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_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_5_entities_1
+
+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: 0.2310
+- Precision: 0.82
+- Recall: 0.8119
+- F1: 0.8159
+- Accuracy: 0.9642
+
+## 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-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  | 0.4044          | 0.5       | 0.0198 | 0.0381 | 0.8884   |
+| No log        | 5.13  | 200  | 0.2363          | 0.7571    | 0.5248 | 0.6199 | 0.9328   |
+| No log        | 7.69  | 300  | 0.1817          | 0.7083    | 0.6733 | 0.6904 | 0.9447   |
+| No log        | 10.26 | 400  | 0.1606          | 0.7551    | 0.7327 | 0.7437 | 0.9523   |
+| 0.2439        | 12.82 | 500  | 0.1592          | 0.79      | 0.7822 | 0.7861 | 0.9577   |
+| 0.2439        | 15.38 | 600  | 0.1676          | 0.8144    | 0.7822 | 0.7980 | 0.9621   |
+| 0.2439        | 17.95 | 700  | 0.1912          | 0.7980    | 0.7822 | 0.7900 | 0.9588   |
+| 0.2439        | 20.51 | 800  | 0.1860          | 0.8404    | 0.7822 | 0.8103 | 0.9642   |
+| 0.2439        | 23.08 | 900  | 0.1990          | 0.7767    | 0.7921 | 0.7843 | 0.9567   |
+| 0.0312        | 25.64 | 1000 | 0.2126          | 0.8081    | 0.7921 | 0.8000 | 0.9610   |
+| 0.0312        | 28.21 | 1100 | 0.2105          | 0.8058    | 0.8218 | 0.8137 | 0.9621   |
+| 0.0312        | 30.77 | 1200 | 0.2127          | 0.8119    | 0.8119 | 0.8119 | 0.9632   |
+| 0.0312        | 33.33 | 1300 | 0.2308          | 0.81      | 0.8020 | 0.8060 | 0.9621   |
+| 0.0312        | 35.9  | 1400 | 0.2211          | 0.82      | 0.8119 | 0.8159 | 0.9642   |
+| 0.0126        | 38.46 | 1500 | 0.2244          | 0.82      | 0.8119 | 0.8159 | 0.9642   |
+| 0.0126        | 41.03 | 1600 | 0.2241          | 0.82      | 0.8119 | 0.8159 | 0.9642   |
+| 0.0126        | 43.59 | 1700 | 0.2332          | 0.82      | 0.8119 | 0.8159 | 0.9642   |
+| 0.0126        | 46.15 | 1800 | 0.2345          | 0.82      | 0.8119 | 0.8159 | 0.9632   |
+| 0.0126        | 48.72 | 1900 | 0.2318          | 0.82      | 0.8119 | 0.8159 | 0.9642   |
+| 0.0069        | 51.28 | 2000 | 0.2310          | 0.82      | 0.8119 | 0.8159 | 0.9642   |
+
+
+### Framework versions
+
+- Transformers 4.29.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.4
+- Tokenizers 0.13.3

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