End of training

README.md

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+---
+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-test
+  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: 0.6647646219686163
+    - name: Recall
+      type: recall
+      value: 0.6763425253991292
+    - name: F1
+      type: f1
+      value: 0.6705035971223021
+    - name: Accuracy
+      type: accuracy
+      value: 0.8582839474362278
+---
+
+<!-- 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-test
+
+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: 0.8293
+- Precision: 0.6648
+- Recall: 0.6763
+- F1: 0.6705
+- Accuracy: 0.8583
+
+## 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
+- 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 |
+|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.5039        | 0.3660 | 250  | 1.1856          | 0.1597    | 0.2785 | 0.2030 | 0.5852   |
+| 0.8176        | 0.7321 | 500  | 0.6027          | 0.4143    | 0.5506 | 0.4728 | 0.8651   |
+| 0.5533        | 1.0981 | 750  | 0.6755          | 0.5946    | 0.6266 | 0.6102 | 0.8649   |
+| 0.4021        | 1.4641 | 1000 | 0.6233          | 0.6017    | 0.6646 | 0.6316 | 0.8804   |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1