---
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_100
  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: 0.9289415247964471
    - name: Recall
      type: recall
      value: 0.9393712574850299
    - name: F1
      type: f1
      value: 0.9341272794938594
    - name: Accuracy
      type: accuracy
      value: 0.9393039049235993
---

<!-- 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: 0.3066
- Precision: 0.9289
- Recall: 0.9394
- F1: 0.9341
- Accuracy: 0.9393

## 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  | 0.9691          | 0.7365    | 0.7867 | 0.7608 | 0.7992   |
| 1.3706        | 8.33  | 500  | 0.5325          | 0.8645    | 0.8885 | 0.8763 | 0.8858   |
| 1.3706        | 12.5  | 750  | 0.3943          | 0.8939    | 0.9139 | 0.9038 | 0.9151   |
| 0.3211        | 16.67 | 1000 | 0.3364          | 0.9209    | 0.9319 | 0.9263 | 0.9342   |
| 0.3211        | 20.83 | 1250 | 0.3217          | 0.9246    | 0.9364 | 0.9305 | 0.9346   |
| 0.1405        | 25.0  | 1500 | 0.3100          | 0.9296    | 0.9394 | 0.9345 | 0.9355   |
| 0.1405        | 29.17 | 1750 | 0.3113          | 0.9275    | 0.9386 | 0.9330 | 0.9363   |
| 0.076         | 33.33 | 2000 | 0.3183          | 0.9280    | 0.9364 | 0.9322 | 0.9351   |
| 0.076         | 37.5  | 2250 | 0.3125          | 0.9211    | 0.9356 | 0.9283 | 0.9363   |
| 0.0549        | 41.67 | 2500 | 0.3066          | 0.9289    | 0.9394 | 0.9341 | 0.9393   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2