---
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_100
  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: 0.9349593495934959
    - name: Recall
      type: recall
      value: 0.9468562874251497
    - name: F1
      type: f1
      value: 0.9408702119747119
    - name: Accuracy
      type: accuracy
      value: 0.9473684210526315
---

<!-- 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: 0.2702
- Precision: 0.9350
- Recall: 0.9469
- F1: 0.9409
- Accuracy: 0.9474

## 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        | 4.17  | 250  | 1.0496          | 0.6714    | 0.7507 | 0.7088 | 0.7746   |
| 1.4245        | 8.33  | 500  | 0.5492          | 0.8401    | 0.8728 | 0.8561 | 0.8735   |
| 1.4245        | 12.5  | 750  | 0.3773          | 0.8934    | 0.9162 | 0.9047 | 0.9240   |
| 0.3461        | 16.67 | 1000 | 0.3212          | 0.9287    | 0.9364 | 0.9325 | 0.9380   |
| 0.3461        | 20.83 | 1250 | 0.2888          | 0.9276    | 0.9401 | 0.9338 | 0.9440   |
| 0.1502        | 25.0  | 1500 | 0.2749          | 0.9299    | 0.9431 | 0.9365 | 0.9474   |
| 0.1502        | 29.17 | 1750 | 0.2741          | 0.9321    | 0.9446 | 0.9383 | 0.9469   |
| 0.0866        | 33.33 | 2000 | 0.2715          | 0.9328    | 0.9454 | 0.9390 | 0.9465   |
| 0.0866        | 37.5  | 2250 | 0.2740          | 0.9314    | 0.9446 | 0.9379 | 0.9452   |
| 0.0635        | 41.67 | 2500 | 0.2702          | 0.9350    | 0.9469 | 0.9409 | 0.9474   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2