---
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: 0.9516369047619048
    - name: Recall
      type: recall
      value: 0.9573353293413174
    - name: F1
      type: f1
      value: 0.9544776119402986
    - name: Accuracy
      type: accuracy
      value: 0.9630730050933786
---

<!-- 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: 0.2085
- Precision: 0.9516
- Recall: 0.9573
- F1: 0.9545
- Accuracy: 0.9631

## 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  | 1.0211          | 0.7533    | 0.8024 | 0.7771 | 0.8073   |
| 1.3874        | 3.12  | 500  | 0.5352          | 0.8488    | 0.8698 | 0.8591 | 0.8778   |
| 1.3874        | 4.69  | 750  | 0.3738          | 0.8865    | 0.9124 | 0.8993 | 0.9228   |
| 0.3827        | 6.25  | 1000 | 0.2868          | 0.9253    | 0.9364 | 0.9308 | 0.9402   |
| 0.3827        | 7.81  | 1250 | 0.2506          | 0.9289    | 0.9394 | 0.9341 | 0.9457   |
| 0.2046        | 9.38  | 1500 | 0.2312          | 0.9427    | 0.9484 | 0.9455 | 0.9537   |
| 0.2046        | 10.94 | 1750 | 0.2194          | 0.9450    | 0.9513 | 0.9482 | 0.9588   |
| 0.1365        | 12.5  | 2000 | 0.2105          | 0.9495    | 0.9566 | 0.9530 | 0.9631   |
| 0.1365        | 14.06 | 2250 | 0.2115          | 0.9509    | 0.9573 | 0.9541 | 0.9631   |
| 0.1066        | 15.62 | 2500 | 0.2085          | 0.9516    | 0.9573 | 0.9545 | 0.9631   |


### Framework versions

- Transformers 4.33.0
- Pytorch 1.12.1+cu116
- Datasets 2.14.4
- Tokenizers 0.12.1