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--- |
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license: mit |
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base_model: SCUT-DLVCLab/lilt-roberta-en-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: lilt-invoices |
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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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# lilt-invoices |
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This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0031 |
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- Endorname: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 177} |
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- Escription: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 183} |
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- Illingaddress: {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161} |
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- Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 175} |
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- Nitprice: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 156} |
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- Nvoicedate: {'precision': 0.9941520467836257, 'recall': 1.0, 'f1': 0.9970674486803519, 'number': 170} |
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- Nvoicetotal: {'precision': 0.9946808510638298, 'recall': 0.9946808510638298, 'f1': 0.9946808510638298, 'number': 188} |
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- Otaltax: {'precision': 1.0, 'recall': 0.9927007299270073, 'f1': 0.9963369963369962, 'number': 137} |
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- Uantity: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 167} |
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- Ubtotal: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 151} |
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- Overall Precision: 0.9988 |
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- Overall Recall: 0.9982 |
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- Overall F1: 0.9985 |
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- Overall Accuracy: 0.9994 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Endorname | Escription | Illingaddress | Mount | Nitprice | Nvoicedate | Nvoicetotal | Otaltax | Uantity | Ubtotal | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.1736 | 21.74 | 500 | 0.0031 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 177} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 183} | {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 175} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 156} | {'precision': 0.9941520467836257, 'recall': 1.0, 'f1': 0.9970674486803519, 'number': 170} | {'precision': 0.9946808510638298, 'recall': 0.9946808510638298, 'f1': 0.9946808510638298, 'number': 188} | {'precision': 1.0, 'recall': 0.9927007299270073, 'f1': 0.9963369963369962, 'number': 137} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 167} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 151} | 0.9988 | 0.9982 | 0.9985 | 0.9994 | |
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### Framework versions |
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- Transformers 4.32.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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