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--- |
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library_name: transformers.js |
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license: cc-by-nc-sa-4.0 |
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base_model: |
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- mp-02/layoutlmv3-base-cord |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mp-02/cord |
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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-base-cord |
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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: mp-02/cord |
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type: mp-02/cord |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9752270850536746 |
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- name: Recall |
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type: recall |
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value: 0.9784589892294946 |
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- name: F1 |
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type: f1 |
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value: 0.976840363937138 |
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- name: Accuracy |
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type: accuracy |
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value: 0.973924977127173 |
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pipeline_tag: token-classification |
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--- |
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# layoutlmv3-base-cord (ONNX) |
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This is an ONNX version of [mp-02/layoutlmv3-base-cord](https://huggingface.co/mp-02/layoutlmv3-base-cord). It was automatically converted and uploaded using [this Hugging Face Space](https://huggingface.co/spaces/onnx-community/convert-to-onnx). |
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## Usage with Transformers.js |
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See the pipeline documentation for `token-classification`: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TokenClassificationPipeline |
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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-base-cord |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the mp-02/cord dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1517 |
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- Precision: 0.9752 |
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- Recall: 0.9785 |
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- F1: 0.9768 |
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- Accuracy: 0.9739 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 3000 |
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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 | 2.0 | 100 | 0.8667 | 0.7592 | 0.8202 | 0.7885 | 0.8097 | |
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| No log | 4.0 | 200 | 0.3443 | 0.9122 | 0.9387 | 0.9253 | 0.9222 | |
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| No log | 6.0 | 300 | 0.2128 | 0.9345 | 0.9569 | 0.9456 | 0.9579 | |
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| No log | 8.0 | 400 | 0.1745 | 0.9440 | 0.9635 | 0.9537 | 0.9629 | |
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| 0.6362 | 10.0 | 500 | 0.1594 | 0.9559 | 0.9702 | 0.9630 | 0.9684 | |
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| 0.6362 | 12.0 | 600 | 0.1720 | 0.9630 | 0.9693 | 0.9661 | 0.9629 | |
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| 0.6362 | 14.0 | 700 | 0.1528 | 0.9607 | 0.9710 | 0.9658 | 0.9675 | |
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| 0.6362 | 16.0 | 800 | 0.1460 | 0.9638 | 0.9718 | 0.9678 | 0.9680 | |
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| 0.6362 | 18.0 | 900 | 0.1609 | 0.9614 | 0.9702 | 0.9658 | 0.9648 | |
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| 0.0536 | 20.0 | 1000 | 0.1517 | 0.9752 | 0.9785 | 0.9768 | 0.9739 | |
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| 0.0536 | 22.0 | 1100 | 0.1901 | 0.9614 | 0.9693 | 0.9653 | 0.9657 | |
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| 0.0536 | 24.0 | 1200 | 0.1867 | 0.9638 | 0.9718 | 0.9678 | 0.9666 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu118 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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