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: 1.6122
+- Answer: {'precision': 0.8905882352941177, 'recall': 0.9265605875152999, 'f1': 0.9082183563287344, 'number': 817}
+- Header: {'precision': 0.6379310344827587, 'recall': 0.6218487394957983, 'f1': 0.6297872340425532, 'number': 119}
+- Question: {'precision': 0.8964252978918423, 'recall': 0.9080779944289693, 'f1': 0.9022140221402215, 'number': 1077}
+- Overall Precision: 0.8794
+- Overall Recall: 0.8987
+- Overall F1: 0.8889
+- Overall Accuracy: 0.8020
+
+## 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
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                   | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4109        | 10.53  | 200  | 0.9316          | {'precision': 0.8169491525423729, 'recall': 0.8849449204406364, 'f1': 0.8495887191539365, 'number': 817} | {'precision': 0.5258620689655172, 'recall': 0.5126050420168067, 'f1': 0.5191489361702126, 'number': 119} | {'precision': 0.8695652173913043, 'recall': 0.8913649025069638, 'f1': 0.8803301237964236, 'number': 1077} | 0.8285            | 0.8664         | 0.8470     | 0.7998           |
+| 0.0432        | 21.05  | 400  | 1.6192          | {'precision': 0.8022099447513812, 'recall': 0.8886168910648715, 'f1': 0.843205574912892, 'number': 817}  | {'precision': 0.5585585585585585, 'recall': 0.5210084033613446, 'f1': 0.5391304347826087, 'number': 119} | {'precision': 0.9002932551319648, 'recall': 0.8551532033426184, 'f1': 0.8771428571428571, 'number': 1077} | 0.8382            | 0.8490         | 0.8435     | 0.7705           |
+| 0.0145        | 31.58  | 600  | 1.4845          | {'precision': 0.8556701030927835, 'recall': 0.9143206854345165, 'f1': 0.8840236686390532, 'number': 817} | {'precision': 0.6274509803921569, 'recall': 0.5378151260504201, 'f1': 0.579185520361991, 'number': 119}  | {'precision': 0.8772241992882562, 'recall': 0.9155060352831941, 'f1': 0.8959563834620626, 'number': 1077} | 0.8561            | 0.8927         | 0.8740     | 0.7985           |
+| 0.0063        | 42.11  | 800  | 1.4909          | {'precision': 0.8667452830188679, 'recall': 0.8996328029375765, 'f1': 0.8828828828828829, 'number': 817} | {'precision': 0.5766423357664233, 'recall': 0.6638655462184874, 'f1': 0.6171874999999999, 'number': 119} | {'precision': 0.891444342226311, 'recall': 0.8997214484679665, 'f1': 0.8955637707948244, 'number': 1077}  | 0.8605            | 0.8857         | 0.8729     | 0.8117           |
+| 0.0041        | 52.63  | 1000 | 1.6197          | {'precision': 0.834056399132321, 'recall': 0.9412484700122399, 'f1': 0.8844163312248418, 'number': 817}  | {'precision': 0.5945945945945946, 'recall': 0.5546218487394958, 'f1': 0.5739130434782609, 'number': 119} | {'precision': 0.8972407231208372, 'recall': 0.8755803156917363, 'f1': 0.8862781954887218, 'number': 1077} | 0.8532            | 0.8833         | 0.8680     | 0.7912           |
+| 0.0043        | 63.16  | 1200 | 1.6797          | {'precision': 0.8763005780346821, 'recall': 0.9277845777233782, 'f1': 0.901307966706302, 'number': 817}  | {'precision': 0.6134453781512605, 'recall': 0.6134453781512605, 'f1': 0.6134453781512605, 'number': 119} | {'precision': 0.8998144712430427, 'recall': 0.9006499535747446, 'f1': 0.9002320185614848, 'number': 1077} | 0.8734            | 0.8947         | 0.8839     | 0.8046           |
+| 0.0015        | 73.68  | 1400 | 1.5586          | {'precision': 0.8683314415437003, 'recall': 0.9363525091799265, 'f1': 0.9010600706713782, 'number': 817} | {'precision': 0.6486486486486487, 'recall': 0.6050420168067226, 'f1': 0.6260869565217391, 'number': 119} | {'precision': 0.9055555555555556, 'recall': 0.9080779944289693, 'f1': 0.9068150208623087, 'number': 1077} | 0.8760            | 0.9016         | 0.8886     | 0.8093           |
+| 0.001         | 84.21  | 1600 | 1.5060          | {'precision': 0.8894230769230769, 'recall': 0.9057527539779682, 'f1': 0.8975136446331109, 'number': 817} | {'precision': 0.711340206185567, 'recall': 0.5798319327731093, 'f1': 0.638888888888889, 'number': 119}   | {'precision': 0.8927927927927928, 'recall': 0.9201485608170845, 'f1': 0.9062642889803384, 'number': 1077} | 0.8828            | 0.8942         | 0.8885     | 0.8202           |
+| 0.0004        | 94.74  | 1800 | 1.6122          | {'precision': 0.8905882352941177, 'recall': 0.9265605875152999, 'f1': 0.9082183563287344, 'number': 817} | {'precision': 0.6379310344827587, 'recall': 0.6218487394957983, 'f1': 0.6297872340425532, 'number': 119} | {'precision': 0.8964252978918423, 'recall': 0.9080779944289693, 'f1': 0.9022140221402215, 'number': 1077} | 0.8794            | 0.8987         | 0.8889     | 0.8020           |
+| 0.0004        | 105.26 | 2000 | 1.6057          | {'precision': 0.8791079812206573, 'recall': 0.9167686658506732, 'f1': 0.8975434391851408, 'number': 817} | {'precision': 0.6260869565217392, 'recall': 0.6050420168067226, 'f1': 0.6153846153846154, 'number': 119} | {'precision': 0.8921124206708976, 'recall': 0.9136490250696379, 'f1': 0.9027522935779816, 'number': 1077} | 0.8720            | 0.8967         | 0.8842     | 0.8057           |
+| 0.0004        | 115.79 | 2200 | 1.6179          | {'precision': 0.8848413631022327, 'recall': 0.9216646266829865, 'f1': 0.9028776978417267, 'number': 817} | {'precision': 0.6206896551724138, 'recall': 0.6050420168067226, 'f1': 0.6127659574468085, 'number': 119} | {'precision': 0.8921124206708976, 'recall': 0.9136490250696379, 'f1': 0.9027522935779816, 'number': 1077} | 0.8739            | 0.8987         | 0.8861     | 0.8070           |
+| 0.0002        | 126.32 | 2400 | 1.6142          | {'precision': 0.8826291079812206, 'recall': 0.9204406364749081, 'f1': 0.9011384062312762, 'number': 817} | {'precision': 0.6068376068376068, 'recall': 0.5966386554621849, 'f1': 0.6016949152542374, 'number': 119} | {'precision': 0.8965201465201466, 'recall': 0.9090064995357474, 'f1': 0.9027201475334256, 'number': 1077} | 0.8743            | 0.8952         | 0.8846     | 0.8074           |
+
+
+### Framework versions
+
+- Transformers 4.30.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.13.1
+- Tokenizers 0.13.3

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