---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- layoutlm_v3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: layoutlm_v3
      type: layoutlm_v3
      config: cord
      split: test
      args: cord
    metrics:
    - name: Precision
      type: precision
      value: 0.9297856614929786
    - name: Recall
      type: recall
      value: 0.9416167664670658
    - name: F1
      type: f1
      value: 0.9356638155448121
    - name: Accuracy
      type: accuracy
      value: 0.9393039049235993
---

<!-- 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 layoutlm_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2976
- Precision: 0.9298
- Recall: 0.9416
- F1: 0.9357
- Accuracy: 0.9393

## 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  | 1.0222          | 0.7468    | 0.7949 | 0.7701 | 0.8014   |
| 1.3962        | 8.33  | 500  | 0.5292          | 0.8414    | 0.8735 | 0.8571 | 0.8778   |
| 1.3962        | 12.5  | 750  | 0.3844          | 0.9049    | 0.9192 | 0.9120 | 0.9249   |
| 0.335         | 16.67 | 1000 | 0.3302          | 0.9243    | 0.9326 | 0.9285 | 0.9342   |
| 0.335         | 20.83 | 1250 | 0.3062          | 0.9204    | 0.9349 | 0.9276 | 0.9406   |
| 0.1419        | 25.0  | 1500 | 0.2931          | 0.9268    | 0.9386 | 0.9327 | 0.9414   |
| 0.1419        | 29.17 | 1750 | 0.2925          | 0.9248    | 0.9386 | 0.9316 | 0.9359   |
| 0.0801        | 33.33 | 2000 | 0.2963          | 0.9276    | 0.9394 | 0.9334 | 0.9359   |
| 0.0801        | 37.5  | 2250 | 0.2916          | 0.9283    | 0.9401 | 0.9342 | 0.9363   |
| 0.0584        | 41.67 | 2500 | 0.2976          | 0.9298    | 0.9416 | 0.9357 | 0.9393   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2