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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_100 |
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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: 0.9472118959107807 |
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- name: Recall |
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type: recall |
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value: 0.9535928143712575 |
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- name: F1 |
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type: f1 |
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value: 0.9503916449086163 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9562818336162988 |
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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_100 |
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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: 0.2152 |
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- Precision: 0.9472 |
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- Recall: 0.9536 |
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- F1: 0.9504 |
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- Accuracy: 0.9563 |
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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: 2500 |
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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 | 0.9909 | 0.7582 | 0.8099 | 0.7832 | 0.8128 | |
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| 1.3653 | 3.12 | 500 | 0.5650 | 0.8392 | 0.8675 | 0.8531 | 0.8756 | |
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| 1.3653 | 4.69 | 750 | 0.3851 | 0.8865 | 0.9177 | 0.9018 | 0.9181 | |
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| 0.3744 | 6.25 | 1000 | 0.3104 | 0.9280 | 0.9364 | 0.9322 | 0.9380 | |
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| 0.3744 | 7.81 | 1250 | 0.2778 | 0.9347 | 0.9424 | 0.9385 | 0.9440 | |
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| 0.1955 | 9.38 | 1500 | 0.2316 | 0.9327 | 0.9446 | 0.9386 | 0.9440 | |
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| 0.1955 | 10.94 | 1750 | 0.2461 | 0.9414 | 0.9491 | 0.9452 | 0.9533 | |
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| 0.1349 | 12.5 | 2000 | 0.2316 | 0.9379 | 0.9491 | 0.9435 | 0.9478 | |
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| 0.1349 | 14.06 | 2250 | 0.2227 | 0.9487 | 0.9551 | 0.9519 | 0.9533 | |
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| 0.1024 | 15.62 | 2500 | 0.2152 | 0.9472 | 0.9536 | 0.9504 | 0.9563 | |
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### Framework versions |
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- Transformers 4.23.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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