generated_from_trainer

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longformer-base-4096-bne-es-finetuned-augmented1

This model is a fine-tuned version of PlanTL-GOB-ES/longformer-base-4096-bne-es on the None dataset. It achieves the following results on the evaluation set:

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:

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.723 1.0 713 0.5777 0.3428 0.5358 0.4181 0.8332
0.3414 2.0 1426 0.7079 0.3337 0.5462 0.4143 0.8210
0.1307 3.0 2139 0.7862 0.4226 0.5868 0.4913 0.8298
0.0792 4.0 2852 0.9581 0.4215 0.5906 0.4919 0.8245
0.0427 5.0 3565 1.0090 0.4451 0.6047 0.5128 0.8303
0.032 6.0 4278 1.0855 0.4865 0.6123 0.5422 0.8450
0.0237 7.0 4991 1.1150 0.4693 0.6066 0.5292 0.8455
0.0171 8.0 5704 1.1544 0.4778 0.5991 0.5316 0.8456
0.0155 9.0 6417 1.1691 0.4812 0.6038 0.5356 0.8421
0.0114 10.0 7130 1.2833 0.4861 0.6104 0.5412 0.8349
0.0137 11.0 7843 1.2716 0.4594 0.6028 0.5214 0.8334
0.0104 12.0 8556 1.2635 0.4571 0.5981 0.5182 0.8459
0.0053 13.0 9269 1.2427 0.4447 0.6066 0.5132 0.8419
0.0067 14.0 9982 1.2834 0.4862 0.6 0.5372 0.8432
0.0068 15.0 10695 1.3774 0.5012 0.6094 0.5500 0.8373
0.0077 16.0 11408 1.3625 0.4871 0.6057 0.5399 0.8428
0.0051 17.0 12121 1.3764 0.5 0.6113 0.5501 0.8445
0.0061 18.0 12834 1.5528 0.4613 0.6009 0.5219 0.8267
0.0049 19.0 13547 1.3307 0.5070 0.6151 0.5558 0.8538
0.0059 20.0 14260 1.3556 0.4903 0.6198 0.5475 0.8439
0.0064 21.0 14973 1.4599 0.5004 0.6123 0.5507 0.8409
0.0057 22.0 15686 1.3506 0.5077 0.6217 0.5589 0.8439
0.0054 23.0 16399 1.5439 0.4914 0.5953 0.5384 0.8377
0.0034 24.0 17112 1.5174 0.5059 0.6066 0.5517 0.8377
0.0048 25.0 17825 1.5228 0.4984 0.6057 0.5468 0.8438
0.0041 26.0 18538 1.4479 0.5224 0.6057 0.5609 0.8403
0.0049 27.0 19251 1.3992 0.5291 0.6349 0.5772 0.8447
0.0048 28.0 19964 1.4971 0.5234 0.6321 0.5726 0.8478
0.0018 29.0 20677 1.4874 0.4981 0.6151 0.5504 0.8390
0.0035 30.0 21390 1.3051 0.5051 0.6094 0.5524 0.8421
0.0031 31.0 22103 1.5998 0.5133 0.6179 0.5608 0.8364
0.0031 32.0 22816 1.4274 0.5085 0.6179 0.5579 0.8458
0.0042 33.0 23529 1.3180 0.5111 0.6066 0.5548 0.8494
0.0022 34.0 24242 1.5043 0.4886 0.6085 0.5420 0.8442
0.0021 35.0 24955 1.5247 0.4962 0.6094 0.5470 0.8425
0.0024 36.0 25668 1.5139 0.4851 0.5981 0.5357 0.8432
0.0027 37.0 26381 1.5214 0.4930 0.6009 0.5417 0.8404
0.0024 38.0 27094 1.4470 0.5087 0.6075 0.5537 0.8472
0.0009 39.0 27807 1.4867 0.5016 0.6094 0.5503 0.8485
0.0015 40.0 28520 1.5234 0.5148 0.6217 0.5632 0.8483
0.0023 41.0 29233 1.5742 0.4926 0.6264 0.5515 0.8407
0.0017 42.0 29946 1.5897 0.5252 0.6 0.5601 0.8403
0.0022 43.0 30659 1.4243 0.4889 0.6038 0.5403 0.8448
0.001 44.0 31372 1.6117 0.5081 0.6179 0.5577 0.8462
0.0015 45.0 32085 1.5342 0.5169 0.6066 0.5582 0.8405
0.0005 46.0 32798 1.5110 0.4687 0.6142 0.5316 0.8432
0.0019 47.0 33511 1.5835 0.5066 0.6132 0.5548 0.8427
