---
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: 0.9458456973293768
    - name: Recall
      type: recall
      value: 0.9543413173652695
    - name: F1
      type: f1
      value: 0.9500745156482863
    - 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: 0.2123
- Precision: 0.9458
- Recall: 0.9543
- 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  | 1.0095          | 0.7120    | 0.7754 | 0.7424 | 0.7946   |
| 1.3738        | 3.12  | 500  | 0.5732          | 0.8473    | 0.8683 | 0.8577 | 0.8714   |
| 1.3738        | 4.69  | 750  | 0.3840          | 0.8893    | 0.9079 | 0.8985 | 0.9181   |
| 0.4085        | 6.25  | 1000 | 0.2933          | 0.9181    | 0.9319 | 0.9250 | 0.9376   |
| 0.4085        | 7.81  | 1250 | 0.2704          | 0.9197    | 0.9349 | 0.9272 | 0.9444   |
| 0.2239        | 9.38  | 1500 | 0.2504          | 0.9369    | 0.9454 | 0.9411 | 0.9508   |
| 0.2239        | 10.94 | 1750 | 0.2375          | 0.9288    | 0.9379 | 0.9333 | 0.9465   |
| 0.1544        | 12.5  | 2000 | 0.2326          | 0.9423    | 0.9528 | 0.9475 | 0.9576   |
| 0.1544        | 14.06 | 2250 | 0.2147          | 0.9530    | 0.9566 | 0.9548 | 0.9610   |
| 0.1231        | 15.62 | 2500 | 0.2123          | 0.9458    | 0.9543 | 0.9501 | 0.9597   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1