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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv2-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutkv
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. -->
# layoutkv
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6316
- Answer: {'precision': 0.06269757639620653, 'recall': 0.14709517923362175, 'f1': 0.08792020687107498, 'number': 809}
- Header: {'precision': 0.02142857142857143, 'recall': 0.025210084033613446, 'f1': 0.023166023166023165, 'number': 119}
- Question: {'precision': 0.17976470588235294, 'recall': 0.3586854460093897, 'f1': 0.2394984326018809, 'number': 1065}
- Overall Precision: 0.1211
- Overall Recall: 0.2529
- Overall F1: 0.1637
- Overall Accuracy: 0.3969
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.9011 | 1.0 | 10 | 1.8281 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2901 |
| 1.7212 | 2.0 | 20 | 1.6755 | {'precision': 0.010714285714285714, 'recall': 0.011124845488257108, 'f1': 0.01091570648878108, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.8, 'recall': 0.003755868544600939, 'f1': 0.007476635514018691, 'number': 1065} | 0.0154 | 0.0065 | 0.0092 | 0.3484 |
| 1.6644 | 3.0 | 30 | 1.7453 | {'precision': 0.0030959752321981426, 'recall': 0.0012360939431396785, 'f1': 0.0017667844522968195, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4895833333333333, 'recall': 0.044131455399061034, 'f1': 0.08096468561584841, 'number': 1065} | 0.1143 | 0.0241 | 0.0398 | 0.3130 |
| 1.5949 | 4.0 | 40 | 1.7670 | {'precision': 0.03335250143760782, 'recall': 0.07169344870210136, 'f1': 0.04552590266875981, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.13106796116504854, 'recall': 0.2535211267605634, 'f1': 0.1728, 'number': 1065} | 0.0859 | 0.1646 | 0.1129 | 0.3285 |
| 1.4559 | 5.0 | 50 | 1.5921 | {'precision': 0.05108940646130729, 'recall': 0.08405438813349815, 'f1': 0.06355140186915888, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.17631224764468373, 'recall': 0.2460093896713615, 'f1': 0.20540964327714623, 'number': 1065} | 0.1171 | 0.1656 | 0.1372 | 0.3763 |
| 1.3707 | 6.0 | 60 | 1.6238 | {'precision': 0.049044914816726896, 'recall': 0.11742892459826947, 'f1': 0.06919155134741442, 'number': 809} | {'precision': 0.006622516556291391, 'recall': 0.008403361344537815, 'f1': 0.007407407407407407, 'number': 119} | {'precision': 0.16497339138848574, 'recall': 0.32018779342723, 'f1': 0.21775223499361432, 'number': 1065} | 0.1052 | 0.2193 | 0.1422 | 0.3786 |
| 1.2836 | 7.0 | 70 | 1.5846 | {'precision': 0.058695652173913045, 'recall': 0.1334981458590853, 'f1': 0.08154020385050964, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.15556492411467115, 'recall': 0.3464788732394366, 'f1': 0.2147221414023858, 'number': 1065} | 0.1120 | 0.2393 | 0.1526 | 0.3851 |
| 1.2161 | 8.0 | 80 | 1.6814 | {'precision': 0.06025974025974026, 'recall': 0.1433868974042027, 'f1': 0.08485735186539868, 'number': 809} | {'precision': 0.009433962264150943, 'recall': 0.008403361344537815, 'f1': 0.008888888888888889, 'number': 119} | {'precision': 0.1631912964641886, 'recall': 0.3380281690140845, 'f1': 0.22011617242433507, 'number': 1065} | 0.1126 | 0.2393 | 0.1531 | 0.3730 |
| 1.1499 | 9.0 | 90 | 1.6027 | {'precision': 0.06253521126760564, 'recall': 0.13720642768850433, 'f1': 0.08591331269349846, 'number': 809} | {'precision': 0.011111111111111112, 'recall': 0.008403361344537815, 'f1': 0.009569377990430622, 'number': 119} | {'precision': 0.1547870097005483, 'recall': 0.34460093896713617, 'f1': 0.21362048894062866, 'number': 1065} | 0.1131 | 0.2403 | 0.1538 | 0.3710 |
| 1.1199 | 10.0 | 100 | 1.6616 | {'precision': 0.055350553505535055, 'recall': 0.12978986402966625, 'f1': 0.07760532150776053, 'number': 809} | {'precision': 0.019867549668874173, 'recall': 0.025210084033613446, 'f1': 0.022222222222222223, 'number': 119} | {'precision': 0.16207042851081885, 'recall': 0.3586854460093897, 'f1': 0.22326125073056693, 'number': 1065} | 0.1112 | 0.2459 | 0.1532 | 0.3719 |
| 1.0651 | 11.0 | 110 | 1.6100 | {'precision': 0.06031016657093624, 'recall': 0.12978986402966625, 'f1': 0.08235294117647059, 'number': 809} | {'precision': 0.015151515151515152, 'recall': 0.01680672268907563, 'f1': 0.01593625498007968, 'number': 119} | {'precision': 0.163854351687389, 'recall': 0.3464788732394366, 'f1': 0.22249020198974978, 'number': 1065} | 0.1154 | 0.2388 | 0.1556 | 0.3805 |
| 1.0454 | 12.0 | 120 | 1.5988 | {'precision': 0.0639269406392694, 'recall': 0.138442521631644, 'f1': 0.08746583365872705, 'number': 809} | {'precision': 0.022222222222222223, 'recall': 0.025210084033613446, 'f1': 0.02362204724409449, 'number': 119} | {'precision': 0.17867298578199053, 'recall': 0.3539906103286385, 'f1': 0.23748031496062993, 'number': 1065} | 0.1231 | 0.2469 | 0.1643 | 0.3977 |
| 1.0279 | 13.0 | 130 | 1.6209 | {'precision': 0.06463104325699745, 'recall': 0.15698393077873918, 'f1': 0.09156452775775054, 'number': 809} | {'precision': 0.022388059701492536, 'recall': 0.025210084033613446, 'f1': 0.02371541501976284, 'number': 119} | {'precision': 0.18118811881188118, 'recall': 0.3436619718309859, 'f1': 0.23727714748784443, 'number': 1065} | 0.1204 | 0.2489 | 0.1623 | 0.3998 |
| 1.008 | 14.0 | 140 | 1.6538 | {'precision': 0.0633116883116883, 'recall': 0.1446229913473424, 'f1': 0.08806925103500188, 'number': 809} | {'precision': 0.022556390977443608, 'recall': 0.025210084033613446, 'f1': 0.023809523809523808, 'number': 119} | {'precision': 0.18536350505536833, 'recall': 0.3615023474178404, 'f1': 0.24506683640992996, 'number': 1065} | 0.1244 | 0.2534 | 0.1669 | 0.3965 |
| 0.9812 | 15.0 | 150 | 1.6316 | {'precision': 0.06269757639620653, 'recall': 0.14709517923362175, 'f1': 0.08792020687107498, 'number': 809} | {'precision': 0.02142857142857143, 'recall': 0.025210084033613446, 'f1': 0.023166023166023165, 'number': 119} | {'precision': 0.17976470588235294, 'recall': 0.3586854460093897, 'f1': 0.2394984326018809, 'number': 1065} | 0.1211 | 0.2529 | 0.1637 | 0.3969 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.0+cu118
- Datasets 2.17.1
- Tokenizers 0.13.2
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