---
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: 0.2879
- Precision: 0.9258
- Recall: 0.9384
- F1: 0.9321
- Accuracy: 0.9474

## 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  | 0.6982          | 0.7974    | 0.8017 | 0.7995 | 0.8265   |
| 0.9999        | 8.33  | 500  | 0.4214          | 0.8603    | 0.8754 | 0.8678 | 0.8936   |
| 0.9999        | 12.5  | 750  | 0.2820          | 0.9081    | 0.9164 | 0.9123 | 0.9364   |
| 0.1912        | 16.67 | 1000 | 0.2710          | 0.9147    | 0.9293 | 0.9220 | 0.9389   |
| 0.1912        | 20.83 | 1250 | 0.2748          | 0.9125    | 0.9271 | 0.9197 | 0.9406   |
| 0.061         | 25.0  | 1500 | 0.2612          | 0.9220    | 0.9339 | 0.9279 | 0.9474   |
| 0.061         | 29.17 | 1750 | 0.2731          | 0.9300    | 0.9384 | 0.9342 | 0.9478   |
| 0.0275        | 33.33 | 2000 | 0.2824          | 0.9279    | 0.9384 | 0.9331 | 0.9487   |
| 0.0275        | 37.5  | 2250 | 0.2886          | 0.9242    | 0.9362 | 0.9302 | 0.9457   |
| 0.0168        | 41.67 | 2500 | 0.2879          | 0.9258    | 0.9384 | 0.9321 | 0.9474   |


### Framework versions

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