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Monster23
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layoutlm-funsd

Token Classification
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TensorBoard
Safetensors
layoutlm
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End of training

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Files changed (5) hide show
  1. README.md +18 -18
  2. config.json +1 -1
  3. logs/events.out.tfevents.1701881950.ysw.244620.0 +3 -0
  4. logs/events.out.tfevents.1701882497.ysw.247112.0 +3 -0
  5. model.safetensors +1 -1
README.md CHANGED
@@ -14,7 +14,7 @@ should probably proofread and complete it, then remove this comment. -->
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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
@@ -50,23 +50,23 @@ The following hyperparameters were used during training:
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  ### Training results
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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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  ### Framework versions
 
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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: 0.0668
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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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  ### Training results
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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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+ | 1.1627 | 1.0 | 1 | 1.1422 | {'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 | 0.2713 |
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+ | 1.1655 | 2.0 | 2 | 1.1422 | {'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 | 0.2713 |
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+ | 1.1695 | 3.0 | 3 | 1.1422 | {'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 | 0.2713 |
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+ | 1.1661 | 4.0 | 4 | 0.8227 | {'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 | 0.8093 |
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+ | 0.8478 | 5.0 | 5 | 0.5718 | {'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 | 0.9744 |
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+ | 0.5975 | 6.0 | 6 | 0.3821 | {'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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+ | 0.4052 | 7.0 | 7 | 0.2537 | {'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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+ | 0.2676 | 8.0 | 8 | 0.1673 | {'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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+ | 0.1775 | 9.0 | 9 | 0.1173 | {'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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+ | 0.1266 | 10.0 | 10 | 0.0942 | {'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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+ | 0.1017 | 11.0 | 11 | 0.0842 | {'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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+ | 0.0891 | 12.0 | 12 | 0.0786 | {'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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+ | 0.0845 | 13.0 | 13 | 0.0741 | {'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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+ | 0.0788 | 14.0 | 14 | 0.0702 | {'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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+ | 0.0763 | 15.0 | 15 | 0.0668 | {'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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  ### Framework versions
config.json CHANGED
@@ -21,7 +21,7 @@
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  },
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  "layer_norm_eps": 1e-12,
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  "max_2d_position_embeddings": 1024,
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- "max_position_embeddings": 512,
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  "model_type": "layoutlm",
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  "num_attention_heads": 12,
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  "num_hidden_layers": 12,
 
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  },
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  "layer_norm_eps": 1e-12,
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  "max_2d_position_embeddings": 1024,
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+ "max_position_embeddings": 2048,
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  "model_type": "layoutlm",
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  "num_attention_heads": 12,
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  "num_hidden_layers": 12,
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