| --- |
| license: cc-by-nc-sa-4.0 |
| tags: |
| - generated_from_trainer |
| - layoutlmv3 |
| - token_classifier |
| - layout_analysis |
| datasets: |
| - pierreguillou/DocLayNet-small |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: layoutlmv3-finetuned-DocLayNet |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: doc_lay_net-small |
| type: doc_lay_net-small |
| config: DocLayNet_2022.08_processed_on_2023.01 |
| split: test |
| args: DocLayNet_2022.08_processed_on_2023.01 |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.6178861788617886 |
| - name: Recall |
| type: recall |
| value: 0.7238095238095238 |
| - name: F1 |
| type: f1 |
| value: 0.6666666666666667 |
| - name: Accuracy |
| type: accuracy |
| value: 0.8719611021069692 |
| language: |
| - en |
| pipeline_tag: token-classification |
| --- |
| |
| <!-- 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-DocLayNet |
|
|
| This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the doc_lay_net-small dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5644 |
| - Precision: 0.6179 |
| - Recall: 0.7238 |
| - F1: 0.6667 |
| - Accuracy: 0.8720 |
|
|
| ## 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: 4 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - training_steps: 1000 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 1.3383 | 0.58 | 200 | 0.8358 | 0.3007 | 0.4381 | 0.3566 | 0.7724 | |
| | 0.8308 | 1.16 | 400 | 0.6735 | 0.4634 | 0.5429 | 0.5 | 0.8084 | |
| | 0.518 | 1.74 | 600 | 0.5706 | 0.5373 | 0.6857 | 0.6025 | 0.8399 | |
| | 0.3856 | 2.33 | 800 | 0.6303 | 0.6032 | 0.7238 | 0.6580 | 0.8648 | |
| | 0.2558 | 2.91 | 1000 | 0.5644 | 0.6179 | 0.7238 | 0.6667 | 0.8720 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.27.3 |
| - Pytorch 1.13.1+cu116 |
| - Datasets 2.10.1 |
| - Tokenizers 0.13.2 |
|
|
|
|
|
|
| ### How to Train & Inference: |
|
|
| Check this out this repo: https://github.com/mit1280/Document-AI |
