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STS-Lora-Fine-Tuning-Capstone-bert-testing-42-with-lower-r-mid

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

  • Loss: 1.4126
  • Accuracy: 0.4199

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: 3e-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: 40

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 90 1.7527 0.2429
No log 2.0 180 1.7436 0.2429
No log 3.0 270 1.7371 0.2429
No log 4.0 360 1.7268 0.2444
No log 5.0 450 1.7015 0.2973
1.6932 6.0 540 1.6853 0.2886
1.6932 7.0 630 1.6676 0.2922
1.6932 8.0 720 1.6423 0.3089
1.6932 9.0 810 1.6182 0.3191
1.6932 10.0 900 1.5953 0.3241
1.6932 11.0 990 1.5797 0.3256
1.5883 12.0 1080 1.5590 0.3358
1.5883 13.0 1170 1.5306 0.3539
1.5883 14.0 1260 1.5157 0.3561
1.5883 15.0 1350 1.4990 0.3604
1.5883 16.0 1440 1.4944 0.3611
1.4756 17.0 1530 1.4822 0.3698
1.4756 18.0 1620 1.4731 0.3735
1.4756 19.0 1710 1.4655 0.3756
1.4756 20.0 1800 1.4603 0.3778
1.4756 21.0 1890 1.4552 0.3974
1.4756 22.0 1980 1.4478 0.3930
1.4113 23.0 2070 1.4439 0.3901
1.4113 24.0 2160 1.4417 0.3930
1.4113 25.0 2250 1.4359 0.4075
1.4113 26.0 2340 1.4316 0.4126
1.4113 27.0 2430 1.4300 0.4061
1.3841 28.0 2520 1.4258 0.4141
1.3841 29.0 2610 1.4237 0.4162
1.3841 30.0 2700 1.4218 0.4133
1.3841 31.0 2790 1.4205 0.4213
1.3841 32.0 2880 1.4189 0.4133
1.3841 33.0 2970 1.4173 0.4162
1.3682 34.0 3060 1.4159 0.4220
1.3682 35.0 3150 1.4146 0.4199
1.3682 36.0 3240 1.4142 0.4213
1.3682 37.0 3330 1.4134 0.4213
1.3682 38.0 3420 1.4129 0.4199
1.3612 39.0 3510 1.4127 0.4184
1.3612 40.0 3600 1.4126 0.4199

Framework versions

  • PEFT 0.10.0
  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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