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20230830203443
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:
- Loss: 0.7274
- Accuracy: 0.5
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:
- learning_rate: 0.0007
- train_batch_size: 16
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 80.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 1.0 | 340 | 0.7532 | 0.5 |
0.7604 | 2.0 | 680 | 0.7283 | 0.5 |
0.7635 | 3.0 | 1020 | 0.7745 | 0.5 |
0.7635 | 4.0 | 1360 | 0.8267 | 0.5 |
0.7685 | 5.0 | 1700 | 0.7674 | 0.5 |
0.7536 | 6.0 | 2040 | 0.7283 | 0.5 |
0.7536 | 7.0 | 2380 | 0.7315 | 0.5 |
0.7457 | 8.0 | 2720 | 0.7962 | 0.5 |
0.7462 | 9.0 | 3060 | 0.7287 | 0.5 |
0.7462 | 10.0 | 3400 | 0.7515 | 0.5 |
0.7445 | 11.0 | 3740 | 0.7305 | 0.5 |
0.7427 | 12.0 | 4080 | 0.7298 | 0.5 |
0.7427 | 13.0 | 4420 | 0.8376 | 0.5 |
0.7506 | 14.0 | 4760 | 0.7391 | 0.4749 |
0.7508 | 15.0 | 5100 | 0.7457 | 0.5 |
0.7508 | 16.0 | 5440 | 0.7366 | 0.5 |
0.7428 | 17.0 | 5780 | 0.7423 | 0.5 |
0.7418 | 18.0 | 6120 | 0.7331 | 0.5 |
0.7418 | 19.0 | 6460 | 0.7340 | 0.5 |
0.7443 | 20.0 | 6800 | 0.7566 | 0.5 |
0.7411 | 21.0 | 7140 | 0.7274 | 0.5 |
0.7411 | 22.0 | 7480 | 0.7503 | 0.5 |
0.7423 | 23.0 | 7820 | 0.7416 | 0.5 |
0.7428 | 24.0 | 8160 | 0.7274 | 0.5 |
0.7406 | 25.0 | 8500 | 0.7313 | 0.5 |
0.7406 | 26.0 | 8840 | 0.7513 | 0.5 |
0.7421 | 27.0 | 9180 | 0.7476 | 0.5 |
0.7423 | 28.0 | 9520 | 0.7274 | 0.5 |
0.7423 | 29.0 | 9860 | 0.7313 | 0.5 |
0.7381 | 30.0 | 10200 | 0.7274 | 0.5 |
0.739 | 31.0 | 10540 | 0.7276 | 0.5 |
0.739 | 32.0 | 10880 | 0.7727 | 0.5 |
0.7392 | 33.0 | 11220 | 0.7287 | 0.5 |
0.7389 | 34.0 | 11560 | 0.7376 | 0.5 |
0.7389 | 35.0 | 11900 | 0.7278 | 0.5 |
0.7391 | 36.0 | 12240 | 0.7296 | 0.5 |
0.7369 | 37.0 | 12580 | 0.7307 | 0.5 |
0.7369 | 38.0 | 12920 | 0.7304 | 0.5 |
0.7391 | 39.0 | 13260 | 0.7358 | 0.5 |
0.7366 | 40.0 | 13600 | 0.7298 | 0.5 |
0.7366 | 41.0 | 13940 | 0.7284 | 0.5 |
0.737 | 42.0 | 14280 | 0.7279 | 0.5 |
0.7343 | 43.0 | 14620 | 0.7334 | 0.5 |
0.7343 | 44.0 | 14960 | 0.7273 | 0.5 |
0.7358 | 45.0 | 15300 | 0.7468 | 0.5 |
0.7341 | 46.0 | 15640 | 0.7277 | 0.5 |
0.7341 | 47.0 | 15980 | 0.7327 | 0.5 |
0.7345 | 48.0 | 16320 | 0.7290 | 0.5 |
0.7357 | 49.0 | 16660 | 0.7518 | 0.5 |
0.7362 | 50.0 | 17000 | 0.7276 | 0.5 |
0.7362 | 51.0 | 17340 | 0.7275 | 0.5 |
0.7313 | 52.0 | 17680 | 0.7279 | 0.5 |
0.7357 | 53.0 | 18020 | 0.7307 | 0.5 |
0.7357 | 54.0 | 18360 | 0.7276 | 0.5 |
0.7323 | 55.0 | 18700 | 0.7294 | 0.5 |
0.7304 | 56.0 | 19040 | 0.7310 | 0.5 |
0.7304 | 57.0 | 19380 | 0.7278 | 0.5 |
0.7326 | 58.0 | 19720 | 0.7289 | 0.5 |
0.7314 | 59.0 | 20060 | 0.7461 | 0.5 |
0.7314 | 60.0 | 20400 | 0.7287 | 0.5 |
0.7319 | 61.0 | 20740 | 0.7337 | 0.5 |
0.7304 | 62.0 | 21080 | 0.7273 | 0.5 |
0.7304 | 63.0 | 21420 | 0.7288 | 0.5 |
0.7313 | 64.0 | 21760 | 0.7285 | 0.5 |
0.7317 | 65.0 | 22100 | 0.7285 | 0.5 |
0.7317 | 66.0 | 22440 | 0.7310 | 0.5 |
0.7294 | 67.0 | 22780 | 0.7274 | 0.5 |
0.7304 | 68.0 | 23120 | 0.7275 | 0.5 |
0.7304 | 69.0 | 23460 | 0.7281 | 0.5 |
0.7286 | 70.0 | 23800 | 0.7276 | 0.5 |
0.7295 | 71.0 | 24140 | 0.7277 | 0.5 |
0.7295 | 72.0 | 24480 | 0.7301 | 0.5 |
0.7292 | 73.0 | 24820 | 0.7277 | 0.5 |
0.7288 | 74.0 | 25160 | 0.7302 | 0.5 |
0.7276 | 75.0 | 25500 | 0.7280 | 0.5 |
0.7276 | 76.0 | 25840 | 0.7275 | 0.5 |
0.7281 | 77.0 | 26180 | 0.7274 | 0.5 |
0.727 | 78.0 | 26520 | 0.7275 | 0.5 |
0.727 | 79.0 | 26860 | 0.7275 | 0.5 |
0.7279 | 80.0 | 27200 | 0.7274 | 0.5 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3