| | --- |
| | license: cc-by-nc-sa-4.0 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - cord-layoutlmv3 |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: project-ocr |
| | results: |
| | - task: |
| | name: Token Classification |
| | type: token-classification |
| | dataset: |
| | name: cord-layoutlmv3 |
| | type: cord-layoutlmv3 |
| | config: cord |
| | split: test |
| | args: cord |
| | metrics: |
| | - name: Precision |
| | type: precision |
| | value: 0.7515745276417075 |
| | - name: Recall |
| | type: recall |
| | value: 0.8038922155688623 |
| | - name: F1 |
| | type: f1 |
| | value: 0.7768535262206148 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.8102716468590832 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # project-ocr |
| |
|
| | This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.9877 |
| | - Precision: 0.7516 |
| | - Recall: 0.8039 |
| | - F1: 0.7769 |
| | - Accuracy: 0.8103 |
| |
|
| | ## 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: 500 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | No log | 0.83 | 50 | 2.6184 | 0.4355 | 0.5404 | 0.4823 | 0.4338 | |
| | | No log | 1.67 | 100 | 1.8766 | 0.5912 | 0.6018 | 0.5964 | 0.5620 | |
| | | No log | 2.5 | 150 | 1.6165 | 0.5737 | 0.6347 | 0.6027 | 0.6150 | |
| | | No log | 3.33 | 200 | 1.4317 | 0.5732 | 0.6737 | 0.6194 | 0.6944 | |
| | | No log | 4.17 | 250 | 1.2787 | 0.6190 | 0.7126 | 0.6625 | 0.7347 | |
| | | No log | 5.0 | 300 | 1.1632 | 0.6729 | 0.7560 | 0.7120 | 0.7759 | |
| | | No log | 5.83 | 350 | 1.0990 | 0.6980 | 0.7665 | 0.7306 | 0.7857 | |
| | | No log | 6.67 | 400 | 1.0327 | 0.7125 | 0.7792 | 0.7444 | 0.7946 | |
| | | No log | 7.5 | 450 | 0.9994 | 0.7526 | 0.8016 | 0.7764 | 0.8065 | |
| | | 1.6589 | 8.33 | 500 | 0.9877 | 0.7516 | 0.8039 | 0.7769 | 0.8103 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.27.1 |
| | - Pytorch 1.13.1+cu116 |
| | - Datasets 2.10.1 |
| | - Tokenizers 0.13.2 |
| |
|
