---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
  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-finetuned-cord_100

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3036
- Precision: 0.9149
- Recall: 0.9309
- F1: 0.9228
- Accuracy: 0.9419

## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 4.17  | 250  | 0.6391          | 0.8080    | 0.8093 | 0.8087 | 0.8312   |
| 0.9327        | 8.33  | 500  | 0.3636          | 0.8790    | 0.8891 | 0.8840 | 0.9088   |
| 0.9327        | 12.5  | 750  | 0.3144          | 0.9001    | 0.9103 | 0.9052 | 0.9288   |
| 0.1743        | 16.67 | 1000 | 0.2957          | 0.9102    | 0.9240 | 0.9170 | 0.9360   |
| 0.1743        | 20.83 | 1250 | 0.2963          | 0.9109    | 0.9248 | 0.9178 | 0.9334   |
| 0.0551        | 25.0  | 1500 | 0.2943          | 0.9207    | 0.9263 | 0.9235 | 0.9411   |
| 0.0551        | 29.17 | 1750 | 0.3034          | 0.9145    | 0.9263 | 0.9203 | 0.9360   |
| 0.0249        | 33.33 | 2000 | 0.3059          | 0.9162    | 0.9301 | 0.9231 | 0.9394   |
| 0.0249        | 37.5  | 2250 | 0.3019          | 0.9147    | 0.9293 | 0.9220 | 0.9385   |
| 0.0153        | 41.67 | 2500 | 0.3036          | 0.9149    | 0.9309 | 0.9228 | 0.9419   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0