---
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: 0.9479940564635958
    - name: Recall
      type: recall
      value: 0.9550898203592815
    - name: F1
      type: f1
      value: 0.9515287099179717
    - name: Accuracy
      type: accuracy
      value: 0.9562818336162988
---

<!-- 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: 0.2163
- Precision: 0.9480
- Recall: 0.9551
- F1: 0.9515
- Accuracy: 0.9563

## 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: 10
- eval_batch_size: 10
- 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        | 3.12  | 250  | 0.8807          | 0.7910    | 0.8301 | 0.8101 | 0.8247   |
| 1.2597        | 6.25  | 500  | 0.4375          | 0.8808    | 0.9072 | 0.8938 | 0.9117   |
| 1.2597        | 9.38  | 750  | 0.3119          | 0.9185    | 0.9364 | 0.9274 | 0.9423   |
| 0.2869        | 12.5  | 1000 | 0.2700          | 0.9340    | 0.9424 | 0.9382 | 0.9452   |
| 0.2869        | 15.62 | 1250 | 0.2401          | 0.9429    | 0.9513 | 0.9471 | 0.9559   |
| 0.1378        | 18.75 | 1500 | 0.2295          | 0.9415    | 0.9521 | 0.9468 | 0.9550   |
| 0.1378        | 21.88 | 1750 | 0.2183          | 0.9523    | 0.9566 | 0.9544 | 0.9580   |
| 0.0866        | 25.0  | 2000 | 0.2190          | 0.9523    | 0.9566 | 0.9544 | 0.9576   |
| 0.0866        | 28.12 | 2250 | 0.2168          | 0.9508    | 0.9551 | 0.9529 | 0.9567   |
| 0.066         | 31.25 | 2500 | 0.2163          | 0.9480    | 0.9551 | 0.9515 | 0.9563   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.10.1
- Tokenizers 0.13.2