Model save

README.md

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+---
+license: cc-by-nc-sa-4.0
+base_model: microsoft/layoutlmv3-base
+tags:
+- generated_from_trainer
+datasets:
+- violations_dataset
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: layoutlmv3-violations-test
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: violations_dataset
+      type: violations_dataset
+      config: ViolationsExtraction
+      split: test
+      args: ViolationsExtraction
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9482758620689655
+    - name: Recall
+      type: recall
+      value: 0.9116022099447514
+    - name: F1
+      type: f1
+      value: 0.9295774647887324
+    - name: Accuracy
+      type: accuracy
+      value: 0.9502762430939227
+---
+
+<!-- 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-violations-test
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the violations_dataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3979
+- Precision: 0.9483
+- Recall: 0.9116
+- F1: 0.9296
+- Accuracy: 0.9503
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 1000
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 9.0909  | 100  | 0.2997          | 0.9543    | 0.9227 | 0.9382 | 0.9558   |
+| No log        | 18.1818 | 200  | 0.3729          | 0.9425    | 0.9061 | 0.9239 | 0.9448   |
+| No log        | 27.2727 | 300  | 0.3408          | 0.9543    | 0.9227 | 0.9382 | 0.9558   |
+| No log        | 36.3636 | 400  | 0.3566          | 0.9483    | 0.9116 | 0.9296 | 0.9503   |
+| 0.0997        | 45.4545 | 500  | 0.3685          | 0.9483    | 0.9116 | 0.9296 | 0.9503   |
+| 0.0997        | 54.5455 | 600  | 0.3736          | 0.9483    | 0.9116 | 0.9296 | 0.9503   |
+| 0.0997        | 63.6364 | 700  | 0.3866          | 0.9483    | 0.9116 | 0.9296 | 0.9503   |
+| 0.0997        | 72.7273 | 800  | 0.3990          | 0.9483    | 0.9116 | 0.9296 | 0.9503   |
+| 0.0997        | 81.8182 | 900  | 0.4018          | 0.9483    | 0.9116 | 0.9296 | 0.9503   |
+| 0.001         | 90.9091 | 1000 | 0.3979          | 0.9483    | 0.9116 | 0.9296 | 0.9503   |
+
+
+### Framework versions
+
+- Transformers 4.42.1
+- Pytorch 2.3.1+cu118
+- Datasets 2.20.0
+- Tokenizers 0.19.1

model.safetensors

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