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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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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.7407 |
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- Education: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} |
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- Email: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} |
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- Github: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} |
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- Location: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} |
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- Name: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} |
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- Name : {'precision': 0.2, 'recall': 0.5, 'f1': 0.28571428571428575, 'number': 2} |
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- Phone Number: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} |
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- Soft Skills: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} |
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- Technical Skills: {'precision': 0.2, 'recall': 0.35714285714285715, 'f1': 0.25641025641025644, 'number': 14} |
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- Overall Precision: 0.1176 |
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- Overall Recall: 0.2 |
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- Overall F1: 0.1481 |
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- Overall Accuracy: 0.1475 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Education | Email | Github | Linkedin | Location | Name | Name | Phone Number | Soft Skills | Technical Skills | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 2.9387 | 1.0 | 2 | 2.8701 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.14285714285714285, 'recall': 0.5, 'f1': 0.22222222222222224, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 14} | 0.0185 | 0.0333 | 0.0238 | 0.0328 | |
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| 2.6716 | 2.0 | 4 | 2.7798 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.2, 'recall': 0.5, 'f1': 0.28571428571428575, 'number': 2}| {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.10526315789473684, 'recall': 0.14285714285714285, 'f1': 0.12121212121212122, 'number': 14}| 0.0612 | 0.1 | 0.0759 | 0.1311 | |
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| 2.5524 | 3.0 | 6 | 2.7407 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.2, 'recall': 0.5, 'f1': 0.28571428571428575, 'number': 2}| {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.2, 'recall': 0.35714285714285715, 'f1': 0.25641025641025644, 'number': 14}| 0.1176 | 0.2 | 0.1481 | 0.1475 | |
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### Framework versions |
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- Transformers 4.35.0 |
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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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