---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LayoutLMv3_5_entities_filtred_13
  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. -->

# LayoutLMv3_5_entities_filtred_13

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: 0.5851
- Precision: 0.875
- Recall: 0.7778
- F1: 0.8235
- Accuracy: 0.9540

## 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 20.0  | 100  | 0.3668          | 0.7917    | 0.7037 | 0.7451 | 0.9425   |
| No log        | 40.0  | 200  | 0.5200          | 0.8182    | 0.6667 | 0.7347 | 0.9368   |
| No log        | 60.0  | 300  | 0.5244          | 0.8333    | 0.7407 | 0.7843 | 0.9483   |
| No log        | 80.0  | 400  | 0.5471          | 0.8261    | 0.7037 | 0.76   | 0.9425   |
| 0.0818        | 100.0 | 500  | 0.5854          | 0.8261    | 0.7037 | 0.76   | 0.9425   |
| 0.0818        | 120.0 | 600  | 0.5497          | 0.875     | 0.7778 | 0.8235 | 0.9540   |
| 0.0818        | 140.0 | 700  | 0.5480          | 0.875     | 0.7778 | 0.8235 | 0.9540   |
| 0.0818        | 160.0 | 800  | 0.5709          | 0.8696    | 0.7407 | 0.8000 | 0.9483   |
| 0.0818        | 180.0 | 900  | 0.5587          | 0.875     | 0.7778 | 0.8235 | 0.9540   |
| 0.0007        | 200.0 | 1000 | 0.5676          | 0.875     | 0.7778 | 0.8235 | 0.9540   |
| 0.0007        | 220.0 | 1100 | 0.5674          | 0.875     | 0.7778 | 0.8235 | 0.9540   |
| 0.0007        | 240.0 | 1200 | 0.5688          | 0.875     | 0.7778 | 0.8235 | 0.9540   |
| 0.0007        | 260.0 | 1300 | 0.5733          | 0.875     | 0.7778 | 0.8235 | 0.9540   |
| 0.0007        | 280.0 | 1400 | 0.5786          | 0.875     | 0.7778 | 0.8235 | 0.9540   |
| 0.0003        | 300.0 | 1500 | 0.5767          | 0.875     | 0.7778 | 0.8235 | 0.9540   |
| 0.0003        | 320.0 | 1600 | 0.5766          | 0.875     | 0.7778 | 0.8235 | 0.9540   |
| 0.0003        | 340.0 | 1700 | 0.5813          | 0.875     | 0.7778 | 0.8235 | 0.9540   |
| 0.0003        | 360.0 | 1800 | 0.5831          | 0.875     | 0.7778 | 0.8235 | 0.9540   |
| 0.0003        | 380.0 | 1900 | 0.5851          | 0.875     | 0.7778 | 0.8235 | 0.9540   |
| 0.0003        | 400.0 | 2000 | 0.5851          | 0.875     | 0.7778 | 0.8235 | 0.9540   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3