---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- cne-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cne_nvidia_100
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: cne-layoutlmv3
      type: cne-layoutlmv3
      config: cne-dataset
      split: test
      args: cne-dataset
    metrics:
    - name: Precision
      type: precision
      value: 0.9950738916256158
    - name: Recall
      type: recall
      value: 0.9950738916256158
    - name: F1
      type: f1
      value: 0.9950738916256159
    - name: Accuracy
      type: accuracy
      value: 0.9992716678805535
---

<!-- 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-cne_nvidia_100

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cne-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0064
- Precision: 0.9951
- Recall: 0.9951
- F1: 0.9951
- Accuracy: 0.9993

## 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: 3
- eval_batch_size: 3
- 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        | 7.81  | 250  | 0.0143          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
| 0.1596        | 15.62 | 500  | 0.0085          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
| 0.1596        | 23.44 | 750  | 0.0074          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
| 0.0195        | 31.25 | 1000 | 0.0068          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
| 0.0195        | 39.06 | 1250 | 0.0067          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
| 0.008         | 46.88 | 1500 | 0.0067          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
| 0.008         | 54.69 | 1750 | 0.0064          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
| 0.0034        | 62.5  | 2000 | 0.0063          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
| 0.0034        | 70.31 | 2250 | 0.0063          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
| 0.0023        | 78.12 | 2500 | 0.0064          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.3
- Tokenizers 0.13.3