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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: bert-base-uncased-Federal-Regulations |
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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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# bert-base-uncased-Federal-Regulations |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6193 |
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- Accuracy: 0.7332 |
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- Precision: 0.7510 |
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- Recall: 0.7332 |
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- F1: 0.7394 |
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- Roc Auc: 0.7821 |
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- Confusion Matrix: [[2590, 795], [498, 963]] |
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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: 4e-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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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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 | Accuracy | Precision | Recall | F1 | Roc Auc | Confusion Matrix | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|:---------------------------:| |
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| 0.5846 | 1.0 | 600 | 0.6114 | 0.6620 | 0.7393 | 0.6620 | 0.6759 | 0.7668 | [[2092, 1293], [345, 1116]] | |
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| 0.5123 | 2.0 | 1200 | 0.5976 | 0.7210 | 0.7535 | 0.7210 | 0.7301 | 0.7848 | [[2465, 920], [432, 1029]] | |
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| 0.4449 | 3.0 | 1800 | 0.6193 | 0.7332 | 0.7510 | 0.7332 | 0.7394 | 0.7821 | [[2590, 795], [498, 963]] | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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