---
license: cc-by-nc-sa-4.0
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: 0.2137
- Precision: 0.9407
- Recall: 0.9499
- F1: 0.9453
- Accuracy: 0.9546

## 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  | 1.0177          | 0.7537    | 0.7994 | 0.7759 | 0.8120   |
| 1.3951        | 3.12  | 500  | 0.5566          | 0.8370    | 0.8645 | 0.8505 | 0.8731   |
| 1.3951        | 4.69  | 750  | 0.4020          | 0.8737    | 0.9012 | 0.8873 | 0.9104   |
| 0.3933        | 6.25  | 1000 | 0.3111          | 0.9182    | 0.9326 | 0.9254 | 0.9385   |
| 0.3933        | 7.81  | 1250 | 0.2657          | 0.9354    | 0.9424 | 0.9389 | 0.9465   |
| 0.2107        | 9.38  | 1500 | 0.2451          | 0.9241    | 0.9386 | 0.9313 | 0.9423   |
| 0.2107        | 10.94 | 1750 | 0.2393          | 0.9292    | 0.9424 | 0.9357 | 0.9474   |
| 0.1443        | 12.5  | 2000 | 0.2255          | 0.9298    | 0.9416 | 0.9357 | 0.9469   |
| 0.1443        | 14.06 | 2250 | 0.2159          | 0.9336    | 0.9476 | 0.9406 | 0.9542   |
| 0.1093        | 15.62 | 2500 | 0.2137          | 0.9407    | 0.9499 | 0.9453 | 0.9546   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3