---
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: 0.1687
- Precision: 0.9382
- Recall: 0.9574
- F1: 0.9477
- Accuracy: 0.9597

## 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        | 1.56  | 250  | 0.3730          | 0.8662    | 0.8708 | 0.8685 | 0.9042   |
| 0.3943        | 3.12  | 500  | 0.2683          | 0.8939    | 0.9027 | 0.8983 | 0.9279   |
| 0.3943        | 4.69  | 750  | 0.2232          | 0.9248    | 0.9339 | 0.9293 | 0.9474   |
| 0.1559        | 6.25  | 1000 | 0.2129          | 0.9301    | 0.9407 | 0.9354 | 0.9504   |
| 0.1559        | 7.81  | 1250 | 0.1782          | 0.9289    | 0.9529 | 0.9407 | 0.9563   |
| 0.082         | 9.38  | 1500 | 0.1876          | 0.9327    | 0.9483 | 0.9405 | 0.9555   |
| 0.082         | 10.94 | 1750 | 0.1746          | 0.9416    | 0.9559 | 0.9487 | 0.9606   |
| 0.0486        | 12.5  | 2000 | 0.1848          | 0.9349    | 0.9498 | 0.9423 | 0.9550   |
| 0.0486        | 14.06 | 2250 | 0.1739          | 0.9439    | 0.9590 | 0.9514 | 0.9623   |
| 0.0351        | 15.62 | 2500 | 0.1687          | 0.9382    | 0.9574 | 0.9477 | 0.9597   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2