End of training

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- funsd-layoutlmv3
+model-index:
+- name: lilt-en-funsd
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# 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: 1.7566
+- Answer: {'precision': 0.8818713450292398, 'recall': 0.9228886168910648, 'f1': 0.9019138755980862, 'number': 817}
+- Header: {'precision': 0.6597938144329897, 'recall': 0.5378151260504201, 'f1': 0.5925925925925926, 'number': 119}
+- Question: {'precision': 0.8944494995450409, 'recall': 0.9127205199628597, 'f1': 0.9034926470588234, 'number': 1077}
+- Overall Precision: 0.8781
+- Overall Recall: 0.8947
+- Overall F1: 0.8863
+- Overall Accuracy: 0.7939
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4354        | 10.53  | 200  | 1.0094          | {'precision': 0.8219954648526077, 'recall': 0.8873929008567931, 'f1': 0.8534432018834609, 'number': 817} | {'precision': 0.5617977528089888, 'recall': 0.42016806722689076, 'f1': 0.4807692307692308, 'number': 119} | {'precision': 0.8493723849372385, 'recall': 0.9424326833797586, 'f1': 0.8934859154929579, 'number': 1077} | 0.8264            | 0.8892         | 0.8567     | 0.7972           |
+| 0.0503        | 21.05  | 400  | 1.2949          | {'precision': 0.8543577981651376, 'recall': 0.9118727050183598, 'f1': 0.8821788040260509, 'number': 817} | {'precision': 0.5658914728682171, 'recall': 0.6134453781512605, 'f1': 0.5887096774193549, 'number': 119}  | {'precision': 0.9066147859922179, 'recall': 0.8653667595171773, 'f1': 0.8855106888361044, 'number': 1077} | 0.8625            | 0.8693         | 0.8659     | 0.8117           |
+| 0.0143        | 31.58  | 600  | 1.3527          | {'precision': 0.8726190476190476, 'recall': 0.8971848225214198, 'f1': 0.8847314423657212, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5714285714285714, 'f1': 0.6153846153846153, 'number': 119}  | {'precision': 0.8533674339300937, 'recall': 0.9294336118848654, 'f1': 0.8897777777777779, 'number': 1077} | 0.8520            | 0.8952         | 0.8731     | 0.8116           |
+| 0.0064        | 42.11  | 800  | 1.6567          | {'precision': 0.8483466362599772, 'recall': 0.9106487148102815, 'f1': 0.8783943329397875, 'number': 817} | {'precision': 0.5564516129032258, 'recall': 0.5798319327731093, 'f1': 0.5679012345679013, 'number': 119}  | {'precision': 0.8949814126394052, 'recall': 0.8941504178272981, 'f1': 0.8945657222480261, 'number': 1077} | 0.8551            | 0.8823         | 0.8685     | 0.7982           |
+| 0.0051        | 52.63  | 1000 | 1.6856          | {'precision': 0.8542141230068337, 'recall': 0.9179926560587516, 'f1': 0.8849557522123894, 'number': 817} | {'precision': 0.66, 'recall': 0.5546218487394958, 'f1': 0.6027397260273973, 'number': 119}                | {'precision': 0.9025069637883009, 'recall': 0.9025069637883009, 'f1': 0.9025069637883009, 'number': 1077} | 0.8701            | 0.8882         | 0.8791     | 0.7925           |
+| 0.0029        | 63.16  | 1200 | 1.5031          | {'precision': 0.8860294117647058, 'recall': 0.8849449204406364, 'f1': 0.8854868340477648, 'number': 817} | {'precision': 0.6147540983606558, 'recall': 0.6302521008403361, 'f1': 0.6224066390041495, 'number': 119}  | {'precision': 0.8724890829694323, 'recall': 0.9275766016713092, 'f1': 0.8991899189918992, 'number': 1077} | 0.8627            | 0.8927         | 0.8774     | 0.8117           |
+| 0.0015        | 73.68  | 1400 | 1.6708          | {'precision': 0.8720657276995305, 'recall': 0.9094247246022031, 'f1': 0.89035350509287, 'number': 817}   | {'precision': 0.5286624203821656, 'recall': 0.6974789915966386, 'f1': 0.6014492753623188, 'number': 119}  | {'precision': 0.8897126969416126, 'recall': 0.8913649025069638, 'f1': 0.8905380333951762, 'number': 1077} | 0.8554            | 0.8872         | 0.8710     | 0.7958           |
+| 0.0012        | 84.21  | 1600 | 1.7566          | {'precision': 0.8818713450292398, 'recall': 0.9228886168910648, 'f1': 0.9019138755980862, 'number': 817} | {'precision': 0.6597938144329897, 'recall': 0.5378151260504201, 'f1': 0.5925925925925926, 'number': 119}  | {'precision': 0.8944494995450409, 'recall': 0.9127205199628597, 'f1': 0.9034926470588234, 'number': 1077} | 0.8781            | 0.8947         | 0.8863     | 0.7939           |
+| 0.0006        | 94.74  | 1800 | 1.8482          | {'precision': 0.8781362007168458, 'recall': 0.8996328029375765, 'f1': 0.8887545344619106, 'number': 817} | {'precision': 0.5862068965517241, 'recall': 0.5714285714285714, 'f1': 0.5787234042553192, 'number': 119}  | {'precision': 0.8949814126394052, 'recall': 0.8941504178272981, 'f1': 0.8945657222480261, 'number': 1077} | 0.8704            | 0.8773         | 0.8738     | 0.7913           |
+| 0.0006        | 105.26 | 2000 | 1.7763          | {'precision': 0.8747072599531616, 'recall': 0.9143206854345165, 'f1': 0.8940754039497306, 'number': 817} | {'precision': 0.6095238095238096, 'recall': 0.5378151260504201, 'f1': 0.5714285714285715, 'number': 119}  | {'precision': 0.8859489051094891, 'recall': 0.9015784586815228, 'f1': 0.8936953520478601, 'number': 1077} | 0.8672            | 0.8852         | 0.8761     | 0.7964           |
+| 0.0003        | 115.79 | 2200 | 1.9186          | {'precision': 0.8813953488372093, 'recall': 0.9277845777233782, 'f1': 0.9039952295766249, 'number': 817} | {'precision': 0.6190476190476191, 'recall': 0.5462184873949579, 'f1': 0.5803571428571429, 'number': 119}  | {'precision': 0.9025304592314901, 'recall': 0.8941504178272981, 'f1': 0.898320895522388, 'number': 1077}  | 0.8789            | 0.8872         | 0.8831     | 0.7971           |
+| 0.0002        | 126.32 | 2400 | 1.8948          | {'precision': 0.8780487804878049, 'recall': 0.9253365973072215, 'f1': 0.901072705601907, 'number': 817}  | {'precision': 0.6261682242990654, 'recall': 0.5630252100840336, 'f1': 0.5929203539823009, 'number': 119}  | {'precision': 0.9033457249070632, 'recall': 0.9025069637883009, 'f1': 0.9029261495587553, 'number': 1077} | 0.8782            | 0.8917         | 0.8849     | 0.7978           |
+
+
+### Framework versions
+
+- Transformers 4.27.3
+- Pytorch 1.13.1+cu116
+- Datasets 2.10.1
+- Tokenizers 0.13.2

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