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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cord |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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base_model: microsoft/layoutlmv3-base |
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model-index: |
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- name: layoutlmv3-finetuned-cord |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: cord |
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type: cord |
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args: cord |
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metrics: |
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- type: precision |
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value: 0.9619686800894854 |
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name: Precision |
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- type: recall |
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value: 0.9655688622754491 |
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name: Recall |
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- type: f1 |
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value: 0.9637654090399701 |
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name: F1 |
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- type: accuracy |
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value: 0.9681663837011885 |
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name: Accuracy |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# layoutlmv3-finetuned-cord |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the CORD dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1845 |
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- Precision: 0.9620 |
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- Recall: 0.9656 |
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- F1: 0.9638 |
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- Accuracy: 0.9682 |
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The script for training can be found here: https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 1000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 2.0 | 100 | 0.5257 | 0.8223 | 0.8555 | 0.8386 | 0.8710 | |
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| No log | 4.0 | 200 | 0.3200 | 0.9118 | 0.9281 | 0.9199 | 0.9317 | |
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| No log | 6.0 | 300 | 0.2449 | 0.9298 | 0.9424 | 0.9361 | 0.9465 | |
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| No log | 8.0 | 400 | 0.1923 | 0.9472 | 0.9536 | 0.9504 | 0.9597 | |
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| 0.4328 | 10.0 | 500 | 0.1857 | 0.9591 | 0.9656 | 0.9623 | 0.9682 | |
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| 0.4328 | 12.0 | 600 | 0.2073 | 0.9597 | 0.9618 | 0.9607 | 0.9656 | |
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| 0.4328 | 14.0 | 700 | 0.1804 | 0.9634 | 0.9663 | 0.9649 | 0.9703 | |
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| 0.4328 | 16.0 | 800 | 0.1882 | 0.9634 | 0.9648 | 0.9641 | 0.9665 | |
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| 0.4328 | 18.0 | 900 | 0.1800 | 0.9619 | 0.9648 | 0.9634 | 0.9677 | |
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| 0.0318 | 20.0 | 1000 | 0.1845 | 0.9620 | 0.9656 | 0.9638 | 0.9682 | |
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### Framework versions |
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- Transformers 4.19.0.dev0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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