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Acc0.8514357053682896, F10.8504605471506504 , Augmented with flang-bert.csv, finetuned on google/electra-base-discriminator
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metadata
license: apache-2.0
base_model: google/electra-base-discriminator
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: electra-base-discriminator_flang-bert
    results: []

electra-base-discriminator_flang-bert

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

  • Loss: 0.4214
  • Accuracy: 0.8830
  • F1: 0.8826
  • Precision: 0.8829
  • Recall: 0.8830

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
0.9314 1.0 91 0.8698 0.6318 0.5743 0.6153 0.6318
0.541 2.0 182 0.4762 0.8237 0.8247 0.8338 0.8237
0.3217 3.0 273 0.3709 0.8580 0.8577 0.8576 0.8580
0.2774 4.0 364 0.4027 0.8596 0.8582 0.8589 0.8596
0.2154 5.0 455 0.4351 0.8424 0.8437 0.8537 0.8424
0.1525 6.0 546 0.4732 0.8487 0.8459 0.8492 0.8487
0.1429 7.0 637 0.4214 0.8830 0.8826 0.8829 0.8830
0.1074 8.0 728 0.5150 0.8674 0.8678 0.8695 0.8674
0.1323 9.0 819 0.5122 0.8705 0.8697 0.8708 0.8705
0.117 10.0 910 0.7296 0.8268 0.8245 0.8294 0.8268
0.1041 11.0 1001 0.5587 0.8643 0.8620 0.8648 0.8643
0.0598 12.0 1092 0.6548 0.8565 0.8564 0.8565 0.8565

Framework versions

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