---
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: 0.9458054936896808
    - name: Recall
      type: recall
      value: 0.9535928143712575
    - name: F1
      type: f1
      value: 0.9496831904584422
    - name: Accuracy
      type: accuracy
      value: 0.9588285229202037
---

<!-- 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: 0.2033
- Precision: 0.9458
- Recall: 0.9536
- F1: 0.9497
- Accuracy: 0.9588

## 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  | 1.0015          | 0.7227    | 0.7822 | 0.7513 | 0.7963   |
| 1.3862        | 3.12  | 500  | 0.5334          | 0.8591    | 0.8765 | 0.8677 | 0.8837   |
| 1.3862        | 4.69  | 750  | 0.3689          | 0.8925    | 0.9072 | 0.8998 | 0.9164   |
| 0.3835        | 6.25  | 1000 | 0.2877          | 0.9281    | 0.9371 | 0.9326 | 0.9431   |
| 0.3835        | 7.81  | 1250 | 0.2506          | 0.9312    | 0.9424 | 0.9368 | 0.9452   |
| 0.2048        | 9.38  | 1500 | 0.2373          | 0.9480    | 0.9543 | 0.9511 | 0.9554   |
| 0.2048        | 10.94 | 1750 | 0.2184          | 0.9379    | 0.9491 | 0.9435 | 0.9542   |
| 0.1365        | 12.5  | 2000 | 0.2057          | 0.9393    | 0.9506 | 0.9449 | 0.9567   |
| 0.1365        | 14.06 | 2250 | 0.2024          | 0.9487    | 0.9543 | 0.9515 | 0.9576   |
| 0.1067        | 15.62 | 2500 | 0.2033          | 0.9458    | 0.9536 | 0.9497 | 0.9588   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1