SCOTUS_AI
This model is a fine-tuned version of raminass/scotus-v10 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7680
- Accuracy: 0.8341
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5767 | 1.0 | 1800 | 0.6222 | 0.8243 |
| 0.2965 | 2.0 | 3600 | 0.6352 | 0.8339 |
| 0.1832 | 3.0 | 5400 | 0.7201 | 0.8261 |
| 0.0991 | 4.0 | 7200 | 0.7398 | 0.8356 |
| 0.0616 | 5.0 | 9000 | 0.7680 | 0.8341 |
Justices
| Justice | Count |
|---|---|
| Thomas | 571 |
| Scalia | 473 |
| Breyer | 443 |
| Stevens | 407 |
| Ginsburg | 390 |
| Kennedy | 326 |
| Alito | 286 |
| Souter | 230 |
| Sotomayor | 226 |
| O'Connor | 167 |
| Kagan | 145 |
| Rehnquist | 144 |
| Roberts | 123 |
| Gorsuch | 109 |
| Kavanaugh | 65 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
- Downloads last month
- 15
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.