---
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: 0.9430473372781065
    - name: Recall
      type: recall
      value: 0.9543413173652695
    - name: F1
      type: f1
      value: 0.9486607142857143
    - name: Accuracy
      type: accuracy
      value: 0.9579796264855688
---

<!-- 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: 0.2188
- Precision: 0.9430
- Recall: 0.9543
- F1: 0.9487
- Accuracy: 0.9580

## 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  | 1.0024          | 0.7392    | 0.7957 | 0.7664 | 0.8060   |
| 1.3949        | 3.12  | 500  | 0.5684          | 0.8330    | 0.8660 | 0.8492 | 0.8727   |
| 1.3949        | 4.69  | 750  | 0.3929          | 0.8931    | 0.9072 | 0.9001 | 0.9160   |
| 0.3964        | 6.25  | 1000 | 0.3312          | 0.9236    | 0.9326 | 0.9281 | 0.9321   |
| 0.3964        | 7.81  | 1250 | 0.2754          | 0.9275    | 0.9386 | 0.9330 | 0.9410   |
| 0.216         | 9.38  | 1500 | 0.2447          | 0.9328    | 0.9454 | 0.9390 | 0.9478   |
| 0.216         | 10.94 | 1750 | 0.2467          | 0.9363    | 0.9461 | 0.9412 | 0.9478   |
| 0.1534        | 12.5  | 2000 | 0.2300          | 0.9436    | 0.9521 | 0.9478 | 0.9537   |
| 0.1534        | 14.06 | 2250 | 0.2155          | 0.9459    | 0.9558 | 0.9509 | 0.9597   |
| 0.119         | 15.62 | 2500 | 0.2188          | 0.9430    | 0.9543 | 0.9487 | 0.9580   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3