---
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.