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metadata
license: mit
library_name: peft
tags:
  - generated_from_trainer
metrics:
  - accuracy
base_model: dslim/bert-base-NER
model-index:
  - name: STS-Lora-Fine-Tuning-Capstone-bert-testing-22-with-lower-r
    results: []

STS-Lora-Fine-Tuning-Capstone-bert-testing-22-with-lower-r

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: 1.4650
  • Accuracy: 0.3843

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: 32
  • eval_batch_size: 32
  • 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
No log 1.0 180 1.7491 0.2429
No log 2.0 360 1.7398 0.2451
1.7057 3.0 540 1.7266 0.2408
1.7057 4.0 720 1.6996 0.2922
1.7057 5.0 900 1.6538 0.2988
1.6492 6.0 1080 1.6283 0.3118
1.6492 7.0 1260 1.5879 0.3270
1.6492 8.0 1440 1.5578 0.3387
1.5479 9.0 1620 1.5355 0.3503
1.5479 10.0 1800 1.5148 0.3561
1.5479 11.0 1980 1.5062 0.3561
1.4735 12.0 2160 1.5005 0.3691
1.4735 13.0 2340 1.4876 0.3843
1.437 14.0 2520 1.4799 0.3800
1.437 15.0 2700 1.4768 0.3785
1.437 16.0 2880 1.4732 0.3851
1.4223 17.0 3060 1.4689 0.3800
1.4223 18.0 3240 1.4684 0.3822
1.4223 19.0 3420 1.4657 0.3822
1.4123 20.0 3600 1.4650 0.3843

Framework versions

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