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--- |
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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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model-index: |
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- name: MiniLMv2-L12-H384-distilled-finetuned-spam-detection |
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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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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: 0.9928263988522238 |
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- task: |
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type: text-classification |
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name: 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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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9919268030139935 |
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verified: true |
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- name: Precision |
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type: precision |
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value: 0.9915966386554622 |
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verified: true |
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- name: Recall |
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type: recall |
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value: 0.9477911646586346 |
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verified: true |
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- name: AUC |
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type: auc |
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value: 0.9765156891636706 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 0.9691991786447638 |
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verified: true |
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- name: loss |
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type: loss |
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value: 0.06180405616760254 |
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verified: true |
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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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# MiniLMv2-L12-H384-distilled-finetuned-spam-detection |
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This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the sms_spam dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0938 |
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- Accuracy: 0.9928 |
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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: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 33 |
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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: 6 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.4101 | 1.0 | 131 | 0.4930 | 0.9763 | |
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| 0.8003 | 2.0 | 262 | 0.3999 | 0.9799 | |
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| 0.377 | 3.0 | 393 | 0.3196 | 0.9828 | |
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| 0.302 | 4.0 | 524 | 0.3462 | 0.9828 | |
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| 0.1945 | 5.0 | 655 | 0.1094 | 0.9928 | |
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| 0.1393 | 6.0 | 786 | 0.0938 | 0.9928 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.10.2+cu113 |
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- Datasets 1.18.4 |
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- Tokenizers 0.12.1 |
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