generated_from_trainer

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20230830102607

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 0.6741 0.5361
0.6968 2.0 680 0.6803 0.5
0.6934 3.0 1020 0.6837 0.5
0.6934 4.0 1360 0.6453 0.5752
0.67 5.0 1700 0.6319 0.6379
0.6229 6.0 2040 0.6657 0.6223
0.6229 7.0 2380 0.6293 0.6693
0.585 8.0 2720 0.6188 0.6771
0.5622 9.0 3060 0.7399 0.6379
0.5622 10.0 3400 0.7147 0.6176
0.5341 11.0 3740 0.6935 0.6552
0.5113 12.0 4080 0.6084 0.6677
0.5113 13.0 4420 0.6515 0.6818
0.4915 14.0 4760 0.8644 0.6176
0.4663 15.0 5100 0.6619 0.6567
0.4663 16.0 5440 0.7789 0.6505
0.4606 17.0 5780 0.7261 0.6489
0.4483 18.0 6120 0.7978 0.6442
0.4483 19.0 6460 0.6810 0.6442
0.4332 20.0 6800 0.8449 0.6505
0.4219 21.0 7140 0.8289 0.6379
0.4219 22.0 7480 0.7733 0.6614
0.4134 23.0 7820 0.7533 0.6536
0.399 24.0 8160 0.7560 0.6395
0.4001 25.0 8500 0.8408 0.6348
0.4001 26.0 8840 0.7573 0.6599
0.3922 27.0 9180 0.7875 0.6113
0.3847 28.0 9520 0.7636 0.6442
0.3847 29.0 9860 0.8088 0.6473
0.3735 30.0 10200 0.7499 0.6505
0.3754 31.0 10540 0.7845 0.6411
0.3754 32.0 10880 0.8677 0.6364
0.3676 33.0 11220 0.7861 0.6411
0.3607 34.0 11560 0.7834 0.6332
0.3607 35.0 11900 0.9127 0.6426
0.3596 36.0 12240 0.8042 0.6395
0.3557 37.0 12580 0.8923 0.6379
0.3557 38.0 12920 0.7978 0.6379
0.3478 39.0 13260 0.8106 0.6458
0.3467 40.0 13600 0.8699 0.6301
0.3467 41.0 13940 0.8522 0.6426
0.3408 42.0 14280 0.8836 0.6317
0.3373 43.0 14620 0.8808 0.6238
0.3373 44.0 14960 0.9437 0.6301
0.3308 45.0 15300 0.8724 0.6317
0.3282 46.0 15640 0.9243 0.6364
0.3282 47.0 15980 0.8446 0.6364
0.32 48.0 16320 0.8618 0.6238
0.3224 49.0 16660 0.8313 0.6270
0.317 50.0 17000 0.8845 0.6348
0.317 51.0 17340 0.9127 0.6442
0.3199 52.0 17680 0.9284 0.6395
0.3158 53.0 18020 0.9305 0.6379
0.3158 54.0 18360 0.8782 0.6379
0.3048 55.0 18700 0.8722 0.6348
0.3086 56.0 19040 0.8814 0.6411
0.3086 57.0 19380 0.8981 0.6364
0.3074 58.0 19720 0.8604 0.6238
0.3012 59.0 20060 0.8734 0.6301
0.3012 60.0 20400 0.9171 0.6332
0.299 61.0 20740 0.9065 0.6317
0.296 62.0 21080 0.9070 0.6332
0.296 63.0 21420 0.9196 0.6254
0.2954 64.0 21760 0.9671 0.6364
0.2964 65.0 22100 0.9451 0.6348
0.2964 66.0 22440 0.8934 0.6411
0.2825 67.0 22780 0.9219 0.6426
0.2997 68.0 23120 0.9352 0.6332
0.2997 69.0 23460 0.9450 0.6332
0.29 70.0 23800 0.9625 0.6301
0.2811 71.0 24140 0.9098 0.6379
0.2811 72.0 24480 0.8967 0.6285
0.2825 73.0 24820 0.9234 0.6317
0.2868 74.0 25160 0.9275 0.6364
0.2846 75.0 25500 0.9291 0.6364
0.2846 76.0 25840 0.9158 0.6379
0.2811 77.0 26180 0.9310 0.6348
0.2754 78.0 26520 0.9423 0.6317
0.2754 79.0 26860 0.9350 0.6332
0.2776 80.0 27200 0.9380 0.6317

Framework versions