---
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: 0.9266666666666666
    - name: Recall
      type: recall
      value: 0.936377245508982
    - name: F1
      type: f1
      value: 0.9314966492926285
    - name: Accuracy
      type: accuracy
      value: 0.9354838709677419
---

<!-- 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: 0.3194
- Precision: 0.9267
- Recall: 0.9364
- F1: 0.9315
- Accuracy: 0.9355

## 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        | 4.17  | 250  | 1.0054          | 0.7555    | 0.8024 | 0.7782 | 0.8081   |
| 1.4019        | 8.33  | 500  | 0.5287          | 0.8320    | 0.8638 | 0.8476 | 0.8739   |
| 1.4019        | 12.5  | 750  | 0.3790          | 0.9043    | 0.9192 | 0.9117 | 0.9236   |
| 0.3185        | 16.67 | 1000 | 0.3253          | 0.9178    | 0.9281 | 0.9230 | 0.9355   |
| 0.3185        | 20.83 | 1250 | 0.3231          | 0.9223    | 0.9334 | 0.9278 | 0.9304   |
| 0.1319        | 25.0  | 1500 | 0.3039          | 0.9317    | 0.9394 | 0.9355 | 0.9419   |
| 0.1319        | 29.17 | 1750 | 0.3142          | 0.9287    | 0.9364 | 0.9325 | 0.9334   |
| 0.0725        | 33.33 | 2000 | 0.2982          | 0.9296    | 0.9386 | 0.9341 | 0.9419   |
| 0.0725        | 37.5  | 2250 | 0.3189          | 0.9288    | 0.9371 | 0.9329 | 0.9346   |
| 0.0549        | 41.67 | 2500 | 0.3194          | 0.9267    | 0.9364 | 0.9315 | 0.9355   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0