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--- |
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license: cc-by-nc-sa-4.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- funsd |
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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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- 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 |
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type: funsd |
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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: 0.7998102466793169 |
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- name: Recall |
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type: recall |
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value: 0.8375558867362146 |
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- name: F1 |
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type: f1 |
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value: 0.8182479980587235 |
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- name: Accuracy |
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type: accuracy |
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value: 0.826102460477832 |
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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 the funsd dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0068 |
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- Precision: 0.7998 |
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- Recall: 0.8376 |
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- F1: 0.8182 |
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- Accuracy: 0.8261 |
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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: 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: 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 | 3.33 | 250 | 0.5828 | 0.7015 | 0.8033 | 0.7490 | 0.8022 | |
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| 0.6702 | 6.67 | 500 | 0.5765 | 0.7499 | 0.8073 | 0.7775 | 0.8253 | |
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| 0.6702 | 10.0 | 750 | 0.7082 | 0.7755 | 0.8236 | 0.7988 | 0.8160 | |
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| 0.1797 | 13.33 | 1000 | 0.7819 | 0.7807 | 0.8366 | 0.8077 | 0.8256 | |
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| 0.1797 | 16.67 | 1250 | 0.8199 | 0.7997 | 0.8311 | 0.8151 | 0.8227 | |
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| 0.0745 | 20.0 | 1500 | 0.9025 | 0.7943 | 0.8286 | 0.8111 | 0.8231 | |
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| 0.0745 | 23.33 | 1750 | 0.9159 | 0.7941 | 0.8470 | 0.8197 | 0.8248 | |
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| 0.041 | 26.67 | 2000 | 1.0012 | 0.7989 | 0.8385 | 0.8182 | 0.8210 | |
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| 0.041 | 30.0 | 2250 | 0.9852 | 0.8024 | 0.8450 | 0.8231 | 0.8301 | |
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| 0.0246 | 33.33 | 2500 | 1.0068 | 0.7998 | 0.8376 | 0.8182 | 0.8261 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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