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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-funsd |
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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-funsd |
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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: 1.1951 |
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- Precision: 0.9104 |
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- Recall: 0.9086 |
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- F1: 0.9095 |
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- Accuracy: 0.8530 |
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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: 2000 |
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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 | 3.3333 | 100 | 0.8172 | 0.8957 | 0.9046 | 0.9001 | 0.8418 | |
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| No log | 6.6667 | 200 | 0.8379 | 0.8870 | 0.9160 | 0.9013 | 0.8381 | |
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| No log | 10.0 | 300 | 0.9611 | 0.8887 | 0.9041 | 0.8963 | 0.8328 | |
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| No log | 13.3333 | 400 | 0.9324 | 0.8888 | 0.9091 | 0.8988 | 0.8438 | |
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| 0.0651 | 16.6667 | 500 | 0.9475 | 0.8928 | 0.9185 | 0.9055 | 0.8511 | |
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| 0.0651 | 20.0 | 600 | 1.1234 | 0.8834 | 0.9031 | 0.8931 | 0.8343 | |
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| 0.0651 | 23.3333 | 700 | 1.1130 | 0.8921 | 0.8957 | 0.8939 | 0.8254 | |
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| 0.0651 | 26.6667 | 800 | 1.0760 | 0.8931 | 0.9175 | 0.9052 | 0.8416 | |
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| 0.0651 | 30.0 | 900 | 1.1777 | 0.8894 | 0.9031 | 0.8962 | 0.8336 | |
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| 0.0115 | 33.3333 | 1000 | 1.2102 | 0.9025 | 0.9101 | 0.9063 | 0.8387 | |
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| 0.0115 | 36.6667 | 1100 | 1.1602 | 0.9012 | 0.9111 | 0.9061 | 0.8467 | |
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| 0.0115 | 40.0 | 1200 | 1.1819 | 0.9011 | 0.9101 | 0.9056 | 0.8433 | |
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| 0.0115 | 43.3333 | 1300 | 1.2095 | 0.9051 | 0.9051 | 0.9051 | 0.8452 | |
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| 0.0115 | 46.6667 | 1400 | 1.1687 | 0.9064 | 0.9185 | 0.9124 | 0.8570 | |
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| 0.0031 | 50.0 | 1500 | 1.1951 | 0.9104 | 0.9086 | 0.9095 | 0.8530 | |
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| 0.0031 | 53.3333 | 1600 | 1.1967 | 0.9041 | 0.9131 | 0.9086 | 0.8530 | |
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| 0.0031 | 56.6667 | 1700 | 1.1989 | 0.9015 | 0.9091 | 0.9053 | 0.8531 | |
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| 0.0031 | 60.0 | 1800 | 1.1973 | 0.9000 | 0.9126 | 0.9063 | 0.8549 | |
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| 0.0031 | 63.3333 | 1900 | 1.2135 | 0.9015 | 0.9096 | 0.9055 | 0.8490 | |
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| 0.0012 | 66.6667 | 2000 | 1.2210 | 0.9015 | 0.9091 | 0.9053 | 0.8469 | |
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### Framework versions |
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- Transformers 4.42.0.dev0 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.1 |
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