distilbert_coarse5_js_1.1
This model is a fine-tuned version of distilbert-base-uncased trained on the dataset PDAP/coarse-labeled-urls-headers. It achieves the following results on the evaluation set:
- Loss: 0.6826
- Accuracy: 0.8039
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
This model is trained on urls/html data belonging to 5 coarse grained labels:
- Police & Public Interactions
- Info About Officers
- Info About Agencies
- Agency-Published Resources
- Jails & Courts Specific
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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
- lr_scheduler_warmup_steps: 100
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 364 | 0.9021 | 0.6830 |
| 1.0729 | 2.0 | 728 | 0.6936 | 0.7712 |
| 0.6279 | 3.0 | 1092 | 0.6766 | 0.7745 |
| 0.6279 | 4.0 | 1456 | 0.6633 | 0.7941 |
| 0.4531 | 5.0 | 1820 | 0.6691 | 0.8137 |
| 0.3527 | 6.0 | 2184 | 0.6826 | 0.8039 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
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