Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gates04/DistilBERT-Network-Intrusion-Detection")
model = AutoModelForSequenceClassification.from_pretrained("gates04/DistilBERT-Network-Intrusion-Detection")Quick Links
results
This model is a fine-tuned version of gates04/DistilBERT-Network-Intrusion-Detection on an unknown dataset.
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- 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: 3.0
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gates04/DistilBERT-Network-Intrusion-Detection")