---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LayoutLM_5
  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. -->

# LayoutLM_5

This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3586
- Precision: 0.8344
- Recall: 0.8344
- F1: 0.8344
- Accuracy: 0.9343

## 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-06
- 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 3.7   | 100  | 0.8644          | 0.0       | 0.0    | 0.0    | 0.7818   |
| No log        | 7.41  | 200  | 0.6214          | 0.7857    | 0.0728 | 0.1333 | 0.8      |
| No log        | 11.11 | 300  | 0.4714          | 0.7303    | 0.4305 | 0.5417 | 0.8657   |
| No log        | 14.81 | 400  | 0.4046          | 0.7955    | 0.6954 | 0.7420 | 0.9189   |
| 0.6176        | 18.52 | 500  | 0.3755          | 0.8194    | 0.7815 | 0.8000 | 0.9301   |
| 0.6176        | 22.22 | 600  | 0.3611          | 0.7935    | 0.8146 | 0.8039 | 0.9245   |
| 0.6176        | 25.93 | 700  | 0.3679          | 0.7848    | 0.8212 | 0.8026 | 0.9245   |
| 0.6176        | 29.63 | 800  | 0.3292          | 0.8289    | 0.8344 | 0.8317 | 0.9357   |
| 0.6176        | 33.33 | 900  | 0.3408          | 0.8289    | 0.8344 | 0.8317 | 0.9315   |
| 0.1555        | 37.04 | 1000 | 0.3479          | 0.8141    | 0.8411 | 0.8274 | 0.9315   |
| 0.1555        | 40.74 | 1100 | 0.3491          | 0.8247    | 0.8411 | 0.8328 | 0.9357   |
| 0.1555        | 44.44 | 1200 | 0.3704          | 0.7888    | 0.8411 | 0.8141 | 0.9245   |
| 0.1555        | 48.15 | 1300 | 0.3591          | 0.8194    | 0.8411 | 0.8301 | 0.9315   |
| 0.1555        | 51.85 | 1400 | 0.3420          | 0.8344    | 0.8344 | 0.8344 | 0.9343   |
| 0.0746        | 55.56 | 1500 | 0.3546          | 0.8421    | 0.8477 | 0.8449 | 0.9357   |
| 0.0746        | 59.26 | 1600 | 0.3442          | 0.8421    | 0.8477 | 0.8449 | 0.9371   |
| 0.0746        | 62.96 | 1700 | 0.3687          | 0.8205    | 0.8477 | 0.8339 | 0.9357   |
| 0.0746        | 66.67 | 1800 | 0.3743          | 0.8258    | 0.8477 | 0.8366 | 0.9343   |
| 0.0746        | 70.37 | 1900 | 0.3626          | 0.8301    | 0.8411 | 0.8355 | 0.9343   |
| 0.0502        | 74.07 | 2000 | 0.3586          | 0.8344    | 0.8344 | 0.8344 | 0.9343   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3