Text Classification
Transformers
PyTorch
distilbert
Network Intrusion Detection
Cybersecurity
Network Packets
text-embeddings-inference
Instructions to use rdpahalavan/bert-network-packet-flow-header-payload with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rdpahalavan/bert-network-packet-flow-header-payload with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rdpahalavan/bert-network-packet-flow-header-payload")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rdpahalavan/bert-network-packet-flow-header-payload") model = AutoModelForSequenceClassification.from_pretrained("rdpahalavan/bert-network-packet-flow-header-payload") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cbf745dd9a3b5c9371c870c79d53c5b405c731531147ad23f9442bb81d3e8e22
- Size of remote file:
- 263 MB
- SHA256:
- 71b8ed2ff231901109c0de64f66590ec303ab198c2d07f66e8d476ed7f878723
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