generated_from_trainer

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tiny_bert_27_mva_intents

This model is a fine-tuned version of prajjwal1/bert-tiny on the None dataset. It achieves the following results on the evaluation set:

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:

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 271 2.2533 0.6050
2.3347 2.0 542 2.0443 0.6570
2.3347 3.0 813 1.8496 0.7089
1.9691 4.0 1084 1.6831 0.7380
1.9691 5.0 1355 1.5359 0.7817
1.6616 6.0 1626 1.4010 0.7942
1.6616 7.0 1897 1.2820 0.8129
1.4124 8.0 2168 1.1748 0.8337
1.4124 9.0 2439 1.0754 0.8503
1.1972 10.0 2710 0.9926 0.8565
1.1972 11.0 2981 0.9165 0.8586
1.0186 12.0 3252 0.8477 0.8690
0.8756 13.0 3523 0.7845 0.8815
0.8756 14.0 3794 0.7295 0.8857
0.7602 15.0 4065 0.6794 0.8940
0.7602 16.0 4336 0.6333 0.9002
0.6497 17.0 4607 0.5906 0.9106
0.6497 18.0 4878 0.5533 0.9168
0.5763 19.0 5149 0.5206 0.9210
0.5763 20.0 5420 0.4859 0.9210
0.4944 21.0 5691 0.4572 0.9231
0.4944 22.0 5962 0.4260 0.9168
0.4367 23.0 6233 0.3999 0.9272
0.3791 24.0 6504 0.3841 0.9272
0.3791 25.0 6775 0.3602 0.9314
0.3364 26.0 7046 0.3426 0.9293
0.3364 27.0 7317 0.3210 0.9376
0.3053 28.0 7588 0.3078 0.9376
0.3053 29.0 7859 0.2881 0.9501
0.2633 30.0 8130 0.2765 0.9501
0.2633 31.0 8401 0.2657 0.9522
0.2384 32.0 8672 0.2518 0.9543
0.2384 33.0 8943 0.2368 0.9563
0.2175 34.0 9214 0.2273 0.9563
0.2175 35.0 9485 0.2188 0.9605
0.1872 36.0 9756 0.2101 0.9605
0.176 37.0 10027 0.2039 0.9626
0.176 38.0 10298 0.1986 0.9605
0.1532 39.0 10569 0.1895 0.9626
0.1532 40.0 10840 0.1827 0.9605
0.1434 41.0 11111 0.1770 0.9626
0.1434 42.0 11382 0.1723 0.9626
0.1309 43.0 11653 0.1647 0.9626
0.1309 44.0 11924 0.1664 0.9605
0.1208 45.0 12195 0.1574 0.9626
0.1208 46.0 12466 0.1549 0.9626
0.1083 47.0 12737 0.1499 0.9647
0.1033 48.0 13008 0.1525 0.9626
0.1033 49.0 13279 0.1422 0.9667
0.0907 50.0 13550 0.1385 0.9688
0.0907 51.0 13821 0.1423 0.9667
0.0888 52.0 14092 0.1395 0.9688
0.0888 53.0 14363 0.1410 0.9688
0.0829 54.0 14634 0.1326 0.9688
0.0829 55.0 14905 0.1274 0.9709
0.0735 56.0 15176 0.1266 0.9709
0.0735 57.0 15447 0.1235 0.9709
0.0737 58.0 15718 0.1223 0.9709
0.0737 59.0 15989 0.1181 0.9709
0.0639 60.0 16260 0.1134 0.9730
0.062 61.0 16531 0.1113 0.9709
0.062 62.0 16802 0.1151 0.9730
0.057 63.0 17073 0.1149 0.9709
0.057 64.0 17344 0.1112 0.9730
0.0565 65.0 17615 0.1125 0.9709
0.0565 66.0 17886 0.1124 0.9730
0.0564 67.0 18157 0.1112 0.9709
0.0564 68.0 18428 0.1097 0.9709
0.0509 69.0 18699 0.1062 0.9730
0.0509 70.0 18970 0.1083 0.9730
0.0496 71.0 19241 0.1076 0.9709
0.0454 72.0 19512 0.1052 0.9688
0.0454 73.0 19783 0.1018 0.9751
0.0451 74.0 20054 0.1050 0.9730
0.0451 75.0 20325 0.1061 0.9709
0.0446 76.0 20596 0.1037 0.9709
0.0446 77.0 20867 0.1054 0.9709
0.0414 78.0 21138 0.1033 0.9709
0.0414 79.0 21409 0.1030 0.9730
0.043 80.0 21680 0.1015 0.9730
0.043 81.0 21951 0.1020 0.9730
0.0402 82.0 22222 0.1011 0.9751
0.0402 83.0 22493 0.1033 0.9751
0.0364 84.0 22764 0.1031 0.9730
0.0376 85.0 23035 0.1030 0.9730
0.0376 86.0 23306 0.1020 0.9751
0.0386 87.0 23577 0.1021 0.9751
0.0386 88.0 23848 0.1009 0.9751
0.0382 89.0 24119 0.1022 0.9751
0.0382 90.0 24390 0.1020 0.9751
0.0352 91.0 24661 0.1020 0.9751
0.0352 92.0 24932 0.1041 0.9730
0.0363 93.0 25203 0.1034 0.9751
0.0363 94.0 25474 0.1026 0.9751
0.0328 95.0 25745 0.1034 0.9751
0.0357 96.0 26016 0.1026 0.9751
0.0357 97.0 26287 0.1033 0.9751
0.0357 98.0 26558 0.1031 0.9751
0.0357 99.0 26829 0.1030 0.9751
0.0352 100.0 27100 0.1029 0.9751

Framework versions