---
library_name: transformers
base_model: microsoft/layoutlm-large-uncased
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: Layoutlmlargetest
  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. -->

# Layoutlmlargetest

This model is a fine-tuned version of [microsoft/layoutlm-large-uncased](https://huggingface.co/microsoft/layoutlm-large-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8743
- F1: 0.7462
- Recall: 0.7244
- Precision: 0.7693
- Pred Bestellnummer: 147
- Percentage Pred Act Bestellnummer: 1.0280
- Pred Kundennr.: 56
- Percentage Pred Act Kundennr.: 1.1667
- Pred Bezug 1: 26
- Percentage Pred Act Bezug 1: 1.8571
- Pred Modell 1: 96
- Percentage Pred Act Modell 1: 0.9697
- Pred Menge1: 25
- Percentage Pred Act Menge1: 1.1905
- Pred Menge4: 13
- Percentage Pred Act Menge4: 1.3
- Pred Möbelhaus: 94
- Percentage Pred Act Möbelhaus: 1.0330
- Pred Termin kundenwunsch - kw: 28
- Percentage Pred Act Termin kundenwunsch - kw: 0.875
- Pred Kommission: 57
- Percentage Pred Act Kommission: 0.9828
- Pred Holz 1: 22
- Percentage Pred Act Holz 1: 1.1579
- Pred Modell 2: 64
- Percentage Pred Act Modell 2: 1.0323
- Pred Zusatz 1: 14
- Percentage Pred Act Zusatz 1: 1.0
- Pred La-anschrift: 6
- Percentage Pred Act La-anschrift: 1.0
- Pred Bezug 2: 2
- Percentage Pred Act Bezug 2: 0.1538
- Pred Holz 2: 25
- Percentage Pred Act Holz 2: 1.1905
- Pred Menge3: 30
- Percentage Pred Act Menge3: 1.3636
- Pred Modell 3: 77
- Percentage Pred Act Modell 3: 1.1667
- Pred Bezug 4: 1
- Percentage Pred Act Bezug 4: 0.1429
- Pred Menge2: 9
- Percentage Pred Act Menge2: 0.5
- Pred Var-ausf 1: 8
- Percentage Pred Act Var-ausf 1: 1.0
- Pred Bezug 3: 9
- Percentage Pred Act Bezug 3: 2.25
- Act Bestellnummer: 143
- Act Kundennr.: 48
- Act Bezug 1: 14
- Act Modell 1: 99
- Act Menge1: 21
- Act Menge4: 10
- Act Möbelhaus: 91
- Act Bezug 2: 13
- Act Zusatz 2: 1
- Act Termin kundenwunsch - kw: 32
- Act Kommission: 58
- Act Holz 1: 19
- Act Menge3: 22
- Act Modell 2: 62
- Act Modell 3: 66
- Act Modell 4: 6
- Act Bezug 4: 7
- Act Zusatz 3: 1
- Act Holz 2: 21
- Act Menge2: 18
- Act Bezug 3: 4
- Act Var-ausf 1: 8
- Act Holz 3: 5
- Act Zusatz 1: 14
- Act Var-ausf. 2: 7
- Act Var-ausf. 3: 4
- Act Pv 3: 1
- Act Holz 4: 1
- Act Var-ausf. 5: 1
- Act Modell 5: 5
- Act La-anschrift: 6
- Act Menge5: 1

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.53.0.dev0
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1