1013 0.5965 0.5535 0.5742 0.9590
0.2652 27.6484 47500 1.1031 0.6048 0.5340 0.5672 0.9588
0.2657 27.9395 48000 1.1053 0.6074 0.5471 0.5757 0.9595
0.2626 28.2305 48500 1.1000 0.6154 0.5452 0.5782 0.9595
0.2596 28.5215 49000 1.1055 0.6012 0.5497 0.5743 0.9594
0.2648 28.8126 49500 1.1101 0.6109 0.5454 0.5763 0.9593
0.2597 29.1036 50000 1.1020 0.6181 0.5455 0.5796 0.9595
0.2598 29.3946 50500 1.1045 0.608 0.5483 0.5766 0.9592
0.2577 29.6857 51000 1.1024 0.6052 0.5559 0.5795 0.9593
0.2622 29.9767 51500 1.1033 0.6036 0.5441 0.5723 0.9593

Framework versions

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