---
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: 0.9393042190969653
    - name: Recall
      type: recall
      value: 0.9498502994011976
    - name: F1
      type: f1
      value: 0.9445478228507629
    - name: Accuracy
      type: accuracy
      value: 0.9494906621392191
---

<!-- 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: 0.2454
- Precision: 0.9393
- Recall: 0.9499
- F1: 0.9445
- Accuracy: 0.9495

## 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        | 2.5   | 250  | 1.0544          | 0.7297    | 0.7822 | 0.7551 | 0.7852   |
| 1.4348        | 5.0   | 500  | 0.5651          | 0.8477    | 0.8705 | 0.8589 | 0.8693   |
| 1.4348        | 7.5   | 750  | 0.4012          | 0.8833    | 0.9012 | 0.8922 | 0.9083   |
| 0.4052        | 10.0  | 1000 | 0.3168          | 0.9208    | 0.9311 | 0.9259 | 0.9338   |
| 0.4052        | 12.5  | 1250 | 0.2823          | 0.9304    | 0.9401 | 0.9352 | 0.9410   |
| 0.2039        | 15.0  | 1500 | 0.2626          | 0.9242    | 0.9394 | 0.9317 | 0.9397   |
| 0.2039        | 17.5  | 1750 | 0.2504          | 0.9305    | 0.9424 | 0.9364 | 0.9448   |
| 0.1333        | 20.0  | 2000 | 0.2425          | 0.9324    | 0.9491 | 0.9407 | 0.9503   |
| 0.1333        | 22.5  | 2250 | 0.2442          | 0.9371    | 0.9484 | 0.9427 | 0.9486   |
| 0.1042        | 25.0  | 2500 | 0.2454          | 0.9393    | 0.9499 | 0.9445 | 0.9495   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3