---
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: 0.9349593495934959
    - name: Recall
      type: recall
      value: 0.9468562874251497
    - name: F1
      type: f1
      value: 0.9408702119747119
    - name: Accuracy
      type: accuracy
      value: 0.9490662139219015
---

<!-- 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: 0.2730
- Precision: 0.9350
- Recall: 0.9469
- F1: 0.9409
- Accuracy: 0.9491

## 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  | 1.0147          | 0.7119    | 0.7807 | 0.7447 | 0.7963   |
| 1.3916        | 8.33  | 500  | 0.5211          | 0.8428    | 0.8705 | 0.8564 | 0.8786   |
| 1.3916        | 12.5  | 750  | 0.3842          | 0.8961    | 0.9169 | 0.9064 | 0.9181   |
| 0.3265        | 16.67 | 1000 | 0.3158          | 0.9225    | 0.9349 | 0.9286 | 0.9393   |
| 0.3265        | 20.83 | 1250 | 0.2874          | 0.9162    | 0.9334 | 0.9247 | 0.9414   |
| 0.139         | 25.0  | 1500 | 0.2738          | 0.9255    | 0.9394 | 0.9324 | 0.9461   |
| 0.139         | 29.17 | 1750 | 0.2774          | 0.9354    | 0.9431 | 0.9392 | 0.9491   |
| 0.0798        | 33.33 | 2000 | 0.2695          | 0.9342    | 0.9461 | 0.9401 | 0.9508   |
| 0.0798        | 37.5  | 2250 | 0.2759          | 0.9356    | 0.9461 | 0.9408 | 0.9495   |
| 0.0592        | 41.67 | 2500 | 0.2730          | 0.9350    | 0.9469 | 0.9409 | 0.9491   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3