---
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: 0.9485842026825634
    - name: Recall
      type: recall
      value: 0.9528443113772455
    - name: F1
      type: f1
      value: 0.9507094846900671
    - name: Accuracy
      type: accuracy
      value: 0.9592529711375212
---

<!-- 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: 0.1978
- Precision: 0.9486
- Recall: 0.9528
- F1: 0.9507
- Accuracy: 0.9593

## 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        | 1.56  | 250  | 0.9543          | 0.7832    | 0.8166 | 0.7996 | 0.8226   |
| 1.3644        | 3.12  | 500  | 0.5338          | 0.8369    | 0.8683 | 0.8523 | 0.8824   |
| 1.3644        | 4.69  | 750  | 0.3658          | 0.8840    | 0.9072 | 0.8955 | 0.9232   |
| 0.3802        | 6.25  | 1000 | 0.3019          | 0.9156    | 0.9251 | 0.9203 | 0.9334   |
| 0.3802        | 7.81  | 1250 | 0.2833          | 0.9094    | 0.9237 | 0.9165 | 0.9346   |
| 0.2061        | 9.38  | 1500 | 0.2241          | 0.9377    | 0.9469 | 0.9423 | 0.9525   |
| 0.2061        | 10.94 | 1750 | 0.2282          | 0.9304    | 0.9409 | 0.9356 | 0.9474   |
| 0.1416        | 12.5  | 2000 | 0.2017          | 0.9509    | 0.9566 | 0.9537 | 0.9610   |
| 0.1416        | 14.06 | 2250 | 0.2006          | 0.9472    | 0.9536 | 0.9504 | 0.9614   |
| 0.1056        | 15.62 | 2500 | 0.1978          | 0.9486    | 0.9528 | 0.9507 | 0.9593   |


### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1