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: 0.6500
Accuracy: 0.64
Auc: 0.7

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
0.6946	1.0	11	0.6352	0.64	0.74
0.6177	2.0	22	0.6500	0.64	0.7

Framework versions

PEFT 0.16.0
Transformers 4.53.3
Pytorch 2.7.1
Datasets 4.0.0
Tokenizers 0.21.2