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_97_1
+  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_97_1
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8446
+- Precision: 0.5939
+- Recall: 0.8376
+- F1: 0.6950
+- Accuracy: 0.8952
+
+## 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: 2000
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 2.44  | 100  | 0.4463          | 0.4830    | 0.7265 | 0.5802 | 0.8599   |
+| No log        | 4.88  | 200  | 0.4064          | 0.5924    | 0.7949 | 0.6788 | 0.8884   |
+| No log        | 7.32  | 300  | 0.4774          | 0.5813    | 0.7949 | 0.6715 | 0.8907   |
+| No log        | 9.76  | 400  | 0.5800          | 0.6013    | 0.7863 | 0.6815 | 0.8907   |
+| 0.2076        | 12.2  | 500  | 0.6426          | 0.6209    | 0.8120 | 0.7037 | 0.8952   |
+| 0.2076        | 14.63 | 600  | 0.6872          | 0.5939    | 0.8376 | 0.6950 | 0.8907   |
+| 0.2076        | 17.07 | 700  | 0.7801          | 0.5915    | 0.8291 | 0.6904 | 0.8918   |
+| 0.2076        | 19.51 | 800  | 0.7865          | 0.5890    | 0.8205 | 0.6857 | 0.8895   |
+| 0.2076        | 21.95 | 900  | 0.8533          | 0.5854    | 0.8205 | 0.6833 | 0.8895   |
+| 0.0109        | 24.39 | 1000 | 0.7738          | 0.5864    | 0.8120 | 0.6810 | 0.8941   |
+| 0.0109        | 26.83 | 1100 | 0.8297          | 0.5854    | 0.8205 | 0.6833 | 0.8872   |
+| 0.0109        | 29.27 | 1200 | 0.7690          | 0.6062    | 0.8291 | 0.7004 | 0.8975   |
+| 0.0109        | 31.71 | 1300 | 0.8629          | 0.5904    | 0.8376 | 0.6926 | 0.8895   |
+| 0.0109        | 34.15 | 1400 | 0.8104          | 0.5976    | 0.8376 | 0.6975 | 0.8941   |
+| 0.0027        | 36.59 | 1500 | 0.7864          | 0.5926    | 0.8205 | 0.6882 | 0.8929   |
+| 0.0027        | 39.02 | 1600 | 0.8002          | 0.6037    | 0.8462 | 0.7046 | 0.8986   |
+| 0.0027        | 41.46 | 1700 | 0.8049          | 0.5964    | 0.8462 | 0.6996 | 0.8964   |
+| 0.0027        | 43.9  | 1800 | 0.8355          | 0.5939    | 0.8376 | 0.6950 | 0.8952   |
+| 0.0027        | 46.34 | 1900 | 0.8402          | 0.5939    | 0.8376 | 0.6950 | 0.8952   |
+| 0.001         | 48.78 | 2000 | 0.8446          | 0.5939    | 0.8376 | 0.6950 | 0.8952   |
+
+
+### Framework versions
+
+- Transformers 4.29.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.4
+- Tokenizers 0.13.3

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