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: 1.6117
+- Answer: {'precision': 0.8821428571428571, 'recall': 0.9069767441860465, 'f1': 0.8943874471937237, 'number': 817}
+- Header: {'precision': 0.6126126126126126, 'recall': 0.5714285714285714, 'f1': 0.591304347826087, 'number': 119}
+- Question: {'precision': 0.9045045045045045, 'recall': 0.9322191272051996, 'f1': 0.9181527206218564, 'number': 1077}
+- Overall Precision: 0.8797
+- Overall Recall: 0.9006
+- Overall F1: 0.8900
+- Overall Accuracy: 0.8204
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0211        | 10.53  | 200  | 1.5528          | {'precision': 0.8458904109589042, 'recall': 0.9069767441860465, 'f1': 0.8753691671588896, 'number': 817} | {'precision': 0.5684210526315789, 'recall': 0.453781512605042, 'f1': 0.5046728971962617, 'number': 119}  | {'precision': 0.896551724137931, 'recall': 0.89322191272052, 'f1': 0.8948837209302325, 'number': 1077}    | 0.8596            | 0.8728         | 0.8662     | 0.8011           |
+| 0.0132        | 21.05  | 400  | 1.3143          | {'precision': 0.8447058823529412, 'recall': 0.8788249694002448, 'f1': 0.8614277144571085, 'number': 817} | {'precision': 0.6020408163265306, 'recall': 0.4957983193277311, 'f1': 0.543778801843318, 'number': 119}  | {'precision': 0.8854262144821264, 'recall': 0.8969359331476323, 'f1': 0.8911439114391144, 'number': 1077} | 0.8548            | 0.8659         | 0.8603     | 0.8095           |
+| 0.0052        | 31.58  | 600  | 1.5747          | {'precision': 0.8482446206115515, 'recall': 0.9167686658506732, 'f1': 0.8811764705882352, 'number': 817} | {'precision': 0.6283185840707964, 'recall': 0.5966386554621849, 'f1': 0.6120689655172413, 'number': 119} | {'precision': 0.8997161778618732, 'recall': 0.883008356545961, 'f1': 0.8912839737582005, 'number': 1077}  | 0.8626            | 0.8798         | 0.8711     | 0.8030           |
+| 0.0073        | 42.11  | 800  | 1.4848          | {'precision': 0.8487972508591065, 'recall': 0.9069767441860465, 'f1': 0.8769230769230769, 'number': 817} | {'precision': 0.5190839694656488, 'recall': 0.5714285714285714, 'f1': 0.5439999999999999, 'number': 119} | {'precision': 0.8941947565543071, 'recall': 0.8867223769730733, 'f1': 0.8904428904428905, 'number': 1077} | 0.8514            | 0.8763         | 0.8636     | 0.7969           |
+| 0.0057        | 52.63  | 1000 | 1.3993          | {'precision': 0.8852071005917159, 'recall': 0.9155446756425949, 'f1': 0.9001203369434416, 'number': 817} | {'precision': 0.5454545454545454, 'recall': 0.6050420168067226, 'f1': 0.5737051792828685, 'number': 119} | {'precision': 0.899090909090909, 'recall': 0.9182915506035283, 'f1': 0.9085898024804776, 'number': 1077}  | 0.8710            | 0.8987         | 0.8846     | 0.8198           |
+| 0.0023        | 63.16  | 1200 | 1.6463          | {'precision': 0.8961201501877347, 'recall': 0.8763769889840881, 'f1': 0.886138613861386, 'number': 817}  | {'precision': 0.5625, 'recall': 0.5294117647058824, 'f1': 0.5454545454545455, 'number': 119}             | {'precision': 0.888, 'recall': 0.9275766016713092, 'f1': 0.9073569482288827, 'number': 1077}              | 0.8733            | 0.8833         | 0.8782     | 0.8082           |
+| 0.001         | 73.68  | 1400 | 1.6476          | {'precision': 0.8676814988290398, 'recall': 0.9069767441860465, 'f1': 0.8868940754039496, 'number': 817} | {'precision': 0.6571428571428571, 'recall': 0.5798319327731093, 'f1': 0.6160714285714286, 'number': 119} | {'precision': 0.908256880733945, 'recall': 0.9192200557103064, 'f1': 0.9137055837563451, 'number': 1077}  | 0.8785            | 0.8942         | 0.8863     | 0.8137           |
+| 0.0014        | 84.21  | 1600 | 1.6493          | {'precision': 0.8814814814814815, 'recall': 0.8739290085679314, 'f1': 0.8776889981561156, 'number': 817} | {'precision': 0.6194690265486725, 'recall': 0.5882352941176471, 'f1': 0.603448275862069, 'number': 119}  | {'precision': 0.894404332129964, 'recall': 0.9201485608170845, 'f1': 0.9070938215102976, 'number': 1077}  | 0.8740            | 0.8818         | 0.8778     | 0.8041           |
+| 0.0006        | 94.74  | 1800 | 1.6193          | {'precision': 0.8766467065868263, 'recall': 0.8959608323133414, 'f1': 0.8861985472154963, 'number': 817} | {'precision': 0.6068376068376068, 'recall': 0.5966386554621849, 'f1': 0.6016949152542374, 'number': 119} | {'precision': 0.8946428571428572, 'recall': 0.9303621169916435, 'f1': 0.912152935821575, 'number': 1077}  | 0.8711            | 0.8967         | 0.8837     | 0.8137           |
+| 0.0001        | 105.26 | 2000 | 1.6048          | {'precision': 0.8751472320376914, 'recall': 0.9094247246022031, 'f1': 0.8919567827130852, 'number': 817} | {'precision': 0.6140350877192983, 'recall': 0.5882352941176471, 'f1': 0.6008583690987125, 'number': 119} | {'precision': 0.9062784349408554, 'recall': 0.924791086350975, 'f1': 0.9154411764705882, 'number': 1077}  | 0.8773            | 0.8987         | 0.8879     | 0.8194           |
+| 0.0001        | 115.79 | 2200 | 1.6117          | {'precision': 0.8821428571428571, 'recall': 0.9069767441860465, 'f1': 0.8943874471937237, 'number': 817} | {'precision': 0.6126126126126126, 'recall': 0.5714285714285714, 'f1': 0.591304347826087, 'number': 119}  | {'precision': 0.9045045045045045, 'recall': 0.9322191272051996, 'f1': 0.9181527206218564, 'number': 1077} | 0.8797            | 0.9006         | 0.8900     | 0.8204           |
+| 0.0001        | 126.32 | 2400 | 1.6163          | {'precision': 0.8799048751486326, 'recall': 0.9057527539779682, 'f1': 0.8926417370325694, 'number': 817} | {'precision': 0.6052631578947368, 'recall': 0.5798319327731093, 'f1': 0.5922746781115881, 'number': 119} | {'precision': 0.9062784349408554, 'recall': 0.924791086350975, 'f1': 0.9154411764705882, 'number': 1077}  | 0.8788            | 0.8967         | 0.8876     | 0.8192           |
+
+
+### Framework versions
+
+- Transformers 4.24.0
+- Pytorch 1.12.1+cu113
+- Datasets 2.7.0
+- Tokenizers 0.12.1

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