---
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: 0.9457652303120356
    - name: Recall
      type: recall
      value: 0.9528443113772455
    - name: F1
      type: f1
      value: 0.9492915734526474
    - name: Accuracy
      type: accuracy
      value: 0.9490662139219015
---

<!-- 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: 0.2296
- Precision: 0.9458
- Recall: 0.9528
- F1: 0.9493
- Accuracy: 0.9491

## 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  | 1.1659          | 0.6767    | 0.7552 | 0.7138 | 0.7738   |
| 1.4723        | 3.12  | 500  | 0.6092          | 0.8320    | 0.8600 | 0.8458 | 0.8667   |
| 1.4723        | 4.69  | 750  | 0.4107          | 0.8730    | 0.9004 | 0.8865 | 0.9045   |
| 0.4246        | 6.25  | 1000 | 0.3370          | 0.9143    | 0.9259 | 0.9200 | 0.9270   |
| 0.4246        | 7.81  | 1250 | 0.2909          | 0.9267    | 0.9371 | 0.9319 | 0.9372   |
| 0.2225        | 9.38  | 1500 | 0.2571          | 0.9355    | 0.9439 | 0.9396 | 0.9414   |
| 0.2225        | 10.94 | 1750 | 0.2547          | 0.9383    | 0.9454 | 0.9418 | 0.9431   |
| 0.1514        | 12.5  | 2000 | 0.2412          | 0.9306    | 0.9431 | 0.9368 | 0.9435   |
| 0.1514        | 14.06 | 2250 | 0.2329          | 0.9443    | 0.9513 | 0.9478 | 0.9478   |
| 0.1168        | 15.62 | 2500 | 0.2296          | 0.9458    | 0.9528 | 0.9493 | 0.9491   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.10.2+cpu
- Datasets 2.8.0
- Tokenizers 0.13.2