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metadata
base_model: nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: MiniLMv2-L6-H384-distilled-from-RoBERTa-Large-agentflow-distil
    results: []

MiniLMv2-L6-H384-distilled-from-RoBERTa-Large-agentflow-distil

This model is a fine-tuned version of nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1540
  • Accuracy: 0.9616
  • F1: 0.9618

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: 7e-05
  • train_batch_size: 10
  • eval_batch_size: 10
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 8

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
No log 0.07 30 3.4249 0.1510 0.0404
No log 0.13 60 3.3994 0.2779 0.1759
No log 0.2 90 3.3313 0.3423 0.2154
No log 0.27 120 3.1475 0.3977 0.3024
No log 0.33 150 2.8961 0.3494 0.2370
No log 0.4 180 2.6867 0.5147 0.4325
No log 0.47 210 2.4676 0.5728 0.4955
No log 0.54 240 2.2129 0.5657 0.4588
No log 0.6 270 1.9712 0.6917 0.6331
No log 0.67 300 1.8016 0.6533 0.5799
No log 0.74 330 1.5721 0.7185 0.6524
No log 0.8 360 1.3381 0.8061 0.7760
No log 0.87 390 1.1876 0.8543 0.8319
No log 0.94 420 0.9877 0.8722 0.8577
No log 1.0 450 0.8819 0.8892 0.8850
No log 1.07 480 0.7511 0.8972 0.8955
2.2047 1.14 510 0.5262 0.9410 0.9408
2.2047 1.21 540 0.5107 0.9294 0.9297
2.2047 1.27 570 0.4612 0.9285 0.9292
2.2047 1.34 600 0.3487 0.9410 0.9407
2.2047 1.41 630 0.3137 0.9374 0.9369
2.2047 1.47 660 0.2951 0.9223 0.9190
2.2047 1.54 690 0.2738 0.9374 0.9377
2.2047 1.61 720 0.2472 0.9446 0.9439
2.2047 1.67 750 0.1988 0.9535 0.9530
2.2047 1.74 780 0.2016 0.9517 0.9519
2.2047 1.81 810 0.2158 0.9428 0.9427
2.2047 1.88 840 0.2519 0.9330 0.9324
2.2047 1.94 870 0.2224 0.9437 0.9436
2.2047 2.01 900 0.3032 0.9285 0.9276
2.2047 2.08 930 0.1815 0.9544 0.9546
2.2047 2.14 960 0.2125 0.9455 0.9455
2.2047 2.21 990 0.2198 0.9455 0.9446
0.2888 2.28 1020 0.1869 0.9571 0.9568
0.2888 2.34 1050 0.1705 0.9571 0.9568
0.2888 2.41 1080 0.1927 0.9526 0.9523
0.2888 2.48 1110 0.1700 0.9562 0.9561
0.2888 2.54 1140 0.2162 0.9464 0.9460
0.2888 2.61 1170 0.1540 0.9616 0.9618
0.2888 2.68 1200 0.1752 0.9562 0.9561
0.2888 2.75 1230 0.1476 0.9607 0.9605
0.2888 2.81 1260 0.2575 0.9410 0.9414
0.2888 2.88 1290 0.1574 0.9616 0.9614
0.2888 2.95 1320 0.1574 0.9598 0.9596
0.2888 3.01 1350 0.1640 0.9580 0.9578
0.2888 3.08 1380 0.1627 0.9598 0.9594
0.2888 3.15 1410 0.1866 0.9544 0.9550
0.2888 3.21 1440 0.1610 0.9526 0.9526
0.2888 3.28 1470 0.2134 0.9419 0.9412

Framework versions

  • Transformers 4.37.0
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.1