End of training
Browse files- README.md +28 -14
- logs/events.out.tfevents.1740668344.DESKTOP-HA84SVN.2309656.2 +2 -2
- model.safetensors +1 -1
README.md
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---
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library_name: transformers
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license: mit
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base_model:
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tags:
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- generated_from_trainer
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datasets:
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# layoutlm-with-funsd
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This model is a fine-tuned version of [
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It achieves the following results on the evaluation set:
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- Loss:
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- Eader: {'precision': 0.
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- Nswer: {'precision': 0.
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- Uestion: {'precision': 0.
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- Overall Precision: 0.
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- Overall Recall: 0.
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- Overall F1: 0.
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- Overall Accuracy: 0.
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## Model description
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- seed: 42
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs:
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Eader
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### Framework versions
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---
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library_name: transformers
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license: mit
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base_model: pabloma09/layoutlm-with-funsd
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tags:
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- generated_from_trainer
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datasets:
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# layoutlm-with-funsd
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This model is a fine-tuned version of [pabloma09/layoutlm-with-funsd](https://huggingface.co/pabloma09/layoutlm-with-funsd) on the funsd dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6555
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- Eader: {'precision': 0.45454545454545453, 'recall': 0.3125, 'f1': 0.3703703703703703, 'number': 16}
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- Nswer: {'precision': 0.4520547945205479, 'recall': 0.4782608695652174, 'f1': 0.46478873239436624, 'number': 69}
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- Uestion: {'precision': 0.44047619047619047, 'recall': 0.5362318840579711, 'f1': 0.48366013071895425, 'number': 69}
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- Overall Precision: 0.4464
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- Overall Recall: 0.4870
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- Overall F1: 0.4658
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- Overall Accuracy: 0.7819
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## Model description
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- seed: 42
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Eader | Nswer | Uestion | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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| 1.2774 | 1.0 | 4 | 1.2327 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16} | {'precision': 0.03164556962025317, 'recall': 0.07246376811594203, 'f1': 0.04405286343612334, 'number': 69} | {'precision': 0.04433497536945813, 'recall': 0.13043478260869565, 'f1': 0.0661764705882353, 'number': 69} | 0.0383 | 0.0909 | 0.0538 | 0.4555 |
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| 1.0828 | 2.0 | 8 | 1.0961 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16} | {'precision': 0.1286549707602339, 'recall': 0.3188405797101449, 'f1': 0.18333333333333332, 'number': 69} | {'precision': 0.12138728323699421, 'recall': 0.30434782608695654, 'f1': 0.17355371900826447, 'number': 69} | 0.125 | 0.2792 | 0.1727 | 0.5891 |
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| 0.9043 | 3.0 | 12 | 1.0033 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16} | {'precision': 0.17391304347826086, 'recall': 0.34782608695652173, 'f1': 0.23188405797101447, 'number': 69} | {'precision': 0.15328467153284672, 'recall': 0.30434782608695654, 'f1': 0.20388349514563106, 'number': 69} | 0.1636 | 0.2922 | 0.2098 | 0.6183 |
