valrad_qa_model
This model is a fine-tuned version of distilbert-base-uncased on the mlqa dataset. It achieves the following results on the evaluation set:
- Loss: 1.8117
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 355 | 2.3752 |
| 3.1802 | 2.0 | 710 | 1.8748 |
| 1.6816 | 3.0 | 1065 | 1.8117 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3