---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- invoice_layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-intellectai
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: invoice_layoutlmv3
      type: invoice_layoutlmv3
      config: intellectai
      split: validation
      args: intellectai
    metrics:
    - name: Precision
      type: precision
      value: 0.7053571428571429
    - name: Recall
      type: recall
      value: 0.8540540540540541
    - name: F1
      type: f1
      value: 0.7726161369193154
    - name: Accuracy
      type: accuracy
      value: 0.9624772313296903
---

<!-- 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-intellectai

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the invoice_layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3645
- Precision: 0.7054
- Recall: 0.8541
- F1: 0.7726
- Accuracy: 0.9625

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.79  | 50   | 1.7979          | 0.0228    | 0.0541 | 0.0321 | 0.1410   |
| No log        | 1.59  | 100  | 1.2400          | 0.0863    | 0.4216 | 0.1433 | 0.2616   |
| No log        | 2.38  | 150  | 0.8691          | 0.1279    | 0.6919 | 0.2159 | 0.4495   |
| No log        | 3.17  | 200  | 0.6001          | 0.2323    | 0.8162 | 0.3617 | 0.7570   |
| No log        | 3.97  | 250  | 0.4709          | 0.4660    | 0.7784 | 0.5830 | 0.9093   |
| No log        | 4.76  | 300  | 0.3986          | 0.5977    | 0.8270 | 0.6939 | 0.9472   |
| No log        | 5.56  | 350  | 0.3762          | 0.5714    | 0.8216 | 0.6741 | 0.9454   |
| No log        | 6.35  | 400  | 0.3763          | 0.7048    | 0.8649 | 0.7767 | 0.9636   |
| No log        | 7.14  | 450  | 0.3696          | 0.6639    | 0.8541 | 0.7470 | 0.9570   |
| 0.71          | 7.94  | 500  | 0.3645          | 0.7054    | 0.8541 | 0.7726 | 0.9625   |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2