0.0063 48.0 34224 1.5688 0.5058 0.5802 0.5404 0.8394
0.0017 49.0 34937 1.5410 0.5075 0.6028 0.5511 0.8419
0.0012 50.0 35650 1.5343 0.5220 0.5943 0.5558 0.8359
0.0009 51.0 36363 1.5190 0.5173 0.6358 0.5705 0.8411
0.0006 52.0 37076 1.6576 0.5066 0.6189 0.5571 0.8311
0.0009 53.0 37789 1.5675 0.5155 0.6283 0.5663 0.8475
0.0007 54.0 38502 1.6993 0.5218 0.6208 0.5670 0.8328
0.0019 55.0 39215 1.6003 0.5284 0.6047 0.5640 0.8365
0.0014 56.0 39928 1.4922 0.5428 0.6226 0.5800 0.8556
0.0004 57.0 40641 1.5974 0.5402 0.6142 0.5748 0.8464
0.0002 58.0 41354 1.7351 0.5501 0.6113 0.5791 0.8417
0.0008 59.0 42067 1.6191 0.5179 0.6132 0.5616 0.8476
0.0006 60.0 42780 1.5721 0.5059 0.6094 0.5528 0.8455
0.0009 61.0 43493 1.6079 0.4980 0.6 0.5443 0.8388
0.0011 62.0 44206 1.7208 0.4907 0.5943 0.5375 0.8288
0.0002 63.0 44919 1.7335 0.5012 0.5925 0.5430 0.8354
0.001 64.0 45632 1.7670 0.5439 0.6189 0.5790 0.8352
0.0002 65.0 46345 1.7687 0.5203 0.6170 0.5645 0.8430
0.0002 66.0 47058 1.7857 0.5059 0.6066 0.5517 0.8375
0.0003 67.0 47771 1.7961 0.5090 0.6104 0.5551 0.8335
0.0009 68.0 48484 1.7072 0.5039 0.6132 0.5532 0.8416
0.0003 69.0 49197 1.7345 0.5147 0.6113 0.5589 0.8421
0.0002 70.0 49910 1.6423 0.5427 0.6179 0.5779 0.8491
0.0007 71.0 50623 1.6966 0.5422 0.6368 0.5857 0.8425
0.0016 72.0 51336 1.7376 0.5153 0.6198 0.5627 0.8349
0.001 73.0 52049 1.6447 0.51 0.6255 0.5619 0.8442
0.0001 74.0 52762 1.7449 0.5204 0.6132 0.5630 0.8421
0.0002 75.0 53475 1.6948 0.5287 0.6179 0.5698 0.8450
0.0005 76.0 54188 1.6546 0.5305 0.6321 0.5768 0.8480
0.0002 77.0 54901 1.7188 0.5224 0.6264 0.5697 0.8444
0.0001 78.0 55614 1.6167 0.5102 0.6142 0.5574 0.8462
0.0005 79.0 56327 1.6709 0.5160 0.6245 0.5651 0.8462
0.0 80.0 57040 1.6883 0.5223 0.6179 0.5661 0.8475
0.0002 81.0 57753 1.7612 0.5051 0.6057 0.5508 0.8436
0.0001 82.0 58466 1.7766 0.5342 0.6189 0.5734 0.8410
0.0001 83.0 59179 1.7235 0.5252 0.6189 0.5682 0.8453
0.0002 84.0 59892 1.7663 0.5319 0.6208 0.5729 0.8440
0.0007 85.0 60605 1.7581 0.5280 0.6217 0.5711 0.8408
0.0002 86.0 61318 1.7467 0.5271 0.6236 0.5713 0.8407
0.0003 87.0 62031 1.7220 0.5275 0.6151 0.5679 0.8437
0.0001 88.0 62744 1.7616 0.5207 0.6179 0.5651 0.8430
0.0 89.0 63457 1.7817 0.5396 0.6170 0.5757 0.8460
0.0 90.0 64170 1.7684 0.5319 0.6132 0.5697 0.8436
0.0 91.0 64883 1.7731 0.5264 0.6208 0.5697 0.8434
0.0 92.0 65596 1.7448 0.5314 0.6236 0.5738 0.8467
0.0 93.0 66309 1.7457 0.5353 0.6302 0.5789 0.8484
0.0 94.0 67022 1.7477 0.5424 0.6274 0.5818 0.8485
0.0 95.0 67735 1.7931 0.5292 0.6160 0.5693 0.8444
0.0002 96.0 68448 1.8056 0.5287 0.6170 0.5694 0.8455
0.0001 97.0 69161 1.7963 0.5247 0.6217 0.5691 0.8450
0.0001 98.0 69874 1.7963 0.5211 0.6179 0.5654 0.8446
0.0001 99.0 70587 1.7950 0.5261 0.6189 0.5687 0.8452
0.0002 100.0 71300 1.7936 0.5307 0.6189 0.5714 0.8447

Framework versions