generated_from_trainer

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20230830145026

This model is a fine-tuned version of bert-large-cased on the super_glue 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 Accuracy
No log 1.0 340 0.7278 0.5219
0.7489 2.0 680 0.7277 0.5
0.7435 3.0 1020 0.7386 0.5
0.7435 4.0 1360 0.7302 0.5
0.7399 5.0 1700 0.7256 0.5
0.7423 6.0 2040 0.7256 0.5
0.7423 7.0 2380 0.7297 0.5
0.7409 8.0 2720 0.7365 0.5
0.7408 9.0 3060 0.7305 0.5
0.7408 10.0 3400 0.7599 0.5
0.7394 11.0 3740 0.7301 0.5
0.7356 12.0 4080 0.7531 0.5
0.7356 13.0 4420 0.7362 0.5
0.7351 14.0 4760 0.7306 0.5
0.7383 15.0 5100 0.7366 0.5
0.7383 16.0 5440 0.7526 0.5
0.7351 17.0 5780 0.7400 0.5
0.7354 18.0 6120 0.7302 0.5
0.7354 19.0 6460 0.7251 0.5
0.7328 20.0 6800 0.7274 0.5
0.733 21.0 7140 0.7263 0.5
0.733 22.0 7480 0.7423 0.5
0.7338 23.0 7820 0.7297 0.5
0.733 24.0 8160 0.7239 0.5
0.733 25.0 8500 0.7245 0.5
0.733 26.0 8840 0.7267 0.5
0.7332 27.0 9180 0.7595 0.5
0.7327 28.0 9520 0.7233 0.5
0.7327 29.0 9860 0.7339 0.5
0.7319 30.0 10200 0.7193 0.5047
0.7283 31.0 10540 0.6910 0.5987
0.7283 32.0 10880 0.7071 0.6614
0.7085 33.0 11220 0.6750 0.6489
0.6778 34.0 11560 0.6710 0.6614
0.6778 35.0 11900 0.7262 0.6426
0.6531 36.0 12240 0.6715 0.6677
0.6288 37.0 12580 0.7028 0.6536
0.6288 38.0 12920 0.7200 0.6505
0.6058 39.0 13260 0.7034 0.6536
0.5953 40.0 13600 0.6975 0.6458
0.5953 41.0 13940 0.6987 0.6489
0.5723 42.0 14280 0.7472 0.6426
0.555 43.0 14620 0.7437 0.6426
0.555 44.0 14960 0.7673 0.6066
0.5439 45.0 15300 0.7111 0.6348
0.533 46.0 15640 0.7657 0.6270
0.533 47.0 15980 0.8004 0.6332
0.525 48.0 16320 0.7423 0.6144
0.5098 49.0 16660 0.7131 0.6505
0.5078 50.0 17000 0.7751 0.6144
0.5078 51.0 17340 0.7272 0.6505
0.4987 52.0 17680 0.7666 0.6473
0.4892 53.0 18020 0.8000 0.6379
0.4892 54.0 18360 0.7950 0.6348
0.4813 55.0 18700 0.7802 0.6317
0.4756 56.0 19040 0.8083 0.6129
0.4756 57.0 19380 0.7916 0.6364
0.4685 58.0 19720 0.8475 0.6066
0.4694 59.0 20060 0.7576 0.6646
0.4694 60.0 20400 0.8712 0.6113
0.4513 61.0 20740 0.8164 0.6160
0.4492 62.0 21080 0.8174 0.6270
0.4492 63.0 21420 0.8198 0.6270
0.4458 64.0 21760 0.8006 0.6395
0.4351 65.0 22100 0.7962 0.6520
0.4351 66.0 22440 0.7911 0.6301
0.4346 67.0 22780 0.8150 0.6395
0.4356 68.0 23120 0.8336 0.6301
0.4356 69.0 23460 0.8777 0.6097
0.4283 70.0 23800 0.8313 0.6191
0.4212 71.0 24140 0.8191 0.6034
0.4212 72.0 24480 0.8384 0.6113
0.4197 73.0 24820 0.8532 0.6176
0.4172 74.0 25160 0.8398 0.6097
0.4112 75.0 25500 0.8536 0.6113
0.4112 76.0 25840 0.8130 0.6176
0.4104 77.0 26180 0.8463 0.6082
0.4128 78.0 26520 0.8342 0.6160
0.4128 79.0 26860 0.8502 0.6176
0.4004 80.0 27200 0.8508 0.6176

Framework versions