File size: 3,246 Bytes
b19e0da 2406ec4 b19e0da 2406ec4 8e66f8c 2406ec4 8e66f8c 2406ec4 8e66f8c 2406ec4 8e66f8c 2406ec4 8e66f8c 2406ec4 b19e0da |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
---
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
|