---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LayoutLMv3_5_entities_1
  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_1

This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2310
- Precision: 0.82
- Recall: 0.8119
- F1: 0.8159
- Accuracy: 0.9642

## 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        | 2.56  | 100  | 0.4044          | 0.5       | 0.0198 | 0.0381 | 0.8884   |
| No log        | 5.13  | 200  | 0.2363          | 0.7571    | 0.5248 | 0.6199 | 0.9328   |
| No log        | 7.69  | 300  | 0.1817          | 0.7083    | 0.6733 | 0.6904 | 0.9447   |
| No log        | 10.26 | 400  | 0.1606          | 0.7551    | 0.7327 | 0.7437 | 0.9523   |
| 0.2439        | 12.82 | 500  | 0.1592          | 0.79      | 0.7822 | 0.7861 | 0.9577   |
| 0.2439        | 15.38 | 600  | 0.1676          | 0.8144    | 0.7822 | 0.7980 | 0.9621   |
| 0.2439        | 17.95 | 700  | 0.1912          | 0.7980    | 0.7822 | 0.7900 | 0.9588   |
| 0.2439        | 20.51 | 800  | 0.1860          | 0.8404    | 0.7822 | 0.8103 | 0.9642   |
| 0.2439        | 23.08 | 900  | 0.1990          | 0.7767    | 0.7921 | 0.7843 | 0.9567   |
| 0.0312        | 25.64 | 1000 | 0.2126          | 0.8081    | 0.7921 | 0.8000 | 0.9610   |
| 0.0312        | 28.21 | 1100 | 0.2105          | 0.8058    | 0.8218 | 0.8137 | 0.9621   |
| 0.0312        | 30.77 | 1200 | 0.2127          | 0.8119    | 0.8119 | 0.8119 | 0.9632   |
| 0.0312        | 33.33 | 1300 | 0.2308          | 0.81      | 0.8020 | 0.8060 | 0.9621   |
| 0.0312        | 35.9  | 1400 | 0.2211          | 0.82      | 0.8119 | 0.8159 | 0.9642   |
| 0.0126        | 38.46 | 1500 | 0.2244          | 0.82      | 0.8119 | 0.8159 | 0.9642   |
| 0.0126        | 41.03 | 1600 | 0.2241          | 0.82      | 0.8119 | 0.8159 | 0.9642   |
| 0.0126        | 43.59 | 1700 | 0.2332          | 0.82      | 0.8119 | 0.8159 | 0.9642   |
| 0.0126        | 46.15 | 1800 | 0.2345          | 0.82      | 0.8119 | 0.8159 | 0.9632   |
| 0.0126        | 48.72 | 1900 | 0.2318          | 0.82      | 0.8119 | 0.8159 | 0.9642   |
| 0.0069        | 51.28 | 2000 | 0.2310          | 0.82      | 0.8119 | 0.8159 | 0.9642   |


### Framework versions

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