---
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_200
  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: 0.9033923303834809
    - name: Recall
      type: recall
      value: 0.9169161676646707
    - name: F1
      type: f1
      value: 0.9101040118870729
    - name: Accuracy
      type: accuracy
      value: 0.9121392190152802
---

<!-- 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_200

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: 0.4529
- Precision: 0.9034
- Recall: 0.9169
- F1: 0.9101
- Accuracy: 0.9121

## 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: 3000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 6.25  | 250  | 1.0785          | 0.6815    | 0.7575 | 0.7175 | 0.7780   |
| 1.3902        | 12.5  | 500  | 0.5871          | 0.8542    | 0.8683 | 0.8612 | 0.8604   |
| 1.3902        | 18.75 | 750  | 0.4572          | 0.8728    | 0.8937 | 0.8831 | 0.8905   |
| 0.298         | 25.0  | 1000 | 0.3947          | 0.8936    | 0.9117 | 0.9026 | 0.9092   |
| 0.298         | 31.25 | 1250 | 0.3925          | 0.8982    | 0.9177 | 0.9078 | 0.9117   |
| 0.1023        | 37.5  | 1500 | 0.4290          | 0.8908    | 0.9102 | 0.9004 | 0.9041   |
| 0.1023        | 43.75 | 1750 | 0.4220          | 0.8980    | 0.9162 | 0.9070 | 0.9117   |
| 0.0475        | 50.0  | 2000 | 0.4755          | 0.8944    | 0.9064 | 0.9004 | 0.8990   |
| 0.0475        | 56.25 | 2250 | 0.4635          | 0.8992    | 0.9147 | 0.9069 | 0.9070   |
| 0.0296        | 62.5  | 2500 | 0.4475          | 0.9019    | 0.9154 | 0.9086 | 0.9117   |
| 0.0296        | 68.75 | 2750 | 0.4484          | 0.9004    | 0.9139 | 0.9071 | 0.9079   |
| 0.0242        | 75.0  | 3000 | 0.4529          | 0.9034    | 0.9169 | 0.9101 | 0.9121   |


### Framework versions

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