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1
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: nan
- F1: 0.0032
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: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 9.5074 | 0.18 | 10 | nan | 0.0060 |
| 8.5442 | 0.37 | 20 | nan | 0.0102 |
| 6.6803 | 0.55 | 30 | nan | 0.0 |
| 7.1123 | 0.73 | 40 | nan | 0.0040 |
| 9.1525 | 0.92 | 50 | nan | 0.0044 |
| 7.3704 | 1.1 | 60 | nan | 0.0 |
| 6.0446 | 1.28 | 70 | nan | 0.0 |
| 6.8367 | 1.47 | 80 | nan | 0.0 |
| 6.3409 | 1.65 | 90 | nan | 0.0144 |
| 7.3165 | 1.83 | 100 | nan | 0.0 |
| 6.2659 | 2.02 | 110 | nan | 0.0 |
| 5.7613 | 2.2 | 120 | nan | 0.0 |
| 6.3813 | 2.39 | 130 | nan | 0.0044 |
| 6.977 | 2.57 | 140 | nan | 0.0102 |
| 6.1388 | 2.75 | 150 | nan | 0.0063 |
| 8.3673 | 2.94 | 160 | nan | 0.0013 |
| 6.9132 | 3.12 | 170 | nan | 0.0059 |
| 7.4859 | 3.3 | 180 | nan | 0.0 |
| 6.5924 | 3.49 | 190 | nan | 0.0061 |
| 6.3331 | 3.67 | 200 | nan | 0.0032 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
