--- license: mit base_model: SCUT-DLVCLab/lilt-roberta-en-base tags: - generated_from_trainer model-index: - name: lilt-en-funsd-layoutlmv3 results: [] --- # lilt-en-funsd-layoutlmv3 This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co./SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5778 - Answer: {'precision': 0.8764705882352941, 'recall': 0.9118727050183598, 'f1': 0.8938212357528493, 'number': 817} - Header: {'precision': 0.6593406593406593, 'recall': 0.5042016806722689, 'f1': 0.5714285714285715, 'number': 119} - Question: {'precision': 0.8901890189018902, 'recall': 0.9182915506035283, 'f1': 0.9040219378427787, 'number': 1077} - Overall Precision: 0.8743 - Overall Recall: 0.8912 - Overall F1: 0.8827 - Overall Accuracy: 0.8242 ## 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.4133 | 10.5263 | 200 | 1.0790 | {'precision': 0.8122941822173436, 'recall': 0.9057527539779682, 'f1': 0.8564814814814816, 'number': 817} | {'precision': 0.5463917525773195, 'recall': 0.44537815126050423, 'f1': 0.4907407407407407, 'number': 119} | {'precision': 0.8778280542986425, 'recall': 0.9006499535747446, 'f1': 0.8890925756186985, 'number': 1077} | 0.8344 | 0.8758 | 0.8546 | 0.7789 | | 0.0495 | 21.0526 | 400 | 1.1160 | {'precision': 0.8738207547169812, 'recall': 0.9069767441860465, 'f1': 0.8900900900900901, 'number': 817} | {'precision': 0.6354166666666666, 'recall': 0.5126050420168067, 'f1': 0.5674418604651162, 'number': 119} | {'precision': 0.8720829732065687, 'recall': 0.9368616527390901, 'f1': 0.9033124440465533, 'number': 1077} | 0.8620 | 0.8997 | 0.8804 | 0.8217 | | 0.0138 | 31.5789 | 600 | 1.3117 | {'precision': 0.8450867052023121, 'recall': 0.8947368421052632, 'f1': 0.8692033293697978, 'number': 817} | {'precision': 0.5348837209302325, 'recall': 0.5798319327731093, 'f1': 0.5564516129032259, 'number': 119} | {'precision': 0.8910433979686058, 'recall': 0.8960074280408542, 'f1': 0.8935185185185185, 'number': 1077} | 0.8498 | 0.8768 | 0.8631 | 0.8064 | | 0.0069 | 42.1053 | 800 | 1.5258 | {'precision': 0.8543352601156069, 'recall': 0.9045287637698899, 'f1': 0.8787158145065399, 'number': 817} | {'precision': 0.5700934579439252, 'recall': 0.5126050420168067, 'f1': 0.5398230088495575, 'number': 119} | {'precision': 0.8849315068493151, 'recall': 0.8997214484679665, 'f1': 0.8922651933701657, 'number': 1077} | 0.8558 | 0.8788 | 0.8672 | 0.8003 | | 0.0035 | 52.6316 | 1000 | 1.5538 | {'precision': 0.86, 'recall': 0.8947368421052632, 'f1': 0.8770245950809838, 'number': 817} | {'precision': 0.5737704918032787, 'recall': 0.5882352941176471, 'f1': 0.5809128630705394, 'number': 119} | {'precision': 0.8893023255813953, 'recall': 0.8876508820798514, 'f1': 0.8884758364312269, 'number': 1077} | 0.8583 | 0.8728 | 0.8655 | 0.8103 | | 0.0025 | 63.1579 | 1200 | 1.4460 | {'precision': 0.8547297297297297, 'recall': 0.9290085679314566, 'f1': 0.8903225806451612, 'number': 817} | {'precision': 0.6483516483516484, 'recall': 0.4957983193277311, 'f1': 0.561904761904762, 'number': 119} | {'precision': 0.8898916967509025, 'recall': 0.9155060352831941, 'f1': 0.9025171624713959, 'number': 1077} | 0.8644 | 0.8962 | 0.88 | 0.8193 | | 0.0014 | 73.6842 | 1400 | 1.5030 | {'precision': 0.8910648714810282, 'recall': 0.8910648714810282, 'f1': 0.8910648714810282, 'number': 817} | {'precision': 0.616822429906542, 'recall': 0.5546218487394958, 'f1': 0.5840707964601769, 'number': 119} | {'precision': 0.8925022583559169, 'recall': 0.9173630454967502, 'f1': 0.9047619047619048, 'number': 1077} | 0.8774 | 0.8852 | 0.8813 | 0.8247 | | 0.0011 | 84.2105 | 1600 | 1.5379 | {'precision': 0.8779342723004695, 'recall': 0.9155446756425949, 'f1': 0.8963451168364289, 'number': 817} | {'precision': 0.6210526315789474, 'recall': 0.4957983193277311, 'f1': 0.5514018691588785, 'number': 119} | {'precision': 0.9005424954792043, 'recall': 0.924791086350975, 'f1': 0.9125057260650481, 'number': 1077} | 0.8782 | 0.8957 | 0.8869 | 0.8281 | | 0.0005 | 94.7368 | 1800 | 1.5872 | {'precision': 0.8630609896432682, 'recall': 0.9179926560587516, 'f1': 0.8896797153024911, 'number': 817} | {'precision': 0.6333333333333333, 'recall': 0.4789915966386555, 'f1': 0.5454545454545454, 'number': 119} | {'precision': 0.8955495004541326, 'recall': 0.9155060352831941, 'f1': 0.9054178145087237, 'number': 1077} | 0.8704 | 0.8907 | 0.8804 | 0.8211 | | 0.0004 | 105.2632 | 2000 | 1.5457 | {'precision': 0.8758782201405152, 'recall': 0.9155446756425949, 'f1': 0.8952722920406941, 'number': 817} | {'precision': 0.6703296703296703, 'recall': 0.5126050420168067, 'f1': 0.5809523809523809, 'number': 119} | {'precision': 0.8824577025823687, 'recall': 0.9201485608170845, 'f1': 0.9009090909090909, 'number': 1077} | 0.8704 | 0.8942 | 0.8821 | 0.8231 | | 0.0002 | 115.7895 | 2200 | 1.5761 | {'precision': 0.8802816901408451, 'recall': 0.9179926560587516, 'f1': 0.8987417615338527, 'number': 817} | {'precision': 0.6630434782608695, 'recall': 0.5126050420168067, 'f1': 0.5781990521327014, 'number': 119} | {'precision': 0.8940754039497307, 'recall': 0.924791086350975, 'f1': 0.9091738931994524, 'number': 1077} | 0.8780 | 0.8977 | 0.8877 | 0.8271 | | 0.0002 | 126.3158 | 2400 | 1.5778 | {'precision': 0.8764705882352941, 'recall': 0.9118727050183598, 'f1': 0.8938212357528493, 'number': 817} | {'precision': 0.6593406593406593, 'recall': 0.5042016806722689, 'f1': 0.5714285714285715, 'number': 119} | {'precision': 0.8901890189018902, 'recall': 0.9182915506035283, 'f1': 0.9040219378427787, 'number': 1077} | 0.8743 | 0.8912 | 0.8827 | 0.8242 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1