distilbert-base-uncased-finetuned-sst-2-english-finetuned-abstract_classification
This model is a fine-tuned version of distilbert/distilbert-base-uncased-finetuned-sst-2-english on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3820
- Accuracy: 0.9803
- F1: 0.9709
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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
1.4187 | 1.0 | 4 | 1.1950 | 0.8071 | 0.7275 |
1.215 | 2.0 | 8 | 1.1708 | 0.8150 | 0.7405 |
1.2073 | 3.0 | 12 | 1.1419 | 0.8110 | 0.7359 |
1.2722 | 4.0 | 16 | 1.1119 | 0.8110 | 0.7360 |
1.1215 | 5.0 | 20 | 1.0880 | 0.8189 | 0.7488 |
1.1604 | 6.0 | 24 | 1.0609 | 0.8268 | 0.7587 |
1.1658 | 7.0 | 28 | 1.0354 | 0.8346 | 0.7683 |
1.1585 | 8.0 | 32 | 1.0155 | 0.8307 | 0.7639 |
1.1995 | 9.0 | 36 | 0.9936 | 0.8268 | 0.7596 |
1.084 | 10.0 | 40 | 0.9698 | 0.8268 | 0.7598 |
1.208 | 11.0 | 44 | 0.9477 | 0.8386 | 0.7755 |
1.0951 | 12.0 | 48 | 0.9297 | 0.8583 | 0.7979 |
1.042 | 13.0 | 52 | 0.9119 | 0.8543 | 0.7924 |
1.0197 | 14.0 | 56 | 0.8913 | 0.8543 | 0.7924 |
1.0083 | 15.0 | 60 | 0.8761 | 0.8583 | 0.7979 |
0.9577 | 16.0 | 64 | 0.8606 | 0.8543 | 0.7927 |
0.9542 | 17.0 | 68 | 0.8418 | 0.8543 | 0.7929 |
0.9632 | 18.0 | 72 | 0.8262 | 0.8543 | 0.7925 |
0.9265 | 19.0 | 76 | 0.8122 | 0.8543 | 0.7924 |
0.978 | 20.0 | 80 | 0.7951 | 0.8622 | 0.8070 |
0.8984 | 21.0 | 84 | 0.7810 | 0.8661 | 0.8124 |
0.8813 | 22.0 | 88 | 0.7684 | 0.8740 | 0.8227 |
0.8821 | 23.0 | 92 | 0.7550 | 0.8819 | 0.8328 |
0.8303 | 24.0 | 96 | 0.7419 | 0.8819 | 0.8341 |
0.833 | 25.0 | 100 | 0.7327 | 0.8898 | 0.8456 |
0.9008 | 26.0 | 104 | 0.7151 | 0.8976 | 0.8559 |
0.838 | 27.0 | 108 | 0.7035 | 0.9016 | 0.8592 |
0.7202 | 28.0 | 112 | 0.6964 | 0.9055 | 0.8641 |
0.7998 | 29.0 | 116 | 0.6803 | 0.9094 | 0.8711 |
0.7539 | 30.0 | 120 | 0.6693 | 0.9055 | 0.8656 |
0.7137 | 31.0 | 124 | 0.6625 | 0.9134 | 0.8766 |
0.8068 | 32.0 | 128 | 0.6536 | 0.9173 | 0.8824 |
0.7688 | 33.0 | 132 | 0.6393 | 0.9173 | 0.8806 |
0.7516 | 34.0 | 136 | 0.6308 | 0.9134 | 0.8777 |
0.7908 | 35.0 | 140 | 0.6251 | 0.9134 | 0.8764 |
0.6659 | 36.0 | 144 | 0.6141 | 0.9134 | 0.8761 |
0.7202 | 37.0 | 148 | 0.6043 | 0.9291 | 0.8986 |
0.6657 | 38.0 | 152 | 0.5966 | 0.9370 | 0.9099 |
0.6988 | 39.0 | 156 | 0.5886 | 0.9409 | 0.9142 |
0.7726 | 40.0 | 160 | 0.5799 | 0.9370 | 0.9100 |
0.5252 | 41.0 | 164 | 0.5716 | 0.9409 | 0.9141 |
0.6311 | 42.0 | 168 | 0.5650 | 0.9409 | 0.9142 |
0.6402 | 43.0 | 172 | 0.5583 | 0.9409 | 0.9147 |
0.6468 | 44.0 | 176 | 0.5513 | 0.9409 | 0.9147 |
0.6197 | 45.0 | 180 | 0.5437 | 0.9449 | 0.9200 |
0.6282 | 46.0 | 184 | 0.5371 | 0.9449 | 0.9200 |
0.6579 | 47.0 | 188 | 0.5313 | 0.9409 | 0.9142 |
0.6682 | 48.0 | 192 | 0.5237 | 0.9409 | 0.9142 |
