--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert_survey_response_classification results: [] --- # distilbert_survey_response_classification This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0013 - Accuracy: 1.0 ## 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: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 1.1299 | 0.125 | 5 | 1.0802 | 0.4051 | | 1.087 | 0.25 | 10 | 1.0728 | 0.4051 | | 1.0963 | 0.375 | 15 | 1.0612 | 0.4051 | | 1.0295 | 0.5 | 20 | 1.0474 | 0.4051 | | 1.0555 | 0.625 | 25 | 1.0342 | 0.4051 | | 1.0977 | 0.75 | 30 | 1.0229 | 0.4177 | | 1.0578 | 0.875 | 35 | 1.0083 | 0.4051 | | 1.0634 | 1.0 | 40 | 0.9898 | 0.4051 | | 1.101 | 1.125 | 45 | 0.9580 | 0.4177 | | 0.975 | 1.25 | 50 | 0.9324 | 0.5696 | | 0.8884 | 1.375 | 55 | 0.9234 | 0.4810 | | 0.952 | 1.5 | 60 | 0.9370 | 0.4430 | | 0.9615 | 1.625 | 65 | 0.9046 | 0.4810 | | 0.9397 | 1.75 | 70 | 0.8182 | 0.5823 | | 0.8171 | 1.875 | 75 | 0.7518 | 0.7215 | | 0.9969 | 2.0 | 80 | 0.7126 | 0.8101 | | 0.6124 | 2.125 | 85 | 0.7107 | 0.7848 | | 0.768 | 2.25 | 90 | 0.6562 | 0.8481 | | 0.5737 | 2.375 | 95 | 0.6030 | 0.8481 | | 0.5334 | 2.5 | 100 | 0.5290 | 0.8734 | | 0.3639 | 2.625 | 105 | 0.5277 | 0.8481 | | 1.0796 | 2.75 | 110 | 0.4561 | 0.8734 | | 0.4922 | 2.875 | 115 | 0.3470 | 0.8987 | | 0.6645 | 3.0 | 120 | 0.3213 | 0.9367 | | 0.3425 | 3.125 | 125 | 0.2897 | 0.9367 | | 0.4311 | 3.25 | 130 | 0.2757 | 0.9241 | | 0.2699 | 3.375 | 135 | 0.2918 | 0.9241 | | 0.5033 | 3.5 | 140 | 0.3021 | 0.8987 | | 0.1645 | 3.625 | 145 | 0.2710 | 0.9114 | | 0.4956 | 3.75 | 150 | 0.2695 | 0.8987 | | 0.4773 | 3.875 | 155 | 0.2175 | 0.9241 | | 0.1566 | 4.0 | 160 | 0.1788 | 0.9494 | | 0.331 | 4.125 | 165 | 0.1332 | 0.9747 | | 0.0865 | 4.25 | 170 | 0.1716 | 0.9367 | | 0.2109 | 4.375 | 175 | 0.1431 | 0.9494 | | 0.0673 | 4.5 | 180 | 0.1536 | 0.9367 | | 0.3188 | 4.625 | 185 | 0.1312 | 0.9494 | | 0.1084 | 4.75 | 190 | 0.0994 | 0.9747 | | 0.0454 | 4.875 | 195 | 0.1158 | 0.9747 | | 0.2056 | 5.0 | 200 | 0.0880 | 0.9873 | | 0.0444 | 5.125 | 205 | 0.0947 | 0.9747 | | 0.0304 | 5.25 | 210 | 0.1205 | 0.9747 | | 0.0187 | 5.375 | 215 | 0.1289 | 0.9747 | | 0.0205 | 5.5 | 220 | 0.1390 | 0.9747 | | 0.022 | 5.625 | 225 | 0.1592 | 0.9620 | | 0.0163 | 5.75 | 230 | 0.1652 | 0.9620 | | 0.7803 | 5.875 | 235 | 0.0975 | 0.9747 | | 0.0137 | 6.0 | 240 | 0.0683 | 0.9873 | | 0.0155 | 6.125 | 245 | 0.0700 | 0.9873 | | 0.4981 | 6.25 | 250 | 0.0743 | 0.9873 | | 0.0127 | 6.375 | 255 | 0.0730 | 0.9873 | | 0.031 | 6.5 | 260 | 0.0733 | 0.9873 | | 0.0139 | 6.625 | 265 | 0.0763 | 0.9873 | | 0.0104 | 6.75 | 270 | 0.1024 | 0.9620 | | 0.0158 | 6.875 | 275 | 0.1015 | 0.9620 | | 0.0081 | 7.0 | 280 | 0.1042 | 0.9747 | | 0.4143 | 7.125 | 285 | 0.1005 | 0.9747 | | 0.0104 | 7.25 | 290 | 0.0916 | 0.9747 | | 0.009 | 7.375 | 295 | 0.1089 | 0.9620 | | 0.0087 | 7.5 | 300 | 0.1176 | 0.9620 | | 0.1324 | 7.625 | 305 | 0.0496 | 0.9873 | | 0.0094 | 7.75 | 310 | 0.0385 | 0.9873 | | 0.0078 | 7.875 | 315 | 0.0336 | 0.9873 | | 0.0077 | 8.0 | 320 | 0.0342 | 0.9873 | | 0.008 | 8.125 | 325 | 0.0450 | 0.9873 | | 0.0064 | 8.25 | 330 | 0.0590 | 0.9873 | | 0.3151 | 8.375 | 335 | 0.0578 | 0.9873 | | 0.007 | 8.5 | 340 | 0.0162 | 0.9873 | | 0.0073 | 8.625 | 345 | 0.0092 | 1.0 | | 0.0063 | 8.75 | 350 | 0.0089 | 1.0 | | 0.0064 | 8.875 | 355 | 0.0074 | 1.0 | | 0.0088 | 9.0 | 360 | 0.0127 | 0.9873 | | 0.0074 | 9.125 | 365 | 0.0260 | 0.9873 | | 0.0055 | 9.25 | 370 | 0.0355 | 0.9873 | | 0.0051 | 9.375 | 375 | 0.0415 | 0.9873 | | 0.0042 | 9.5 | 380 | 0.0409 | 0.9873 | | 0.0053 | 9.625 | 385 | 0.0408 | 0.9873 | | 0.3642 | 9.75 | 390 | 0.0096 | 1.0 | | 0.0049 | 9.875 | 395 | 0.0032 | 1.0 | | 0.0043 | 10.0 | 400 | 0.0029 | 1.0 | | 0.0059 | 10.125 | 405 | 0.0029 | 1.0 | | 0.0048 | 10.25 | 410 | 0.0030 | 1.0 | | 0.0067 | 10.375 | 415 | 0.0030 | 1.0 | | 0.0035 | 10.5 | 420 | 0.0029 | 1.0 | | 0.0041 | 10.625 | 425 | 0.0042 | 1.0 | | 0.0039 | 10.75 | 430 | 0.0084 | 1.0 | | 0.2433 | 10.875 | 435 | 0.0027 | 1.0 | | 0.0043 | 11.0 | 440 | 0.0024 | 1.0 | | 0.0036 | 11.125 | 445 | 0.0024 | 1.0 | | 0.0042 | 11.25 | 450 | 0.0024 | 1.0 | | 0.0037 | 11.375 | 455 | 0.0025 | 1.0 | | 0.0074 | 11.5 | 460 | 0.0023 | 1.0 | | 0.0034 | 11.625 | 465 | 0.0022 | 1.0 | | 0.0039 | 11.75 | 470 | 0.0021 | 1.0 | | 0.0038 | 11.875 | 475 | 0.0021 | 1.0 | | 0.0029 | 12.0 | 480 | 0.0021 | 1.0 | | 0.0033 | 12.125 | 485 | 0.0020 | 1.0 | | 0.0029 | 12.25 | 490 | 0.0020 | 1.0 | | 0.0032 | 12.375 | 495 | 0.0020 | 1.0 | | 0.0029 | 12.5 | 500 | 0.0020 | 1.0 | | 0.0039 | 12.625 | 505 | 0.0019 | 1.0 | | 0.0041 | 12.75 | 510 | 0.0019 | 1.0 | | 0.0028 | 12.875 | 515 | 0.0020 | 1.0 | | 0.0028 | 13.0 | 520 | 0.0020 | 1.0 | | 0.0027 | 13.125 | 525 | 0.0020 | 1.0 | | 0.0028 | 13.25 | 530 | 0.0020 | 1.0 | | 0.0024 | 13.375 | 535 | 0.0019 | 1.0 | | 0.0032 | 13.5 | 540 | 0.0019 | 1.0 | | 0.0039 | 13.625 | 545 | 0.0019 | 1.0 | | 0.0021 | 13.75 | 550 | 0.0018 | 1.0 | | 0.0037 | 13.875 | 555 | 0.0018 | 1.0 | | 0.0033 | 14.0 | 560 | 0.0017 | 1.0 | | 0.003 | 14.125 | 565 | 0.0017 | 1.0 | | 0.0025 | 14.25 | 570 | 0.0016 | 1.0 | | 0.0032 | 14.375 | 575 | 0.0016 | 1.0 | | 0.0025 | 14.5 | 580 | 0.0016 | 1.0 | | 0.0026 | 14.625 | 585 | 0.0016 | 1.0 | | 0.0024 | 14.75 | 590 | 0.0016 | 1.0 | | 0.003 | 14.875 | 595 | 0.0016 | 1.0 | | 0.0028 | 15.0 | 600 | 0.0015 | 1.0 | | 0.0025 | 15.125 | 605 | 0.0015 | 1.0 | | 0.0024 | 15.25 | 610 | 0.0015 | 1.0 | | 0.0026 | 15.375 | 615 | 0.0015 | 1.0 | | 0.0042 | 15.5 | 620 | 0.0015 | 1.0 | | 0.0024 | 15.625 | 625 | 0.0015 | 1.0 | | 0.0025 | 15.75 | 630 | 0.0015 | 1.0 | | 0.0024 | 15.875 | 635 | 0.0015 | 1.0 | | 0.0029 | 16.0 | 640 | 0.0015 | 1.0 | | 0.0027 | 16.125 | 645 | 0.0015 | 1.0 | | 0.0022 | 16.25 | 650 | 0.0015 | 1.0 | | 0.0024 | 16.375 | 655 | 0.0014 | 1.0 | | 0.0025 | 16.5 | 660 | 0.0014 | 1.0 | | 0.0023 | 16.625 | 665 | 0.0014 | 1.0 | | 0.0024 | 16.75 | 670 | 0.0014 | 1.0 | | 0.0024 | 16.875 | 675 | 0.0014 | 1.0 | | 0.0021 | 17.0 | 680 | 0.0014 | 1.0 | | 0.0022 | 17.125 | 685 | 0.0014 | 1.0 | | 0.0025 | 17.25 | 690 | 0.0014 | 1.0 | | 0.0022 | 17.375 | 695 | 0.0014 | 1.0 | | 0.0023 | 17.5 | 700 | 0.0014 | 1.0 | | 0.0023 | 17.625 | 705 | 0.0013 | 1.0 | | 0.0036 | 17.75 | 710 | 0.0013 | 1.0 | | 0.0022 | 17.875 | 715 | 0.0013 | 1.0 | | 0.0024 | 18.0 | 720 | 0.0013 | 1.0 | | 0.0023 | 18.125 | 725 | 0.0013 | 1.0 | | 0.0019 | 18.25 | 730 | 0.0013 | 1.0 | | 0.0021 | 18.375 | 735 | 0.0013 | 1.0 | | 0.0023 | 18.5 | 740 | 0.0013 | 1.0 | | 0.003 | 18.625 | 745 | 0.0013 | 1.0 | | 0.0021 | 18.75 | 750 | 0.0013 | 1.0 | | 0.0021 | 18.875 | 755 | 0.0013 | 1.0 | | 0.0018 | 19.0 | 760 | 0.0013 | 1.0 | | 0.002 | 19.125 | 765 | 0.0013 | 1.0 | | 0.0017 | 19.25 | 770 | 0.0013 | 1.0 | | 0.0025 | 19.375 | 775 | 0.0013 | 1.0 | | 0.0022 | 19.5 | 780 | 0.0013 | 1.0 | | 0.0027 | 19.625 | 785 | 0.0013 | 1.0 | | 0.0024 | 19.75 | 790 | 0.0013 | 1.0 | | 0.0024 | 19.875 | 795 | 0.0013 | 1.0 | | 0.0025 | 20.0 | 800 | 0.0013 | 1.0 | ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.2 - Datasets 2.19.1 - Tokenizers 0.19.1