---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: legistorm-categorizer-seqclass-deberta-v1
  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. -->

# legistorm-categorizer-seqclass-deberta-v1

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0265
- Accuracy: 0.9954
- F1: 0.9623
- Precision: 0.9814
- Recall: 0.9439

## 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: 2e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 0.25  | 456   | 0.0191          | 0.9957   | 0.9644 | 0.9973    | 0.9337 |
| 0.0725        | 0.5   | 912   | 0.0176          | 0.9960   | 0.9671 | 1.0       | 0.9362 |
| 0.0044        | 0.75  | 1368  | 0.0161          | 0.9963   | 0.9699 | 0.9973    | 0.9439 |
| 0.0015        | 1.0   | 1824  | 0.0183          | 0.9963   | 0.9699 | 0.9973    | 0.9439 |
| 0.0007        | 1.25  | 2280  | 0.0189          | 0.9962   | 0.9686 | 0.9946    | 0.9439 |
| 0.0004        | 1.5   | 2736  | 0.0197          | 0.9962   | 0.9686 | 0.9946    | 0.9439 |
| 0.0003        | 1.75  | 3192  | 0.0181          | 0.9963   | 0.9699 | 0.9946    | 0.9464 |
| 0.0002        | 2.0   | 3648  | 0.0210          | 0.9957   | 0.9647 | 0.9893    | 0.9413 |
| 0.0001        | 2.25  | 4104  | 0.0224          | 0.9959   | 0.9661 | 0.9893    | 0.9439 |
| 0.0001        | 2.5   | 4560  | 0.0228          | 0.9959   | 0.9661 | 0.9893    | 0.9439 |
| 0.0001        | 2.75  | 5016  | 0.0231          | 0.9957   | 0.9648 | 0.9867    | 0.9439 |
| 0.0001        | 3.0   | 5472  | 0.0224          | 0.9957   | 0.9648 | 0.9867    | 0.9439 |
| 0.0001        | 3.25  | 5928  | 0.0218          | 0.9959   | 0.9661 | 0.9893    | 0.9439 |
| 0.0           | 3.5   | 6384  | 0.0232          | 0.9955   | 0.9635 | 0.9840    | 0.9439 |
| 0.0           | 3.75  | 6840  | 0.0229          | 0.9957   | 0.9649 | 0.9841    | 0.9464 |
| 0.0           | 4.0   | 7296  | 0.0240          | 0.9955   | 0.9635 | 0.9840    | 0.9439 |
| 0.0           | 4.25  | 7752  | 0.0249          | 0.9957   | 0.9648 | 0.9867    | 0.9439 |
| 0.0           | 4.5   | 8208  | 0.0251          | 0.9954   | 0.9623 | 0.9814    | 0.9439 |
| 0.0           | 4.74  | 8664  | 0.0248          | 0.9954   | 0.9623 | 0.9814    | 0.9439 |
| 0.0           | 4.99  | 9120  | 0.0251          | 0.9954   | 0.9623 | 0.9814    | 0.9439 |
| 0.0           | 5.24  | 9576  | 0.0253          | 0.9954   | 0.9623 | 0.9814    | 0.9439 |
| 0.0           | 5.49  | 10032 | 0.0266          | 0.9954   | 0.9623 | 0.9814    | 0.9439 |
| 0.0           | 5.74  | 10488 | 0.0264          | 0.9954   | 0.9623 | 0.9814    | 0.9439 |
| 0.0           | 5.99  | 10944 | 0.0265          | 0.9954   | 0.9623 | 0.9814    | 0.9439 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0