---
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: 0.8132267441860465
    - name: Recall
      type: recall
      value: 0.8375748502994012
    - name: F1
      type: f1
      value: 0.8252212389380531
    - name: Accuracy
      type: accuracy
      value: 0.8408319185059423
---

<!-- 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: 1.0498
- Precision: 0.8132
- Recall: 0.8376
- F1: 0.8252
- Accuracy: 0.8408

## 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        | 41.67  | 250  | 1.0301          | 0.7519    | 0.7964 | 0.7735 | 0.7954   |
| 1.0019        | 83.33  | 500  | 0.8414          | 0.7996    | 0.8331 | 0.8160 | 0.8396   |
| 1.0019        | 125.0  | 750  | 0.9117          | 0.8097    | 0.8376 | 0.8234 | 0.8417   |
| 0.0561        | 166.67 | 1000 | 0.9434          | 0.8097    | 0.8376 | 0.8234 | 0.8408   |
| 0.0561        | 208.33 | 1250 | 1.0014          | 0.8049    | 0.8338 | 0.8191 | 0.8404   |
| 0.0208        | 250.0  | 1500 | 1.0132          | 0.8081    | 0.8353 | 0.8215 | 0.8400   |
| 0.0208        | 291.67 | 1750 | 1.0279          | 0.8154    | 0.8398 | 0.8274 | 0.8417   |
| 0.0133        | 333.33 | 2000 | 1.0402          | 0.8119    | 0.8368 | 0.8242 | 0.8408   |
| 0.0133        | 375.0  | 2250 | 1.0495          | 0.8154    | 0.8398 | 0.8274 | 0.8417   |
| 0.0108        | 416.67 | 2500 | 1.0498          | 0.8132    | 0.8376 | 0.8252 | 0.8408   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3