0.6592 | 49.0 | 196 | 0.5168 | 0.9488 | 0.9258 |
0.547 | 50.0 | 200 | 0.5104 | 0.9488 | 0.9257 |
0.5069 | 51.0 | 204 | 0.5042 | 0.9488 | 0.9257 |
0.6015 | 52.0 | 208 | 0.4995 | 0.9567 | 0.9367 |
0.549 | 53.0 | 212 | 0.4935 | 0.9606 | 0.9425 |
0.6206 | 54.0 | 216 | 0.4870 | 0.9646 | 0.9482 |
0.5396 | 55.0 | 220 | 0.4821 | 0.9685 | 0.9541 |
0.5753 | 56.0 | 224 | 0.4773 | 0.9646 | 0.9482 |
0.5867 | 57.0 | 228 | 0.4732 | 0.9685 | 0.9542 |
0.5553 | 58.0 | 232 | 0.4685 | 0.9724 | 0.9596 |
0.4751 | 59.0 | 236 | 0.4641 | 0.9724 | 0.9596 |
0.5857 | 60.0 | 240 | 0.4588 | 0.9685 | 0.9538 |
0.5199 | 61.0 | 244 | 0.4563 | 0.9724 | 0.9596 |
0.5616 | 62.0 | 248 | 0.4535 | 0.9685 | 0.9538 |
0.5698 | 63.0 | 252 | 0.4481 | 0.9685 | 0.9538 |
0.5302 | 64.0 | 256 | 0.4435 | 0.9646 | 0.9479 |
0.5311 | 65.0 | 260 | 0.4405 | 0.9685 | 0.9537 |
0.5204 | 66.0 | 264 | 0.4385 | 0.9685 | 0.9537 |
0.4678 | 67.0 | 268 | 0.4334 | 0.9764 | 0.9653 |
0.5635 | 68.0 | 272 | 0.4297 | 0.9724 | 0.9595 |
0.5404 | 69.0 | 276 | 0.4275 | 0.9764 | 0.9653 |
0.5246 | 70.0 | 280 | 0.4256 | 0.9764 | 0.9653 |
0.4557 | 71.0 | 284 | 0.4236 | 0.9764 | 0.9653 |
0.5924 | 72.0 | 288 | 0.4215 | 0.9764 | 0.9653 |
0.5166 | 73.0 | 292 | 0.4178 | 0.9764 | 0.9653 |
0.375 | 74.0 | 296 | 0.4141 | 0.9764 | 0.9653 |
0.5337 | 75.0 | 300 | 0.4111 | 0.9764 | 0.9653 |
0.4728 | 76.0 | 304 | 0.4088 | 0.9764 | 0.9653 |
0.516 | 77.0 | 308 | 0.4070 | 0.9764 | 0.9653 |
0.4553 | 78.0 | 312 | 0.4051 | 0.9764 | 0.9653 |
0.4761 | 79.0 | 316 | 0.4034 | 0.9764 | 0.9653 |
0.4672 | 80.0 | 320 | 0.4011 | 0.9724 | 0.9595 |
0.5029 | 81.0 | 324 | 0.3990 | 0.9764 | 0.9653 |
0.4754 | 82.0 | 328 | 0.3973 | 0.9764 | 0.9653 |
0.4678 | 83.0 | 332 | 0.3962 | 0.9764 | 0.9653 |
0.4717 | 84.0 | 336 | 0.3950 | 0.9803 | 0.9708 |
0.4518 | 85.0 | 340 | 0.3935 | 0.9803 | 0.9709 |
0.5682 | 86.0 | 344 | 0.3916 | 0.9803 | 0.9709 |
0.4313 | 87.0 | 348 | 0.3900 | 0.9803 | 0.9709 |
0.4528 | 88.0 | 352 | 0.3883 | 0.9803 | 0.9709 |
0.5075 | 89.0 | 356 | 0.3871 | 0.9803 | 0.9709 |
0.4255 | 90.0 | 360 | 0.3865 | 0.9803 | 0.9709 |
0.4278 | 91.0 | 364 | 0.3860 | 0.9803 | 0.9709 |
0.5074 | 92.0 | 368 | 0.3855 | 0.9803 | 0.9709 |
0.5244 | 93.0 | 372 | 0.3848 | 0.9803 | 0.9709 |
0.4806 | 94.0 | 376 | 0.3839 | 0.9803 | 0.9709 |
0.4271 | 95.0 | 380 | 0.3832 | 0.9803 | 0.9709 |
0.4829 | 96.0 | 384 | 0.3827 | 0.9803 | 0.9709 |
0.4356 | 97.0 | 388 | 0.3823 | 0.9803 | 0.9709 |
0.5412 | 98.0 | 392 | 0.3821 | 0.9803 | 0.9709 |
0.4539 | 99.0 | 396 | 0.3820 | 0.9803 | 0.9709 |
0.4462 | 100.0 | 400 | 0.3820 | 0.9803 | 0.9709 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
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