Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mariadg/AttackVectorClassifier")
model = AutoModelForSequenceClassification.from_pretrained("mariadg/AttackVectorClassifier", device_map="auto")Quick Links
AttackVectorClassifier
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2148
- Accuracy: 0.9548
- F1: 0.9577
- Precision: 0.9646
- Recall: 0.9509
- Roc Auc: 0.9898
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc |
|---|---|---|---|---|---|---|---|---|
| 0.1444 | 1.0 | 2393 | 0.1713 | 0.9517 | 0.9542 | 0.9768 | 0.9325 | 0.9887 |
| 0.1601 | 2.0 | 4786 | 0.1616 | 0.9487 | 0.9529 | 0.9439 | 0.9620 | 0.9904 |
| 0.0945 | 3.0 | 7179 | 0.2148 | 0.9548 | 0.9577 | 0.9646 | 0.9509 | 0.9898 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Downloads last month
- 9
Model tree for mariadg/AttackVectorClassifier
Base model
FacebookAI/roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mariadg/AttackVectorClassifier")