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xlm-roberta-large-finetuned-augument-visquad2-5-4-2023-1
This model is a fine-tuned version of xlm-roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Best F1: 76.9467
- Loss: 2.4868
- Exact: 38.7333
- F1: 56.7311
- Total: 3821
- Hasans Exact: 55.1451
- Hasans F1: 81.0666
- Hasans Total: 2653
- Noans Exact: 1.4555
- Noans F1: 1.4555
- Noans Total: 1168
- Best Exact: 60.7694
- Best Exact Thresh: 0.8788
- Best F1 Thresh: 0.9686
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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Best F1 | Validation Loss | Exact | F1 | Total | Hasans Exact | Hasans F1 | Hasans Total | Noans Exact | Noans F1 | Noans Total | Best Exact | Best Exact Thresh | Best F1 Thresh |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.1009 | 1.0 | 4221 | 69.8405 | 1.2010 | 36.1685 | 54.2642 | 3821 | 52.0920 | 78.1543 | 2653 | 0.0 | 0.0 | 1168 | 55.4305 | 0.8416 | 0.9065 |
| 0.4716 | 2.0 | 8443 | 74.1358 | 1.0553 | 38.2884 | 56.0896 | 3821 | 54.9943 | 80.6326 | 2653 | 0.3425 | 0.3425 | 1168 | 58.8328 | 0.8002 | 0.9118 |
| 0.3487 | 3.0 | 12664 | 76.3875 | 1.1176 | 39.3876 | 56.5884 | 3821 | 56.6905 | 81.4641 | 2653 | 0.0856 | 0.0856 | 1168 | 61.3190 | 0.7923 | 0.9324 |
| 0.2747 | 4.0 | 16886 | 76.3938 | 1.2634 | 38.6548 | 56.3082 | 3821 | 55.6728 | 81.0982 | 2653 | 0.0 | 0.0 | 1168 | 60.4030 | 0.7414 | 0.9059 |
| 0.217 | 5.0 | 21107 | 76.5504 | 1.3581 | 39.3353 | 56.9569 | 3821 | 56.4644 | 81.8441 | 2653 | 0.4281 | 0.4281 | 1168 | 61.0050 | 0.8307 | 0.8701 |
| 0.1758 | 6.0 | 25329 | 77.2312 | 1.5473 | 39.4399 | 56.6673 | 3821 | 56.7282 | 81.5401 | 2653 | 0.1712 | 0.1712 | 1168 | 61.4761 | 0.8283 | 0.8996 |
| 0.1429 | 7.0 | 29550 | 77.2045 | 1.7840 | 38.8642 | 56.8934 | 3821 | 55.7105 | 81.6773 | 2653 | 0.5993 | 0.5993 | 1168 | 61.3190 | 0.7413 | 0.9449 |
| 0.1159 | 8.0 | 33772 | 76.6868 | 2.1179 | 38.7071 | 56.7210 | 3821 | 55.2959 | 81.2405 | 2653 | 1.0274 | 1.0274 | 1168 | 60.4292 | 0.5939 | 0.9917 |
| 0.0997 | 9.0 | 37993 | 77.1237 | 2.3697 | 38.6548 | 56.6046 | 3821 | 55.4467 | 81.2990 | 2653 | 0.5137 | 0.5137 | 1168 | 60.8479 | 0.9509 | 0.9636 |
| 0.0873 | 10.0 | 42210 | 76.9467 | 2.4868 | 38.7333 | 56.7311 | 3821 | 55.1451 | 81.0666 | 2653 | 1.4555 | 1.4555 | 1168 | 60.7694 | 0.8788 | 0.9686 |
Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
