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tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-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. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6820
- Answer: {'precision': 0.7084257206208425, 'recall': 0.7898640296662547, 'f1': 0.7469316189362946, 'number': 809}
- Header: {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119}
- Question: {'precision': 0.7870619946091644, 'recall': 0.8225352112676056, 'f1': 0.8044077134986226, 'number': 1065}
- Overall Precision: 0.7194
- Overall Recall: 0.7797
- Overall F1: 0.7484
- Overall Accuracy: 0.8102
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7857 | 1.0 | 10 | 1.5985 | {'precision': 0.009248554913294798, 'recall': 0.009888751545117428, 'f1': 0.00955794504181601, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.1273972602739726, 'recall': 0.08732394366197183, 'f1': 0.10362116991643454, 'number': 1065} | 0.0633 | 0.0507 | 0.0563 | 0.3562 |
| 1.4597 | 2.0 | 20 | 1.2331 | {'precision': 0.18717683557394002, 'recall': 0.22373300370828184, 'f1': 0.20382882882882883, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4439461883408072, 'recall': 0.5577464788732395, 'f1': 0.4943820224719101, 'number': 1065} | 0.3362 | 0.3889 | 0.3606 | 0.6007 |
| 1.0902 | 3.0 | 30 | 0.9489 | {'precision': 0.4371069182389937, 'recall': 0.515451174289246, 'f1': 0.47305728871242203, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.6257615317667538, 'recall': 0.6751173708920187, 'f1': 0.6495031616982836, 'number': 1065} | 0.5311 | 0.5700 | 0.5499 | 0.6910 |
| 0.8339 | 4.0 | 40 | 0.7979 | {'precision': 0.5977366255144033, 'recall': 0.7181705809641533, 'f1': 0.652442448062886, 'number': 809} | {'precision': 0.13513513513513514, 'recall': 0.08403361344537816, 'f1': 0.10362694300518135, 'number': 119} | {'precision': 0.6854545454545454, 'recall': 0.707981220657277, 'f1': 0.6965357967667436, 'number': 1065} | 0.6267 | 0.6749 | 0.6499 | 0.7453 |
| 0.6983 | 5.0 | 50 | 0.7659 | {'precision': 0.6392896781354052, 'recall': 0.7119901112484549, 'f1': 0.6736842105263159, 'number': 809} | {'precision': 0.19626168224299065, 'recall': 0.17647058823529413, 'f1': 0.18584070796460178, 'number': 119} | {'precision': 0.6688102893890675, 'recall': 0.7812206572769953, 'f1': 0.7206582936336077, 'number': 1065} | 0.6345 | 0.7170 | 0.6733 | 0.7610 |
| 0.5815 | 6.0 | 60 | 0.6907 | {'precision': 0.6410256410256411, 'recall': 0.7725587144622992, 'f1': 0.7006726457399104, 'number': 809} | {'precision': 0.23863636363636365, 'recall': 0.17647058823529413, 'f1': 0.20289855072463767, 'number': 119} | {'precision': 0.7027463651050081, 'recall': 0.8169014084507042, 'f1': 0.7555362570560139, 'number': 1065} | 0.6588 | 0.7607 | 0.7061 | 0.7913 |
| 0.5044 | 7.0 | 70 | 0.6802 | {'precision': 0.6727078891257996, 'recall': 0.7799752781211372, 'f1': 0.7223812249570692, 'number': 809} | {'precision': 0.26605504587155965, 'recall': 0.24369747899159663, 'f1': 0.2543859649122807, 'number': 119} | {'precision': 0.7305699481865285, 'recall': 0.7943661971830986, 'f1': 0.7611336032388665, 'number': 1065} | 0.6830 | 0.7556 | 0.7175 | 0.7902 |
| 0.4534 | 8.0 | 80 | 0.6595 | {'precision': 0.7018701870187019, 'recall': 0.788627935723115, 'f1': 0.7427240977881256, 'number': 809} | {'precision': 0.234375, 'recall': 0.25210084033613445, 'f1': 0.242914979757085, 'number': 119} | {'precision': 0.7378559463986599, 'recall': 0.8272300469483568, 'f1': 0.779991146525011, 'number': 1065} | 0.6943 | 0.7772 | 0.7334 | 0.8074 |
| 0.3971 | 9.0 | 90 | 0.6625 | {'precision': 0.6967032967032967, 'recall': 0.7836835599505563, 'f1': 0.7376381617219313, 'number': 809} | {'precision': 0.27007299270072993, 'recall': 0.31092436974789917, 'f1': 0.2890625, 'number': 119} | {'precision': 0.7433930093776641, 'recall': 0.8187793427230047, 'f1': 0.7792672028596961, 'number': 1065} | 0.6950 | 0.7742 | 0.7325 | 0.8060 |
| 0.3593 | 10.0 | 100 | 0.6634 | {'precision': 0.7079152731326644, 'recall': 0.7849196538936959, 'f1': 0.7444314185228605, 'number': 809} | {'precision': 0.2714285714285714, 'recall': 0.31932773109243695, 'f1': 0.29343629343629346, 'number': 119} | {'precision': 0.7571305099394987, 'recall': 0.8225352112676056, 'f1': 0.7884788478847885, 'number': 1065} | 0.7060 | 0.7772 | 0.7399 | 0.8115 |
| 0.3209 | 11.0 | 110 | 0.6655 | {'precision': 0.6973262032085561, 'recall': 0.8059332509270705, 'f1': 0.7477064220183487, 'number': 809} | {'precision': 0.2903225806451613, 'recall': 0.3025210084033613, 'f1': 0.2962962962962963, 'number': 119} | {'precision': 0.7788632326820604, 'recall': 0.8234741784037559, 'f1': 0.8005476951163851, 'number': 1065} | 0.7162 | 0.7852 | 0.7492 | 0.8129 |
| 0.3064 | 12.0 | 120 | 0.6771 | {'precision': 0.7104072398190046, 'recall': 0.7762669962917181, 'f1': 0.74187832250443, 'number': 809} | {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119} | {'precision': 0.7795698924731183, 'recall': 0.8169014084507042, 'f1': 0.797799174690509, 'number': 1065} | 0.7166 | 0.7712 | 0.7429 | 0.8088 |
| 0.286 | 13.0 | 130 | 0.6765 | {'precision': 0.7030905077262694, 'recall': 0.7873918417799752, 'f1': 0.7428571428571429, 'number': 809} | {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119} | {'precision': 0.769298245614035, 'recall': 0.8234741784037559, 'f1': 0.7954648526077097, 'number': 1065} | 0.7088 | 0.7792 | 0.7424 | 0.8111 |
| 0.2806 | 14.0 | 140 | 0.6820 | {'precision': 0.7052980132450332, 'recall': 0.7898640296662547, 'f1': 0.7451895043731779, 'number': 809} | {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119} | {'precision': 0.7793594306049823, 'recall': 0.8225352112676056, 'f1': 0.8003654636820466, 'number': 1065} | 0.7145 | 0.7797 | 0.7457 | 0.8106 |
| 0.2736 | 15.0 | 150 | 0.6820 | {'precision': 0.7084257206208425, 'recall': 0.7898640296662547, 'f1': 0.7469316189362946, 'number': 809} | {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119} | {'precision': 0.7870619946091644, 'recall': 0.8225352112676056, 'f1': 0.8044077134986226, 'number': 1065} | 0.7194 | 0.7797 | 0.7484 | 0.8102 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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