---
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: 0.9296817172464841
    - name: Recall
      type: recall
      value: 0.9401197604790419
    - name: F1
      type: f1
      value: 0.9348716040193524
    - name: Accuracy
      type: accuracy
      value: 0.9435483870967742
---

<!-- 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: 0.2908
- Precision: 0.9297
- Recall: 0.9401
- F1: 0.9349
- Accuracy: 0.9435

## 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  | 1.0995          | 0.6869    | 0.7635 | 0.7231 | 0.7789   |
| 1.4568        | 8.33  | 500  | 0.5676          | 0.8382    | 0.8765 | 0.8569 | 0.8773   |
| 1.4568        | 12.5  | 750  | 0.4044          | 0.8920    | 0.9147 | 0.9032 | 0.9202   |
| 0.3562        | 16.67 | 1000 | 0.3518          | 0.9086    | 0.9229 | 0.9157 | 0.9270   |
| 0.3562        | 20.83 | 1250 | 0.3060          | 0.9245    | 0.9349 | 0.9297 | 0.9372   |
| 0.1509        | 25.0  | 1500 | 0.3032          | 0.9261    | 0.9379 | 0.9319 | 0.9419   |
| 0.1509        | 29.17 | 1750 | 0.2980          | 0.9261    | 0.9386 | 0.9323 | 0.9368   |
| 0.0848        | 33.33 | 2000 | 0.2996          | 0.9226    | 0.9371 | 0.9298 | 0.9385   |
| 0.0848        | 37.5  | 2250 | 0.2924          | 0.9276    | 0.9394 | 0.9334 | 0.9440   |
| 0.0619        | 41.67 | 2500 | 0.2908          | 0.9297    | 0.9401 | 0.9349 | 0.9435   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3