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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- sms_spam |
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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-finetuned-smsspam |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: sms_spam |
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type: sms_spam |
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config: plain_text |
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split: train |
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args: plain_text |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9904420549581839 |
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- name: Precision |
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type: precision |
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value: 0.9814814814814815 |
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- name: Recall |
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type: recall |
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value: 0.9464285714285714 |
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- name: F1 |
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type: f1 |
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value: 0.9636363636363636 |
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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-finetuned-smsspam |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the sms_spam dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0637 |
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- Accuracy: 0.9904 |
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- Precision: 0.9815 |
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- Recall: 0.9464 |
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- F1: 0.9636 |
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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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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.0828 | 1.0 | 593 | 0.0538 | 0.9892 | 0.9725 | 0.9464 | 0.9593 | |
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| 0.0269 | 2.0 | 1186 | 0.1792 | 0.9677 | 0.8244 | 0.9643 | 0.8889 | |
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| 0.0229 | 3.0 | 1779 | 0.0623 | 0.9916 | 0.9817 | 0.9554 | 0.9683 | |
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| 0.0043 | 4.0 | 2372 | 0.0637 | 0.9904 | 0.9815 | 0.9464 | 0.9636 | |
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### Framework versions |
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- Transformers 4.31.0 |
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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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