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layoutlm-funsd
This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the funsd dataset. It achieves the following results on the evaluation set:
- Loss: 1.1493
- Answer: {'precision': 0.22598870056497175, 'recall': 0.19777503090234858, 'f1': 0.2109426499670402, 'number': 809}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.5350523771152297, 'recall': 0.6234741784037559, 'f1': 0.5758889852558543, 'number': 1065}
- Overall Precision: 0.4228
- Overall Recall: 0.4134
- Overall F1: 0.4181
- Overall Accuracy: 0.6373
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|
| 1.6256 | 1.0 | 10 | 1.4524 | {'precision': 0.05670665212649945, 'recall': 0.06427688504326329, 'f1': 0.060254924681344156, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3111913357400722, 'recall': 0.40469483568075115, 'f1': 0.35183673469387755, 'number': 1065} | 0.2098 | 0.2423 | 0.2249 | 0.4826 |
| 1.3478 | 2.0 | 20 | 1.2324 | {'precision': 0.14285714285714285, 'recall': 0.1211372064276885, 'f1': 0.1311036789297659, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4789156626506024, 'recall': 0.5971830985915493, 'f1': 0.5315503552026745, 'number': 1065} | 0.3644 | 0.3683 | 0.3664 | 0.5977 |
| 1.155 | 3.0 | 30 | 1.1493 | {'precision': 0.22598870056497175, 'recall': 0.19777503090234858, 'f1': 0.2109426499670402, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5350523771152297, 'recall': 0.6234741784037559, 'f1': 0.5758889852558543, 'number': 1065} | 0.4228 | 0.4134 | 0.4181 | 0.6373 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3
