---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: cord-layoutlmv3
      type: cord-layoutlmv3
      config: cord
      split: test
      args: cord
    metrics:
    - name: Precision
      type: precision
      value: 0.9569093610698366
    - name: Recall
      type: recall
      value: 0.9640718562874252
    - name: F1
      type: f1
      value: 0.9604772557792692
    - name: Accuracy
      type: accuracy
      value: 0.9681663837011885
---

<!-- 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 the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1720
- Precision: 0.9569
- Recall: 0.9641
- F1: 0.9605
- Accuracy: 0.9682

## 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  | 0.3320          | 0.9011    | 0.9207 | 0.9108 | 0.9253   |
| 0.3502        | 3.12  | 500  | 0.2811          | 0.9281    | 0.9371 | 0.9326 | 0.9427   |
| 0.3502        | 4.69  | 750  | 0.2429          | 0.9210    | 0.9341 | 0.9275 | 0.9435   |
| 0.162         | 6.25  | 1000 | 0.2264          | 0.9385    | 0.9476 | 0.9430 | 0.9542   |
| 0.162         | 7.81  | 1250 | 0.1996          | 0.9373    | 0.9513 | 0.9443 | 0.9601   |
| 0.0971        | 9.38  | 1500 | 0.1686          | 0.9569    | 0.9633 | 0.9601 | 0.9690   |
| 0.0971        | 10.94 | 1750 | 0.1814          | 0.9532    | 0.9603 | 0.9567 | 0.9652   |
| 0.0704        | 12.5  | 2000 | 0.1915          | 0.9539    | 0.9611 | 0.9575 | 0.9656   |
| 0.0704        | 14.06 | 2250 | 0.1833          | 0.9590    | 0.9633 | 0.9612 | 0.9677   |
| 0.0513        | 15.62 | 2500 | 0.1720          | 0.9569    | 0.9641 | 0.9605 | 0.9682   |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3