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tags: |
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- generated_from_trainer |
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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: layoutlmv1-cord-ner |
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results: [] |
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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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# layoutlmv1-cord-ner |
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1438 |
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- Precision: 0.9336 |
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- Recall: 0.9453 |
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- F1: 0.9394 |
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- Accuracy: 0.9767 |
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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: 8 |
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- eval_batch_size: 8 |
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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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- num_epochs: 10 |
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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 | 1.0 | 113 | 0.1251 | 0.9054 | 0.9184 | 0.9119 | 0.9651 | |
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| No log | 2.0 | 226 | 0.1343 | 0.9002 | 0.9261 | 0.9130 | 0.9635 | |
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| No log | 3.0 | 339 | 0.1264 | 0.9189 | 0.9357 | 0.9272 | 0.9647 | |
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| No log | 4.0 | 452 | 0.1235 | 0.9122 | 0.9376 | 0.9248 | 0.9681 | |
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| 0.1371 | 5.0 | 565 | 0.1353 | 0.9378 | 0.9405 | 0.9391 | 0.9717 | |
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| 0.1371 | 6.0 | 678 | 0.1431 | 0.9233 | 0.9357 | 0.9295 | 0.9709 | |
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| 0.1371 | 7.0 | 791 | 0.1473 | 0.9289 | 0.9405 | 0.9347 | 0.9759 | |
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| 0.1371 | 8.0 | 904 | 0.1407 | 0.9473 | 0.9491 | 0.9482 | 0.9784 | |
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| 0.0106 | 9.0 | 1017 | 0.1440 | 0.9301 | 0.9453 | 0.9376 | 0.9769 | |
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| 0.0106 | 10.0 | 1130 | 0.1438 | 0.9336 | 0.9453 | 0.9394 | 0.9767 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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