--- license: mit base_model: SCUT-DLVCLab/lilt-roberta-en-base tags: - generated_from_trainer 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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6470 - Answer: {'precision': 0.8841607565011821, 'recall': 0.9155446756425949, 'f1': 0.899579073962718, 'number': 817} - Header: {'precision': 0.6574074074074074, 'recall': 0.5966386554621849, 'f1': 0.6255506607929515, 'number': 119} - Question: {'precision': 0.900804289544236, 'recall': 0.935933147632312, 'f1': 0.9180327868852459, 'number': 1077} - Overall Precision: 0.8813 - Overall Recall: 0.9076 - Overall F1: 0.8943 - Overall Accuracy: 0.8114 ## 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 | |:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.4017 | 10.53 | 200 | 1.0898 | {'precision': 0.8257491675915649, 'recall': 0.9106487148102815, 'f1': 0.8661233993015134, 'number': 817} | {'precision': 0.49166666666666664, 'recall': 0.4957983193277311, 'f1': 0.4937238493723849, 'number': 119} | {'precision': 0.8689591078066915, 'recall': 0.8681522748375116, 'f1': 0.8685555039479796, 'number': 1077} | 0.8288 | 0.8634 | 0.8457 | 0.7811 | | 0.0453 | 21.05 | 400 | 1.1928 | {'precision': 0.8400900900900901, 'recall': 0.9130966952264382, 'f1': 0.8750733137829912, 'number': 817} | {'precision': 0.5575221238938053, 'recall': 0.5294117647058824, 'f1': 0.543103448275862, 'number': 119} | {'precision': 0.8885869565217391, 'recall': 0.9108635097493036, 'f1': 0.8995873452544704, 'number': 1077} | 0.8504 | 0.8892 | 0.8694 | 0.8044 | | 0.0148 | 31.58 | 600 | 1.3654 | {'precision': 0.8174946004319654, 'recall': 0.9265605875152999, 'f1': 0.8686173264486519, 'number': 817} | {'precision': 0.5391304347826087, 'recall': 0.5210084033613446, 'f1': 0.52991452991453, 'number': 119} | {'precision': 0.8866171003717472, 'recall': 0.8857938718662952, 'f1': 0.8862052949372968, 'number': 1077} | 0.8375 | 0.8808 | 0.8586 | 0.8086 | | 0.0073 | 42.11 | 800 | 1.4733 | {'precision': 0.8653395784543325, 'recall': 0.9045287637698899, 'f1': 0.8845002992220227, 'number': 817} | {'precision': 0.6597938144329897, 'recall': 0.5378151260504201, 'f1': 0.5925925925925926, 'number': 119} | {'precision': 0.8881057268722466, 'recall': 0.935933147632312, 'f1': 0.9113924050632911, 'number': 1077} | 0.8682 | 0.8997 | 0.8836 | 0.8111 | | 0.008 | 52.63 | 1000 | 1.6415 | {'precision': 0.864963503649635, 'recall': 0.8702570379436965, 'f1': 0.8676021964612569, 'number': 817} | {'precision': 0.5636363636363636, 'recall': 0.5210084033613446, 'f1': 0.5414847161572053, 'number': 119} | {'precision': 0.8630490956072352, 'recall': 0.9303621169916435, 'f1': 0.8954423592493298, 'number': 1077} | 0.8481 | 0.8818 | 0.8646 | 0.7959 | | 0.0026 | 63.16 | 1200 | 1.5876 | {'precision': 0.8830049261083743, 'recall': 0.8776009791921665, 'f1': 0.8802946593001842, 'number': 817} | {'precision': 0.5564516129032258, 'recall': 0.5798319327731093, 'f1': 0.5679012345679013, 'number': 119} | {'precision': 0.8905693950177936, 'recall': 0.9294336118848654, 'f1': 0.9095865515674693, 'number': 1077} | 0.8675 | 0.8877 | 0.8775 | 0.8032 | | 0.0013 | 73.68 | 1400 | 1.5427 | {'precision': 0.8733572281959379, 'recall': 0.8947368421052632, 'f1': 0.8839177750906893, 'number': 817} | {'precision': 0.6271186440677966, 'recall': 0.6218487394957983, 'f1': 0.6244725738396625, 'number': 119} | {'precision': 0.9108455882352942, 'recall': 0.9201485608170845, 'f1': 0.9154734411085451, 'number': 1077} | 0.8791 | 0.8922 | 0.8856 | 0.8115 | | 0.0012 | 84.21 | 1600 | 1.5540 | {'precision': 0.8790035587188612, 'recall': 0.9069767441860465, 'f1': 0.8927710843373493, 'number': 817} | {'precision': 0.6216216216216216, 'recall': 0.5798319327731093, 'f1': 0.6000000000000001, 'number': 119} | {'precision': 0.9066543438077634, 'recall': 0.9108635097493036, 'f1': 0.9087540528022232, 'number': 1077} | 0.8797 | 0.8897 | 0.8847 | 0.8096 | | 0.0007 | 94.74 | 1800 | 1.6470 | {'precision': 0.8841607565011821, 'recall': 0.9155446756425949, 'f1': 0.899579073962718, 'number': 817} | {'precision': 0.6574074074074074, 'recall': 0.5966386554621849, 'f1': 0.6255506607929515, 'number': 119} | {'precision': 0.900804289544236, 'recall': 0.935933147632312, 'f1': 0.9180327868852459, 'number': 1077} | 0.8813 | 0.9076 | 0.8943 | 0.8114 | | 0.0004 | 105.26 | 2000 | 1.6446 | {'precision': 0.877906976744186, 'recall': 0.9241126070991432, 'f1': 0.9004174120453191, 'number': 817} | {'precision': 0.6739130434782609, 'recall': 0.5210084033613446, 'f1': 0.5876777251184835, 'number': 119} | {'precision': 0.8982142857142857, 'recall': 0.9340761374187558, 'f1': 0.915794264906691, 'number': 1077} | 0.8798 | 0.9056 | 0.8925 | 0.8196 | | 0.0003 | 115.79 | 2200 | 1.6375 | {'precision': 0.8638443935926774, 'recall': 0.9241126070991432, 'f1': 0.892962743938498, 'number': 817} | {'precision': 0.7375, 'recall': 0.4957983193277311, 'f1': 0.592964824120603, 'number': 119} | {'precision': 0.8999098286744815, 'recall': 0.9266480965645311, 'f1': 0.9130832570905763, 'number': 1077} | 0.8783 | 0.9001 | 0.8891 | 0.8225 | | 0.0003 | 126.32 | 2400 | 1.6489 | {'precision': 0.8751458576429405, 'recall': 0.9179926560587516, 'f1': 0.8960573476702508, 'number': 817} | {'precision': 0.6559139784946236, 'recall': 0.5126050420168067, 'f1': 0.5754716981132076, 'number': 119} | {'precision': 0.9026149684400361, 'recall': 0.9294336118848654, 'f1': 0.9158279963403477, 'number': 1077} | 0.8800 | 0.9001 | 0.8900 | 0.8166 | ### Framework versions - Transformers 4.38.0 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2