---
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: 0.4115296803652968
    - name: Recall
      type: recall
      value: 0.5396706586826348
    - name: F1
      type: f1
      value: 0.46696891191709844
    - name: Accuracy
      type: accuracy
      value: 0.4350594227504245
---

<!-- 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: 2.5624
- Precision: 0.4115
- Recall: 0.5397
- F1: 0.4670
- Accuracy: 0.4351

## 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: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.06  | 10   | 3.8065          | 0.1637    | 0.2582 | 0.2003 | 0.2585   |
| No log        | 0.12  | 20   | 3.4787          | 0.4661    | 0.3862 | 0.4224 | 0.3353   |
| No log        | 0.19  | 30   | 3.2587          | 0.4332    | 0.4731 | 0.4522 | 0.3667   |
| No log        | 0.25  | 40   | 3.0615          | 0.4144    | 0.4873 | 0.4479 | 0.3846   |
| No log        | 0.31  | 50   | 2.9052          | 0.3993    | 0.5090 | 0.4475 | 0.4024   |
| No log        | 0.38  | 60   | 2.7819          | 0.3876    | 0.5165 | 0.4429 | 0.4143   |
| No log        | 0.44  | 70   | 2.6853          | 0.3891    | 0.5202 | 0.4452 | 0.4164   |
| No log        | 0.5   | 80   | 2.6245          | 0.3942    | 0.5269 | 0.4510 | 0.4236   |
| No log        | 0.56  | 90   | 2.5777          | 0.4056    | 0.5352 | 0.4614 | 0.4312   |
| No log        | 0.62  | 100  | 2.5624          | 0.4115    | 0.5397 | 0.4670 | 0.4351   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cpu
- Datasets 2.12.0
- Tokenizers 0.13.3