metadata
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: cyber_bert
results: []
cyber_bert
This model is a fine-tuned version of google-bert/bert-base-multilingual-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4547
- Accuracy: 0.8159
- F1: 0.8066
- Precision: 0.8011
- Recall: 0.8300
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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
---|---|---|---|---|---|---|---|
0.4657 | 1.0 | 144 | 0.4140 | 0.7960 | 0.7774 | 0.7732 | 0.7833 |
0.3537 | 2.0 | 288 | 0.4115 | 0.8023 | 0.7904 | 0.7843 | 0.8083 |
0.3624 | 3.0 | 432 | 0.4559 | 0.7986 | 0.7906 | 0.7885 | 0.8192 |
0.312 | 4.0 | 576 | 0.4269 | 0.8174 | 0.8066 | 0.8000 | 0.8257 |
0.2929 | 5.0 | 720 | 0.4547 | 0.8159 | 0.8066 | 0.8011 | 0.8300 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1