---
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: 0.596078431372549
    - name: Recall
      type: recall
      value: 0.6826347305389222
    - name: F1
      type: f1
      value: 0.636427076064201
    - name: Accuracy
      type: accuracy
      value: 0.684634974533107
---

<!-- 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: 1.9357
- Precision: 0.5961
- Recall: 0.6826
- F1: 0.6364
- Accuracy: 0.6846

## 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        | 250.0  | 250  | 1.5298          | 0.5778    | 0.6781 | 0.6240 | 0.6825   |
| 0.6654        | 500.0  | 500  | 1.6175          | 0.5942    | 0.6849 | 0.6363 | 0.6880   |
| 0.6654        | 750.0  | 750  | 1.7087          | 0.5947    | 0.6841 | 0.6363 | 0.6876   |
| 0.0208        | 1000.0 | 1000 | 1.7729          | 0.5948    | 0.6834 | 0.6360 | 0.6859   |
| 0.0208        | 1250.0 | 1250 | 1.8273          | 0.5949    | 0.6826 | 0.6358 | 0.6851   |
| 0.0099        | 1500.0 | 1500 | 1.8693          | 0.5957    | 0.6826 | 0.6362 | 0.6846   |
| 0.0099        | 1750.0 | 1750 | 1.8969          | 0.5950    | 0.6819 | 0.6355 | 0.6842   |
| 0.0066        | 2000.0 | 2000 | 1.9196          | 0.5972    | 0.6826 | 0.6371 | 0.6842   |
| 0.0066        | 2250.0 | 2250 | 1.9312          | 0.5946    | 0.6819 | 0.6353 | 0.6838   |
| 0.0054        | 2500.0 | 2500 | 1.9357          | 0.5961    | 0.6826 | 0.6364 | 0.6846   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1