---
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: train
      args: cord
    metrics:
    - name: Precision
      type: precision
      value: 0.8719646799116998
    - name: Recall
      type: recall
      value: 0.8869760479041916
    - name: F1
      type: f1
      value: 0.8794063079777364
    - name: Accuracy
      type: accuracy
      value: 0.8790322580645161
---

<!-- 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: 0.7215
- Precision: 0.8720
- Recall: 0.8870
- F1: 0.8794
- Accuracy: 0.8790

## 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        | 12.5  | 250  | 1.0892          | 0.7345    | 0.7867 | 0.7597 | 0.7806   |
| 1.3039        | 25.0  | 500  | 0.7150          | 0.8054    | 0.8428 | 0.8237 | 0.8281   |
| 1.3039        | 37.5  | 750  | 0.6320          | 0.8335    | 0.8615 | 0.8473 | 0.8540   |
| 0.2171        | 50.0  | 1000 | 0.6427          | 0.8651    | 0.8832 | 0.8741 | 0.8722   |
| 0.2171        | 62.5  | 1250 | 0.6640          | 0.8672    | 0.8847 | 0.8759 | 0.8765   |
| 0.0654        | 75.0  | 1500 | 0.6758          | 0.8650    | 0.8825 | 0.8737 | 0.8731   |
| 0.0654        | 87.5  | 1750 | 0.7028          | 0.8684    | 0.8840 | 0.8761 | 0.8765   |
| 0.0338        | 100.0 | 2000 | 0.7252          | 0.8710    | 0.8847 | 0.8778 | 0.8769   |
| 0.0338        | 112.5 | 2250 | 0.7227          | 0.8710    | 0.8847 | 0.8778 | 0.8778   |
| 0.0257        | 125.0 | 2500 | 0.7215          | 0.8720    | 0.8870 | 0.8794 | 0.8790   |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1