Model save

README.md

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+---
+library_name: transformers
+license: cc-by-nc-sa-4.0
+base_model: microsoft/layoutlmv3-base
+tags:
+- generated_from_trainer
+datasets:
+- doc_lay_net-small
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: layoutlmv3-finetuned-DocLayNet
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: doc_lay_net-small
+      type: doc_lay_net-small
+      config: DocLayNet_2022.08_processed_on_2023.01
+      split: test
+      args: DocLayNet_2022.08_processed_on_2023.01
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.876231416801003
+    - name: Recall
+      type: recall
+      value: 0.876231416801003
+    - name: F1
+      type: f1
+      value: 0.876231416801003
+    - name: Accuracy
+      type: accuracy
+      value: 0.876231416801003
+---
+
+<!-- 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-finetuned-DocLayNet
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the doc_lay_net-small dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4878
+- Precision: 0.8762
+- Recall: 0.8762
+- F1: 0.8762
+- Accuracy: 0.8762
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- training_steps: 1000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.1244        | 2.9070  | 250  | 0.7630          | 0.7337    | 0.7337 | 0.7337 | 0.7337   |
+| 0.2934        | 5.8140  | 500  | 0.4878          | 0.8762    | 0.8762 | 0.8762 | 0.8762   |
+| 0.1028        | 8.7209  | 750  | 0.5626          | 0.8752    | 0.8752 | 0.8752 | 0.8752   |
+| 0.0539        | 11.6279 | 1000 | 0.6090          | 0.8719    | 0.8719 | 0.8719 | 0.8719   |
+
+
+### Framework versions
+
+- Transformers 4.45.2
+- Pytorch 2.5.0+cu124
+- Datasets 3.0.1
+- Tokenizers 0.20.1

model.safetensors

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