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: 1.4726
+- Answer: {'precision': 0.8964677222898904, 'recall': 0.9008567931456548, 'f1': 0.8986568986568988, 'number': 817}
+- Header: {'precision': 0.7446808510638298, 'recall': 0.5882352941176471, 'f1': 0.6572769953051643, 'number': 119}
+- Question: {'precision': 0.8958517210944396, 'recall': 0.9424326833797586, 'f1': 0.918552036199095, 'number': 1077}
+- Overall Precision: 0.8892
+- Overall Recall: 0.9046
+- Overall F1: 0.8968
+- Overall Accuracy: 0.8387
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4172        | 10.53  | 200  | 0.8947          | {'precision': 0.8194444444444444, 'recall': 0.8665850673194615, 'f1': 0.842355740630577, 'number': 817}  | {'precision': 0.5284552845528455, 'recall': 0.5462184873949579, 'f1': 0.5371900826446281, 'number': 119}  | {'precision': 0.845414847161572, 'recall': 0.8987929433611885, 'f1': 0.8712871287128714, 'number': 1077}  | 0.8166            | 0.8649         | 0.8400     | 0.8019           |
+| 0.0368        | 21.05  | 400  | 1.1681          | {'precision': 0.8507972665148064, 'recall': 0.9143206854345165, 'f1': 0.8814159292035397, 'number': 817} | {'precision': 0.45962732919254656, 'recall': 0.6218487394957983, 'f1': 0.5285714285714286, 'number': 119} | {'precision': 0.888671875, 'recall': 0.8449396471680595, 'f1': 0.866254164683484, 'number': 1077}         | 0.8391            | 0.8599         | 0.8494     | 0.8104           |
+| 0.0132        | 31.58  | 600  | 1.3663          | {'precision': 0.8438914027149321, 'recall': 0.9130966952264382, 'f1': 0.8771310993533216, 'number': 817} | {'precision': 0.6511627906976745, 'recall': 0.47058823529411764, 'f1': 0.5463414634146342, 'number': 119} | {'precision': 0.8687943262411347, 'recall': 0.9099350046425255, 'f1': 0.888888888888889, 'number': 1077}  | 0.8494            | 0.8852         | 0.8669     | 0.8101           |
+| 0.0061        | 42.11  | 800  | 1.4360          | {'precision': 0.8648018648018648, 'recall': 0.9082007343941249, 'f1': 0.8859701492537313, 'number': 817} | {'precision': 0.6867469879518072, 'recall': 0.4789915966386555, 'f1': 0.5643564356435644, 'number': 119}  | {'precision': 0.8886910062333037, 'recall': 0.9266480965645311, 'f1': 0.9072727272727273, 'number': 1077} | 0.8706            | 0.8927         | 0.8815     | 0.8045           |
+| 0.0043        | 52.63  | 1000 | 1.4084          | {'precision': 0.8550057537399309, 'recall': 0.9094247246022031, 'f1': 0.8813760379596678, 'number': 817} | {'precision': 0.6344086021505376, 'recall': 0.4957983193277311, 'f1': 0.5566037735849056, 'number': 119}  | {'precision': 0.8842010771992819, 'recall': 0.914577530176416, 'f1': 0.8991328160657235, 'number': 1077}  | 0.8608            | 0.8877         | 0.8741     | 0.8265           |
+| 0.002         | 63.16  | 1200 | 1.4017          | {'precision': 0.8716136631330977, 'recall': 0.9057527539779682, 'f1': 0.8883553421368547, 'number': 817} | {'precision': 0.6593406593406593, 'recall': 0.5042016806722689, 'f1': 0.5714285714285715, 'number': 119}  | {'precision': 0.8825088339222615, 'recall': 0.9275766016713092, 'f1': 0.9044816659121775, 'number': 1077} | 0.8682            | 0.8937         | 0.8808     | 0.8194           |
+| 0.0018        | 73.68  | 1400 | 1.4379          | {'precision': 0.857307249712313, 'recall': 0.9118727050183598, 'f1': 0.8837485172004744, 'number': 817}  | {'precision': 0.6761904761904762, 'recall': 0.5966386554621849, 'f1': 0.6339285714285715, 'number': 119}  | {'precision': 0.8941068139963168, 'recall': 0.9015784586815228, 'f1': 0.8978270920018492, 'number': 1077} | 0.8675            | 0.8877         | 0.8775     | 0.8242           |
+| 0.0014        | 84.21  | 1600 | 1.4741          | {'precision': 0.8871359223300971, 'recall': 0.8947368421052632, 'f1': 0.890920170627666, 'number': 817}  | {'precision': 0.7590361445783133, 'recall': 0.5294117647058824, 'f1': 0.6237623762376238, 'number': 119}  | {'precision': 0.8777969018932874, 'recall': 0.947075208913649, 'f1': 0.9111210361768646, 'number': 1077}  | 0.8768            | 0.9011         | 0.8888     | 0.8407           |
+| 0.0005        | 94.74  | 1800 | 1.5542          | {'precision': 0.871824480369515, 'recall': 0.9241126070991432, 'f1': 0.8972073677956032, 'number': 817}  | {'precision': 0.7111111111111111, 'recall': 0.5378151260504201, 'f1': 0.6124401913875598, 'number': 119}  | {'precision': 0.9029038112522686, 'recall': 0.9238625812441968, 'f1': 0.9132629646626893, 'number': 1077} | 0.8814            | 0.9011         | 0.8912     | 0.8219           |
+| 0.0008        | 105.26 | 2000 | 1.4726          | {'precision': 0.8964677222898904, 'recall': 0.9008567931456548, 'f1': 0.8986568986568988, 'number': 817} | {'precision': 0.7446808510638298, 'recall': 0.5882352941176471, 'f1': 0.6572769953051643, 'number': 119}  | {'precision': 0.8958517210944396, 'recall': 0.9424326833797586, 'f1': 0.918552036199095, 'number': 1077}  | 0.8892            | 0.9046         | 0.8968     | 0.8387           |
+| 0.0003        | 115.79 | 2200 | 1.5233          | {'precision': 0.8910179640718563, 'recall': 0.9106487148102815, 'f1': 0.900726392251816, 'number': 817}  | {'precision': 0.71, 'recall': 0.5966386554621849, 'f1': 0.6484018264840181, 'number': 119}                | {'precision': 0.9049773755656109, 'recall': 0.9285051067780873, 'f1': 0.916590284142988, 'number': 1077}  | 0.8897            | 0.9016         | 0.8956     | 0.8354           |
+| 0.0001        | 126.32 | 2400 | 1.5261          | {'precision': 0.8817966903073287, 'recall': 0.9130966952264382, 'f1': 0.8971737823211066, 'number': 817} | {'precision': 0.7319587628865979, 'recall': 0.5966386554621849, 'f1': 0.6574074074074073, 'number': 119}  | {'precision': 0.8998194945848376, 'recall': 0.9257195914577531, 'f1': 0.9125858123569794, 'number': 1077} | 0.8844            | 0.9011         | 0.8927     | 0.8362           |
+
+
+### Framework versions
+
+- Transformers 4.27.1
+- Pytorch 1.13.1+cu116
+- Datasets 2.10.1
+- Tokenizers 0.13.2

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