metadata
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-funsd
results: []
layoutlm-funsd
This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the funsd dataset. It achieves the following results on the evaluation set:
- Loss: 0.6674
- Answer: {'precision': 0.6954595791805094, 'recall': 0.7762669962917181, 'f1': 0.7336448598130841, 'number': 809}
- Header: {'precision': 0.32061068702290074, 'recall': 0.35294117647058826, 'f1': 0.336, 'number': 119}
- Question: {'precision': 0.7581196581196581, 'recall': 0.8328638497652582, 'f1': 0.7937360178970916, 'number': 1065}
- Overall Precision: 0.7064
- Overall Recall: 0.7812
- Overall F1: 0.7420
- Overall Accuracy: 0.8062
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|
| 1.7631 | 1.0 | 10 | 1.5642 | {'precision': 0.020954598370197905, 'recall': 0.022249690976514216, 'f1': 0.02158273381294964, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2592137592137592, 'recall': 0.19812206572769953, 'f1': 0.22458754656732305, 'number': 1065} | 0.1369 | 0.1149 | 0.1249 | 0.3744 |
| 1.4027 | 2.0 | 20 | 1.2058 | {'precision': 0.2994871794871795, 'recall': 0.36093943139678614, 'f1': 0.32735426008968616, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.43074691805656273, 'recall': 0.5577464788732395, 'f1': 0.48608837970540103, 'number': 1065} | 0.3764 | 0.4446 | 0.4076 | 0.6093 |
| 1.0483 | 3.0 | 30 | 0.9188 | {'precision': 0.5052854122621564, 'recall': 0.5908529048207664, 'f1': 0.5447293447293446, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5563218390804597, 'recall': 0.6816901408450704, 'f1': 0.6126582278481012, 'number': 1065} | 0.5325 | 0.6041 | 0.5661 | 0.7192 |
| 0.8029 | 4.0 | 40 | 0.7734 | {'precision': 0.6087408949011447, 'recall': 0.723114956736712, 'f1': 0.6610169491525424, 'number': 809} | {'precision': 0.0967741935483871, 'recall': 0.05042016806722689, 'f1': 0.06629834254143646, 'number': 119} | {'precision': 0.646677471636953, 'recall': 0.7492957746478873, 'f1': 0.6942148760330579, 'number': 1065} | 0.6154 | 0.6969 | 0.6536 | 0.7601 |
| 0.6459 | 5.0 | 50 | 0.7058 | {'precision': 0.6804932735426009, 'recall': 0.7503090234857849, 'f1': 0.713697824808936, 'number': 809} | {'precision': 0.1951219512195122, 'recall': 0.13445378151260504, 'f1': 0.15920398009950248, 'number': 119} | {'precision': 0.697065820777161, 'recall': 0.8253521126760563, 'f1': 0.7558039552880481, 'number': 1065} | 0.6720 | 0.7536 | 0.7105 | 0.7922 |
| 0.5563 | 6.0 | 60 | 0.6929 | {'precision': 0.6706008583690987, 'recall': 0.7725587144622992, 'f1': 0.7179781734635267, 'number': 809} | {'precision': 0.25287356321839083, 'recall': 0.18487394957983194, 'f1': 0.21359223300970878, 'number': 119} | {'precision': 0.730999146029035, 'recall': 0.8037558685446009, 'f1': 0.7656529516994633, 'number': 1065} | 0.6863 | 0.7541 | 0.7186 | 0.7852 |
| 0.4789 | 7.0 | 70 | 0.6587 | {'precision': 0.6681127982646421, 'recall': 0.761433868974042, 'f1': 0.7117273252455227, 'number': 809} | {'precision': 0.24242424242424243, 'recall': 0.20168067226890757, 'f1': 0.2201834862385321, 'number': 119} | {'precision': 0.7401837928153717, 'recall': 0.831924882629108, 'f1': 0.7833775419982316, 'number': 1065} | 0.6880 | 0.7657 | 0.7248 | 0.7977 |
