AnonymousCS's picture
End of training
a13f975 verified
metadata
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_base
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: populism_classifier_bsample_191
    results: []

populism_classifier_bsample_191

This model is a fine-tuned version of AnonymousCS/populism_xlmr_base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8839
  • Accuracy: 0.0552
  • 1-f1: 0.1047
  • 1-recall: 1.0
  • 1-precision: 0.0552
  • Balanced Acc: 0.5

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: 1e-06
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.06
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy 1-f1 1-recall 1-precision Balanced Acc
0.7631 1.0 14 1.0478 0.0552 0.1047 1.0 0.0552 0.5
0.9633 2.0 28 1.0210 0.0552 0.1047 1.0 0.0552 0.5
0.5929 3.0 42 0.9960 0.0552 0.1047 1.0 0.0552 0.5
0.746 4.0 56 0.9764 0.0552 0.1047 1.0 0.0552 0.5
0.7519 5.0 70 0.9590 0.0552 0.1047 1.0 0.0552 0.5
0.8273 6.0 84 0.9430 0.0552 0.1047 1.0 0.0552 0.5
0.7775 7.0 98 0.9296 0.0552 0.1047 1.0 0.0552 0.5
0.7445 8.0 112 0.9189 0.0552 0.1047 1.0 0.0552 0.5
0.7983 9.0 126 0.9096 0.0552 0.1047 1.0 0.0552 0.5
0.6858 10.0 140 0.9034 0.0552 0.1047 1.0 0.0552 0.5
0.8285 11.0 154 0.8959 0.0552 0.1047 1.0 0.0552 0.5
0.9547 12.0 168 0.8905 0.0552 0.1047 1.0 0.0552 0.5
0.6147 13.0 182 0.8866 0.0552 0.1047 1.0 0.0552 0.5
0.7742 14.0 196 0.8848 0.0552 0.1047 1.0 0.0552 0.5
0.6978 15.0 210 0.8839 0.0552 0.1047 1.0 0.0552 0.5

Framework versions

  • Transformers 4.46.3
  • Pytorch 2.4.1+cu121
  • Datasets 3.1.0
  • Tokenizers 0.20.3