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nb-bert-base-user-needs

Text Classification
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PyTorch
bert
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nb-bert-base-user-needs / README.md
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metadata
license: cc-by-4.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: nb-bert-base-user-needs
    results: []

nb-bert-base-user-needs

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

  • Loss: 1.0600
  • Accuracy: 0.8479
  • F1: 0.8319
  • Precision: 0.8315
  • Recall: 0.8479

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: 16
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 25
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
No log 1.0 98 1.1222 0.6263 0.5185 0.5076 0.6263
No log 2.0 196 1.0066 0.7216 0.6436 0.5899 0.7216
No log 3.0 294 0.8540 0.7577 0.7037 0.6760 0.7577
No log 4.0 392 0.8621 0.7603 0.6998 0.6568 0.7603
No log 5.0 490 0.8062 0.7887 0.7500 0.7449 0.7887
0.91 6.0 588 0.7465 0.8041 0.7660 0.7636 0.8041
0.91 7.0 686 0.6324 0.8247 0.8163 0.8187 0.8247
0.91 8.0 784 0.7333 0.7964 0.7703 0.7740 0.7964
0.91 9.0 882 0.6590 0.8325 0.8208 0.8106 0.8325
0.91 10.0 980 0.9854 0.8196 0.7890 0.7920 0.8196
0.4246 11.0 1078 0.7023 0.8247 0.8054 0.8138 0.8247
0.4246 12.0 1176 0.8995 0.8325 0.8120 0.8068 0.8325
0.4246 13.0 1274 0.8589 0.8299 0.8145 0.8058 0.8299
0.4246 14.0 1372 0.9859 0.8376 0.8151 0.8123 0.8376
0.4246 15.0 1470 0.8452 0.8402 0.8318 0.8341 0.8402
0.1637 16.0 1568 1.1156 0.8351 0.8157 0.8196 0.8351
0.1637 17.0 1666 1.1514 0.8325 0.8122 0.8218 0.8325
0.1637 18.0 1764 1.0092 0.8428 0.8266 0.8320 0.8428
0.1637 19.0 1862 1.0368 0.8351 0.8229 0.8287 0.8351
0.1637 20.0 1960 1.0600 0.8479 0.8319 0.8315 0.8479
0.0391 21.0 2058 1.1046 0.8428 0.8293 0.8269 0.8428
0.0391 22.0 2156 1.1178 0.8454 0.8262 0.8280 0.8454
0.0391 23.0 2254 1.1103 0.8428 0.8268 0.8295 0.8428
0.0391 24.0 2352 1.1179 0.8428 0.8274 0.8313 0.8428
0.0391 25.0 2450 1.1134 0.8402 0.8233 0.8254 0.8402

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.10.2+cu113
  • Datasets 2.3.2
  • Tokenizers 0.12.1