---
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: 0.9386094674556213
    - name: Recall
      type: recall
      value: 0.9498502994011976
    - name: F1
      type: f1
      value: 0.9441964285714285
    - 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: 0.2039
- Precision: 0.9386
- Recall: 0.9499
- F1: 0.9442
- 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: 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  | 1.0093          | 0.7425    | 0.7964 | 0.7685 | 0.7984   |
| 1.3757        | 3.12  | 500  | 0.5393          | 0.8493    | 0.8735 | 0.8613 | 0.8816   |
| 1.3757        | 4.69  | 750  | 0.3774          | 0.8857    | 0.9049 | 0.8952 | 0.9160   |
| 0.3755        | 6.25  | 1000 | 0.2909          | 0.9153    | 0.9304 | 0.9228 | 0.9338   |
| 0.3755        | 7.81  | 1250 | 0.2511          | 0.9174    | 0.9311 | 0.9242 | 0.9393   |
| 0.1939        | 9.38  | 1500 | 0.2213          | 0.9385    | 0.9484 | 0.9434 | 0.9529   |
| 0.1939        | 10.94 | 1750 | 0.2176          | 0.9383    | 0.9454 | 0.9418 | 0.9525   |
| 0.1358        | 12.5  | 2000 | 0.2180          | 0.9314    | 0.9454 | 0.9383 | 0.9503   |
| 0.1358        | 14.06 | 2250 | 0.2057          | 0.9357    | 0.9484 | 0.9420 | 0.9546   |
| 0.1035        | 15.62 | 2500 | 0.2039          | 0.9386    | 0.9499 | 0.9442 | 0.9563   |


### Framework versions

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