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--- |
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license: cc-by-nc-sa-4.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cord-layoutlmv3 |
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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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model-index: |
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- name: layoutlmv3-finetuned-cord_800 |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: cord-layoutlmv3 |
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type: cord-layoutlmv3 |
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config: cord |
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split: train |
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args: cord |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9445266272189349 |
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- name: Recall |
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type: recall |
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value: 0.9558383233532934 |
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- name: F1 |
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type: f1 |
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value: 0.9501488095238095 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9605263157894737 |
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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_800 |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2042 |
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- Precision: 0.9445 |
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- Recall: 0.9558 |
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- F1: 0.9501 |
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- Accuracy: 0.9605 |
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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: 1e-05 |
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- train_batch_size: 5 |
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- eval_batch_size: 5 |
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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: 4000 |
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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 | 1.56 | 250 | 0.9737 | 0.7787 | 0.8166 | 0.7972 | 0.8188 | |
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| 1.3706 | 3.12 | 500 | 0.5489 | 0.8480 | 0.8645 | 0.8562 | 0.8680 | |
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| 1.3706 | 4.69 | 750 | 0.3857 | 0.8913 | 0.9087 | 0.8999 | 0.9147 | |
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| 0.3693 | 6.25 | 1000 | 0.3192 | 0.9117 | 0.9274 | 0.9195 | 0.9317 | |
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| 0.3693 | 7.81 | 1250 | 0.2816 | 0.9189 | 0.9326 | 0.9257 | 0.9355 | |
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| 0.1903 | 9.38 | 1500 | 0.2521 | 0.9277 | 0.9409 | 0.9342 | 0.9465 | |
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| 0.1903 | 10.94 | 1750 | 0.2353 | 0.9357 | 0.9476 | 0.9416 | 0.9550 | |
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| 0.1231 | 12.5 | 2000 | 0.2361 | 0.9293 | 0.9446 | 0.9369 | 0.9516 | |
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| 0.1231 | 14.06 | 2250 | 0.2194 | 0.9402 | 0.9528 | 0.9465 | 0.9576 | |
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| 0.0766 | 15.62 | 2500 | 0.2133 | 0.9416 | 0.9528 | 0.9472 | 0.9580 | |
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| 0.0766 | 17.19 | 2750 | 0.2117 | 0.9438 | 0.9558 | 0.9498 | 0.9597 | |
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| 0.0585 | 18.75 | 3000 | 0.2152 | 0.9417 | 0.9551 | 0.9483 | 0.9605 | |
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| 0.0585 | 20.31 | 3250 | 0.2070 | 0.9431 | 0.9551 | 0.9491 | 0.9588 | |
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| 0.0454 | 21.88 | 3500 | 0.2093 | 0.9489 | 0.9588 | 0.9538 | 0.9622 | |
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| 0.0454 | 23.44 | 3750 | 0.2034 | 0.9453 | 0.9566 | 0.9509 | 0.9610 | |
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| 0.0409 | 25.0 | 4000 | 0.2042 | 0.9445 | 0.9558 | 0.9501 | 0.9605 | |
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### Framework versions |
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- Transformers 4.21.2 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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