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layoutlm-funsd

Token Classification
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  1. README.md +77 -0
  2. config.json +36 -0
  3. logs/events.out.tfevents.1701505020.ysw.15130.0 +3 -0
  4. logs/events.out.tfevents.1701874828.ysw.210629.0 +3 -0
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  16. model.safetensors +3 -0
  17. preprocessor_config.json +14 -0
  18. special_tokens_map.json +37 -0
  19. tokenizer.json +0 -0
  20. tokenizer_config.json +80 -0
  21. training_args.bin +3 -0
  22. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ base_model: microsoft/layoutlm-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: layoutlm-funsd
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+ results: []
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+ ---
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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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+
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+ # layoutlm-funsd
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+
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+ This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0664
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+ - Number-a: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
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+ - Number-q: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
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+ - Overall Precision: 0.0
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+ - Overall Recall: 0.0
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+ - Overall F1: 0.0
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+ - Overall Accuracy: 0.9848
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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+ - train_batch_size: 16
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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: 15
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Number-a | Number-q | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 1.057 | 1.0 | 1 | 1.0490 | {'precision': 0.02127659574468085, 'recall': 1.0, 'f1': 0.04166666666666667, 'number': 4} | {'precision': 0.017857142857142856, 'recall': 1.0, 'f1': 0.03508771929824561, 'number': 4} | 0.0194 | 1.0 | 0.0381 | 0.4905 |
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+ | 1.0545 | 2.0 | 2 | 1.0490 | {'precision': 0.02127659574468085, 'recall': 1.0, 'f1': 0.04166666666666667, 'number': 4} | {'precision': 0.017857142857142856, 'recall': 1.0, 'f1': 0.03508771929824561, 'number': 4} | 0.0194 | 1.0 | 0.0381 | 0.4905 |
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+ | 1.0577 | 3.0 | 3 | 1.0490 | {'precision': 0.02127659574468085, 'recall': 1.0, 'f1': 0.04166666666666667, 'number': 4} | {'precision': 0.017857142857142856, 'recall': 1.0, 'f1': 0.03508771929824561, 'number': 4} | 0.0194 | 1.0 | 0.0381 | 0.4905 |
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+ | 1.0569 | 4.0 | 4 | 0.7616 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9431 |
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+ | 0.7741 | 5.0 | 5 | 0.5454 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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+ | 0.5582 | 6.0 | 6 | 0.3809 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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+ | 0.3957 | 7.0 | 7 | 0.2636 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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+ | 0.271 | 8.0 | 8 | 0.1836 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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+ | 0.1909 | 9.0 | 9 | 0.1327 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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+ | 0.1385 | 10.0 | 10 | 0.1026 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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+ | 0.1066 | 11.0 | 11 | 0.0860 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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+ | 0.0894 | 12.0 | 12 | 0.0773 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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+ | 0.0812 | 13.0 | 13 | 0.0725 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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+ | 0.0754 | 14.0 | 14 | 0.0692 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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+ | 0.0729 | 15.0 | 15 | 0.0664 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.1+cu121
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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