generated_from_trainer

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20230830151725

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 0.7462 0.4984
0.7527 2.0 680 0.7272 0.5047
0.7493 3.0 1020 0.7466 0.5
0.7493 4.0 1360 0.7254 0.5
0.7484 5.0 1700 0.7484 0.5
0.7446 6.0 2040 0.7319 0.5
0.7446 7.0 2380 0.7327 0.5
0.7385 8.0 2720 0.7423 0.5
0.7404 9.0 3060 0.7351 0.4875
0.7404 10.0 3400 0.7567 0.5
0.7576 11.0 3740 0.7305 0.5
0.745 12.0 4080 0.7374 0.5
0.745 13.0 4420 0.7735 0.5
0.7346 14.0 4760 0.7443 0.5
0.7407 15.0 5100 0.7278 0.5
0.7407 16.0 5440 0.7490 0.5
0.7367 17.0 5780 0.7443 0.5
0.7377 18.0 6120 0.7330 0.5
0.7377 19.0 6460 0.7249 0.5
0.7325 20.0 6800 0.7362 0.5
0.7357 21.0 7140 0.7240 0.5
0.7357 22.0 7480 0.7470 0.5
0.7356 23.0 7820 0.7296 0.5
0.734 24.0 8160 0.7255 0.5
0.7327 25.0 8500 0.7249 0.5
0.7327 26.0 8840 0.7262 0.5
0.7345 27.0 9180 0.7743 0.5
0.735 28.0 9520 0.7276 0.5
0.735 29.0 9860 0.7271 0.5
0.7309 30.0 10200 0.7239 0.5
0.7345 31.0 10540 0.7241 0.5
0.7345 32.0 10880 0.7599 0.5
0.733 33.0 11220 0.7245 0.5
0.7325 34.0 11560 0.7385 0.5
0.7325 35.0 11900 0.7245 0.5
0.7318 36.0 12240 0.7242 0.5
0.7308 37.0 12580 0.7239 0.5
0.7308 38.0 12920 0.7241 0.5
0.73 39.0 13260 0.7317 0.5
0.7288 40.0 13600 0.7258 0.5
0.7288 41.0 13940 0.7241 0.5
0.7311 42.0 14280 0.7241 0.5
0.7284 43.0 14620 0.7344 0.5
0.7284 44.0 14960 0.7297 0.5
0.73 45.0 15300 0.7393 0.5
0.7269 46.0 15640 0.7239 0.5
0.7269 47.0 15980 0.7282 0.5
0.7284 48.0 16320 0.7240 0.5
0.729 49.0 16660 0.7343 0.5
0.7281 50.0 17000 0.7240 0.5
0.7281 51.0 17340 0.7245 0.5
0.7264 52.0 17680 0.7291 0.5
0.7294 53.0 18020 0.7255 0.5
0.7294 54.0 18360 0.7251 0.5
0.7263 55.0 18700 0.7256 0.5
0.7255 56.0 19040 0.7294 0.5
0.7255 57.0 19380 0.7242 0.5
0.7261 58.0 19720 0.7243 0.5
0.7265 59.0 20060 0.7315 0.5
0.7265 60.0 20400 0.7239 0.5
0.7253 61.0 20740 0.7246 0.5
0.7265 62.0 21080 0.7248 0.5
0.7265 63.0 21420 0.7244 0.5
0.7247 64.0 21760 0.7250 0.5
0.7273 65.0 22100 0.7240 0.5
0.7273 66.0 22440 0.7251 0.5
0.7233 67.0 22780 0.7239 0.5
0.7268 68.0 23120 0.7239 0.5
0.7268 69.0 23460 0.7251 0.5
0.7246 70.0 23800 0.7241 0.5
0.7249 71.0 24140 0.7242 0.5
0.7249 72.0 24480 0.7259 0.5
0.7241 73.0 24820 0.7247 0.5
0.7237 74.0 25160 0.7257 0.5
0.7244 75.0 25500 0.7245 0.5
0.7244 76.0 25840 0.7251 0.5
0.7234 77.0 26180 0.7240 0.5
0.7231 78.0 26520 0.7243 0.5
0.7231 79.0 26860 0.7243 0.5
0.7235 80.0 27200 0.7242 0.5

Framework versions