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source-role-model

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PyTorch
roberta
Generated from Trainer
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source-role-model / README.md
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metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - f1
base_model: roberta-base
model-index:
  - name: source-role-model
    results: []

source-role-model

This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 2.5543
  • F1: 0.5814

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: 5e-05
  • train_batch_size: 5
  • eval_batch_size: 5
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10.0

Training results

Training Loss Epoch Step Validation Loss F1
No log 0.12 100 1.0000 0.3391
No log 0.25 200 0.8371 0.5055
No log 0.37 300 0.8684 0.5019
No log 0.49 400 0.8668 0.5208
0.9644 0.62 500 0.8473 0.5422
0.9644 0.74 600 0.8852 0.4956
0.9644 0.86 700 0.8368 0.5124
0.9644 0.99 800 0.7913 0.5848
0.9644 1.11 900 1.0570 0.4950
0.8375 1.23 1000 0.9402 0.5280
0.8375 1.35 1100 0.8023 0.5084
0.8375 1.48 1200 0.9299 0.4807
0.8375 1.6 1300 0.9661 0.5194
0.8375 1.72 1400 0.8014 0.6016
0.8149 1.85 1500 0.8608 0.6105
0.8149 1.97 1600 0.9195 0.5741
0.8149 2.09 1700 1.2378 0.5964
0.8149 2.22 1800 1.0415 0.5902
0.8149 2.34 1900 1.0499 0.5526
0.6932 2.46 2000 1.0600 0.5832
0.6932 2.59 2100 0.9368 0.6074
0.6932 2.71 2200 1.0872 0.6270
0.6932 2.83 2300 1.0912 0.5707
0.6932 2.96 2400 0.8815 0.5602
0.6214 3.08 2500 1.1650 0.5993
0.6214 3.2 2600 1.4485 0.5821
0.6214 3.33 2700 1.5382 0.5775
0.6214 3.45 2800 1.3999 0.5696
0.6214 3.57 2900 1.3702 0.6114
0.5686 3.69 3000 1.3840 0.5635
0.5686 3.82 3100 1.3547 0.5403
0.5686 3.94 3200 1.0283 0.5723
0.5686 4.06 3300 1.3593 0.6242
0.5686 4.19 3400 1.5985 0.6004
0.4807 4.31 3500 1.5351 0.6177
0.4807 4.43 3600 1.4109 0.5779
0.4807 4.56 3700 1.6972 0.5637
0.4807 4.68 3800 1.5336 0.6047
0.4807 4.8 3900 1.7811 0.5909
0.4387 4.93 4000 1.5862 0.5869
0.4387 5.05 4100 1.7106 0.5637
0.4387 5.17 4200 1.5251 0.5624
0.4387 5.3 4300 1.5519 0.5944
0.4387 5.42 4400 1.7315 0.5908
0.3219 5.54 4500 1.7588 0.6015
0.3219 5.67 4600 1.9277 0.5635
0.3219 5.79 4700 1.7663 0.5891
0.3219 5.91 4800 1.8401 0.5917
0.3219 6.03 4900 2.0516 0.5845
0.2311 6.16 5000 2.0510 0.6166
0.2311 6.28 5100 2.1673 0.5732
0.2311 6.4 5200 2.0931 0.5819
0.2311 6.53 5300 2.2803 0.5961
0.2311 6.65 5400 1.9985 0.6010
0.1669 6.77 5500 2.1742 0.5664
0.1669 6.9 5600 2.1021 0.5732
0.1669 7.02 5700 2.2043 0.5641
0.1669 7.14 5800 2.2018 0.5837
0.1669 7.27 5900 2.3575 0.5721
0.1698 7.39 6000 2.4663 0.5662
0.1698 7.51 6100 2.2658 0.5851
0.1698 7.64 6200 2.1585 0.5676
0.1698 7.76 6300 2.1755 0.5774
0.1698 7.88 6400 2.2680 0.5696
0.1378 8.0 6500 2.3505 0.5615
0.1378 8.13 6600 2.2773 0.5705
0.1378 8.25 6700 2.3112 0.5662
0.1378 8.37 6800 2.4572 0.5679
0.1378 8.5 6900 2.4642 0.5766
0.0756 8.62 7000 2.4643 0.5885
0.0756 8.74 7100 2.5096 0.5779
0.0756 8.87 7200 2.4261 0.5789
0.0756 8.99 7300 2.3973 0.5757
0.0756 9.11 7400 2.4137 0.5906
0.0842 9.24 7500 2.4577 0.5844
0.0842 9.36 7600 2.5034 0.5840
0.0842 9.48 7700 2.5176 0.5810
0.0842 9.61 7800 2.5240 0.5852
0.0842 9.73 7900 2.5141 0.5824
0.0634 9.85 8000 2.5482 0.5814
0.0634 9.98 8100 2.5543 0.5814

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.13.1
  • Tokenizers 0.13.3