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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/layoutlm-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- funsd |
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model-index: |
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- name: layoutlm-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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# layoutlm-funsd |
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7076 |
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- Answer: {'precision': 0.7112831858407079, 'recall': 0.7948084054388134, 'f1': 0.7507297139521306, 'number': 809} |
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- Header: {'precision': 0.2932330827067669, 'recall': 0.3277310924369748, 'f1': 0.30952380952380953, 'number': 119} |
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- Question: {'precision': 0.768303186907838, 'recall': 0.8375586854460094, 'f1': 0.8014375561545374, 'number': 1065} |
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- Overall Precision: 0.7161 |
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- Overall Recall: 0.7898 |
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- Overall F1: 0.7511 |
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- Overall Accuracy: 0.8076 |
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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: 3e-05 |
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- train_batch_size: 16 |
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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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- num_epochs: 15 |
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- mixed_precision_training: Native AMP |
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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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| 1.8402 | 1.0 | 10 | 1.5987 | {'precision': 0.021546261089987327, 'recall': 0.021013597033374538, 'f1': 0.02127659574468085, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.18303571428571427, 'recall': 0.11549295774647887, 'f1': 0.14162348877374784, 'number': 1065} | 0.0958 | 0.0702 | 0.0811 | 0.3696 | |
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| 1.4639 | 2.0 | 20 | 1.2465 | {'precision': 0.11695906432748537, 'recall': 0.09888751545117429, 'f1': 0.10716677829872738, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4292786421499293, 'recall': 0.5699530516431925, 'f1': 0.48971359419120614, 'number': 1065} | 0.3275 | 0.3447 | 0.3359 | 0.5870 | |
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| 1.1099 | 3.0 | 30 | 0.9764 | {'precision': 0.4803921568627451, 'recall': 0.5451174289245982, 'f1': 0.5107122177185871, 'number': 809} | {'precision': 0.1282051282051282, 'recall': 0.04201680672268908, 'f1': 0.06329113924050632, 'number': 119} | {'precision': 0.5776255707762558, 'recall': 0.7126760563380282, 'f1': 0.6380832282471627, 'number': 1065} | 0.5306 | 0.6046 | 0.5652 | 0.7145 | |
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| 0.8446 | 4.0 | 40 | 0.7999 | {'precision': 0.6241900647948164, 'recall': 0.7144622991347342, 'f1': 0.6662824207492796, 'number': 809} | {'precision': 0.23880597014925373, 'recall': 0.13445378151260504, 'f1': 0.17204301075268816, 'number': 119} | {'precision': 0.629976580796253, 'recall': 0.7577464788732394, 'f1': 0.6879795396419438, 'number': 1065} | 0.6161 | 0.7030 | 0.6567 | 0.7649 | |
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| 0.6679 | 5.0 | 50 | 0.7382 | {'precision': 0.6525515743756786, 'recall': 0.7428924598269468, 'f1': 0.6947976878612716, 'number': 809} | {'precision': 0.2891566265060241, 'recall': 0.20168067226890757, 'f1': 0.23762376237623764, 'number': 119} | {'precision': 0.6767441860465117, 'recall': 0.819718309859155, 'f1': 0.7414012738853504, 'number': 1065} | 0.6530 | 0.7516 | 0.6989 | 0.7784 | |
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| 0.5711 | 6.0 | 60 | 0.7029 | {'precision': 0.6705882352941176, 'recall': 0.7750309023485785, 'f1': 0.7190366972477065, 'number': 809} | {'precision': 0.3150684931506849, 'recall': 0.19327731092436976, 'f1': 0.23958333333333334, 'number': 119} | {'precision': 0.7184873949579832, 'recall': 0.8028169014084507, 'f1': 0.7583148558758316, 'number': 1065} | 0.6847 | 0.7551 | 0.7182 | 0.7926 | |
