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Regression_longformer
This model is a fine-tuned version of allenai/longformer-base-4096 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0973
- Mse: 0.0973
- Mae: 0.2464
- R2: 0.9498
- Accuracy: 0.8571
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 4 | 3.1150 | 3.1150 | 1.4245 | -1.9228 | 0.2857 |
| No log | 2.0 | 8 | 2.4934 | 2.4934 | 1.2570 | -1.3396 | 0.4286 |
| No log | 3.0 | 12 | 1.5812 | 1.5812 | 1.1308 | -0.4836 | 0.1429 |
| No log | 4.0 | 16 | 1.4004 | 1.4004 | 1.0230 | -0.3140 | 0.4286 |
| No log | 5.0 | 20 | 2.4083 | 2.4083 | 1.2028 | -1.2597 | 0.4286 |
| No log | 6.0 | 24 | 2.4795 | 2.4795 | 1.2186 | -1.3265 | 0.2857 |
| No log | 7.0 | 28 | 1.9781 | 1.9781 | 1.0441 | -0.8560 | 0.5714 |
| No log | 8.0 | 32 | 1.6604 | 1.6604 | 0.9238 | -0.5579 | 0.5714 |
| No log | 9.0 | 36 | 1.5105 | 1.5105 | 0.9193 | -0.4172 | 0.5714 |
| No log | 10.0 | 40 | 0.9750 | 0.9750 | 0.7766 | 0.0852 | 0.5714 |
| No log | 11.0 | 44 | 0.6147 | 0.6147 | 0.6262 | 0.4233 | 0.2857 |
| No log | 12.0 | 48 | 0.5429 | 0.5429 | 0.5941 | 0.4906 | 0.4286 |
| No log | 13.0 | 52 | 0.4183 | 0.4183 | 0.5999 | 0.6075 | 0.4286 |
| No log | 14.0 | 56 | 0.5570 | 0.5570 | 0.6746 | 0.4774 | 0.2857 |
| No log | 15.0 | 60 | 0.2450 | 0.2450 | 0.4159 | 0.7701 | 0.7143 |
| No log | 16.0 | 64 | 0.1650 | 0.1650 | 0.3241 | 0.8452 | 0.7143 |
| No log | 17.0 | 68 | 0.1367 | 0.1367 | 0.2701 | 0.8718 | 0.8571 |
| No log | 18.0 | 72 | 0.1225 | 0.1225 | 0.2905 | 0.8850 | 0.8571 |
| No log | 19.0 | 76 | 0.1169 | 0.1169 | 0.2825 | 0.8903 | 0.8571 |
| No log | 20.0 | 80 | 0.1309 | 0.1309 | 0.3046 | 0.8772 | 0.8571 |
| No log | 21.0 | 84 | 0.4585 | 0.4585 | 0.5426 | 0.5698 | 0.5714 |
| No log | 22.0 | 88 | 0.2451 | 0.2451 | 0.4216 | 0.7700 | 0.5714 |
| No log | 23.0 | 92 | 0.1123 | 0.1123 | 0.2678 | 0.8946 | 0.8571 |
| No log | 24.0 | 96 | 0.0942 | 0.0942 | 0.2816 | 0.9116 | 1.0 |
| No log | 25.0 | 100 | 0.1239 | 0.1239 | 0.2445 | 0.8838 | 0.8571 |
| No log | 26.0 | 104 | 0.2268 | 0.2268 | 0.3540 | 0.7872 | 0.8571 |
| No log | 27.0 | 108 | 0.3575 | 0.3575 | 0.4408 | 0.6645 | 0.5714 |
| No log | 28.0 | 112 | 0.3418 | 0.3418 | 0.4349 | 0.6793 | 0.5714 |
| No log | 29.0 | 116 | 0.2677 | 0.2677 | 0.3958 | 0.7488 | 0.5714 |
| No log | 30.0 | 120 | 0.2235 | 0.2235 | 0.3717 | 0.7903 | 0.5714 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
