ontology-trial

scales-okn/ontology-trial

ontology-trial is a text classification model from scales-okn. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as gpl-3.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
License: gpl-3.0
Model type: deberta-v2
Access: Public
Created: Aug 18, 2022
Updated: Aug 6, 2026
SHA: 78ce2cb8

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Model card

From the Hugging Face model README

ontology-trial

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: 0.0681
Accuracy: 0.9881
F1: 0.9825
Precision: 0.9882
Recall: 0.9767

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
0.0782	0.56	100	0.0941	0.9802	0.9711	0.9655	0.9767
0.1477	1.12	200	0.0911	0.9841	0.9765	0.9881	0.9651
0.1786	1.67	300	0.0862	0.9841	0.9767	0.9767	0.9767
0.0018	2.23	400	0.0807	0.9881	0.9825	0.9882	0.9767
0.0869	2.79	500	0.0817	0.9841	0.9767	0.9767	0.9767
0.001	3.35	600	0.0681	0.9881	0.9825	0.9882	0.9767
0.0005	3.91	700	0.0717	0.9841	0.9767	0.9767	0.9767
0.0004	4.47	800	0.0892	0.9802	0.9711	0.9655	0.9767

Framework versions

Transformers 4.23.1
Pytorch 1.13.0+cu117
Datasets 2.6.1
Tokenizers 0.13.1

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.

Task: Text Classification
Library: transformers
Type: deberta-v2
License: gpl-3.0
Created: Aug 18, 2022
Updated: Aug 6, 2026