--- license: mit tags: - generated_from_trainer datasets: - funsd-layoutlmv3 model-index: - name: lilt-en-funsd results: [] --- # lilt-en-funsd This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set: - Loss: 1.5278 - Answer: {'precision': 0.8726415094339622, 'recall': 0.9057527539779682, 'f1': 0.8888888888888888, 'number': 817} - Header: {'precision': 0.6701030927835051, 'recall': 0.5462184873949579, 'f1': 0.6018518518518517, 'number': 119} - Question: {'precision': 0.9128440366972477, 'recall': 0.9238625812441968, 'f1': 0.9183202584217812, 'number': 1077} - Overall Precision: 0.8845 - Overall Recall: 0.8942 - Overall F1: 0.8893 - Overall Accuracy: 0.8213 ## 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 | |:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.4258 | 10.53 | 200 | 0.9877 | {'precision': 0.8354143019296254, 'recall': 0.9008567931456548, 'f1': 0.866902237926973, 'number': 817} | {'precision': 0.5491803278688525, 'recall': 0.5630252100840336, 'f1': 0.5560165975103735, 'number': 119} | {'precision': 0.8691756272401434, 'recall': 0.9006499535747446, 'f1': 0.8846329229366164, 'number': 1077} | 0.8367 | 0.8808 | 0.8582 | 0.8030 | | 0.0396 | 21.05 | 400 | 1.2891 | {'precision': 0.8314855875831486, 'recall': 0.9179926560587516, 'f1': 0.8726003490401396, 'number': 817} | {'precision': 0.5289256198347108, 'recall': 0.5378151260504201, 'f1': 0.5333333333333334, 'number': 119} | {'precision': 0.9008579599618685, 'recall': 0.8774373259052924, 'f1': 0.8889934148635935, 'number': 1077} | 0.8489 | 0.8738 | 0.8612 | 0.8071 | | 0.0145 | 31.58 | 600 | 1.1878 | {'precision': 0.8541666666666666, 'recall': 0.9033047735618115, 'f1': 0.8780487804878049, 'number': 817} | {'precision': 0.5943396226415094, 'recall': 0.5294117647058824, 'f1': 0.5599999999999999, 'number': 119} | {'precision': 0.8793565683646113, 'recall': 0.9136490250696379, 'f1': 0.896174863387978, 'number': 1077} | 0.8545 | 0.8867 | 0.8703 | 0.8139 | | 0.0093 | 42.11 | 800 | 1.3968 | {'precision': 0.8727272727272727, 'recall': 0.8812729498164015, 'f1': 0.8769792935444579, 'number': 817} | {'precision': 0.5454545454545454, 'recall': 0.6050420168067226, 'f1': 0.5737051792828685, 'number': 119} | {'precision': 0.8951686417502279, 'recall': 0.9117920148560817, 'f1': 0.9034038638454462, 'number': 1077} | 0.8637 | 0.8813 | 0.8724 | 0.8054 | | 0.0042 | 52.63 | 1000 | 1.5509 | {'precision': 0.8372093023255814, 'recall': 0.9253365973072215, 'f1': 0.8790697674418605, 'number': 817} | {'precision': 0.6304347826086957, 'recall': 0.48739495798319327, 'f1': 0.5497630331753555, 'number': 119} | {'precision': 0.9044048734770385, 'recall': 0.8960074280408542, 'f1': 0.9001865671641791, 'number': 1077} | 0.8628 | 0.8838 | 0.8731 | 0.8044 | | 0.0026 | 63.16 | 1200 | 1.5696 | {'precision': 0.8618266978922716, 'recall': 0.9008567931456548, 'f1': 0.8809096349491322, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5210084033613446, 'f1': 0.5849056603773585, 'number': 119} | {'precision': 0.8935978358881875, 'recall': 0.9201485608170845, 'f1': 0.9066788655077767, 'number': 1077} | 0.8701 | 0.8887 | 0.8793 | 0.8116 | | 0.001 | 73.68 | 1400 | 1.7209 | {'precision': 0.8396860986547086, 'recall': 0.9167686658506732, 'f1': 0.8765359859566998, 'number': 817} | {'precision': 0.6781609195402298, 'recall': 0.4957983193277311, 'f1': 0.5728155339805825, 'number': 119} | {'precision': 0.8969359331476323, 'recall': 0.8969359331476323, 'f1': 0.8969359331476322, 'number': 1077} | 0.8628 | 0.8813 | 0.8720 | 0.7977 | | 0.0011 | 84.21 | 1600 | 1.5329 | {'precision': 0.8646188850967008, 'recall': 0.9302325581395349, 'f1': 0.8962264150943396, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5042016806722689, 'f1': 0.5741626794258373, 'number': 119} | {'precision': 0.9050691244239631, 'recall': 0.9117920148560817, 'f1': 0.9084181313598519, 'number': 1077} | 0.8773 | 0.8952 | 0.8862 | 0.8267 | | 0.0006 | 94.74 | 1800 | 1.5523 | {'precision': 0.8748510131108462, 'recall': 0.8984088127294981, 'f1': 0.8864734299516908, 'number': 817} | {'precision': 0.5811965811965812, 'recall': 0.5714285714285714, 'f1': 0.576271186440678, 'number': 119} | {'precision': 0.9045412418906394, 'recall': 0.9062209842154132, 'f1': 0.9053803339517627, 'number': 1077} | 0.8737 | 0.8833 | 0.8785 | 0.8196 | | 0.0005 | 105.26 | 2000 | 1.5178 | {'precision': 0.8758949880668258, 'recall': 0.8984088127294981, 'f1': 0.8870090634441088, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119} | {'precision': 0.8995475113122172, 'recall': 0.9229340761374187, 'f1': 0.9110907424381303, 'number': 1077} | 0.8775 | 0.8897 | 0.8836 | 0.8253 | | 0.0004 | 115.79 | 2200 | 1.5493 | {'precision': 0.8597701149425288, 'recall': 0.9155446756425949, 'f1': 0.8867812685240072, 'number': 817} | {'precision': 0.6631578947368421, 'recall': 0.5294117647058824, 'f1': 0.5887850467289719, 'number': 119} | {'precision': 0.9107635694572217, 'recall': 0.9192200557103064, 'f1': 0.9149722735674676, 'number': 1077} | 0.8777 | 0.8947 | 0.8861 | 0.8217 | | 0.0003 | 126.32 | 2400 | 1.5278 | {'precision': 0.8726415094339622, 'recall': 0.9057527539779682, 'f1': 0.8888888888888888, 'number': 817} | {'precision': 0.6701030927835051, 'recall': 0.5462184873949579, 'f1': 0.6018518518518517, 'number': 119} | {'precision': 0.9128440366972477, 'recall': 0.9238625812441968, 'f1': 0.9183202584217812, 'number': 1077} | 0.8845 | 0.8942 | 0.8893 | 0.8213 | ### Framework versions - Transformers 4.28.1 - Pytorch 1.13.1+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3