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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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- Document Layout |
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- LayoutLMv3 |
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datasets: |
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- funsd-layoutlmv3 |
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metrics: |
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- f1 |
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- accuracy |
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- recall |
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- precision |
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model-index: |
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- name: layoutlmv3-base-fine_tuned-FUNSD_dataset |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: funsd-layoutlmv3 |
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type: funsd-layoutlmv3 |
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config: funsd |
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split: test |
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args: funsd |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8978890525282278 |
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- name: Recall |
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type: recall |
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value: 0.9085941381023348 |
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- name: F1 |
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type: f1 |
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value: 0.9032098765432099 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8461904195887318 |
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language: |
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- en |
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--- |
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# layoutlmv3-base-fine_tuned-FUNSD_dataset |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co./microsoft/layoutlmv3-base) on the funsd-layoutlmv3 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2956 |
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- Precision: 0.8979 |
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- Recall: 0.9086 |
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- F1: 0.9032 |
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- Accuracy: 0.8462 |
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## Model description |
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For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Document%20AI/Document%20Layout/LayoutLMv3%20with%20FUNSD/Fine%20tuning%20%26%20Evaluation%20-%20LayoutLMv3%20with%20FUNSD.ipynb |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://huggingface.co./datasets/nielsr/funsd-layoutlmv3 |
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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: 2 |
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- eval_batch_size: 2 |
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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: 2000 |
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### Training results |
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| Train Loss | Epoch | Step | Valid. Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.2149 | 1.33 | 100 | 0.2402 | 0.7469 | 0.8212 | 0.7823 | 0.7758 | |
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| 0.1466 | 2.67 | 200 | 0.1869 | 0.8161 | 0.8838 | 0.8486 | 0.8273 | |
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| 0.1122 | 4.0 | 300 | 0.1902 | 0.8538 | 0.8997 | 0.8761 | 0.8316 | |
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| 0.0757 | 5.33 | 400 | 0.1857 | 0.8354 | 0.8927 | 0.8631 | 0.8349 | |
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| 0.0427 | 6.67 | 500 | 0.2091 | 0.8792 | 0.8897 | 0.8844 | 0.8446 | |
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| 0.0495 | 8.0 | 600 | 0.2235 | 0.8825 | 0.9031 | 0.8927 | 0.8370 | |
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| 0.0369 | 9.33 | 700 | 0.2532 | 0.8826 | 0.9146 | 0.8983 | 0.8349 | |
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| 0.0329 | 10.67 | 800 | 0.2576 | 0.8829 | 0.8992 | 0.8910 | 0.8474 | |
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| 0.0229 | 12.0 | 900 | 0.2579 | 0.8827 | 0.8937 | 0.8882 | 0.8443 | |
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| 0.0219 | 13.33 | 1000 | 0.2710 | 0.8710 | 0.8987 | 0.8846 | 0.8347 | |
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| 0.0191 | 14.67 | 1100 | 0.2582 | 0.8889 | 0.9061 | 0.8974 | 0.8454 | |
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| 0.0179 | 16.0 | 1200 | 0.2646 | 0.8870 | 0.9006 | 0.8938 | 0.8356 | |
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| 0.0135 | 17.33 | 1300 | 0.2798 | 0.8949 | 0.9180 | 0.9063 | 0.8512 | |
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| 0.007 | 18.67 | 1400 | 0.2944 | 0.8988 | 0.9091 | 0.9039 | 0.8455 | |
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| 0.0064 | 20.0 | 1500 | 0.2822 | 0.8938 | 0.9071 | 0.9004 | 0.8452 | |
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| 0.0089 | 21.33 | 1600 | 0.3003 | 0.8941 | 0.9101 | 0.9020 | 0.8484 | |
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| 0.0099 | 22.67 | 1700 | 0.3008 | 0.8942 | 0.9071 | 0.9006 | 0.8439 | |
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| 0.0069 | 24.0 | 1800 | 0.2965 | 0.8942 | 0.9071 | 0.9006 | 0.8386 | |
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| 0.0048 | 25.33 | 1900 | 0.2973 | 0.9027 | 0.9076 | 0.9051 | 0.8501 | |
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| 0.0069 | 26.67 | 2000 | 0.2956 | 0.8979 | 0.9086 | 0.9032 | 0.8462 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