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| 0.5019 | 7.0 | 70 | 0.6781 | {'precision': 0.6785714285714286, 'recall': 0.7750309023485785, 'f1': 0.7236006924408541, 'number': 809} | {'precision': 0.3263157894736842, 'recall': 0.2605042016806723, 'f1': 0.2897196261682243, 'number': 119} | {'precision': 0.7317686504610227, 'recall': 0.819718309859155, 'f1': 0.7732506643046944, 'number': 1065} | 0.6921 | 0.7682 | 0.7282 | 0.7984 | |
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| 0.4481 | 8.0 | 80 | 0.6777 | {'precision': 0.678609062170706, 'recall': 0.796044499381953, 'f1': 0.732650739476678, 'number': 809} | {'precision': 0.29523809523809524, 'recall': 0.2605042016806723, 'f1': 0.2767857142857143, 'number': 119} | {'precision': 0.7263763352506163, 'recall': 0.8300469483568075, 'f1': 0.7747589833479404, 'number': 1065} | 0.6865 | 0.7822 | 0.7312 | 0.8020 | |
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| 0.3933 | 9.0 | 90 | 0.6705 | {'precision': 0.7040704070407041, 'recall': 0.7911001236093943, 'f1': 0.7450523864959255, 'number': 809} | {'precision': 0.30275229357798167, 'recall': 0.2773109243697479, 'f1': 0.28947368421052627, 'number': 119} | {'precision': 0.7557643040136636, 'recall': 0.8309859154929577, 'f1': 0.7915921288014313, 'number': 1065} | 0.7117 | 0.7817 | 0.7451 | 0.8054 | |
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| 0.387 | 10.0 | 100 | 0.6786 | {'precision': 0.6973969631236443, 'recall': 0.7948084054388134, 'f1': 0.7429231658001155, 'number': 809} | {'precision': 0.26717557251908397, 'recall': 0.29411764705882354, 'f1': 0.28, 'number': 119} | {'precision': 0.7676240208877284, 'recall': 0.828169014084507, 'f1': 0.7967479674796749, 'number': 1065} | 0.7084 | 0.7827 | 0.7437 | 0.8098 | |
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| 0.3298 | 11.0 | 110 | 0.6932 | {'precision': 0.6968716289104638, 'recall': 0.7985166872682324, 'f1': 0.7442396313364056, 'number': 809} | {'precision': 0.2824427480916031, 'recall': 0.31092436974789917, 'f1': 0.29600000000000004, 'number': 119} | {'precision': 0.7559121621621622, 'recall': 0.8403755868544601, 'f1': 0.7959092930191196, 'number': 1065} | 0.7038 | 0.7918 | 0.7452 | 0.8026 | |
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| 0.3049 | 12.0 | 120 | 0.6984 | {'precision': 0.7032115171650055, 'recall': 0.7849196538936959, 'f1': 0.7418224299065419, 'number': 809} | {'precision': 0.30833333333333335, 'recall': 0.31092436974789917, 'f1': 0.3096234309623431, 'number': 119} | {'precision': 0.7710320901994796, 'recall': 0.8347417840375587, 'f1': 0.8016230838593327, 'number': 1065} | 0.7174 | 0.7832 | 0.7489 | 0.8083 | |
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| 0.293 | 13.0 | 130 | 0.7063 | {'precision': 0.706140350877193, 'recall': 0.796044499381953, 'f1': 0.7484020918070889, 'number': 809} | {'precision': 0.2932330827067669, 'recall': 0.3277310924369748, 'f1': 0.30952380952380953, 'number': 119} | {'precision': 0.7664359861591695, 'recall': 0.831924882629108, 'f1': 0.7978388113462405, 'number': 1065} | 0.7129 | 0.7873 | 0.7482 | 0.8041 | |
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| 0.2732 | 14.0 | 140 | 0.7057 | {'precision': 0.7161862527716186, 'recall': 0.7985166872682324, 'f1': 0.7551139684395091, 'number': 809} | {'precision': 0.2932330827067669, 'recall': 0.3277310924369748, 'f1': 0.30952380952380953, 'number': 119} | {'precision': 0.7714285714285715, 'recall': 0.8366197183098592, 'f1': 0.8027027027027026, 'number': 1065} | 0.7196 | 0.7908 | 0.7535 | 0.8080 | |
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| 0.2762 | 15.0 | 150 | 0.7076 | {'precision': 0.7112831858407079, 'recall': 0.7948084054388134, 'f1': 0.7507297139521306, 'number': 809} | {'precision': 0.2932330827067669, 'recall': 0.3277310924369748, 'f1': 0.30952380952380953, 'number': 119} | {'precision': 0.768303186907838, 'recall': 0.8375586854460094, 'f1': 0.8014375561545374, 'number': 1065} | 0.7161 | 0.7898 | 0.7511 | 0.8076 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 3.0.0 |
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- Tokenizers 0.19.1 |
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