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--- |
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library_name: transformers |
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base_model: microsoft/layoutlm-large-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: Layoutlmlargetest |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Layoutlmlargetest |
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This model is a fine-tuned version of [microsoft/layoutlm-large-uncased](https://huggingface.co/microsoft/layoutlm-large-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8743 |
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- F1: 0.7462 |
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- Recall: 0.7244 |
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- Precision: 0.7693 |
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- Pred Bestellnummer: 147 |
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- Percentage Pred Act Bestellnummer: 1.0280 |
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- Pred Kundennr.: 56 |
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- Percentage Pred Act Kundennr.: 1.1667 |
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- Pred Bezug 1: 26 |
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- Percentage Pred Act Bezug 1: 1.8571 |
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- Pred Modell 1: 96 |
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- Percentage Pred Act Modell 1: 0.9697 |
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- Pred Menge1: 25 |
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- Percentage Pred Act Menge1: 1.1905 |
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- Pred Menge4: 13 |
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- Percentage Pred Act Menge4: 1.3 |
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- Pred Möbelhaus: 94 |
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- Percentage Pred Act Möbelhaus: 1.0330 |
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- Pred Termin kundenwunsch - kw: 28 |
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- Percentage Pred Act Termin kundenwunsch - kw: 0.875 |
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- Pred Kommission: 57 |
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- Percentage Pred Act Kommission: 0.9828 |
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- Pred Holz 1: 22 |
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- Percentage Pred Act Holz 1: 1.1579 |
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- Pred Modell 2: 64 |
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- Percentage Pred Act Modell 2: 1.0323 |
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- Pred Zusatz 1: 14 |
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- Percentage Pred Act Zusatz 1: 1.0 |
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- Pred La-anschrift: 6 |
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- Percentage Pred Act La-anschrift: 1.0 |
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- Pred Bezug 2: 2 |
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- Percentage Pred Act Bezug 2: 0.1538 |
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- Pred Holz 2: 25 |
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- Percentage Pred Act Holz 2: 1.1905 |
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- Pred Menge3: 30 |
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- Percentage Pred Act Menge3: 1.3636 |
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- Pred Modell 3: 77 |
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- Percentage Pred Act Modell 3: 1.1667 |
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- Pred Bezug 4: 1 |
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- Percentage Pred Act Bezug 4: 0.1429 |
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- Pred Menge2: 9 |
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- Percentage Pred Act Menge2: 0.5 |
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- Pred Var-ausf 1: 8 |
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- Percentage Pred Act Var-ausf 1: 1.0 |
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- Pred Bezug 3: 9 |
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- Percentage Pred Act Bezug 3: 2.25 |
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- Act Bestellnummer: 143 |
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- Act Kundennr.: 48 |
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- Act Bezug 1: 14 |
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- Act Modell 1: 99 |
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- Act Menge1: 21 |
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- Act Menge4: 10 |
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- Act Möbelhaus: 91 |
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- Act Bezug 2: 13 |
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- Act Zusatz 2: 1 |
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- Act Termin kundenwunsch - kw: 32 |
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- Act Kommission: 58 |
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- Act Holz 1: 19 |
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- Act Menge3: 22 |
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- Act Modell 2: 62 |
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- Act Modell 3: 66 |
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- Act Modell 4: 6 |
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- Act Bezug 4: 7 |
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- Act Zusatz 3: 1 |
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- Act Holz 2: 21 |
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- Act Menge2: 18 |
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- Act Bezug 3: 4 |
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- Act Var-ausf 1: 8 |
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- Act Holz 3: 5 |
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- Act Zusatz 1: 14 |
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- Act Var-ausf. 2: 7 |
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- Act Var-ausf. 3: 4 |
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- Act Pv 3: 1 |
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- Act Holz 4: 1 |
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- Act Var-ausf. 5: 1 |
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- Act Modell 5: 5 |
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- Act La-anschrift: 6 |
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- Act Menge5: 1 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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### Framework versions |
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- Transformers 4.53.0.dev0 |
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- Pytorch 2.7.0+cu126 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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