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da-discourse-coherence-base
This model is a fine-tuned version of NbAiLab/nb-bert-base on the DDisco dataset. It achieves the following results on the evaluation set:
- Loss: 0.7487
- Accuracy: 0.6915
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 703
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 6.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3422 | 0.4 | 5 | 1.0166 | 0.5721 |
| 0.9645 | 0.8 | 10 | 0.8966 | 0.5721 |
| 0.9854 | 1.24 | 15 | 0.8499 | 0.5721 |
| 0.8628 | 1.64 | 20 | 0.8379 | 0.6517 |
| 0.9046 | 2.08 | 25 | 0.8228 | 0.5721 |
| 0.8361 | 2.48 | 30 | 0.7980 | 0.5821 |
| 0.8158 | 2.88 | 35 | 0.8095 | 0.5821 |
| 0.8689 | 3.32 | 40 | 0.7989 | 0.6169 |
| 0.8125 | 3.72 | 45 | 0.7730 | 0.6965 |
| 0.843 | 4.16 | 50 | 0.7566 | 0.6418 |
| 0.7421 | 4.56 | 55 | 0.7840 | 0.6517 |
| 0.7949 | 4.96 | 60 | 0.7531 | 0.6915 |
| 0.828 | 5.4 | 65 | 0.7464 | 0.6816 |
| 0.7438 | 5.8 | 70 | 0.7487 | 0.6915 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.0a0+d0d6b1f
- Datasets 2.9.0
- Tokenizers 0.13.2
