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Regression_bert_2
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.8766
- Mse: 3.8766
- Mae: 1.3858
- R2: -1.0002
- Accuracy: 0.5714
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 1 | 3.5920 | 3.5920 | 1.5846 | -2.3703 | 0.2857 |
| No log | 2.0 | 2 | 3.3981 | 3.3981 | 1.5187 | -2.1884 | 0.2857 |
| No log | 3.0 | 3 | 3.2200 | 3.2200 | 1.4559 | -2.0213 | 0.2857 |
| No log | 4.0 | 4 | 3.0496 | 3.0496 | 1.3924 | -1.8614 | 0.4286 |
| No log | 5.0 | 5 | 2.8847 | 2.8847 | 1.3402 | -1.7067 | 0.4286 |
| No log | 6.0 | 6 | 2.7261 | 2.7261 | 1.2955 | -1.5579 | 0.4286 |
| No log | 7.0 | 7 | 2.5772 | 2.5772 | 1.2590 | -1.4182 | 0.4286 |
| No log | 8.0 | 8 | 2.4361 | 2.4361 | 1.2302 | -1.2858 | 0.4286 |
| No log | 9.0 | 9 | 2.3055 | 2.3055 | 1.2027 | -1.1632 | 0.4286 |
| No log | 10.0 | 10 | 2.1844 | 2.1844 | 1.1765 | -1.0496 | 0.4286 |
| No log | 11.0 | 11 | 2.0725 | 2.0725 | 1.1546 | -0.9446 | 0.4286 |
| No log | 12.0 | 12 | 1.9723 | 1.9723 | 1.1457 | -0.8506 | 0.4286 |
| No log | 13.0 | 13 | 1.8851 | 1.8851 | 1.1381 | -0.7688 | 0.4286 |
| No log | 14.0 | 14 | 1.8103 | 1.8103 | 1.1315 | -0.6985 | 0.2857 |
| No log | 15.0 | 15 | 1.7472 | 1.7472 | 1.1258 | -0.6394 | 0.2857 |
| No log | 16.0 | 16 | 1.6959 | 1.6959 | 1.1211 | -0.5912 | 0.2857 |
| No log | 17.0 | 17 | 1.6558 | 1.6558 | 1.1174 | -0.5536 | 0.2857 |
| No log | 18.0 | 18 | 1.6262 | 1.6262 | 1.1146 | -0.5259 | 0.2857 |
| No log | 19.0 | 19 | 1.6067 | 1.6067 | 1.1128 | -0.5076 | 0.2857 |
| No log | 20.0 | 20 | 1.5970 | 1.5970 | 1.1118 | -0.4984 | 0.2857 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
