---
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: train
      args: cord
    metrics:
    - name: Precision
      type: precision
      value: 0.9304733727810651
    - name: Recall
      type: recall
      value: 0.9416167664670658
    - name: F1
      type: f1
      value: 0.9360119047619048
    - name: Accuracy
      type: accuracy
      value: 0.9435483870967742
---

<!-- 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: 0.2920
- Precision: 0.9305
- Recall: 0.9416
- F1: 0.9360
- Accuracy: 0.9435

## 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  | 1.1252          | 0.6774    | 0.7530 | 0.7132 | 0.7721   |
| 1.4792        | 8.33  | 500  | 0.5864          | 0.8395    | 0.8653 | 0.8522 | 0.8667   |
| 1.4792        | 12.5  | 750  | 0.4279          | 0.8666    | 0.8997 | 0.8828 | 0.9032   |
| 0.3807        | 16.67 | 1000 | 0.3512          | 0.9067    | 0.9237 | 0.9151 | 0.9317   |
| 0.3807        | 20.83 | 1250 | 0.3030          | 0.9167    | 0.9311 | 0.9239 | 0.9368   |
| 0.1615        | 25.0  | 1500 | 0.3022          | 0.9239    | 0.9356 | 0.9297 | 0.9385   |
| 0.1615        | 29.17 | 1750 | 0.2931          | 0.9198    | 0.9356 | 0.9276 | 0.9385   |
| 0.0879        | 33.33 | 2000 | 0.2968          | 0.9276    | 0.9401 | 0.9338 | 0.9427   |
| 0.0879        | 37.5  | 2250 | 0.2853          | 0.9298    | 0.9424 | 0.9361 | 0.9448   |
| 0.0641        | 41.67 | 2500 | 0.2920          | 0.9305    | 0.9416 | 0.9360 | 0.9435   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2