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| 0.8002 | 4.0 | 16 | 0.8873 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16} | {'precision': 0.2204724409448819, 'recall': 0.4057971014492754, 'f1': 0.2857142857142857, 'number': 69} | {'precision': 0.22580645161290322, 'recall': 0.4057971014492754, 'f1': 0.29015544041450775, 'number': 69} | 0.2222 | 0.3636 | 0.2759 | 0.6336 |
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| 0.6937 | 5.0 | 20 | 0.7914 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16} | {'precision': 0.308411214953271, 'recall': 0.4782608695652174, 'f1': 0.37499999999999994, 'number': 69} | {'precision': 0.2672413793103448, 'recall': 0.4492753623188406, 'f1': 0.33513513513513515, 'number': 69} | 0.2844 | 0.4156 | 0.3377 | 0.6866 |
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| 0.5711 | 6.0 | 24 | 0.7298 | {'precision': 0.1, 'recall': 0.0625, 'f1': 0.07692307692307693, 'number': 16} | {'precision': 0.375, 'recall': 0.5217391304347826, 'f1': 0.43636363636363634, 'number': 69} | {'precision': 0.30973451327433627, 'recall': 0.5072463768115942, 'f1': 0.3846153846153846, 'number': 69} | 0.3288 | 0.4675 | 0.3861 | 0.7273 |
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| 0.4902 | 7.0 | 28 | 0.6971 | {'precision': 0.125, 'recall': 0.125, 'f1': 0.125, 'number': 16} | {'precision': 0.42168674698795183, 'recall': 0.5072463768115942, 'f1': 0.4605263157894737, 'number': 69} | {'precision': 0.3684210526315789, 'recall': 0.5072463768115942, 'f1': 0.4268292682926829, 'number': 69} | 0.3711 | 0.4675 | 0.4138 | 0.7519 |
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| 0.4757 | 8.0 | 32 | 0.6605 | {'precision': 0.11764705882352941, 'recall': 0.125, 'f1': 0.12121212121212122, 'number': 16} | {'precision': 0.43373493975903615, 'recall': 0.5217391304347826, 'f1': 0.4736842105263158, 'number': 69} | {'precision': 0.38636363636363635, 'recall': 0.4927536231884058, 'f1': 0.4331210191082802, 'number': 69} | 0.3830 | 0.4675 | 0.4211 | 0.7634 |
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| 0.4611 | 9.0 | 36 | 0.6649 | {'precision': 0.14285714285714285, 'recall': 0.125, 'f1': 0.13333333333333333, 'number': 16} | {'precision': 0.4533333333333333, 'recall': 0.4927536231884058, 'f1': 0.4722222222222222, 'number': 69} | {'precision': 0.40476190476190477, 'recall': 0.4927536231884058, 'f1': 0.4444444444444444, 'number': 69} | 0.4046 | 0.4545 | 0.4281 | 0.7704 |
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| 0.3752 | 10.0 | 40 | 0.6528 | {'precision': 0.2727272727272727, 'recall': 0.1875, 'f1': 0.2222222222222222, 'number': 16} | {'precision': 0.4594594594594595, 'recall': 0.4927536231884058, 'f1': 0.4755244755244755, 'number': 69} | {'precision': 0.40963855421686746, 'recall': 0.4927536231884058, 'f1': 0.44736842105263164, 'number': 69} | 0.4226 | 0.4610 | 0.4410 | 0.7742 |
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| 0.3663 | 11.0 | 44 | 0.6418 | {'precision': 0.3076923076923077, 'recall': 0.25, 'f1': 0.27586206896551724, 'number': 16} | {'precision': 0.4533333333333333, 'recall': 0.4927536231884058, 'f1': 0.4722222222222222, 'number': 69} | {'precision': 0.43373493975903615, 'recall': 0.5217391304347826, 'f1': 0.4736842105263158, 'number': 69} | 0.4327 | 0.4805 | 0.4554 | 0.7811 |
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| 0.3244 | 12.0 | 48 | 0.6540 | {'precision': 0.5, 'recall': 0.3125, 'f1': 0.38461538461538464, 'number': 16} | {'precision': 0.4583333333333333, 'recall': 0.4782608695652174, 'f1': 0.4680851063829787, 'number': 69} | {'precision': 0.42857142857142855, 'recall': 0.5217391304347826, 'f1': 0.47058823529411764, 'number': 69} | 0.4458 | 0.4805 | 0.4625 | 0.7803 |
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| 0.3294 | 13.0 | 52 | 0.6585 | {'precision': 0.5, 'recall': 0.3125, 'f1': 0.38461538461538464, 'number': 16} | {'precision': 0.4266666666666667, 'recall': 0.463768115942029, 'f1': 0.4444444444444445, 'number': 69} | {'precision': 0.43373493975903615, 'recall': 0.5217391304347826, 'f1': 0.4736842105263158, 'number': 69} | 0.4345 | 0.4740 | 0.4534 | 0.7803 |
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| 0.2977 | 14.0 | 56 | 0.6577 | {'precision': 0.5, 'recall': 0.3125, 'f1': 0.38461538461538464, 'number': 16} | {'precision': 0.4383561643835616, 'recall': 0.463768115942029, 'f1': 0.45070422535211263, 'number': 69} | {'precision': 0.42857142857142855, 'recall': 0.5217391304347826, 'f1': 0.47058823529411764, 'number': 69} | 0.4371 | 0.4740 | 0.4548 | 0.7796 |
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| 0.2874 | 15.0 | 60 | 0.6555 | {'precision': 0.45454545454545453, 'recall': 0.3125, 'f1': 0.3703703703703703, 'number': 16} | {'precision': 0.4520547945205479, 'recall': 0.4782608695652174, 'f1': 0.46478873239436624, 'number': 69} | {'precision': 0.44047619047619047, 'recall': 0.5362318840579711, 'f1': 0.48366013071895425, 'number': 69} | 0.4464 | 0.4870 | 0.4658 | 0.7819 |
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### Framework versions
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logs/events.out.tfevents.1740668344.DESKTOP-HA84SVN.2309656.2
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model.safetensors
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