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
- Downloads last month
- 8
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for PDAP/coarse-url-classifier
Base model
distilbert/distilbert-base-uncased