---
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: 0.9561011904761905
    - name: Recall
      type: recall
      value: 0.9618263473053892
    - name: F1
      type: f1
      value: 0.958955223880597
    - name: Accuracy
      type: accuracy
      value: 0.9702886247877759
---

<!-- 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: 0.1726
- Precision: 0.9561
- Recall: 0.9618
- F1: 0.9590
- Accuracy: 0.9703

## 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: 3000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.56  | 250  | 1.0075          | 0.7597    | 0.8046 | 0.7815 | 0.8145   |
| 1.3907        | 3.12  | 500  | 0.5155          | 0.8388    | 0.8683 | 0.8533 | 0.8841   |
| 1.3907        | 4.69  | 750  | 0.3486          | 0.8917    | 0.9117 | 0.9016 | 0.9283   |
| 0.3755        | 6.25  | 1000 | 0.2722          | 0.9211    | 0.9356 | 0.9283 | 0.9435   |
| 0.3755        | 7.81  | 1250 | 0.2399          | 0.9356    | 0.9461 | 0.9408 | 0.9533   |
| 0.1857        | 9.38  | 1500 | 0.2170          | 0.9376    | 0.9454 | 0.9415 | 0.9542   |
| 0.1857        | 10.94 | 1750 | 0.1917          | 0.9510    | 0.9588 | 0.9549 | 0.9660   |
| 0.1236        | 12.5  | 2000 | 0.1821          | 0.9502    | 0.9573 | 0.9538 | 0.9652   |
| 0.1236        | 14.06 | 2250 | 0.1870          | 0.9538    | 0.9588 | 0.9563 | 0.9669   |
| 0.0858        | 15.62 | 2500 | 0.1741          | 0.9583    | 0.9633 | 0.9608 | 0.9711   |
| 0.0858        | 17.19 | 2750 | 0.1726          | 0.9561    | 0.9611 | 0.9586 | 0.9690   |
| 0.0708        | 18.75 | 3000 | 0.1726          | 0.9561    | 0.9618 | 0.9590 | 0.9703   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0