---
license: apache-2.0
base_model: docketanalyzer/docket-lm-xs
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: label-complaint
  results: []
---

<!-- 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. -->

# label-complaint

This model is a fine-tuned version of [docketanalyzer/docket-lm-xs](https://huggingface.co/docketanalyzer/docket-lm-xs) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0230
- F1: 0.9915

## 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: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | F1     |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0112        | 0.0418 | 300  | 0.0576          | 0.9771 |
| 0.0551        | 0.0836 | 600  | 0.0362          | 0.9857 |
| 0.2331        | 0.1254 | 900  | 0.0354          | 0.9839 |
| 0.0009        | 0.1672 | 1200 | 0.0396          | 0.9868 |
| 0.005         | 0.2090 | 1500 | 0.0526          | 0.9867 |
| 0.0948        | 0.2508 | 1800 | 0.0434          | 0.9865 |
| 0.016         | 0.2926 | 2100 | 0.0297          | 0.9876 |
| 0.0047        | 0.3344 | 2400 | 0.0394          | 0.9882 |
| 0.0007        | 0.3763 | 2700 | 0.0422          | 0.9864 |
| 0.0037        | 0.4181 | 3000 | 0.0248          | 0.9910 |
| 0.002         | 0.4599 | 3300 | 0.0271          | 0.9909 |
| 0.0005        | 0.5017 | 3600 | 0.0283          | 0.9902 |
| 0.0155        | 0.5435 | 3900 | 0.0227          | 0.9910 |
| 0.0017        | 0.5853 | 4200 | 0.0290          | 0.9907 |
| 0.0002        | 0.6271 | 4500 | 0.0264          | 0.9899 |
| 0.0051        | 0.6689 | 4800 | 0.0294          | 0.9907 |
| 0.0152        | 0.7107 | 5100 | 0.0253          | 0.9903 |
| 0.0096        | 0.7525 | 5400 | 0.0232          | 0.9909 |
| 0.1812        | 0.7943 | 5700 | 0.0295          | 0.9915 |
| 0.0007        | 0.8361 | 6000 | 0.0235          | 0.9912 |
| 0.0081        | 0.8779 | 6300 | 0.0247          | 0.9910 |
| 0.0684        | 0.9197 | 6600 | 0.0236          | 0.9905 |
| 0.0003        | 0.9615 | 6900 | 0.0230          | 0.9914 |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.14.4
- Tokenizers 0.19.1