---
library_name: transformers.js
license: cc-by-nc-sa-4.0
base_model:
- mp-02/layoutlmv3-base-cord
tags:
- generated_from_trainer
datasets:
- mp-02/cord
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-base-cord
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: mp-02/cord
      type: mp-02/cord
    metrics:
    - name: Precision
      type: precision
      value: 0.9752270850536746
    - name: Recall
      type: recall
      value: 0.9784589892294946
    - name: F1
      type: f1
      value: 0.976840363937138
    - name: Accuracy
      type: accuracy
      value: 0.973924977127173
pipeline_tag: token-classification
---



# layoutlmv3-base-cord (ONNX)


This is an ONNX version of [mp-02/layoutlmv3-base-cord](https://huggingface.co/mp-02/layoutlmv3-base-cord). It was automatically converted and uploaded using [this Hugging Face Space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).


## Usage with Transformers.js


See the pipeline documentation for `token-classification`: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TokenClassificationPipeline


---


<!-- 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-base-cord

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the mp-02/cord dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1517
- Precision: 0.9752
- Recall: 0.9785
- F1: 0.9768
- Accuracy: 0.9739

## 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: 2e-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: 3000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 2.0   | 100  | 0.8667          | 0.7592    | 0.8202 | 0.7885 | 0.8097   |
| No log        | 4.0   | 200  | 0.3443          | 0.9122    | 0.9387 | 0.9253 | 0.9222   |
| No log        | 6.0   | 300  | 0.2128          | 0.9345    | 0.9569 | 0.9456 | 0.9579   |
| No log        | 8.0   | 400  | 0.1745          | 0.9440    | 0.9635 | 0.9537 | 0.9629   |
| 0.6362        | 10.0  | 500  | 0.1594          | 0.9559    | 0.9702 | 0.9630 | 0.9684   |
| 0.6362        | 12.0  | 600  | 0.1720          | 0.9630    | 0.9693 | 0.9661 | 0.9629   |
| 0.6362        | 14.0  | 700  | 0.1528          | 0.9607    | 0.9710 | 0.9658 | 0.9675   |
| 0.6362        | 16.0  | 800  | 0.1460          | 0.9638    | 0.9718 | 0.9678 | 0.9680   |
| 0.6362        | 18.0  | 900  | 0.1609          | 0.9614    | 0.9702 | 0.9658 | 0.9648   |
| 0.0536        | 20.0  | 1000 | 0.1517          | 0.9752    | 0.9785 | 0.9768 | 0.9739   |
| 0.0536        | 22.0  | 1100 | 0.1901          | 0.9614    | 0.9693 | 0.9653 | 0.9657   |
| 0.0536        | 24.0  | 1200 | 0.1867          | 0.9638    | 0.9718 | 0.9678 | 0.9666   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1