albert-qa-checkpoint
This model is a fine-tuned version of albert-base-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3667
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3388 | 1.0 | 773 | 0.1976 |
| 0.1564 | 2.0 | 1546 | 0.2122 |
| 0.1269 | 3.0 | 2319 | 0.2261 |
| 0.1037 | 4.0 | 3092 | 0.1888 |
| 0.0797 | 5.0 | 3865 | 0.2339 |
| 0.064 | 6.0 | 4638 | 0.2969 |
| 0.0489 | 7.0 | 5411 | 0.3004 |
| 0.0386 | 8.0 | 6184 | 0.3253 |
| 0.0355 | 9.0 | 6957 | 0.3448 |
| 0.0247 | 10.0 | 7730 | 0.3667 |
Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3