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--- |
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license: mit |
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base_model: SCUT-DLVCLab/lilt-roberta-en-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- funsd-layoutlmv3 |
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model-index: |
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- name: lilt-en-funsd |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# lilt-en-funsd |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5526 |
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- Answer: {'precision': 0.8434684684684685, 'recall': 0.9167686658506732, 'f1': 0.8785923753665689, 'number': 817} |
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- Header: {'precision': 0.6494845360824743, 'recall': 0.5294117647058824, 'f1': 0.5833333333333334, 'number': 119} |
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- Question: {'precision': 0.8929219600725953, 'recall': 0.9136490250696379, 'f1': 0.9031665901789812, 'number': 1077} |
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- Overall Precision: 0.8606 |
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- Overall Recall: 0.8922 |
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- Overall F1: 0.8761 |
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- Overall Accuracy: 0.7988 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 2500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.1122 | 10.53 | 200 | 0.3663 | {'precision': 0.7907488986784141, 'recall': 0.8788249694002448, 'f1': 0.832463768115942, 'number': 817} | {'precision': 0.4888888888888889, 'recall': 0.5546218487394958, 'f1': 0.5196850393700787, 'number': 119} | {'precision': 0.8885630498533724, 'recall': 0.8440111420612814, 'f1': 0.8657142857142858, 'number': 1077} | 0.8195 | 0.8410 | 0.8301 | 0.7813 | |
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| 0.0121 | 21.05 | 400 | 0.3856 | {'precision': 0.8256880733944955, 'recall': 0.8812729498164015, 'f1': 0.8525754884547069, 'number': 817} | {'precision': 0.6021505376344086, 'recall': 0.47058823529411764, 'f1': 0.5283018867924528, 'number': 119} | {'precision': 0.8739946380697051, 'recall': 0.9080779944289693, 'f1': 0.8907103825136613, 'number': 1077} | 0.8417 | 0.8713 | 0.8562 | 0.8060 | |
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| 0.0038 | 31.58 | 600 | 0.4187 | {'precision': 0.8352534562211982, 'recall': 0.8873929008567931, 'f1': 0.8605341246290802, 'number': 817} | {'precision': 0.59, 'recall': 0.4957983193277311, 'f1': 0.5388127853881278, 'number': 119} | {'precision': 0.8956197576887233, 'recall': 0.8922934076137419, 'f1': 0.8939534883720931, 'number': 1077} | 0.8550 | 0.8669 | 0.8609 | 0.8082 | |
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| 0.0018 | 42.11 | 800 | 0.4984 | {'precision': 0.8299319727891157, 'recall': 0.8959608323133414, 'f1': 0.8616833431430253, 'number': 817} | {'precision': 0.6111111111111112, 'recall': 0.46218487394957986, 'f1': 0.5263157894736842, 'number': 119} | {'precision': 0.8860055607043559, 'recall': 0.8876508820798514, 'f1': 0.8868274582560296, 'number': 1077} | 0.8498 | 0.8659 | 0.8578 | 0.7950 | |
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| 0.0015 | 52.63 | 1000 | 0.4974 | {'precision': 0.830316742081448, 'recall': 0.8984088127294981, 'f1': 0.8630217519106408, 'number': 817} | {'precision': 0.6161616161616161, 'recall': 0.5126050420168067, 'f1': 0.5596330275229358, 'number': 119} | {'precision': 0.9015151515151515, 'recall': 0.8839368616527391, 'f1': 0.8926394749179559, 'number': 1077} | 0.8568 | 0.8679 | 0.8623 | 0.7888 | |
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| 0.0012 | 63.16 | 1200 | 0.5089 | {'precision': 0.8566473988439306, 'recall': 0.9069767441860465, 'f1': 0.8810939357907253, 'number': 817} | {'precision': 0.5981308411214953, 'recall': 0.5378151260504201, 'f1': 0.5663716814159291, 'number': 119} | {'precision': 0.9059590316573557, 'recall': 0.903435468895079, 'f1': 0.904695490469549, 'number': 1077} | 0.8690 | 0.8833 | 0.8761 | 0.8067 | |
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| 0.0006 | 73.68 | 1400 | 0.5012 | {'precision': 0.8409090909090909, 'recall': 0.9057527539779682, 'f1': 0.8721272834413673, 'number': 817} | {'precision': 0.6043956043956044, 'recall': 0.46218487394957986, 'f1': 0.5238095238095237, 'number': 119} | {'precision': 0.8840182648401826, 'recall': 0.8987929433611885, 'f1': 0.8913443830570903, 'number': 1077} | 0.8533 | 0.8758 | 0.8644 | 0.7997 | |
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| 0.0003 | 84.21 | 1600 | 0.5316 | {'precision': 0.8506944444444444, 'recall': 0.8996328029375765, 'f1': 0.874479476502082, 'number': 817} | {'precision': 0.5775862068965517, 'recall': 0.5630252100840336, 'f1': 0.5702127659574467, 'number': 119} | {'precision': 0.8932481751824818, 'recall': 0.9090064995357474, 'f1': 0.9010584445467096, 'number': 1077} | 0.8579 | 0.8847 | 0.8711 | 0.8003 | |
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| 0.0002 | 94.74 | 1800 | 0.5347 | {'precision': 0.8617401668653158, 'recall': 0.8849449204406364, 'f1': 0.8731884057971013, 'number': 817} | {'precision': 0.5317460317460317, 'recall': 0.5630252100840336, 'f1': 0.5469387755102041, 'number': 119} | {'precision': 0.8999081726354453, 'recall': 0.9099350046425255, 'f1': 0.9048938134810711, 'number': 1077} | 0.8617 | 0.8793 | 0.8704 | 0.7979 | |
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| 0.0002 | 105.26 | 2000 | 0.5526 | {'precision': 0.8434684684684685, 'recall': 0.9167686658506732, 'f1': 0.8785923753665689, 'number': 817} | {'precision': 0.6494845360824743, 'recall': 0.5294117647058824, 'f1': 0.5833333333333334, 'number': 119} | {'precision': 0.8929219600725953, 'recall': 0.9136490250696379, 'f1': 0.9031665901789812, 'number': 1077} | 0.8606 | 0.8922 | 0.8761 | 0.7988 | |
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| 0.0001 | 115.79 | 2200 | 0.5488 | {'precision': 0.8368953880764904, 'recall': 0.9106487148102815, 'f1': 0.8722157092614302, 'number': 817} | {'precision': 0.5833333333333334, 'recall': 0.5294117647058824, 'f1': 0.5550660792951542, 'number': 119} | {'precision': 0.8835740072202166, 'recall': 0.9090064995357474, 'f1': 0.8961098398169337, 'number': 1077} | 0.8485 | 0.8872 | 0.8674 | 0.8017 | |
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| 0.0001 | 126.32 | 2400 | 0.5510 | {'precision': 0.8372615039281706, 'recall': 0.9130966952264382, 'f1': 0.8735362997658079, 'number': 817} | {'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119} | {'precision': 0.895264116575592, 'recall': 0.9127205199628597, 'f1': 0.9039080459770115, 'number': 1077} | 0.8559 | 0.8912 | 0.8732 | 0.8024 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0 |
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- Datasets 2.9.0 |
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- Tokenizers 0.14.1 |
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