generated_from_trainer

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20230830152959

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 0.7372 0.5
0.7583 2.0 680 0.7264 0.5
0.7541 3.0 1020 0.7485 0.5
0.7541 4.0 1360 0.7304 0.5
0.7509 5.0 1700 0.7363 0.5
0.7455 6.0 2040 0.7360 0.5
0.7455 7.0 2380 0.7240 0.5
0.7493 8.0 2720 0.7806 0.5
0.7515 9.0 3060 0.7284 0.5
0.7515 10.0 3400 0.7375 0.5
0.7489 11.0 3740 0.7340 0.5
0.7452 12.0 4080 0.7255 0.5
0.7452 13.0 4420 0.8158 0.5
0.7463 14.0 4760 0.7256 0.5
0.75 15.0 5100 0.7364 0.5
0.75 16.0 5440 0.7260 0.5
0.7411 17.0 5780 0.7320 0.5
0.7482 18.0 6120 0.7296 0.5
0.7482 19.0 6460 0.7312 0.5
0.7424 20.0 6800 0.7405 0.5
0.7374 21.0 7140 0.7258 0.5
0.7374 22.0 7480 0.7609 0.5
0.7388 23.0 7820 0.7608 0.5
0.7382 24.0 8160 0.7239 0.5
0.7385 25.0 8500 0.7315 0.5
0.7385 26.0 8840 0.7472 0.5
0.7392 27.0 9180 0.7863 0.5
0.737 28.0 9520 0.7261 0.5
0.737 29.0 9860 0.7403 0.5
0.7322 30.0 10200 0.7245 0.5
0.7359 31.0 10540 0.7239 0.5
0.7359 32.0 10880 0.7555 0.5
0.7368 33.0 11220 0.7239 0.5
0.7349 34.0 11560 0.7380 0.5
0.7349 35.0 11900 0.7279 0.5
0.7367 36.0 12240 0.7263 0.5
0.7343 37.0 12580 0.7252 0.5
0.7343 38.0 12920 0.7299 0.5
0.7347 39.0 13260 0.7344 0.5
0.7315 40.0 13600 0.7247 0.5
0.7315 41.0 13940 0.7277 0.5
0.7324 42.0 14280 0.7246 0.5
0.7319 43.0 14620 0.7289 0.5
0.7319 44.0 14960 0.7297 0.5
0.7321 45.0 15300 0.7389 0.5
0.7304 46.0 15640 0.7245 0.5
0.7304 47.0 15980 0.7306 0.5
0.7316 48.0 16320 0.7239 0.5
0.7325 49.0 16660 0.7312 0.5
0.7311 50.0 17000 0.7241 0.5
0.7311 51.0 17340 0.7239 0.5
0.7292 52.0 17680 0.7244 0.5
0.732 53.0 18020 0.7250 0.5
0.732 54.0 18360 0.7239 0.5
0.727 55.0 18700 0.7257 0.5
0.7277 56.0 19040 0.7247 0.5
0.7277 57.0 19380 0.7256 0.5
0.7296 58.0 19720 0.7269 0.5
0.7284 59.0 20060 0.7340 0.5
0.7284 60.0 20400 0.7257 0.5
0.7269 61.0 20740 0.7254 0.5
0.7266 62.0 21080 0.7240 0.5
0.7266 63.0 21420 0.7245 0.5
0.7274 64.0 21760 0.7261 0.5
0.7278 65.0 22100 0.7240 0.5
0.7278 66.0 22440 0.7492 0.5
0.7252 67.0 22780 0.7254 0.5
0.727 68.0 23120 0.7241 0.5
0.727 69.0 23460 0.7244 0.5
0.7253 70.0 23800 0.7242 0.5
0.7249 71.0 24140 0.7247 0.5
0.7249 72.0 24480 0.7280 0.5
0.7244 73.0 24820 0.7267 0.5
0.7232 74.0 25160 0.7284 0.5
0.7234 75.0 25500 0.7272 0.5
0.7234 76.0 25840 0.7294 0.5
0.7233 77.0 26180 0.7257 0.5
0.7222 78.0 26520 0.7284 0.5
0.7222 79.0 26860 0.7276 0.5
0.7225 80.0 27200 0.7271 0.5

Framework versions