scales-okn / ner-entry-date-section

huggingface.co
Total runs: 33
24-hour runs: 0
7-day runs: -10
30-day runs: 13
Model's Last Updated: August 06 2026
token-classification

Introduction of ner-entry-date-section

Model Details of ner-entry-date-section

ner-entry-date-section

This model is a fine-tuned version of scales-okn/docket-language-model on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0001
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: 5e-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_ratio: 0.06
  • num_epochs: 5
Training results
Training Loss Epoch Step Validation Loss
0.0012 0.83 30 0.0008
0.0002 1.67 60 0.0001
0.0012 2.5 90 0.0006
0.0012 3.33 120 0.0006
0.0005 4.17 150 0.0002
0.0007 5.0 180 0.0003
Framework versions
  • Transformers 4.20.0.dev0
  • Pytorch 1.10.0+cu102
  • Datasets 1.15.1
  • 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.

Runs of scales-okn ner-entry-date-section on huggingface.co

33
Total runs
0
24-hour runs
2
3-day runs
-10
7-day runs
13
30-day runs

More Information About ner-entry-date-section huggingface.co Model

More ner-entry-date-section license Visit here:

https://choosealicense.com/licenses/gpl-3.0

ner-entry-date-section huggingface.co

ner-entry-date-section huggingface.co is an AI model on huggingface.co that provides ner-entry-date-section's model effect (), which can be used instantly with this scales-okn ner-entry-date-section model. huggingface.co supports a free trial of the ner-entry-date-section model, and also provides paid use of the ner-entry-date-section. Support call ner-entry-date-section model through api, including Node.js, Python, http.

ner-entry-date-section huggingface.co Url

https://huggingface.co/scales-okn/ner-entry-date-section

scales-okn ner-entry-date-section online free

ner-entry-date-section huggingface.co is an online trial and call api platform, which integrates ner-entry-date-section's modeling effects, including api services, and provides a free online trial of ner-entry-date-section, you can try ner-entry-date-section online for free by clicking the link below.

scales-okn ner-entry-date-section online free url in huggingface.co:

https://huggingface.co/scales-okn/ner-entry-date-section

ner-entry-date-section install

ner-entry-date-section is an open source model from GitHub that offers a free installation service, and any user can find ner-entry-date-section on GitHub to install. At the same time, huggingface.co provides the effect of ner-entry-date-section install, users can directly use ner-entry-date-section installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

ner-entry-date-section install url in huggingface.co:

https://huggingface.co/scales-okn/ner-entry-date-section

Url of ner-entry-date-section

ner-entry-date-section huggingface.co Url

Provider of ner-entry-date-section huggingface.co

scales-okn
ORGANIZATIONS

Other API from scales-okn