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20230831143012
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: 0.6198
- 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: 0.0003
- 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 | 0.6258 | 0.5 |
0.6312 | 2.0 | 680 | 0.6164 | 0.5 |
0.6295 | 3.0 | 1020 | 0.6237 | 0.5 |
0.6295 | 4.0 | 1360 | 0.6170 | 0.5 |
0.6241 | 5.0 | 1700 | 0.6181 | 0.5 |
0.6236 | 6.0 | 2040 | 0.6191 | 0.5 |
0.6236 | 7.0 | 2380 | 0.6189 | 0.5 |
0.6239 | 8.0 | 2720 | 0.6261 | 0.5 |
0.6189 | 9.0 | 3060 | 0.6188 | 0.5 |
0.6189 | 10.0 | 3400 | 0.6264 | 0.5 |
0.623 | 11.0 | 3740 | 0.6200 | 0.5 |
0.6207 | 12.0 | 4080 | 0.6273 | 0.5 |
0.6207 | 13.0 | 4420 | 0.6450 | 0.5 |
0.6183 | 14.0 | 4760 | 0.6217 | 0.5 |
0.6235 | 15.0 | 5100 | 0.6226 | 0.5 |
0.6235 | 16.0 | 5440 | 0.6237 | 0.5 |
0.623 | 17.0 | 5780 | 0.6185 | 0.5 |
0.6176 | 18.0 | 6120 | 0.6202 | 0.5 |
0.6176 | 19.0 | 6460 | 0.6180 | 0.5 |
0.6204 | 20.0 | 6800 | 0.6195 | 0.5 |
0.6186 | 21.0 | 7140 | 0.6174 | 0.5 |
0.6186 | 22.0 | 7480 | 0.6283 | 0.5 |
0.621 | 23.0 | 7820 | 0.6254 | 0.5 |
0.6196 | 24.0 | 8160 | 0.6169 | 0.5 |
0.6218 | 25.0 | 8500 | 0.6170 | 0.5 |
0.6218 | 26.0 | 8840 | 0.6256 | 0.5 |
0.621 | 27.0 | 9180 | 0.6479 | 0.5 |
0.6189 | 28.0 | 9520 | 0.6170 | 0.5 |
0.6189 | 29.0 | 9860 | 0.6219 | 0.5 |
0.619 | 30.0 | 10200 | 0.6169 | 0.5 |
0.6175 | 31.0 | 10540 | 0.6169 | 0.5 |
0.6175 | 32.0 | 10880 | 0.6379 | 0.5 |
0.6181 | 33.0 | 11220 | 0.6193 | 0.5 |
0.6185 | 34.0 | 11560 | 0.6219 | 0.5 |
0.6185 | 35.0 | 11900 | 0.6188 | 0.5 |
0.6186 | 36.0 | 12240 | 0.6196 | 0.5 |
0.6185 | 37.0 | 12580 | 0.6170 | 0.5 |
0.6185 | 38.0 | 12920 | 0.6238 | 0.5 |
0.6167 | 39.0 | 13260 | 0.6332 | 0.5 |
0.6164 | 40.0 | 13600 | 0.6207 | 0.5 |
0.6164 | 41.0 | 13940 | 0.6176 | 0.5 |
0.6174 | 42.0 | 14280 | 0.6190 | 0.5 |
0.6137 | 43.0 | 14620 | 0.6190 | 0.5 |
0.6137 | 44.0 | 14960 | 0.6175 | 0.5 |
0.6179 | 45.0 | 15300 | 0.6263 | 0.5 |
0.6141 | 46.0 | 15640 | 0.6183 | 0.5 |
0.6141 | 47.0 | 15980 | 0.6275 | 0.5 |
0.6176 | 48.0 | 16320 | 0.6174 | 0.5 |
0.616 | 49.0 | 16660 | 0.6224 | 0.5 |
0.6162 | 50.0 | 17000 | 0.6173 | 0.5 |
0.6162 | 51.0 | 17340 | 0.6191 | 0.5 |
0.6135 | 52.0 | 17680 | 0.6187 | 0.5 |
0.6186 | 53.0 | 18020 | 0.6232 | 0.5 |
0.6186 | 54.0 | 18360 | 0.6191 | 0.5 |
0.6135 | 55.0 | 18700 | 0.6184 | 0.5 |
0.6138 | 56.0 | 19040 | 0.6186 | 0.5 |
0.6138 | 57.0 | 19380 | 0.6176 | 0.5 |
0.6137 | 58.0 | 19720 | 0.6236 | 0.5 |
0.6153 | 59.0 | 20060 | 0.6251 | 0.5 |
0.6153 | 60.0 | 20400 | 0.6166 | 0.5 |
0.6132 | 61.0 | 20740 | 0.6175 | 0.5 |
0.6131 | 62.0 | 21080 | 0.6199 | 0.5 |
0.6131 | 63.0 | 21420 | 0.6178 | 0.5 |
0.6121 | 64.0 | 21760 | 0.6212 | 0.5 |
0.6169 | 65.0 | 22100 | 0.6183 | 0.5 |
0.6169 | 66.0 | 22440 | 0.6252 | 0.5 |
0.6079 | 67.0 | 22780 | 0.6191 | 0.5 |
0.6151 | 68.0 | 23120 | 0.6170 | 0.5 |
0.6151 | 69.0 | 23460 | 0.6182 | 0.5 |
0.6128 | 70.0 | 23800 | 0.6191 | 0.5 |
0.6118 | 71.0 | 24140 | 0.6194 | 0.5 |
0.6118 | 72.0 | 24480 | 0.6224 | 0.5 |
0.6112 | 73.0 | 24820 | 0.6199 | 0.5 |
0.6129 | 74.0 | 25160 | 0.6210 | 0.5 |
0.6109 | 75.0 | 25500 | 0.6193 | 0.5 |
0.6109 | 76.0 | 25840 | 0.6210 | 0.5 |
0.612 | 77.0 | 26180 | 0.6187 | 0.5 |
0.6109 | 78.0 | 26520 | 0.6203 | 0.5 |
0.6109 | 79.0 | 26860 | 0.6197 | 0.5 |
0.6115 | 80.0 | 27200 | 0.6198 | 0.5 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3