---
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: 0.8760330578512396
    - name: Recall
      type: recall
      value: 0.8983050847457628
    - name: F1
      type: f1
      value: 0.8870292887029289
    - name: Accuracy
      type: accuracy
      value: 0.9146919431279621
---

<!-- 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: 0.4548
- Precision: 0.8760
- Recall: 0.8983
- F1: 0.8870
- Accuracy: 0.9147

## 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        | 31.25  | 250  | 0.7175          | 0.7874    | 0.8475 | 0.8163 | 0.8768   |
| 1.0535        | 62.5   | 500  | 0.4279          | 0.8843    | 0.9068 | 0.8954 | 0.9147   |
| 1.0535        | 93.75  | 750  | 0.4042          | 0.8760    | 0.8983 | 0.8870 | 0.9147   |
| 0.0727        | 125.0  | 1000 | 0.4065          | 0.8760    | 0.8983 | 0.8870 | 0.9194   |
| 0.0727        | 156.25 | 1250 | 0.4290          | 0.8843    | 0.9068 | 0.8954 | 0.9147   |
| 0.0245        | 187.5  | 1500 | 0.4511          | 0.8760    | 0.8983 | 0.8870 | 0.9100   |
| 0.0245        | 218.75 | 1750 | 0.4594          | 0.8760    | 0.8983 | 0.8870 | 0.9147   |
| 0.0155        | 250.0  | 2000 | 0.4566          | 0.8760    | 0.8983 | 0.8870 | 0.9147   |
| 0.0155        | 281.25 | 2250 | 0.4489          | 0.8760    | 0.8983 | 0.8870 | 0.9147   |
| 0.0125        | 312.5  | 2500 | 0.4548          | 0.8760    | 0.8983 | 0.8870 | 0.9147   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3