---
tags:
- generated_from_trainer
datasets:
- cord
metrics:
- precision
- recall
- f1
- accuracy
base_model: microsoft/layoutlmv3-base
model-index:
- name: layoutlmv3-finetuned-cord
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: cord
      type: cord
      args: cord
    metrics:
    - type: precision
      value: 0.9619686800894854
      name: Precision
    - type: recall
      value: 0.9655688622754491
      name: Recall
    - type: f1
      value: 0.9637654090399701
      name: F1
    - type: accuracy
      value: 0.9681663837011885
      name: Accuracy
---

<!-- 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

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the CORD dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1845
- Precision: 0.9620
- Recall: 0.9656
- F1: 0.9638
- Accuracy: 0.9682

The script for training can be found here: https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 2.0   | 100  | 0.5257          | 0.8223    | 0.8555 | 0.8386 | 0.8710   |
| No log        | 4.0   | 200  | 0.3200          | 0.9118    | 0.9281 | 0.9199 | 0.9317   |
| No log        | 6.0   | 300  | 0.2449          | 0.9298    | 0.9424 | 0.9361 | 0.9465   |
| No log        | 8.0   | 400  | 0.1923          | 0.9472    | 0.9536 | 0.9504 | 0.9597   |
| 0.4328        | 10.0  | 500  | 0.1857          | 0.9591    | 0.9656 | 0.9623 | 0.9682   |
| 0.4328        | 12.0  | 600  | 0.2073          | 0.9597    | 0.9618 | 0.9607 | 0.9656   |
| 0.4328        | 14.0  | 700  | 0.1804          | 0.9634    | 0.9663 | 0.9649 | 0.9703   |
| 0.4328        | 16.0  | 800  | 0.1882          | 0.9634    | 0.9648 | 0.9641 | 0.9665   |
| 0.4328        | 18.0  | 900  | 0.1800          | 0.9619    | 0.9648 | 0.9634 | 0.9677   |
| 0.0318        | 20.0  | 1000 | 0.1845          | 0.9620    | 0.9656 | 0.9638 | 0.9682   |


### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6