---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: cord-layoutlmv
      type: cord-layoutlmv
      config: default
      split: train
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.8426395939086294
    - name: Recall
      type: recall
      value: 0.8877005347593583
    - name: F1
      type: f1
      value: 0.8645833333333333
    - name: Accuracy
      type: accuracy
      value: 0.9807981927710844
---

<!-- 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-layoutlmv dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1563
- Precision: 0.8426
- Recall: 0.8877
- F1: 0.8646
- Accuracy: 0.9808

## 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: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 27.78 | 250  | 0.2591          | 0.7179    | 0.7487 | 0.7330 | 0.9529   |
| 0.4762        | 55.56 | 500  | 0.1563          | 0.8426    | 0.8877 | 0.8646 | 0.9808   |


### Framework versions

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