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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: 2.7407
- Education: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
- Email: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
- Github: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0}
- Location: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
- Name: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
- Name : {'precision': 0.2, 'recall': 0.5, 'f1': 0.28571428571428575, 'number': 2}
- Phone Number: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
- Soft Skills: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0}
- Technical Skills: {'precision': 0.2, 'recall': 0.35714285714285715, 'f1': 0.25641025641025644, 'number': 14}
- Overall Precision: 0.1176
- Overall Recall: 0.2
- Overall F1: 0.1481
- Overall Accuracy: 0.1475
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: 2
- eval_batch_size: 2
- 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 | Education | Github | Location | Name | Name | Phone Number | Soft Skills | Technical Skills | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.9387 | 1.0 | 2 | 2.8701 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.14285714285714285, 'recall': 0.5, 'f1': 0.22222222222222224, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 14} | 0.0185 | 0.0333 | 0.0238 | 0.0328 |
| 2.6716 | 2.0 | 4 | 2.7798 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.2, 'recall': 0.5, 'f1': 0.28571428571428575, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.10526315789473684, 'recall': 0.14285714285714285, 'f1': 0.12121212121212122, 'number': 14} | 0.0612 | 0.1 | 0.0759 | 0.1311 | |
| 2.5524 | 3.0 | 6 | 2.7407 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.2, 'recall': 0.5, 'f1': 0.28571428571428575, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.2, 'recall': 0.35714285714285715, 'f1': 0.25641025641025644, 'number': 14} | 0.1176 | 0.2 | 0.1481 | 0.1475 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
