End of training

README.md

@@ -0,0 +1,77 @@
+---
+license: mit
+base_model: SCUT-DLVCLab/lilt-roberta-en-base
+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: 0.5526
+- Answer: {'precision': 0.8434684684684685, 'recall': 0.9167686658506732, 'f1': 0.8785923753665689, 'number': 817}
+- Header: {'precision': 0.6494845360824743, 'recall': 0.5294117647058824, 'f1': 0.5833333333333334, 'number': 119}
+- Question: {'precision': 0.8929219600725953, 'recall': 0.9136490250696379, 'f1': 0.9031665901789812, 'number': 1077}
+- Overall Precision: 0.8606
+- Overall Recall: 0.8922
+- Overall F1: 0.8761
+- Overall Accuracy: 0.7988
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.1122        | 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           |
+| 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           |
+| 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           |
+| 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           |
+| 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           |
+| 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           |
+| 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           |
+| 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           |
+| 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           |
+| 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           |
+| 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           |
+| 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           |
+
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch 2.1.0
+- Datasets 2.9.0
+- Tokenizers 0.14.1

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preprocessor_config.json

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