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: 1.1695
- Answer: {'precision': 0.35993485342019543, 'recall': 0.546353522867738, 'f1': 0.4339715267550319, 'number': 809}
- Header: {'precision': 0.3488372093023256, 'recall': 0.25210084033613445, 'f1': 0.2926829268292683, 'number': 119}
- Question: {'precision': 0.5217762596071733, 'recall': 0.5737089201877934, 'f1': 0.5465116279069767, 'number': 1065}
- Overall Precision: 0.4358
- Overall Recall: 0.5434
- Overall F1: 0.4837
- Overall Accuracy: 0.5907
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 |
|---|---|---|---|---|---|---|---|---|---|---|
| 1.7983 | 1.0 | 10 | 1.5799 | {'precision': 0.02035830618892508, 'recall': 0.030902348578491966, 'f1': 0.024545900834560624, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.19827586206896552, 'recall': 0.1295774647887324, 'f1': 0.15672913117546847, 'number': 1065} | 0.0847 | 0.0818 | 0.0832 | 0.3629 |
| 1.5573 | 2.0 | 20 | 1.4500 | {'precision': 0.11375661375661375, 'recall': 0.2126081582200247, 'f1': 0.14821197759586383, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.21101928374655649, 'recall': 0.3596244131455399, 'f1': 0.2659722222222222, 'number': 1065} | 0.1668 | 0.2785 | 0.2086 | 0.4045 |
| 1.3717 | 3.0 | 30 | 1.2959 | {'precision': 0.16349108789182545, 'recall': 0.3288009888751545, 'f1': 0.21839080459770116, 'number': 809} | {'precision': 0.061224489795918366, 'recall': 0.025210084033613446, 'f1': 0.03571428571428571, 'number': 119} | {'precision': 0.2739408009286129, 'recall': 0.4431924882629108, 'f1': 0.3385939741750359, 'number': 1065} | 0.2180 | 0.3718 | 0.2749 | 0.4466 |
| 1.2568 | 4.0 | 40 | 1.2032 | {'precision': 0.20166898470097358, 'recall': 0.3584672435105068, 'f1': 0.258121940364931, 'number': 809} | {'precision': 0.23809523809523808, 'recall': 0.16806722689075632, 'f1': 0.19704433497536947, 'number': 119} | {'precision': 0.3354214123006834, 'recall': 0.5530516431924882, 'f1': 0.4175824175824176, 'number': 1065} | 0.2743 | 0.4511 | 0.3411 | 0.4942 |
| 1.1591 | 5.0 | 50 | 1.1897 | {'precision': 0.23584277148567623, 'recall': 0.43757725587144625, 'f1': 0.30649350649350654, 'number': 809} | {'precision': 0.27848101265822783, 'recall': 0.18487394957983194, 'f1': 0.2222222222222222, 'number': 119} | {'precision': 0.36172765446910615, 'recall': 0.5661971830985916, 'f1': 0.44143484626647145, 'number': 1065} | 0.3015 | 0.4912 | 0.3737 | 0.5069 |
| 1.075 | 6.0 | 60 | 1.1092 | {'precision': 0.2832044975404076, 'recall': 0.49814585908529047, 'f1': 0.36111111111111116, 'number': 809} | {'precision': 0.34375, 'recall': 0.18487394957983194, 'f1': 0.24043715846994534, 'number': 119} | {'precision': 0.4609571788413098, 'recall': 0.5154929577464789, 'f1': 0.48670212765957444, 'number': 1065} | 0.3637 | 0.4887 | 0.4170 | 0.5650 |
| 1.0011 | 7.0 | 70 | 1.0638 | {'precision': 0.30843373493975906, 'recall': 0.4746600741656366, 'f1': 0.3739045764362221, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.19327731092436976, 'f1': 0.24468085106382975, 'number': 119} | {'precision': 0.47068021892103207, 'recall': 0.5652582159624413, 'f1': 0.5136518771331057, 'number': 1065} | 0.3891 | 0.5063 | 0.4400 | 0.5852 |
