transient_data
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: 0.7541
- Accuracy: 0.7980
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: 256
- 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 | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 204 | 0.1647 | 0.9579 |
| No log | 1.0 | 204 | 0.6233 | 0.8001 |
| No log | 2.0 | 408 | 0.1083 | 0.9699 |
| No log | 2.0 | 408 | 0.7254 | 0.7910 |
| 0.2523 | 3.0 | 612 | 0.0809 | 0.9797 |
| 0.2523 | 3.0 | 612 | 0.7541 | 0.7980 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1+cpu
- Datasets 2.17.1
- Tokenizers 0.15.2
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Model tree for Jingni/transient_data
Base model
distilbert/distilbert-base-uncased