---
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: test
      args: cord
    metrics:
    - name: Precision
      type: precision
      value: 0.9295774647887324
    - name: Recall
      type: recall
      value: 0.938622754491018
    - name: F1
      type: f1
      value: 0.9340782122905028
    - name: Accuracy
      type: accuracy
      value: 0.9303904923599321
---

<!-- 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: 0.3404
- Precision: 0.9296
- Recall: 0.9386
- F1: 0.9341
- Accuracy: 0.9304

## 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  | 0.9987          | 0.7470    | 0.7957 | 0.7706 | 0.8043   |
| 1.3632        | 8.33  | 500  | 0.5299          | 0.8641    | 0.8855 | 0.8747 | 0.8829   |
| 1.3632        | 12.5  | 750  | 0.3861          | 0.8853    | 0.9124 | 0.8986 | 0.9126   |
| 0.3151        | 16.67 | 1000 | 0.3392          | 0.9154    | 0.9311 | 0.9232 | 0.9321   |
| 0.3151        | 20.83 | 1250 | 0.3382          | 0.9247    | 0.9371 | 0.9309 | 0.9308   |
| 0.1265        | 25.0  | 1500 | 0.3364          | 0.9225    | 0.9356 | 0.9290 | 0.9300   |
| 0.1265        | 29.17 | 1750 | 0.3333          | 0.9304    | 0.9401 | 0.9352 | 0.9321   |
| 0.0716        | 33.33 | 2000 | 0.3381          | 0.9296    | 0.9394 | 0.9345 | 0.9312   |
| 0.0716        | 37.5  | 2250 | 0.3474          | 0.9290    | 0.9409 | 0.9349 | 0.9321   |
| 0.0525        | 41.67 | 2500 | 0.3404          | 0.9296    | 0.9386 | 0.9341 | 0.9304   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2