Training in progress, step 200

README.md

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+---
+license: mit
+base_model: SCUT-DLVCLab/lilt-roberta-en-base
+tags:
+- generated_from_trainer
+model-index:
+- name: lilt-en-funsd-layoutlmv3
+  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-layoutlmv3
+
+This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.5778
+- Answer: {'precision': 0.8764705882352941, 'recall': 0.9118727050183598, 'f1': 0.8938212357528493, 'number': 817}
+- Header: {'precision': 0.6593406593406593, 'recall': 0.5042016806722689, 'f1': 0.5714285714285715, 'number': 119}
+- Question: {'precision': 0.8901890189018902, 'recall': 0.9182915506035283, 'f1': 0.9040219378427787, 'number': 1077}
+- Overall Precision: 0.8743
+- Overall Recall: 0.8912
+- Overall F1: 0.8827
+- Overall Accuracy: 0.8242
+
+## 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 |
+|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4133        | 10.5263  | 200  | 1.0790          | {'precision': 0.8122941822173436, 'recall': 0.9057527539779682, 'f1': 0.8564814814814816, 'number': 817} | {'precision': 0.5463917525773195, 'recall': 0.44537815126050423, 'f1': 0.4907407407407407, 'number': 119} | {'precision': 0.8778280542986425, 'recall': 0.9006499535747446, 'f1': 0.8890925756186985, 'number': 1077} | 0.8344            | 0.8758         | 0.8546     | 0.7789           |
+| 0.0495        | 21.0526  | 400  | 1.1160          | {'precision': 0.8738207547169812, 'recall': 0.9069767441860465, 'f1': 0.8900900900900901, 'number': 817} | {'precision': 0.6354166666666666, 'recall': 0.5126050420168067, 'f1': 0.5674418604651162, 'number': 119}  | {'precision': 0.8720829732065687, 'recall': 0.9368616527390901, 'f1': 0.9033124440465533, 'number': 1077} | 0.8620            | 0.8997         | 0.8804     | 0.8217           |
+| 0.0138        | 31.5789  | 600  | 1.3117          | {'precision': 0.8450867052023121, 'recall': 0.8947368421052632, 'f1': 0.8692033293697978, 'number': 817} | {'precision': 0.5348837209302325, 'recall': 0.5798319327731093, 'f1': 0.5564516129032259, 'number': 119}  | {'precision': 0.8910433979686058, 'recall': 0.8960074280408542, 'f1': 0.8935185185185185, 'number': 1077} | 0.8498            | 0.8768         | 0.8631     | 0.8064           |
+| 0.0069        | 42.1053  | 800  | 1.5258          | {'precision': 0.8543352601156069, 'recall': 0.9045287637698899, 'f1': 0.8787158145065399, 'number': 817} | {'precision': 0.5700934579439252, 'recall': 0.5126050420168067, 'f1': 0.5398230088495575, 'number': 119}  | {'precision': 0.8849315068493151, 'recall': 0.8997214484679665, 'f1': 0.8922651933701657, 'number': 1077} | 0.8558            | 0.8788         | 0.8672     | 0.8003           |
+| 0.0035        | 52.6316  | 1000 | 1.5538          | {'precision': 0.86, 'recall': 0.8947368421052632, 'f1': 0.8770245950809838, 'number': 817}               | {'precision': 0.5737704918032787, 'recall': 0.5882352941176471, 'f1': 0.5809128630705394, 'number': 119}  | {'precision': 0.8893023255813953, 'recall': 0.8876508820798514, 'f1': 0.8884758364312269, 'number': 1077} | 0.8583            | 0.8728         | 0.8655     | 0.8103           |
+| 0.0025        | 63.1579  | 1200 | 1.4460          | {'precision': 0.8547297297297297, 'recall': 0.9290085679314566, 'f1': 0.8903225806451612, 'number': 817} | {'precision': 0.6483516483516484, 'recall': 0.4957983193277311, 'f1': 0.561904761904762, 'number': 119}   | {'precision': 0.8898916967509025, 'recall': 0.9155060352831941, 'f1': 0.9025171624713959, 'number': 1077} | 0.8644            | 0.8962         | 0.88       | 0.8193           |
+| 0.0014        | 73.6842  | 1400 | 1.5030          | {'precision': 0.8910648714810282, 'recall': 0.8910648714810282, 'f1': 0.8910648714810282, 'number': 817} | {'precision': 0.616822429906542, 'recall': 0.5546218487394958, 'f1': 0.5840707964601769, 'number': 119}   | {'precision': 0.8925022583559169, 'recall': 0.9173630454967502, 'f1': 0.9047619047619048, 'number': 1077} | 0.8774            | 0.8852         | 0.8813     | 0.8247           |
+| 0.0011        | 84.2105  | 1600 | 1.5379          | {'precision': 0.8779342723004695, 'recall': 0.9155446756425949, 'f1': 0.8963451168364289, 'number': 817} | {'precision': 0.6210526315789474, 'recall': 0.4957983193277311, 'f1': 0.5514018691588785, 'number': 119}  | {'precision': 0.9005424954792043, 'recall': 0.924791086350975, 'f1': 0.9125057260650481, 'number': 1077}  | 0.8782            | 0.8957         | 0.8869     | 0.8281           |
+| 0.0005        | 94.7368  | 1800 | 1.5872          | {'precision': 0.8630609896432682, 'recall': 0.9179926560587516, 'f1': 0.8896797153024911, 'number': 817} | {'precision': 0.6333333333333333, 'recall': 0.4789915966386555, 'f1': 0.5454545454545454, 'number': 119}  | {'precision': 0.8955495004541326, 'recall': 0.9155060352831941, 'f1': 0.9054178145087237, 'number': 1077} | 0.8704            | 0.8907         | 0.8804     | 0.8211           |
+| 0.0004        | 105.2632 | 2000 | 1.5457          | {'precision': 0.8758782201405152, 'recall': 0.9155446756425949, 'f1': 0.8952722920406941, 'number': 817} | {'precision': 0.6703296703296703, 'recall': 0.5126050420168067, 'f1': 0.5809523809523809, 'number': 119}  | {'precision': 0.8824577025823687, 'recall': 0.9201485608170845, 'f1': 0.9009090909090909, 'number': 1077} | 0.8704            | 0.8942         | 0.8821     | 0.8231           |
+| 0.0002        | 115.7895 | 2200 | 1.5761          | {'precision': 0.8802816901408451, 'recall': 0.9179926560587516, 'f1': 0.8987417615338527, 'number': 817} | {'precision': 0.6630434782608695, 'recall': 0.5126050420168067, 'f1': 0.5781990521327014, 'number': 119}  | {'precision': 0.8940754039497307, 'recall': 0.924791086350975, 'f1': 0.9091738931994524, 'number': 1077}  | 0.8780            | 0.8977         | 0.8877     | 0.8271           |
+| 0.0002        | 126.3158 | 2400 | 1.5778          | {'precision': 0.8764705882352941, 'recall': 0.9118727050183598, 'f1': 0.8938212357528493, 'number': 817} | {'precision': 0.6593406593406593, 'recall': 0.5042016806722689, 'f1': 0.5714285714285715, 'number': 119}  | {'precision': 0.8901890189018902, 'recall': 0.9182915506035283, 'f1': 0.9040219378427787, 'number': 1077} | 0.8743            | 0.8912         | 0.8827     | 0.8242           |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

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