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: 1.6502
+- Answer: {'precision': 0.8637413394919169, 'recall': 0.9155446756425949, 'f1': 0.888888888888889, 'number': 817}
+- Header: {'precision': 0.625, 'recall': 0.5042016806722689, 'f1': 0.5581395348837209, 'number': 119}
+- Question: {'precision': 0.8934280639431617, 'recall': 0.9340761374187558, 'f1': 0.9133000453926464, 'number': 1077}
+- Overall Precision: 0.8688
+- Overall Recall: 0.9011
+- Overall F1: 0.8847
+- Overall Accuracy: 0.8015
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4362        | 10.53  | 200  | 0.9773          | {'precision': 0.8193832599118943, 'recall': 0.9106487148102815, 'f1': 0.862608695652174, 'number': 817}  | {'precision': 0.6666666666666666, 'recall': 0.3865546218487395, 'f1': 0.4893617021276595, 'number': 119} | {'precision': 0.8725490196078431, 'recall': 0.9090064995357474, 'f1': 0.8904047294224647, 'number': 1077} | 0.8428            | 0.8788         | 0.8604     | 0.7932           |
+| 0.0418        | 21.05  | 400  | 1.4204          | {'precision': 0.8056460369163952, 'recall': 0.9082007343941249, 'f1': 0.85385500575374, 'number': 817}   | {'precision': 0.5684210526315789, 'recall': 0.453781512605042, 'f1': 0.5046728971962617, 'number': 119}  | {'precision': 0.8854262144821264, 'recall': 0.8969359331476323, 'f1': 0.8911439114391144, 'number': 1077} | 0.8363            | 0.8753         | 0.8553     | 0.7870           |
+| 0.0118        | 31.58  | 600  | 1.5084          | {'precision': 0.8661137440758294, 'recall': 0.8947368421052632, 'f1': 0.8801926550270921, 'number': 817} | {'precision': 0.5575221238938053, 'recall': 0.5294117647058824, 'f1': 0.543103448275862, 'number': 119}  | {'precision': 0.8864864864864865, 'recall': 0.9136490250696379, 'f1': 0.8998628257887517, 'number': 1077} | 0.8602            | 0.8833         | 0.8716     | 0.7938           |
+| 0.0116        | 42.11  | 800  | 1.4934          | {'precision': 0.8497109826589595, 'recall': 0.8996328029375765, 'f1': 0.8739595719381689, 'number': 817} | {'precision': 0.6068376068376068, 'recall': 0.5966386554621849, 'f1': 0.6016949152542374, 'number': 119} | {'precision': 0.8835489833641405, 'recall': 0.8876508820798514, 'f1': 0.8855951829550718, 'number': 1077} | 0.8537            | 0.8753         | 0.8644     | 0.7963           |
+| 0.0046        | 52.63  | 1000 | 1.6502          | {'precision': 0.8637413394919169, 'recall': 0.9155446756425949, 'f1': 0.888888888888889, 'number': 817}  | {'precision': 0.625, 'recall': 0.5042016806722689, 'f1': 0.5581395348837209, 'number': 119}              | {'precision': 0.8934280639431617, 'recall': 0.9340761374187558, 'f1': 0.9133000453926464, 'number': 1077} | 0.8688            | 0.9011         | 0.8847     | 0.8015           |
+| 0.0025        | 63.16  | 1200 | 1.6009          | {'precision': 0.8503480278422274, 'recall': 0.8971848225214198, 'f1': 0.8731387730792138, 'number': 817} | {'precision': 0.651685393258427, 'recall': 0.48739495798319327, 'f1': 0.5576923076923077, 'number': 119} | {'precision': 0.8716392020815265, 'recall': 0.9331476323119777, 'f1': 0.9013452914798208, 'number': 1077} | 0.8536            | 0.8922         | 0.8725     | 0.8073           |
+| 0.0016        | 73.68  | 1400 | 1.6601          | {'precision': 0.8872727272727273, 'recall': 0.8959608323133414, 'f1': 0.8915956151035324, 'number': 817} | {'precision': 0.67, 'recall': 0.5630252100840336, 'f1': 0.6118721461187214, 'number': 119}               | {'precision': 0.8820375335120644, 'recall': 0.9164345403899722, 'f1': 0.8989071038251366, 'number': 1077} | 0.8738            | 0.8872         | 0.8805     | 0.7977           |
+| 0.0006        | 84.21  | 1600 | 1.6735          | {'precision': 0.8774038461538461, 'recall': 0.8935128518971848, 'f1': 0.8853850818677986, 'number': 817} | {'precision': 0.6636363636363637, 'recall': 0.6134453781512605, 'f1': 0.6375545851528385, 'number': 119} | {'precision': 0.8782452999104745, 'recall': 0.9108635097493036, 'f1': 0.8942570647219691, 'number': 1077} | 0.8664            | 0.8862         | 0.8762     | 0.7997           |
+| 0.0006        | 94.74  | 1800 | 1.6672          | {'precision': 0.8755980861244019, 'recall': 0.8959608323133414, 'f1': 0.8856624319419237, 'number': 817} | {'precision': 0.6545454545454545, 'recall': 0.6050420168067226, 'f1': 0.62882096069869, 'number': 119}   | {'precision': 0.8800705467372134, 'recall': 0.9266480965645311, 'f1': 0.902758932609679, 'number': 1077}  | 0.8663            | 0.8952         | 0.8805     | 0.8010           |
+| 0.0004        | 105.26 | 2000 | 1.6652          | {'precision': 0.8880866425992779, 'recall': 0.9033047735618115, 'f1': 0.895631067961165, 'number': 817}  | {'precision': 0.6086956521739131, 'recall': 0.5882352941176471, 'f1': 0.5982905982905983, 'number': 119} | {'precision': 0.8776785714285714, 'recall': 0.9127205199628597, 'f1': 0.8948566226672735, 'number': 1077} | 0.8669            | 0.8897         | 0.8782     | 0.8059           |
+| 0.0004        | 115.79 | 2200 | 1.6698          | {'precision': 0.8993865030674847, 'recall': 0.8971848225214198, 'f1': 0.8982843137254903, 'number': 817} | {'precision': 0.631578947368421, 'recall': 0.6050420168067226, 'f1': 0.6180257510729613, 'number': 119}  | {'precision': 0.8808243727598566, 'recall': 0.9127205199628597, 'f1': 0.8964888280893752, 'number': 1077} | 0.8743            | 0.8882         | 0.8812     | 0.8096           |
+| 0.0002        | 126.32 | 2400 | 1.7190          | {'precision': 0.8888888888888888, 'recall': 0.9008567931456548, 'f1': 0.8948328267477204, 'number': 817} | {'precision': 0.6542056074766355, 'recall': 0.5882352941176471, 'f1': 0.6194690265486726, 'number': 119} | {'precision': 0.8815672306322351, 'recall': 0.9192200557103064, 'f1': 0.9, 'number': 1077}                | 0.8727            | 0.8922         | 0.8823     | 0.8045           |
+
+
+### Framework versions
+
+- Transformers 4.27.3
+- Pytorch 1.13.1+cu116
+- Datasets 2.10.1
+- Tokenizers 0.13.2

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