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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:
- Loss: 0.7271
- 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 | 0.7372 | 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
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3