End of training

README.md

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+---
+license: cc-by-nc-sa-4.0
+base_model: microsoft/layoutlmv3-base
+tags:
+- generated_from_trainer
+datasets:
+- my_csv_dataset3
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: passive_invoices_v4.7_refined
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: my_csv_dataset3
+      type: my_csv_dataset3
+      config: discharge
+      split: test
+      args: discharge
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8837680590965549
+    - name: Recall
+      type: recall
+      value: 0.9081687491602848
+    - name: F1
+      type: f1
+      value: 0.895802272802571
+    - name: Accuracy
+      type: accuracy
+      value: 0.9791788856304985
+---
+
+<!-- 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. -->
+
+# passive_invoices_v4.7_refined
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the my_csv_dataset3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0915
+- Precision: 0.8838
+- Recall: 0.9082
+- F1: 0.8958
+- Accuracy: 0.9792
+
+## 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: 1e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 16000
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.0499        | 0.27  | 500   | 0.8340          | 0.1650    | 0.0685 | 0.0968 | 0.7864   |
+| 0.6058        | 0.53  | 1000  | 0.5578          | 0.3949    | 0.3288 | 0.3588 | 0.8551   |
+| 0.4061        | 0.8   | 1500  | 0.3891          | 0.5604    | 0.5187 | 0.5388 | 0.8984   |
+| 0.2779        | 1.07  | 2000  | 0.3063          | 0.6178    | 0.6270 | 0.6223 | 0.9156   |
+| 0.2234        | 1.33  | 2500  | 0.2566          | 0.6489    | 0.6511 | 0.6500 | 0.9244   |
+| 0.185         | 1.6   | 3000  | 0.2230          | 0.7019    | 0.7136 | 0.7077 | 0.9381   |
+| 0.1524        | 1.87  | 3500  | 0.2003          | 0.7038    | 0.7484 | 0.7254 | 0.9433   |
+| 0.1249        | 2.14  | 4000  | 0.1652          | 0.7548    | 0.7728 | 0.7637 | 0.9546   |
+| 0.1101        | 2.4   | 4500  | 0.1480          | 0.7760    | 0.7986 | 0.7872 | 0.9589   |
+| 0.1054        | 2.67  | 5000  | 0.1455          | 0.7852    | 0.8163 | 0.8004 | 0.9601   |
+| 0.0846        | 2.94  | 5500  | 0.1413          | 0.7828    | 0.8261 | 0.8039 | 0.9610   |
+| 0.0822        | 3.2   | 6000  | 0.1285          | 0.8133    | 0.8213 | 0.8173 | 0.9649   |
+| 0.0725        | 3.47  | 6500  | 0.1256          | 0.8112    | 0.8444 | 0.8275 | 0.9670   |
+| 0.0653        | 3.74  | 7000  | 0.1210          | 0.8178    | 0.8552 | 0.8361 | 0.9673   |
+| 0.0682        | 4.0   | 7500  | 0.1123          | 0.8347    | 0.8624 | 0.8483 | 0.9703   |
+| 0.0562        | 4.27  | 8000  | 0.1084          | 0.8439    | 0.8635 | 0.8536 | 0.9723   |
+| 0.0553        | 4.54  | 8500  | 0.1098          | 0.8323    | 0.8761 | 0.8536 | 0.9710   |
+| 0.0527        | 4.81  | 9000  | 0.1035          | 0.8408    | 0.8819 | 0.8609 | 0.9732   |
+| 0.0446        | 5.07  | 9500  | 0.1037          | 0.8594    | 0.8839 | 0.8715 | 0.9747   |
+| 0.047         | 5.34  | 10000 | 0.1080          | 0.8631    | 0.8825 | 0.8727 | 0.9731   |
+| 0.0402        | 5.61  | 10500 | 0.0955          | 0.8696    | 0.8871 | 0.8783 | 0.9768   |
+| 0.0428        | 5.87  | 11000 | 0.0948          | 0.8685    | 0.8957 | 0.8819 | 0.9765   |
+| 0.0422        | 6.14  | 11500 | 0.0992          | 0.8724    | 0.8957 | 0.8839 | 0.9762   |
+| 0.0365        | 6.41  | 12000 | 0.0951          | 0.8731    | 0.9032 | 0.8879 | 0.9777   |
+| 0.0351        | 6.67  | 12500 | 0.0930          | 0.8818    | 0.9018 | 0.8917 | 0.9786   |
+| 0.0353        | 6.94  | 13000 | 0.0973          | 0.8654    | 0.9010 | 0.8828 | 0.9765   |
+| 0.0304        | 7.21  | 13500 | 0.0946          | 0.8795    | 0.9053 | 0.8923 | 0.9784   |
+| 0.0324        | 7.47  | 14000 | 0.0954          | 0.8805    | 0.9048 | 0.8925 | 0.9782   |
+| 0.0327        | 7.74  | 14500 | 0.0920          | 0.8825    | 0.9048 | 0.8935 | 0.9786   |
+| 0.0293        | 8.01  | 15000 | 0.0916          | 0.8810    | 0.9068 | 0.8937 | 0.9789   |
+| 0.0259        | 8.28  | 15500 | 0.0921          | 0.8823    | 0.9062 | 0.8941 | 0.9790   |
+| 0.0337        | 8.54  | 16000 | 0.0915          | 0.8838    | 0.9082 | 0.8958 | 0.9792   |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 2.1.0+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2