bert-tiny-finetuned-pile-of-law-tos
This model is a MLM fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the pile-of-law/tos dataset. It achieves the following results on the evaluation set:
- Loss: 3.3545
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: 15
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 264 | 3.5896 |
| 3.8119 | 2.0 | 528 | 3.5598 |
| 3.8119 | 3.0 | 792 | 3.5263 |
| 3.7028 | 4.0 | 1056 | 3.4982 |
| 3.7028 | 5.0 | 1320 | 3.5170 |
| 3.6286 | 6.0 | 1584 | 3.5143 |
| 3.6286 | 7.0 | 1848 | 3.4477 |
| 3.553 | 8.0 | 2112 | 3.4044 |
| 3.553 | 9.0 | 2376 | 3.4670 |
| 3.5179 | 10.0 | 2640 | 3.3991 |
| 3.5179 | 11.0 | 2904 | 3.4330 |
| 3.4784 | 12.0 | 3168 | 3.4671 |
| 3.4784 | 13.0 | 3432 | 3.3489 |
| 3.4535 | 14.0 | 3696 | 3.4354 |
| 3.4535 | 15.0 | 3960 | 3.4023 |
Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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