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--- |
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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-er-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-er-ner |
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This model is a fine-tuned version of [renjithks/layoutlmv1-cord-ner](https://huggingface.co/renjithks/layoutlmv1-cord-ner) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2936 |
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- Precision: 0.6097 |
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- Recall: 0.6192 |
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- F1: 0.6144 |
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- Accuracy: 0.9479 |
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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: 20 |
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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 | 22 | 0.3856 | 0.3047 | 0.1453 | 0.1968 | 0.8885 | |
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| No log | 2.0 | 44 | 0.2637 | 0.3725 | 0.3625 | 0.3674 | 0.9197 | |
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| No log | 3.0 | 66 | 0.2184 | 0.5117 | 0.4612 | 0.4852 | 0.9361 | |
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| No log | 4.0 | 88 | 0.2321 | 0.4714 | 0.5585 | 0.5113 | 0.9361 | |
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| No log | 5.0 | 110 | 0.2183 | 0.5453 | 0.5853 | 0.5646 | 0.9440 | |
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| No log | 6.0 | 132 | 0.2243 | 0.5977 | 0.5867 | 0.5922 | 0.9459 | |
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| No log | 7.0 | 154 | 0.2451 | 0.5716 | 0.5910 | 0.5811 | 0.9410 | |
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| No log | 8.0 | 176 | 0.2387 | 0.5881 | 0.5839 | 0.5860 | 0.9474 | |
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| No log | 9.0 | 198 | 0.2702 | 0.5794 | 0.6023 | 0.5906 | 0.9430 | |
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| No log | 10.0 | 220 | 0.2450 | 0.5920 | 0.6079 | 0.5999 | 0.9480 | |
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| No log | 11.0 | 242 | 0.2697 | 0.6151 | 0.5994 | 0.6071 | 0.9467 | |
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| No log | 12.0 | 264 | 0.2607 | 0.6022 | 0.6234 | 0.6126 | 0.9497 | |
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| No log | 13.0 | 286 | 0.2737 | 0.6172 | 0.6276 | 0.6224 | 0.9488 | |
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| No log | 14.0 | 308 | 0.2840 | 0.6117 | 0.6333 | 0.6223 | 0.9474 | |
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| No log | 15.0 | 330 | 0.2833 | 0.6030 | 0.6192 | 0.6110 | 0.9476 | |
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| No log | 16.0 | 352 | 0.3009 | 0.6161 | 0.6135 | 0.6148 | 0.9449 | |
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| No log | 17.0 | 374 | 0.2920 | 0.6098 | 0.6150 | 0.6124 | 0.9473 | |
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| No log | 18.0 | 396 | 0.2931 | 0.6017 | 0.6135 | 0.6075 | 0.9471 | |
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| No log | 19.0 | 418 | 0.2935 | 0.6103 | 0.6206 | 0.6154 | 0.9476 | |
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| No log | 20.0 | 440 | 0.2936 | 0.6097 | 0.6192 | 0.6144 | 0.9479 | |
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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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