End of training

README.md

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+---
+license: cc-by-nc-sa-4.0
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: LayoutLMv3_5_entities_4
+  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. -->
+
+# LayoutLMv3_5_entities_4
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2514
+- Precision: 0.8762
+- Recall: 0.8519
+- F1: 0.8638
+- Accuracy: 0.9739
+
+## 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-06
+- 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: 2000
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 2.56  | 100  | 0.2241          | 0.8571    | 0.8333 | 0.8451 | 0.9691   |
+| No log        | 5.13  | 200  | 0.2210          | 0.8952    | 0.8704 | 0.8826 | 0.9758   |
+| No log        | 7.69  | 300  | 0.2300          | 0.9029    | 0.8611 | 0.8815 | 0.9758   |
+| No log        | 10.26 | 400  | 0.2630          | 0.8922    | 0.8426 | 0.8667 | 0.9720   |
+| 0.0021        | 12.82 | 500  | 0.2692          | 0.8980    | 0.8148 | 0.8544 | 0.9710   |
+| 0.0021        | 15.38 | 600  | 0.2414          | 0.9       | 0.8333 | 0.8654 | 0.9729   |
+| 0.0021        | 17.95 | 700  | 0.2617          | 0.875     | 0.8426 | 0.8585 | 0.9729   |
+| 0.0021        | 20.51 | 800  | 0.2558          | 0.8713    | 0.8148 | 0.8421 | 0.9720   |
+| 0.0021        | 23.08 | 900  | 0.2581          | 0.8725    | 0.8241 | 0.8476 | 0.9729   |
+| 0.0006        | 25.64 | 1000 | 0.2574          | 0.8679    | 0.8519 | 0.8598 | 0.9739   |
+| 0.0006        | 28.21 | 1100 | 0.2806          | 0.88      | 0.8148 | 0.8462 | 0.9710   |
+| 0.0006        | 30.77 | 1200 | 0.3032          | 0.8958    | 0.7963 | 0.8431 | 0.9691   |
+| 0.0006        | 33.33 | 1300 | 0.2627          | 0.8889    | 0.8148 | 0.8502 | 0.9729   |
+| 0.0006        | 35.9  | 1400 | 0.2661          | 0.8980    | 0.8148 | 0.8544 | 0.9720   |
+| 0.0006        | 38.46 | 1500 | 0.2650          | 0.9       | 0.8333 | 0.8654 | 0.9739   |
+| 0.0006        | 41.03 | 1600 | 0.2543          | 0.8835    | 0.8426 | 0.8626 | 0.9729   |
+| 0.0006        | 43.59 | 1700 | 0.2593          | 0.8911    | 0.8333 | 0.8612 | 0.9739   |
+| 0.0006        | 46.15 | 1800 | 0.2494          | 0.8857    | 0.8611 | 0.8732 | 0.9749   |
+| 0.0006        | 48.72 | 1900 | 0.2494          | 0.8857    | 0.8611 | 0.8732 | 0.9749   |
+| 0.0002        | 51.28 | 2000 | 0.2514          | 0.8762    | 0.8519 | 0.8638 | 0.9739   |
+
+
+### Framework versions
+
+- Transformers 4.29.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.4
+- Tokenizers 0.13.3

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