distilroberta-current
This model classifies articles as current (covering or discussing current events) or not current (not relating to current events).
The model is a fine-tuned version of distilroberta-base on a dataset of articles labeled using weak-supervision and manual labeling
It achieves the following results on the evaluation set:
- Loss: 0.1745
- Acc: 0.9355
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 12345
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 |
|---|---|---|---|---|
| No log | 1.0 | 11 | 0.6559 | 0.7097 |
| 0.6762 | 2.0 | 22 | 0.5627 | 0.7097 |
| 0.5432 | 3.0 | 33 | 0.4606 | 0.7097 |
| 0.5432 | 4.0 | 44 | 0.3651 | 0.8065 |
| 0.411 | 5.0 | 55 | 0.2512 | 0.9194 |
| 0.269 | 6.0 | 66 | 0.2774 | 0.9355 |
| 0.269 | 7.0 | 77 | 0.2062 | 0.8710 |
| 0.2294 | 8.0 | 88 | 0.2598 | 0.9355 |
| 0.1761 | 9.0 | 99 | 0.1745 | 0.9355 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
