metadata
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: []
twitter_bank_scam_classifier
This model is a fine-tuned version of google-bert/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1238
- Accuracy: 0.96
- Auc: 1.0
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 |
|---|---|---|---|---|---|
| 0.3922 | 1.0 | 27 | 0.0940 | 0.96 | 1.0 |
| 0.1191 | 2.0 | 54 | 0.1238 | 0.96 | 1.0 |
Framework versions
- PEFT 0.16.0
- Transformers 4.53.3
- Pytorch 2.7.1
- Datasets 4.0.0
- Tokenizers 0.21.2