| library_name: peft | |
| license: apache-2.0 | |
| base_model: google-bert/bert-base-uncased | |
| tags: | |
| - base_model:adapter:google-bert/bert-base-uncased | |
| - lora | |
| - transformers | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: twitter_bank_scam_classifier | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # twitter_bank_scam_classifier | |
| This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.7974 | |
| - Accuracy: 0.55 | |
| - Auc: 0.54 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.001 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:----:| | |
| | 0.6974 | 1.0 | 11 | 0.7115 | 0.59 | 0.53 | | |
| | 0.6953 | 2.0 | 22 | 0.7974 | 0.55 | 0.54 | | |
| ### Framework versions | |
| - PEFT 0.16.0 | |
| - Transformers 4.53.3 | |
| - Pytorch 2.7.1+cu126 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.21.2 |