---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- dataset
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: sougemi_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: dataset
      type: dataset
      config: discharge
      split: test
      args: discharge
    metrics:
    - name: Precision
      type: precision
      value: 0.845360824742268
    - name: Recall
      type: recall
      value: 0.8913043478260869
    - name: F1
      type: f1
      value: 0.8677248677248677
    - name: Accuracy
      type: accuracy
      value: 0.9533678756476683
---

<!-- 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. -->

# sougemi_model

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the dataset created.
It achieves the following results on the evaluation set:
- Loss: 0.1812
- Precision: 0.8454
- Recall: 0.8913
- F1: 0.8677
- Accuracy: 0.9534

## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 33.33  | 100  | 0.7803          | 0.8966    | 0.8478 | 0.8715 | 0.9663   |
| No log        | 66.67  | 200  | 0.3016          | 0.8696    | 0.8696 | 0.8696 | 0.9767   |
| No log        | 100.0  | 300  | 0.1623          | 0.9130    | 0.9130 | 0.9130 | 0.9819   |
| No log        | 133.33 | 400  | 0.1680          | 0.8454    | 0.8913 | 0.8677 | 0.9637   |
| 0.5801        | 166.67 | 500  | 0.1812          | 0.8454    | 0.8913 | 0.8677 | 0.9534   |
| 0.5801        | 200.0  | 600  | 0.1231          | 0.8947    | 0.9239 | 0.9091 | 0.9715   |
| 0.5801        | 233.33 | 700  | 0.1363          | 0.8617    | 0.8804 | 0.8710 | 0.9663   |
| 0.5801        | 266.67 | 800  | 0.1949          | 0.8333    | 0.8696 | 0.8511 | 0.9508   |
| 0.5801        | 300.0  | 900  | 0.1749          | 0.8163    | 0.8696 | 0.8421 | 0.9534   |
| 0.0607        | 333.33 | 1000 | 0.1817          | 0.8163    | 0.8696 | 0.8421 | 0.9534   |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2