---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
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: 0.9458456973293768
    - name: Recall
      type: recall
      value: 0.9543413173652695
    - name: F1
      type: f1
      value: 0.9500745156482863
    - name: Accuracy
      type: accuracy
      value: 0.9605263157894737
---

<!-- 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: 0.2092
- Precision: 0.9458
- Recall: 0.9543
- F1: 0.9501
- Accuracy: 0.9605

## 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  | 0.9809          | 0.7284    | 0.7829 | 0.7547 | 0.7890   |
| 1.3679        | 3.12  | 500  | 0.5431          | 0.8426    | 0.8653 | 0.8538 | 0.8727   |
| 1.3679        | 4.69  | 750  | 0.3871          | 0.8939    | 0.9147 | 0.9042 | 0.9198   |
| 0.3879        | 6.25  | 1000 | 0.3038          | 0.9175    | 0.9326 | 0.9250 | 0.9389   |
| 0.3879        | 7.81  | 1250 | 0.2561          | 0.9255    | 0.9386 | 0.9320 | 0.9448   |
| 0.2076        | 9.38  | 1500 | 0.2329          | 0.9342    | 0.9454 | 0.9397 | 0.9533   |
| 0.2076        | 10.94 | 1750 | 0.2166          | 0.9458    | 0.9536 | 0.9497 | 0.9605   |
| 0.1404        | 12.5  | 2000 | 0.2144          | 0.9488    | 0.9566 | 0.9527 | 0.9622   |
| 0.1404        | 14.06 | 2250 | 0.2147          | 0.9495    | 0.9573 | 0.9534 | 0.9626   |
| 0.109         | 15.62 | 2500 | 0.2092          | 0.9458    | 0.9543 | 0.9501 | 0.9605   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1