metadata
library_name: transformers
license: apache-2.0
base_model: deepvk/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-finetuned-spam-detection
results: []
bert-base-uncased-finetuned-spam-detection
This model is a fine-tuned version of deepvk/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0508
- Accuracy: 0.9862
- F1: 0.9862
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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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 | F1 |
|---|---|---|---|---|---|
| 0.0626 | 1.0 | 967 | 0.0504 | 0.9838 | 0.9838 |
| 0.0258 | 2.0 | 1934 | 0.0508 | 0.9862 | 0.9862 |
Framework versions
- Transformers 4.46.0
- Pytorch 2.4.1
- Datasets 3.0.2
- Tokenizers 0.20.1