--- license: mit base_model: microsoft/layoutlm-base-uncased tags: - generated_from_trainer datasets: - layoutlmv3 model-index: - name: Inkaso_beta results: [] --- # Inkaso_beta This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the layoutlmv3 dataset. It achieves the following results on the evaluation set: - Loss: 0.0801 - Creditor address: {'precision': 1.0, 'recall': 0.875, 'f1': 0.9333333333333333, 'number': 48} - Creditor name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34} - Creditor proxy: {'precision': 0.8333333333333334, 'recall': 0.8108108108108109, 'f1': 0.8219178082191781, 'number': 37} - Debtor address: {'precision': 0.9636363636363636, 'recall': 1.0, 'f1': 0.9814814814814815, 'number': 53} - Debtor name: {'precision': 0.9428571428571428, 'recall': 1.0, 'f1': 0.9705882352941176, 'number': 33} - Doc id: {'precision': 0.85, 'recall': 0.8947368421052632, 'f1': 0.8717948717948718, 'number': 19} - Title: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34} - Overall Precision: 0.9492 - Overall Recall: 0.9419 - Overall F1: 0.9455 - Overall Accuracy: 0.9831 ## 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 - lr_scheduler_warmup_steps: 10 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Creditor address | Creditor name | Creditor proxy | Debtor address | Debtor name | Doc id | Title | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-------:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.4642 | 6.6667 | 20 | 0.2502 | {'precision': 0.782608695652174, 'recall': 0.75, 'f1': 0.7659574468085107, 'number': 48} | {'precision': 0.9354838709677419, 'recall': 0.8529411764705882, 'f1': 0.8923076923076922, 'number': 34} | {'precision': 0.8, 'recall': 0.6486486486486487, 'f1': 0.7164179104477612, 'number': 37} | {'precision': 0.8205128205128205, 'recall': 0.6037735849056604, 'f1': 0.6956521739130435, 'number': 53} | {'precision': 0.95, 'recall': 0.5757575757575758, 'f1': 0.7169811320754716, 'number': 33} | {'precision': 1.0, 'recall': 0.2631578947368421, 'f1': 0.4166666666666667, 'number': 19} | {'precision': 0.8461538461538461, 'recall': 0.3235294117647059, 'f1': 0.46808510638297873, 'number': 34} | 0.8478 | 0.6047 | 0.7059 | 0.9330 | | 0.1387 | 13.3333 | 40 | 0.0914 | {'precision': 1.0, 'recall': 0.9166666666666666, 'f1': 0.9565217391304348, 'number': 48} | {'precision': 0.9714285714285714, 'recall': 1.0, 'f1': 0.9855072463768115, 'number': 34} | {'precision': 0.7777777777777778, 'recall': 0.7567567567567568, 'f1': 0.7671232876712328, 'number': 37} | {'precision': 0.9444444444444444, 'recall': 0.9622641509433962, 'f1': 0.9532710280373832, 'number': 53} | {'precision': 0.8918918918918919, 'recall': 1.0, 'f1': 0.9428571428571428, 'number': 33} | {'precision': 0.8095238095238095, 'recall': 0.8947368421052632, 'f1': 0.8500000000000001, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34} | 0.9234 | 0.9341 | 0.9287 | 0.9795 | | 0.0431 | 20.0 | 60 | 0.0774 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 48} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34} | {'precision': 0.8181818181818182, 'recall': 0.7297297297297297, 'f1': 0.7714285714285715, 'number': 37} | {'precision': 0.9636363636363636, 'recall': 1.0, 'f1': 0.9814814814814815, 'number': 53} | {'precision': 0.9428571428571428, 'recall': 1.0, 'f1': 0.9705882352941176, 'number': 33} | {'precision': 0.7727272727272727, 'recall': 0.8947368421052632, 'f1': 0.8292682926829269, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34} | 0.9425 | 0.9535 | 0.9480 | 0.9837 | | 0.0216 | 26.6667 | 80 | 0.0842 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 48} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34} | {'precision': 0.7631578947368421, 'recall': 0.7837837837837838, 'f1': 0.7733333333333334, 'number': 37} | {'precision': 0.9454545454545454, 'recall': 0.9811320754716981, 'f1': 0.9629629629629629, 'number': 53} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 33} | {'precision': 0.8095238095238095, 'recall': 0.8947368421052632, 'f1': 0.8500000000000001, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34} | 0.9286 | 0.9574 | 0.9427 | 0.9825 | | 0.0142 | 33.3333 | 100 | 0.0840 | {'precision': 1.0, 'recall': 0.875, 'f1': 0.9333333333333333, 'number': 48} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34} | {'precision': 0.8333333333333334, 'recall': 0.8108108108108109, 'f1': 0.8219178082191781, 'number': 37} | {'precision': 0.9629629629629629, 'recall': 0.9811320754716981, 'f1': 0.9719626168224299, 'number': 53} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 33} | {'precision': 0.8095238095238095, 'recall': 0.8947368421052632, 'f1': 0.8500000000000001, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34} | 0.9416 | 0.9380 | 0.9398 | 0.9819 | | 0.0105 | 40.0 | 120 | 0.0838 | {'precision': 0.9772727272727273, 'recall': 0.8958333333333334, 'f1': 0.9347826086956522, 'number': 48} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34} | {'precision': 0.8333333333333334, 'recall': 0.8108108108108109, 'f1': 0.8219178082191781, 'number': 37} | {'precision': 0.9636363636363636, 'recall': 1.0, 'f1': 0.9814814814814815, 'number': 53} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 33} | {'precision': 0.8095238095238095, 'recall': 0.8947368421052632, 'f1': 0.8500000000000001, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34} | 0.9385 | 0.9457 | 0.9421 | 0.9819 | | 0.0081 | 46.6667 | 140 | 0.0801 | {'precision': 1.0, 'recall': 0.875, 'f1': 0.9333333333333333, 'number': 48} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34} | {'precision': 0.8333333333333334, 'recall': 0.8108108108108109, 'f1': 0.8219178082191781, 'number': 37} | {'precision': 0.9636363636363636, 'recall': 1.0, 'f1': 0.9814814814814815, 'number': 53} | {'precision': 0.9428571428571428, 'recall': 1.0, 'f1': 0.9705882352941176, 'number': 33} | {'precision': 0.85, 'recall': 0.8947368421052632, 'f1': 0.8717948717948718, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34} | 0.9492 | 0.9419 | 0.9455 | 0.9831 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1