---
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: 0.9135893648449039
    - name: Recall
      type: recall
      value: 0.9258982035928144
    - name: F1
      type: f1
      value: 0.9197026022304833
    - name: Accuracy
      type: accuracy
      value: 0.9252971137521222
---

<!-- 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: 0.3248
- Precision: 0.9136
- Recall: 0.9259
- F1: 0.9197
- Accuracy: 0.9253

## 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  | 1.0188          | 0.7447    | 0.7949 | 0.7690 | 0.8031   |
| 1.4061        | 8.33  | 500  | 0.5545          | 0.8420    | 0.8653 | 0.8535 | 0.8616   |
| 1.4061        | 12.5  | 750  | 0.4298          | 0.8884    | 0.9057 | 0.8970 | 0.9045   |
| 0.3563        | 16.67 | 1000 | 0.3477          | 0.9094    | 0.9244 | 0.9169 | 0.9295   |
| 0.3563        | 20.83 | 1250 | 0.3189          | 0.9137    | 0.9274 | 0.9205 | 0.9312   |
| 0.1617        | 25.0  | 1500 | 0.3189          | 0.9210    | 0.9341 | 0.9275 | 0.9393   |
| 0.1617        | 29.17 | 1750 | 0.3158          | 0.9096    | 0.9259 | 0.9177 | 0.9300   |
| 0.0942        | 33.33 | 2000 | 0.3198          | 0.9117    | 0.9274 | 0.9195 | 0.9283   |
| 0.0942        | 37.5  | 2250 | 0.3259          | 0.9112    | 0.9289 | 0.9199 | 0.9300   |
| 0.0674        | 41.67 | 2500 | 0.3248          | 0.9136    | 0.9259 | 0.9197 | 0.9253   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2