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:
- 81f32156d518d5d40086f7068951cf740545e9d67c6cd8c951335843aec1362d
- Size of remote file:
- 14.6 kB
- SHA256:
- 8cbb33081bdb51ced60d6dc33a197664b22a258d9de6179c60d35d681ba7bb60
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