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RoBERTa-our-data

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metadata
license: mit
base_model: roberta-base
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: our_data
    results: []

our_data

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

  • Loss: 1.4250
  • Precision: 0.4759
  • Recall: 0.5476
  • F1: 0.5092
  • Accuracy: 0.7455

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: 2e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • 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 Precision Recall F1 Accuracy
1.8353 0.4 500 1.6175 0.1212 0.1217 0.1215 0.5907
1.4071 0.81 1000 1.3137 0.2618 0.3228 0.2891 0.6518
1.1532 1.21 1500 1.2950 0.3154 0.3558 0.3344 0.6739
0.9969 1.61 2000 1.1882 0.3266 0.4034 0.3609 0.6783
0.922 2.01 2500 1.2653 0.3471 0.3995 0.3715 0.6873
0.739 2.42 3000 1.1592 0.3538 0.4339 0.3898 0.7034
0.6866 2.82 3500 1.2015 0.3521 0.4299 0.3871 0.7017
0.5554 3.22 4000 1.2555 0.4398 0.4643 0.4517 0.7329
0.5009 3.63 4500 1.2871 0.4098 0.4868 0.4450 0.7230
0.5117 4.03 5000 1.2482 0.4030 0.4974 0.4452 0.7279
0.3771 4.43 5500 1.3005 0.4300 0.4960 0.4607 0.7261
0.4357 4.83 6000 1.2412 0.4516 0.5251 0.4856 0.7395
0.3151 5.24 6500 1.3410 0.4423 0.5225 0.4791 0.7333
0.3219 5.64 7000 1.2903 0.425 0.5172 0.4666 0.7366
0.3405 6.04 7500 1.3366 0.4470 0.5304 0.4852 0.7471
0.2856 6.45 8000 1.3243 0.4415 0.5344 0.4835 0.7474
0.2723 6.85 8500 1.3962 0.4540 0.5291 0.4887 0.7398
0.2307 7.25 9000 1.4783 0.4671 0.5357 0.4991 0.7440
0.2484 7.66 9500 1.4250 0.4759 0.5476 0.5092 0.7455
0.2361 8.06 10000 1.4695 0.4700 0.5384 0.5018 0.7518
0.186 8.46 10500 1.5283 0.4587 0.5516 0.5009 0.7520
0.2188 8.86 11000 1.4357 0.4478 0.5450 0.4916 0.7471
0.2072 9.27 11500 1.4810 0.4770 0.5357 0.5047 0.7527
0.1817 9.67 12000 1.5041 0.4719 0.5450 0.5058 0.7532

Framework versions

  • Transformers 4.36.0.dev0
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0