| 0.4306 | 8.0 | 80 | 0.6571 | {'precision': 0.6606765327695561, 'recall': 0.7725587144622992, 'f1': 0.7122507122507123, 'number': 809} | {'precision': 0.25, 'recall': 0.25210084033613445, 'f1': 0.2510460251046025, 'number': 119} | {'precision': 0.7340513670256835, 'recall': 0.831924882629108, 'f1': 0.7799295774647887, 'number': 1065} | 0.6780 | 0.7732 | 0.7225 | 0.8007 |
| 0.3749 | 9.0 | 90 | 0.6571 | {'precision': 0.6827133479212254, 'recall': 0.7713226205191595, 'f1': 0.7243180499129426, 'number': 809} | {'precision': 0.30434782608695654, 'recall': 0.29411764705882354, 'f1': 0.29914529914529914, 'number': 119} | {'precision': 0.7328370554177006, 'recall': 0.831924882629108, 'f1': 0.7792436235708003, 'number': 1065} | 0.6903 | 0.7752 | 0.7303 | 0.8022 |
| 0.3722 | 10.0 | 100 | 0.6581 | {'precision': 0.6774193548387096, 'recall': 0.7787391841779975, 'f1': 0.7245543415756182, 'number': 809} | {'precision': 0.3, 'recall': 0.3025210084033613, 'f1': 0.301255230125523, 'number': 119} | {'precision': 0.7462311557788944, 'recall': 0.8366197183098592, 'f1': 0.7888446215139443, 'number': 1065} | 0.6939 | 0.7812 | 0.7350 | 0.8036 |
| 0.3155 | 11.0 | 110 | 0.6649 | {'precision': 0.6891304347826087, 'recall': 0.7836835599505563, 'f1': 0.733371891266628, 'number': 809} | {'precision': 0.32786885245901637, 'recall': 0.33613445378151263, 'f1': 0.33195020746887965, 'number': 119} | {'precision': 0.7489469250210615, 'recall': 0.8347417840375587, 'f1': 0.7895204262877443, 'number': 1065} | 0.7012 | 0.7842 | 0.7404 | 0.8070 |
| 0.2968 | 12.0 | 120 | 0.6687 | {'precision': 0.6901098901098901, 'recall': 0.7762669962917181, 'f1': 0.7306573589296103, 'number': 809} | {'precision': 0.3106060606060606, 'recall': 0.3445378151260504, 'f1': 0.32669322709163345, 'number': 119} | {'precision': 0.752129471890971, 'recall': 0.8291079812206573, 'f1': 0.7887449754354622, 'number': 1065} | 0.7004 | 0.7787 | 0.7375 | 0.8053 |
| 0.2871 | 13.0 | 130 | 0.6709 | {'precision': 0.6904761904761905, 'recall': 0.788627935723115, 'f1': 0.7362954414310445, 'number': 809} | {'precision': 0.33064516129032256, 'recall': 0.3445378151260504, 'f1': 0.33744855967078186, 'number': 119} | {'precision': 0.7709790209790209, 'recall': 0.828169014084507, 'f1': 0.7985513807152557, 'number': 1065} | 0.7121 | 0.7832 | 0.7460 | 0.8055 |
| 0.2642 | 14.0 | 140 | 0.6679 | {'precision': 0.6991051454138703, 'recall': 0.7725587144622992, 'f1': 0.7339988256018791, 'number': 809} | {'precision': 0.31851851851851853, 'recall': 0.36134453781512604, 'f1': 0.33858267716535434, 'number': 119} | {'precision': 0.7606112054329371, 'recall': 0.8413145539906103, 'f1': 0.7989300044583147, 'number': 1065} | 0.7087 | 0.7847 | 0.7448 | 0.8074 |
| 0.2651 | 15.0 | 150 | 0.6674 | {'precision': 0.6954595791805094, 'recall': 0.7762669962917181, 'f1': 0.7336448598130841, 'number': 809} | {'precision': 0.32061068702290074, 'recall': 0.35294117647058826, 'f1': 0.336, 'number': 119} | {'precision': 0.7581196581196581, 'recall': 0.8328638497652582, 'f1': 0.7937360178970916, 'number': 1065} | 0.7064 | 0.7812 | 0.7420 | 0.8062 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1