---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LayoutLMv3_5_entities_filtred_12
  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_12

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: 0.1405
- Precision: 0.9474
- Recall: 0.9474
- F1: 0.9474
- Accuracy: 0.9856

## 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        | 50.0   | 100  | 0.1150          | 0.9       | 0.9474 | 0.9231 | 0.9784   |
| No log        | 100.0  | 200  | 0.1241          | 0.9474    | 0.9474 | 0.9474 | 0.9856   |
| No log        | 150.0  | 300  | 0.1328          | 0.9474    | 0.9474 | 0.9474 | 0.9856   |
| No log        | 200.0  | 400  | 0.1954          | 0.9       | 0.9474 | 0.9231 | 0.9784   |
| 0.0457        | 250.0  | 500  | 0.1845          | 0.8571    | 0.9474 | 0.9    | 0.9712   |
| 0.0457        | 300.0  | 600  | 0.0843          | 1.0       | 0.9474 | 0.9730 | 0.9928   |
| 0.0457        | 350.0  | 700  | 0.0896          | 1.0       | 0.9474 | 0.9730 | 0.9928   |
| 0.0457        | 400.0  | 800  | 0.0947          | 0.9474    | 0.9474 | 0.9474 | 0.9856   |
| 0.0457        | 450.0  | 900  | 0.1026          | 0.9474    | 0.9474 | 0.9474 | 0.9856   |
| 0.0005        | 500.0  | 1000 | 0.1118          | 0.9474    | 0.9474 | 0.9474 | 0.9856   |
| 0.0005        | 550.0  | 1100 | 0.1196          | 0.9474    | 0.9474 | 0.9474 | 0.9856   |
| 0.0005        | 600.0  | 1200 | 0.1257          | 0.9474    | 0.9474 | 0.9474 | 0.9856   |
| 0.0005        | 650.0  | 1300 | 0.1297          | 0.9474    | 0.9474 | 0.9474 | 0.9856   |
| 0.0005        | 700.0  | 1400 | 0.1334          | 0.9474    | 0.9474 | 0.9474 | 0.9856   |
| 0.0002        | 750.0  | 1500 | 0.1360          | 0.9474    | 0.9474 | 0.9474 | 0.9856   |
| 0.0002        | 800.0  | 1600 | 0.1381          | 0.9474    | 0.9474 | 0.9474 | 0.9856   |
| 0.0002        | 850.0  | 1700 | 0.1389          | 0.9474    | 0.9474 | 0.9474 | 0.9856   |
| 0.0002        | 900.0  | 1800 | 0.1396          | 0.9474    | 0.9474 | 0.9474 | 0.9856   |
| 0.0002        | 950.0  | 1900 | 0.1402          | 0.9474    | 0.9474 | 0.9474 | 0.9856   |
| 0.0002        | 1000.0 | 2000 | 0.1405          | 0.9474    | 0.9474 | 0.9474 | 0.9856   |


### Framework versions

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