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distilroberta-propaganda-2class

This model is a fine-tuned version of distilroberta-base on the QCRI propaganda dataset.

It achieves the following results on the evaluation set:

  • Loss: 0.5087
  • Acc: 0.7424

Training and evaluation data

Training data is the 19-class QCRI propaganda data, with all propaganda classes collapsed to a single catch-all 'prop' class.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 12345
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 16
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Acc
0.5737 1.0 493 0.5998 0.6515
0.4954 2.0 986 0.5530 0.7080
0.4774 3.0 1479 0.5331 0.7258
0.4846 4.0 1972 0.5247 0.7339
0.4749 5.0 2465 0.5392 0.7199
0.502 6.0 2958 0.5124 0.7466
0.457 7.0 3451 0.5167 0.7432
0.4899 8.0 3944 0.5160 0.7428
0.4833 9.0 4437 0.5280 0.7339
0.5114 10.0 4930 0.5112 0.7436
0.4419 11.0 5423 0.5060 0.7525
0.4743 12.0 5916 0.5031 0.7547
0.4597 13.0 6409 0.5043 0.7517
0.4861 14.0 6902 0.5055 0.7487
0.499 15.0 7395 0.5091 0.7419
0.501 16.0 7888 0.5037 0.7521
0.4659 17.0 8381 0.5087 0.7424

Framework versions

  • Transformers 4.11.2
  • Pytorch 1.7.1
  • Datasets 1.11.0
  • Tokenizers 0.10.3
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