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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: google-bert/bert-base-uncased |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: bert-finetuned-spam |
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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-finetuned-spam |
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1942 |
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- Accuracy: 0.952 |
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- F1: 0.9502 |
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- Precision: 0.9871 |
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- Recall: 0.916 |
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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: 2.5719605731158755e-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 2 |
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- seed: 19 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.2449 | 1.0 | 2250 | 0.2435 | 0.901 | 0.8930 | 0.9718 | 0.826 | |
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| 0.2545 | 2.0 | 4500 | 0.2138 | 0.937 | 0.9336 | 0.9866 | 0.886 | |
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| 0.1397 | 3.0 | 6750 | 0.2162 | 0.944 | 0.9413 | 0.9890 | 0.898 | |
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| 0.1184 | 4.0 | 9000 | 0.2134 | 0.946 | 0.9436 | 0.9869 | 0.904 | |
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| 0.2056 | 5.0 | 11250 | 0.1942 | 0.952 | 0.9502 | 0.9871 | 0.916 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |