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: 1.3685
+- Answer: {'precision': 0.8654073199527745, 'recall': 0.8971848225214198, 'f1': 0.8810096153846154, 'number': 817}
+- Header: {'precision': 0.6041666666666666, 'recall': 0.48739495798319327, 'f1': 0.5395348837209302, 'number': 119}
+- Question: {'precision': 0.8660245183887916, 'recall': 0.9182915506035283, 'f1': 0.8913925191527715, 'number': 1077}
+- Overall Precision: 0.8537
+- Overall Recall: 0.8843
+- Overall F1: 0.8687
+- Overall Accuracy: 0.8073
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4208        | 10.53  | 200  | 0.9422          | {'precision': 0.8501742160278746, 'recall': 0.8959608323133414, 'f1': 0.872467222884386, 'number': 817}  | {'precision': 0.47580645161290325, 'recall': 0.4957983193277311, 'f1': 0.48559670781893005, 'number': 119} | {'precision': 0.8847884788478848, 'recall': 0.9127205199628597, 'f1': 0.8985374771480805, 'number': 1077} | 0.8464            | 0.8813         | 0.8635     | 0.8109           |
+| 0.043         | 21.05  | 400  | 1.3865          | {'precision': 0.8387096774193549, 'recall': 0.9228886168910648, 'f1': 0.8787878787878787, 'number': 817} | {'precision': 0.5423728813559322, 'recall': 0.5378151260504201, 'f1': 0.540084388185654, 'number': 119}    | {'precision': 0.8985507246376812, 'recall': 0.8635097493036211, 'f1': 0.8806818181818181, 'number': 1077} | 0.8519            | 0.8684         | 0.8600     | 0.7941           |
+| 0.0126        | 31.58  | 600  | 1.3685          | {'precision': 0.8654073199527745, 'recall': 0.8971848225214198, 'f1': 0.8810096153846154, 'number': 817} | {'precision': 0.6041666666666666, 'recall': 0.48739495798319327, 'f1': 0.5395348837209302, 'number': 119}  | {'precision': 0.8660245183887916, 'recall': 0.9182915506035283, 'f1': 0.8913925191527715, 'number': 1077} | 0.8537            | 0.8843         | 0.8687     | 0.8073           |
+| 0.006         | 42.11  | 800  | 1.6230          | {'precision': 0.8476977567886659, 'recall': 0.8788249694002448, 'f1': 0.8629807692307692, 'number': 817} | {'precision': 0.6, 'recall': 0.4789915966386555, 'f1': 0.5327102803738317, 'number': 119}                  | {'precision': 0.872207327971403, 'recall': 0.9062209842154132, 'f1': 0.8888888888888888, 'number': 1077}  | 0.8496            | 0.8698         | 0.8596     | 0.7927           |
+| 0.005         | 52.63  | 1000 | 1.5735          | {'precision': 0.8246392896781354, 'recall': 0.9094247246022031, 'f1': 0.8649592549476135, 'number': 817} | {'precision': 0.5897435897435898, 'recall': 0.5798319327731093, 'f1': 0.5847457627118645, 'number': 119}   | {'precision': 0.8932316491897044, 'recall': 0.8700092850510678, 'f1': 0.8814675446848542, 'number': 1077} | 0.8462            | 0.8689         | 0.8574     | 0.7970           |
+| 0.0025        | 63.16  | 1200 | 1.6481          | {'precision': 0.8327721661054994, 'recall': 0.9082007343941249, 'f1': 0.8688524590163934, 'number': 817} | {'precision': 0.5688073394495413, 'recall': 0.5210084033613446, 'f1': 0.543859649122807, 'number': 119}    | {'precision': 0.9084778420038536, 'recall': 0.8755803156917363, 'f1': 0.8917257683215132, 'number': 1077} | 0.8572            | 0.8679         | 0.8625     | 0.7981           |
+| 0.002         | 73.68  | 1400 | 1.5911          | {'precision': 0.8459743290548425, 'recall': 0.8873929008567931, 'f1': 0.8661887694145758, 'number': 817} | {'precision': 0.6122448979591837, 'recall': 0.5042016806722689, 'f1': 0.5529953917050692, 'number': 119}   | {'precision': 0.8736462093862816, 'recall': 0.8987929433611885, 'f1': 0.8860411899313502, 'number': 1077} | 0.8497            | 0.8708         | 0.8602     | 0.8120           |
+| 0.0009        | 84.21  | 1600 | 1.6905          | {'precision': 0.8041575492341356, 'recall': 0.8996328029375765, 'f1': 0.8492201039861352, 'number': 817} | {'precision': 0.6020408163265306, 'recall': 0.4957983193277311, 'f1': 0.543778801843318, 'number': 119}    | {'precision': 0.900375939849624, 'recall': 0.8895078922934077, 'f1': 0.8949089210649229, 'number': 1077}  | 0.8439            | 0.8703         | 0.8569     | 0.7985           |
+| 0.0007        | 94.74  | 1800 | 1.7006          | {'precision': 0.8256467941507312, 'recall': 0.8984088127294981, 'f1': 0.8604923798358733, 'number': 817} | {'precision': 0.5573770491803278, 'recall': 0.5714285714285714, 'f1': 0.5643153526970954, 'number': 119}   | {'precision': 0.8952830188679245, 'recall': 0.8811513463324049, 'f1': 0.888160973327094, 'number': 1077}  | 0.8455            | 0.8698         | 0.8575     | 0.7981           |
+| 0.0002        | 105.26 | 2000 | 1.6531          | {'precision': 0.8352402745995423, 'recall': 0.8935128518971848, 'f1': 0.8633944411590775, 'number': 817} | {'precision': 0.5784313725490197, 'recall': 0.4957983193277311, 'f1': 0.5339366515837104, 'number': 119}   | {'precision': 0.8848987108655617, 'recall': 0.8922934076137419, 'f1': 0.888580674988442, 'number': 1077}  | 0.8487            | 0.8693         | 0.8589     | 0.8140           |
+| 0.0002        | 115.79 | 2200 | 1.6520          | {'precision': 0.8342857142857143, 'recall': 0.8935128518971848, 'f1': 0.8628841607565011, 'number': 817} | {'precision': 0.625, 'recall': 0.5462184873949579, 'f1': 0.5829596412556054, 'number': 119}                | {'precision': 0.8868445262189513, 'recall': 0.8950789229340761, 'f1': 0.8909426987060998, 'number': 1077} | 0.8514            | 0.8738         | 0.8625     | 0.8153           |
+| 0.0003        | 126.32 | 2400 | 1.6411          | {'precision': 0.830316742081448, 'recall': 0.8984088127294981, 'f1': 0.8630217519106408, 'number': 817}  | {'precision': 0.6055045871559633, 'recall': 0.5546218487394958, 'f1': 0.5789473684210525, 'number': 119}   | {'precision': 0.8976744186046511, 'recall': 0.8960074280408542, 'f1': 0.8968401486988847, 'number': 1077} | 0.8535            | 0.8768         | 0.8650     | 0.8183           |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.0+cu117
+- Datasets 2.7.1
+- Tokenizers 0.13.2

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