| 0.9357 | 8.0 | 80 | 1.1937 | {'precision': 0.3132867132867133, 'recall': 0.553770086526576, 'f1': 0.4001786511835641, 'number': 809} | {'precision': 0.2987012987012987, 'recall': 0.19327731092436976, 'f1': 0.23469387755102045, 'number': 119} | {'precision': 0.48117839607201307, 'recall': 0.5521126760563381, 'f1': 0.5142107564494972, 'number': 1065} | 0.3881 | 0.5314 | 0.4485 | 0.5559 |
| 0.8751 | 9.0 | 90 | 1.0553 | {'precision': 0.3413173652694611, 'recall': 0.4932014833127318, 'f1': 0.40343781597573314, 'number': 809} | {'precision': 0.29577464788732394, 'recall': 0.17647058823529413, 'f1': 0.2210526315789474, 'number': 119} | {'precision': 0.47987851176917234, 'recall': 0.5934272300469483, 'f1': 0.5306465155331653, 'number': 1065} | 0.4114 | 0.5278 | 0.4624 | 0.5986 |
| 0.8585 | 10.0 | 100 | 1.1844 | {'precision': 0.33745454545454545, 'recall': 0.5735475896168108, 'f1': 0.4249084249084249, 'number': 809} | {'precision': 0.37333333333333335, 'recall': 0.23529411764705882, 'f1': 0.28865979381443296, 'number': 119} | {'precision': 0.5013333333333333, 'recall': 0.5295774647887324, 'f1': 0.5150684931506849, 'number': 1065} | 0.4101 | 0.5299 | 0.4623 | 0.5706 |
| 0.7894 | 11.0 | 110 | 1.1752 | {'precision': 0.3395311236863379, 'recall': 0.519159456118665, 'f1': 0.41055718475073316, 'number': 809} | {'precision': 0.4, 'recall': 0.23529411764705882, 'f1': 0.29629629629629634, 'number': 119} | {'precision': 0.5020885547201337, 'recall': 0.564319248826291, 'f1': 0.5313881520778072, 'number': 1065} | 0.4189 | 0.5263 | 0.4665 | 0.5781 |
| 0.7809 | 12.0 | 120 | 1.2025 | {'precision': 0.34820031298904536, 'recall': 0.5500618046971569, 'f1': 0.4264494489698131, 'number': 809} | {'precision': 0.37037037037037035, 'recall': 0.25210084033613445, 'f1': 0.3, 'number': 119} | {'precision': 0.5093062605752962, 'recall': 0.5652582159624413, 'f1': 0.5358255451713396, 'number': 1065} | 0.4238 | 0.5404 | 0.4751 | 0.5800 |
| 0.7452 | 13.0 | 130 | 1.1974 | {'precision': 0.3550436854646545, 'recall': 0.5525339925834364, 'f1': 0.4323017408123791, 'number': 809} | {'precision': 0.358974358974359, 'recall': 0.23529411764705882, 'f1': 0.28426395939086296, 'number': 119} | {'precision': 0.5247787610619469, 'recall': 0.5568075117370892, 'f1': 0.5403189066059226, 'number': 1065} | 0.4329 | 0.5359 | 0.4789 | 0.5830 |
| 0.7108 | 14.0 | 140 | 1.1267 | {'precision': 0.373015873015873, 'recall': 0.522867737948084, 'f1': 0.43540916109109623, 'number': 809} | {'precision': 0.3448275862068966, 'recall': 0.25210084033613445, 'f1': 0.2912621359223301, 'number': 119} | {'precision': 0.5193548387096775, 'recall': 0.6046948356807512, 'f1': 0.5587852494577007, 'number': 1065} | 0.4458 | 0.5504 | 0.4926 | 0.6044 |
| 0.702 | 15.0 | 150 | 1.1695 | {'precision': 0.35993485342019543, 'recall': 0.546353522867738, 'f1': 0.4339715267550319, 'number': 809} | {'precision': 0.3488372093023256, 'recall': 0.25210084033613445, 'f1': 0.2926829268292683, 'number': 119} | {'precision': 0.5217762596071733, 'recall': 0.5737089201877934, 'f1': 0.5465116279069767, 'number': 1065} | 0.4358 | 0.5434 | 0.4837 | 0.5907 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2