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--- |
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base_model: microsoft/layoutlm-base-uncased |
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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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model-index: |
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- name: layoutlm-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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# layoutlm-funsd |
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0643 |
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- Answer: {'precision': 0.384928716904277, 'recall': 0.4672435105067985, 'f1': 0.4221105527638191, 'number': 809} |
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- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} |
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- Question: {'precision': 0.5277088502894954, 'recall': 0.5990610328638498, 'f1': 0.5611257695690414, 'number': 1065} |
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- Overall Precision: 0.4583 |
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- Overall Recall: 0.5098 |
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- Overall F1: 0.4827 |
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- Overall Accuracy: 0.6395 |
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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: 3e-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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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 1.4286 | 1.0 | 75 | 1.0643 | {'precision': 0.384928716904277, 'recall': 0.4672435105067985, 'f1': 0.4221105527638191, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5277088502894954, 'recall': 0.5990610328638498, 'f1': 0.5611257695690414, 'number': 1065} | 0.4583 | 0.5098 | 0.4827 | 0.6395 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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