deberta-v3-large-survey-related_passage_consistency-rater-all
This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7600
- Krippendorff: 0.7377
- Spearman: 0.6526
- Absolute Agreement: 0.7546
- Agreement Within One: 0.9470
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: 6e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Krippendorff | Spearman | Absolute Agreement | Agreement Within One |
---|---|---|---|---|---|---|---|
No log | 1.0 | 55 | 1.9111 | -0.2279 | nan | 0.4167 | 0.8333 |
No log | 2.0 | 110 | 1.8873 | -0.2279 | nan | 0.4167 | 0.8333 |
No log | 3.0 | 165 | 1.8559 | -0.2279 | nan | 0.4167 | 0.8333 |
No log | 4.0 | 220 | 1.7568 | -0.2279 | nan | 0.4167 | 0.8333 |
No log | 5.0 | 275 | 1.6846 | -0.2279 | nan | 0.4167 | 0.8333 |
No log | 6.0 | 330 | 1.5749 | -0.2279 | nan | 0.4167 | 0.8333 |
No log | 7.0 | 385 | 1.5052 | -0.2279 | nan | 0.4167 | 0.8333 |
No log | 8.0 | 440 | 1.4261 | -0.2279 | nan | 0.4167 | 0.8333 |
No log | 9.0 | 495 | 1.4044 | 0.3239 | 0.3369 | 0.4167 | 0.9028 |
1.2329 | 10.0 | 550 | 1.3616 | 0.2662 | 0.2785 | 0.4167 | 0.8889 |
1.2329 | 11.0 | 605 | 1.2619 | 0.2754 | 0.2653 | 0.4722 | 0.8889 |
1.2329 | 12.0 | 660 | 1.2314 | 0.2592 | 0.3128 | 0.4306 | 0.8889 |
1.2329 | 13.0 | 715 | 1.1903 | 0.3049 | 0.3317 | 0.5417 | 0.8889 |
1.2329 | 14.0 | 770 | 1.3300 | 0.2890 | 0.3347 | 0.5278 | 0.8889 |
1.2329 | 15.0 | 825 | 1.2220 | 0.2192 | 0.3622 | 0.4722 | 0.875 |
1.2329 | 16.0 | 880 | 1.1202 | 0.3028 | 0.3719 | 0.5139 | 0.8889 |
1.2329 | 17.0 | 935 | 1.1119 | 0.1911 | 0.1013 | 0.5556 | 0.875 |
1.2329 | 18.0 | 990 | 1.0345 | 0.2221 | 0.1646 | 0.5556 | 0.875 |
0.6021 | 19.0 | 1045 | 1.1044 | 0.2988 | 0.3099 | 0.5417 | 0.8889 |
0.6021 | 20.0 | 1100 | 1.0117 | 0.2274 | 0.1782 | 0.5556 | 0.875 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.12.1
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