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
