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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: microsoft/layoutlmv3-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: layoutLMv3-finetuned-confluence |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# layoutLMv3-finetuned-confluence |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1354 |
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- Precision: 0.8992 |
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- Recall: 0.9126 |
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- F1: 0.9058 |
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- Accuracy: 0.8578 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 5 |
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- eval_batch_size: 5 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 2500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 8.33 | 250 | 0.9563 | 0.8807 | 0.9056 | 0.8930 | 0.8505 | |
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| 0.0199 | 16.67 | 500 | 1.0827 | 0.8792 | 0.9041 | 0.8915 | 0.8393 | |
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| 0.0199 | 25.0 | 750 | 1.0539 | 0.8834 | 0.9036 | 0.8934 | 0.8493 | |
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| 0.0048 | 33.33 | 1000 | 1.1217 | 0.8944 | 0.9131 | 0.9036 | 0.8583 | |
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| 0.0048 | 41.67 | 1250 | 1.1195 | 0.9004 | 0.9071 | 0.9037 | 0.8616 | |
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| 0.0025 | 50.0 | 1500 | 1.1927 | 0.8923 | 0.9056 | 0.8989 | 0.8467 | |
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| 0.0025 | 58.33 | 1750 | 1.1155 | 0.9017 | 0.9116 | 0.9066 | 0.8640 | |
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| 0.0008 | 66.67 | 2000 | 1.1871 | 0.8971 | 0.9056 | 0.9014 | 0.8395 | |
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| 0.0008 | 75.0 | 2250 | 1.1709 | 0.9007 | 0.9106 | 0.9056 | 0.8420 | |
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| 0.0006 | 83.33 | 2500 | 1.1354 | 0.8992 | 0.9126 | 0.9058 | 0.8578 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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