---
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_800
  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: 0.9445266272189349
    - name: Recall
      type: recall
      value: 0.9558383233532934
    - name: F1
      type: f1
      value: 0.9501488095238095
    - name: Accuracy
      type: accuracy
      value: 0.9605263157894737
---

<!-- 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_800

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: 0.2042
- Precision: 0.9445
- Recall: 0.9558
- F1: 0.9501
- Accuracy: 0.9605

## 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: 4000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.56  | 250  | 0.9737          | 0.7787    | 0.8166 | 0.7972 | 0.8188   |
| 1.3706        | 3.12  | 500  | 0.5489          | 0.8480    | 0.8645 | 0.8562 | 0.8680   |
| 1.3706        | 4.69  | 750  | 0.3857          | 0.8913    | 0.9087 | 0.8999 | 0.9147   |
| 0.3693        | 6.25  | 1000 | 0.3192          | 0.9117    | 0.9274 | 0.9195 | 0.9317   |
| 0.3693        | 7.81  | 1250 | 0.2816          | 0.9189    | 0.9326 | 0.9257 | 0.9355   |
| 0.1903        | 9.38  | 1500 | 0.2521          | 0.9277    | 0.9409 | 0.9342 | 0.9465   |
| 0.1903        | 10.94 | 1750 | 0.2353          | 0.9357    | 0.9476 | 0.9416 | 0.9550   |
| 0.1231        | 12.5  | 2000 | 0.2361          | 0.9293    | 0.9446 | 0.9369 | 0.9516   |
| 0.1231        | 14.06 | 2250 | 0.2194          | 0.9402    | 0.9528 | 0.9465 | 0.9576   |
| 0.0766        | 15.62 | 2500 | 0.2133          | 0.9416    | 0.9528 | 0.9472 | 0.9580   |
| 0.0766        | 17.19 | 2750 | 0.2117          | 0.9438    | 0.9558 | 0.9498 | 0.9597   |
| 0.0585        | 18.75 | 3000 | 0.2152          | 0.9417    | 0.9551 | 0.9483 | 0.9605   |
| 0.0585        | 20.31 | 3250 | 0.2070          | 0.9431    | 0.9551 | 0.9491 | 0.9588   |
| 0.0454        | 21.88 | 3500 | 0.2093          | 0.9489    | 0.9588 | 0.9538 | 0.9622   |
| 0.0454        | 23.44 | 3750 | 0.2034          | 0.9453    | 0.9566 | 0.9509 | 0.9610   |
| 0.0409        | 25.0  | 4000 | 0.2042          | 0.9445    | 0.9558 | 0.9501 | 0.9605   |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1