ontology-dismiss-with-prejudice
This model is a fine-tuned version of
scales-okn/docket-language-model on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0236
- Accuracy: 0.9961
- F1: 0.9937
- Precision: 0.9875
- Recall: 1.0
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|
| 0.0015 | 0.78 | 200 | 0.0705 | 0.9785 | 0.9664 | 0.9349 | 1.0 |
| 0.0116 | 1.56 | 400 | 0.0812 | 0.9883 | 0.9808 | 0.9935 | 0.9684 |
| 0.0002 | 2.34 | 600 | 0.0400 | 0.9863 | 0.9782 | 0.9632 | 0.9937 |
| 0.0001 | 3.12 | 800 | 0.0294 | 0.9961 | 0.9937 | 0.9875 | 1.0 |
| 0.0001 | 3.9 | 1000 | 0.0252 | 0.9941 | 0.9906 | 0.9814 | 1.0 |
| 0.0001 | 4.69 | 1200 | 0.0236 | 0.9961 | 0.9937 | 0.9875 | 1.0 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
Public release information
This model is released by the SCALES Open Knowledge Network under the GNU General
Public License v3.0. It is derived from scales-okn/docket-language-model and is
intended for research and development involving legal-document classification or
information extraction. It is not legal advice.
The organization has reviewed the release decision and confirmed that the model's
training data and resulting weights are legally and ethically releasable. Users are
responsible for evaluating accuracy, bias, privacy, and fitness for their own use.
The repository includes PyTorch .bin artifacts. Hugging Face's server-side security
scan reported no file issues before publication. As with any serialized model
artifact, load it only with maintained libraries and in an appropriately isolated
environment.
