---
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: 0.9465478841870824
    - name: Recall
      type: recall
      value: 0.9543413173652695
    - name: F1
      type: f1
      value: 0.9504286246738725
    - name: Accuracy
      type: accuracy
      value: 0.9584040747028862
---

<!-- 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: 0.2090
- Precision: 0.9465
- Recall: 0.9543
- F1: 0.9504
- Accuracy: 0.9584

## 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  | 1.0347          | 0.6965    | 0.7695 | 0.7312 | 0.7861   |
| 1.4031        | 3.12  | 500  | 0.5641          | 0.8491    | 0.8720 | 0.8604 | 0.8744   |
| 1.4031        | 4.69  | 750  | 0.3899          | 0.8810    | 0.9087 | 0.8946 | 0.9138   |
| 0.4005        | 6.25  | 1000 | 0.3025          | 0.9202    | 0.9319 | 0.9260 | 0.9355   |
| 0.4005        | 7.81  | 1250 | 0.2641          | 0.9211    | 0.9349 | 0.9279 | 0.9402   |
| 0.2161        | 9.38  | 1500 | 0.2406          | 0.9277    | 0.9416 | 0.9346 | 0.9474   |
| 0.2161        | 10.94 | 1750 | 0.2250          | 0.9343    | 0.9469 | 0.9405 | 0.9516   |
| 0.1474        | 12.5  | 2000 | 0.2238          | 0.9415    | 0.9513 | 0.9464 | 0.9542   |
| 0.1474        | 14.06 | 2250 | 0.2128          | 0.9451    | 0.9536 | 0.9493 | 0.9571   |
| 0.1128        | 15.62 | 2500 | 0.2090          | 0.9465    | 0.9543 | 0.9504 | 0.9584   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3