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layoutlm-funsd
ethangclark/layoutlm-funsd
layoutlm-funsd is a token classification model from ethangclark. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- layoutlm
- Access
- Public
- Created
- Mar 20, 2024
- Updated
- Mar 23, 2024
- SHA
- 19dde248
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
layoutlm-funsd
This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6097
- Answer: {'precision': 0.43703703703703706, 'recall': 0.6413043478260869, 'f1': 0.5198237885462555, 'number': 92}
- Header: {'precision': 0.2894736842105263, 'recall': 0.34375, 'f1': 0.3142857142857143, 'number': 32}
- Overall Precision: 0.4046
- Overall Recall: 0.5645
- Overall F1: 0.4714
- Overall Accuracy: 0.8656
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|
| 1.4561 | 1.0 | 2 | 1.0789 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 92} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32} | 0.0 | 0.0 | 0.0 | 0.8182 |
| 0.7649 | 2.0 | 4 | 0.9219 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 92} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32} | 0.0 | 0.0 | 0.0 | 0.8182 |
| 0.5601 | 3.0 | 6 | 0.8338 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 92} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32} | 0.0 | 0.0 | 0.0 | 0.8182 |
| 0.4611 | 4.0 | 8 | 0.7533 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 92} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32} | 0.0 | 0.0 | 0.0 | 0.8182 |
| 0.3306 | 5.0 | 10 | 0.6861 | {'precision': 0.75, 'recall': 0.03260869565217391, 'f1': 0.06249999999999999, 'number': 92} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32} | 0.75 | 0.0242 | 0.0469 | 0.8207 |
| 0.3001 | 6.0 | 12 | 0.6509 | {'precision': 0.43243243243243246, 'recall': 0.5217391304347826, 'f1': 0.47290640394088673, 'number': 92} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32} | 0.4324 | 0.3871 | 0.4085 | 0.8592 |
| 0.3436 | 7.0 | 14 | 0.6713 | {'precision': 0.33689839572192515, 'recall': 0.6847826086956522, 'f1': 0.45161290322580644, 'number': 92} | {'precision': 0.14285714285714285, 'recall': 0.03125, 'f1': 0.05128205128205128, 'number': 32} | 0.3299 | 0.5161 | 0.4025 | 0.8284 |
| 0.3624 | 8.0 | 16 | 0.6454 | {'precision': 0.3516483516483517, 'recall': 0.6956521739130435, 'f1': 0.46715328467153283, 'number': 92} | {'precision': 0.4, 'recall': 0.0625, 'f1': 0.10810810810810811, 'number': 32} | 0.3529 | 0.5323 | 0.4244 | 0.8387 |
| 0.4258 | 9.0 | 18 | 0.6192 | {'precision': 0.3668639053254438, 'recall': 0.6739130434782609, 'f1': 0.475095785440613, 'number': 92} | {'precision': 0.5555555555555556, 'recall': 0.15625, 'f1': 0.24390243902439024, 'number': 32} | 0.3764 | 0.5403 | 0.4437 | 0.8528 |
| 0.2221 | 10.0 | 20 | 0.6282 | {'precision': 0.36942675159235666, 'recall': 0.6304347826086957, 'f1': 0.465863453815261, 'number': 92} | {'precision': 0.3181818181818182, 'recall': 0.21875, 'f1': 0.25925925925925924, 'number': 32} | 0.3631 | 0.5242 | 0.4290 | 0.8476 |
| 0.2069 | 11.0 | 22 | 0.6241 | {'precision': 0.40559440559440557, 'recall': 0.6304347826086957, 'f1': 0.4936170212765958, 'number': 92} | {'precision': 0.34375, 'recall': 0.34375, 'f1': 0.34375, 'number': 32} | 0.3943 | 0.5565 | 0.4615 | 0.8592 |
| 0.2035 | 12.0 | 24 | 0.6218 | {'precision': 0.4084507042253521, 'recall': 0.6304347826086957, 'f1': 0.49572649572649574, 'number': 92} | {'precision': 0.3125, 'recall': 0.3125, 'f1': 0.3125, 'number': 32} | 0.3908 | 0.5484 | 0.4564 | 0.8604 |
| 0.1729 | 13.0 | 26 | 0.6175 | {'precision': 0.41843971631205673, 'recall': 0.6413043478260869, 'f1': 0.5064377682403434, 'number': 92} | {'precision': 0.3125, 'recall': 0.3125, 'f1': 0.3125, 'number': 32} | 0.3988 | 0.5565 | 0.4646 | 0.8643 |
| 0.1759 | 14.0 | 28 | 0.6127 | {'precision': 0.427536231884058, 'recall': 0.6413043478260869, 'f1': 0.5130434782608696, 'number': 92} | {'precision': 0.3142857142857143, 'recall': 0.34375, 'f1': 0.3283582089552239, 'number': 32} | 0.4046 | 0.5645 | 0.4714 | 0.8656 |
| 0.2299 | 15.0 | 30 | 0.6097 | {'precision': 0.43703703703703706, 'recall': 0.6413043478260869, 'f1': 0.5198237885462555, 'number': 92} | {'precision': 0.2894736842105263, 'recall': 0.34375, 'f1': 0.3142857142857143, 'number': 32} | 0.4046 | 0.5645 | 0.4714 | 0.8656 |
Framework versions
- Transformers 4.39.0
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Token Classification
- Library
- transformers
- Type
- layoutlm
- License
- mit
- Created
- Mar 20, 2024
- Updated
- Mar 23, 2024