| | --- |
| | license: cc-by-nc-sa-4.0 |
| | base_model: microsoft/layoutlmv3-base |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - funsd-layoutlmv3 |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: test |
| | results: |
| | - task: |
| | name: Token Classification |
| | type: token-classification |
| | dataset: |
| | name: funsd-layoutlmv3 |
| | type: funsd-layoutlmv3 |
| | config: funsd |
| | split: test |
| | args: funsd |
| | metrics: |
| | - name: Precision |
| | type: precision |
| | value: 0.8808265257087938 |
| | - name: Recall |
| | type: recall |
| | value: 0.910581222056632 |
| | - name: F1 |
| | type: f1 |
| | value: 0.895456765999023 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.8507072387970998 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # test |
| |
|
| | This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the funsd-layoutlmv3 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.5799 |
| | - Precision: 0.8808 |
| | - Recall: 0.9106 |
| | - F1: 0.8955 |
| | - Accuracy: 0.8507 |
| |
|
| | ## 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: 1000 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | No log | 1.3333 | 100 | 0.6686 | 0.7452 | 0.8251 | 0.7831 | 0.7535 | |
| | | No log | 2.6667 | 200 | 0.4724 | 0.8064 | 0.8713 | 0.8376 | 0.8389 | |
| | | No log | 4.0 | 300 | 0.4922 | 0.8612 | 0.8942 | 0.8774 | 0.8481 | |
| | | No log | 5.3333 | 400 | 0.4632 | 0.8587 | 0.8997 | 0.8787 | 0.8521 | |
| | | 0.544 | 6.6667 | 500 | 0.4850 | 0.8632 | 0.9031 | 0.8827 | 0.8474 | |
| | | 0.544 | 8.0 | 600 | 0.5024 | 0.8744 | 0.8992 | 0.8866 | 0.8451 | |
| | | 0.544 | 9.3333 | 700 | 0.5394 | 0.8768 | 0.9155 | 0.8957 | 0.8565 | |
| | | 0.544 | 10.6667 | 800 | 0.5647 | 0.8800 | 0.9146 | 0.8970 | 0.8550 | |
| | | 0.544 | 12.0 | 900 | 0.5798 | 0.8847 | 0.9106 | 0.8974 | 0.8545 | |
| | | 0.1288 | 13.3333 | 1000 | 0.5799 | 0.8808 | 0.9106 | 0.8955 | 0.8507 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.41.0.dev0 |
| | - Pytorch 2.1.1+cu118 |
| | - Datasets 2.15.0 |
| | - Tokenizers 0.19.1 |
| |
|
