| --- |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: ner-entry-date-section |
| results: [] |
| license: gpl-3.0 |
|
|
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # ner-entry-date-section |
|
|
| This model is a fine-tuned version of [scales-okn/docket-language-model](https://huggingface.co/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. |
| |
| |
