End of training

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- funsd-layoutlmv3
+model-index:
+- name: ananth-docai2
+  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. -->
+
+# ananth-docai2
+
+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: 1.4203
+- Answer: {'precision': 0.8505747126436781, 'recall': 0.9057527539779682, 'f1': 0.8772969768820391, 'number': 817}
+- Header: {'precision': 0.6476190476190476, 'recall': 0.5714285714285714, 'f1': 0.6071428571428571, 'number': 119}
+- Question: {'precision': 0.9104477611940298, 'recall': 0.9062209842154132, 'f1': 0.9083294555607259, 'number': 1077}
+- Overall Precision: 0.8715
+- Overall Recall: 0.8862
+- Overall F1: 0.8788
+- Overall Accuracy: 0.8269
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4218        | 10.53  | 200  | 1.0024          | {'precision': 0.8727272727272727, 'recall': 0.8812729498164015, 'f1': 0.8769792935444579, 'number': 817} | {'precision': 0.4036144578313253, 'recall': 0.5630252100840336, 'f1': 0.47017543859649125, 'number': 119} | {'precision': 0.8674812030075187, 'recall': 0.8570102135561746, 'f1': 0.8622139187295657, 'number': 1077} | 0.8321            | 0.8495         | 0.8407     | 0.7973           |
+| 0.0532        | 21.05  | 400  | 1.1791          | {'precision': 0.8563218390804598, 'recall': 0.9118727050183598, 'f1': 0.8832246591582691, 'number': 817} | {'precision': 0.5486725663716814, 'recall': 0.5210084033613446, 'f1': 0.5344827586206897, 'number': 119}  | {'precision': 0.9044943820224719, 'recall': 0.8969359331476323, 'f1': 0.9006993006993008, 'number': 1077} | 0.8645            | 0.8808         | 0.8725     | 0.8103           |
+| 0.0117        | 31.58  | 600  | 1.5177          | {'precision': 0.8064516129032258, 'recall': 0.9179926560587516, 'f1': 0.8586147681740126, 'number': 817} | {'precision': 0.6046511627906976, 'recall': 0.4369747899159664, 'f1': 0.5073170731707317, 'number': 119}  | {'precision': 0.9019607843137255, 'recall': 0.8542246982358404, 'f1': 0.8774439675727229, 'number': 1077} | 0.8458            | 0.8554         | 0.8506     | 0.7952           |
+| 0.0067        | 42.11  | 800  | 1.4884          | {'precision': 0.8443935926773455, 'recall': 0.9033047735618115, 'f1': 0.872856298048492, 'number': 817}  | {'precision': 0.515625, 'recall': 0.5546218487394958, 'f1': 0.5344129554655871, 'number': 119}            | {'precision': 0.8784530386740331, 'recall': 0.8857938718662952, 'f1': 0.8821081830790567, 'number': 1077} | 0.8420            | 0.8733         | 0.8574     | 0.7963           |
+| 0.0034        | 52.63  | 1000 | 1.4203          | {'precision': 0.8505747126436781, 'recall': 0.9057527539779682, 'f1': 0.8772969768820391, 'number': 817} | {'precision': 0.6476190476190476, 'recall': 0.5714285714285714, 'f1': 0.6071428571428571, 'number': 119}  | {'precision': 0.9104477611940298, 'recall': 0.9062209842154132, 'f1': 0.9083294555607259, 'number': 1077} | 0.8715            | 0.8862         | 0.8788     | 0.8269           |
+| 0.0023        | 63.16  | 1200 | 1.5225          | {'precision': 0.834096109839817, 'recall': 0.8922888616891065, 'f1': 0.8622117090479007, 'number': 817}  | {'precision': 0.5689655172413793, 'recall': 0.5546218487394958, 'f1': 0.5617021276595745, 'number': 119}  | {'precision': 0.8962001853568119, 'recall': 0.8978644382544104, 'f1': 0.8970315398886828, 'number': 1077} | 0.8516            | 0.8753         | 0.8633     | 0.8096           |
+| 0.0013        | 73.68  | 1400 | 1.6801          | {'precision': 0.848, 'recall': 0.9082007343941249, 'f1': 0.8770685579196217, 'number': 817}              | {'precision': 0.6741573033707865, 'recall': 0.5042016806722689, 'f1': 0.576923076923077, 'number': 119}   | {'precision': 0.8977695167286245, 'recall': 0.8969359331476323, 'f1': 0.8973525313516025, 'number': 1077} | 0.8667            | 0.8783         | 0.8724     | 0.7977           |
+| 0.0014        | 84.21  | 1600 | 1.6236          | {'precision': 0.8876543209876543, 'recall': 0.8800489596083231, 'f1': 0.8838352796558081, 'number': 817} | {'precision': 0.6237623762376238, 'recall': 0.5294117647058824, 'f1': 0.5727272727272728, 'number': 119}  | {'precision': 0.8656330749354005, 'recall': 0.9331476323119777, 'f1': 0.8981233243967828, 'number': 1077} | 0.8625            | 0.8877         | 0.8749     | 0.8072           |
+| 0.0006        | 94.74  | 1800 | 1.7231          | {'precision': 0.8619883040935673, 'recall': 0.9020807833537332, 'f1': 0.881578947368421, 'number': 817}  | {'precision': 0.6883116883116883, 'recall': 0.44537815126050423, 'f1': 0.5408163265306123, 'number': 119} | {'precision': 0.8748890860692103, 'recall': 0.9155060352831941, 'f1': 0.8947368421052633, 'number': 1077} | 0.8626            | 0.8823         | 0.8723     | 0.8019           |
+| 0.0005        | 105.26 | 2000 | 1.8217          | {'precision': 0.8342665173572228, 'recall': 0.9118727050183598, 'f1': 0.871345029239766, 'number': 817}  | {'precision': 0.6, 'recall': 0.5042016806722689, 'f1': 0.547945205479452, 'number': 119}                  | {'precision': 0.9049858889934148, 'recall': 0.89322191272052, 'f1': 0.8990654205607476, 'number': 1077}   | 0.8594            | 0.8778         | 0.8685     | 0.7964           |
+| 0.0004        | 115.79 | 2200 | 1.7688          | {'precision': 0.8561484918793504, 'recall': 0.9033047735618115, 'f1': 0.8790946992257296, 'number': 817} | {'precision': 0.6555555555555556, 'recall': 0.4957983193277311, 'f1': 0.5645933014354068, 'number': 119}  | {'precision': 0.8827272727272727, 'recall': 0.9015784586815228, 'f1': 0.8920532843362425, 'number': 1077} | 0.8616            | 0.8783         | 0.8699     | 0.7956           |
+| 0.0002        | 126.32 | 2400 | 1.7726          | {'precision': 0.8458904109589042, 'recall': 0.9069767441860465, 'f1': 0.8753691671588896, 'number': 817} | {'precision': 0.6741573033707865, 'recall': 0.5042016806722689, 'f1': 0.576923076923077, 'number': 119}   | {'precision': 0.8878676470588235, 'recall': 0.8969359331476323, 'f1': 0.892378752886836, 'number': 1077}  | 0.8607            | 0.8778         | 0.8692     | 0.7961           |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.1+cu117
+- Datasets 2.7.1
+- Tokenizers 0.13.2

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