# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Colabng/twitter_bank_scam_classifier")
model = AutoModelForSequenceClassification.from_pretrained("Colabng/twitter_bank_scam_classifier")Quick Links
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: 0.6400
- Accuracy: 0.64
- Auc: 0.75
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 |
|---|---|---|---|---|---|
| 0.7725 | 1.0 | 11 | 0.6326 | 0.73 | 0.74 |
| 0.7237 | 2.0 | 22 | 0.6400 | 0.64 | 0.75 |
Framework versions
- PEFT 0.16.0
- Transformers 4.53.3
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.2
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Model tree for Colabng/twitter_bank_scam_classifier
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Colabng/twitter_bank_scam_classifier")