---
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: 0.9451851851851852
    - name: Recall
      type: recall
      value: 0.9550898203592815
    - name: F1
      type: f1
      value: 0.9501116902457185
    - name: Accuracy
      type: accuracy
      value: 0.9596774193548387
---

<!-- 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: 0.9452
- Recall: 0.9551
- F1: 0.9501
- Accuracy: 0.9597

## 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  | 0.9547          | 0.7300    | 0.7912 | 0.7593 | 0.8065   |
| 1.2994        | 3.12  | 500  | 0.5497          | 0.8410    | 0.8630 | 0.8519 | 0.8714   |
| 1.2994        | 4.69  | 750  | 0.3688          | 0.8846    | 0.9064 | 0.8954 | 0.9189   |
| 0.3917        | 6.25  | 1000 | 0.3156          | 0.9152    | 0.9289 | 0.9220 | 0.9359   |
| 0.3917        | 7.81  | 1250 | 0.2468          | 0.9326    | 0.9424 | 0.9375 | 0.9457   |
| 0.2136        | 9.38  | 1500 | 0.2290          | 0.9299    | 0.9431 | 0.9365 | 0.9499   |
| 0.2136        | 10.94 | 1750 | 0.2101          | 0.9429    | 0.9513 | 0.9471 | 0.9571   |
| 0.1388        | 12.5  | 2000 | 0.2090          | 0.9380    | 0.9513 | 0.9446 | 0.9571   |
| 0.1388        | 14.06 | 2250 | 0.2049          | 0.9423    | 0.9528 | 0.9475 | 0.9580   |
| 0.111         | 15.62 | 2500 | 0.2033          | 0.9452    | 0.9551 | 0.9501 | 0.9597   |


### Framework versions

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