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FLANG-BERT_bert-base-uncased

This model is a fine-tuned version of SALT-NLP/FLANG-BERT on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7138
  • Accuracy: 0.8643
  • F1: 0.8645
  • Precision: 0.8681
  • Recall: 0.8643

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: 0.0001
  • 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
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 25

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
0.8614 1.0 91 0.8043 0.6443 0.6279 0.6398 0.6443
0.5386 2.0 182 0.4807 0.8112 0.8113 0.8150 0.8112
0.3422 3.0 273 0.4452 0.8300 0.8304 0.8351 0.8300
0.2617 4.0 364 0.5424 0.8190 0.8177 0.8259 0.8190
0.164 5.0 455 0.5162 0.8424 0.8414 0.8424 0.8424
0.1278 6.0 546 0.5737 0.8440 0.8439 0.8440 0.8440
0.0599 7.0 637 0.6869 0.8268 0.8236 0.8311 0.8268
0.1184 8.0 728 0.5331 0.8471 0.8475 0.8493 0.8471
0.1126 9.0 819 0.6979 0.8237 0.8221 0.8332 0.8237
0.0737 10.0 910 0.7481 0.8362 0.8362 0.8381 0.8362
0.1425 11.0 1001 0.7602 0.8315 0.8308 0.8331 0.8315
0.0666 12.0 1092 0.6645 0.8612 0.8612 0.8615 0.8612
0.0523 13.0 1183 0.7138 0.8643 0.8645 0.8681 0.8643
0.0168 14.0 1274 0.7317 0.8534 0.8525 0.8527 0.8534
0.0336 15.0 1365 0.8575 0.8456 0.8454 0.8553 0.8456
0.0424 16.0 1456 0.9331 0.8409 0.8386 0.8423 0.8409
0.0188 17.0 1547 0.7885 0.8596 0.8595 0.8599 0.8596
0.0032 18.0 1638 0.8774 0.8596 0.8584 0.8592 0.8596

Framework versions

  • Transformers 4.37.0
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.1
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